Relative Positional Encoding for Speech Recognition and Direct Translation

Transformer models are powerful sequence-to-sequence architectures that are capable of directly mapping speech inputs to transcriptions or translations. However, the mechanism for modeling positions in this model was tailored for text modeling, and thus is less ideal for acoustic inputs. In this work, we adapt the relative position encoding scheme to the Speech Transformer, where the key addition is relative distance between input states in the self-attention network. As a result, the network can better adapt to the variable distributions present in speech data. Our experiments show that our resulting model achieves the best recognition result on the Switchboard benchmark in the non-augmentation condition, and the best published result in the MuST-C speech translation benchmark. We also show that this model is able to better utilize synthetic data than the Transformer, and adapts better to variable sentence segmentation quality for speech translation.Comment: Submitted to Interspeech 202

SEASONAL VARIATION OF PHYTOPLANKTON FUNCTIONAL GROUPS IN TUYEN LAM RESERVOIR, CENTRAL HIGHLANDS, VIETNAM

Seasonal changes in freshwater phytoplankton assemblages at Tuyen Lam Reservoir in the Central Highlands of Vietnam were classified into 23 functional groups based on physiological, morphological, and ecological characteristics. A total of 168 species were recorded during 10 surveys from 2015 to 2019 at 7 sampling sites, with Chlorophyta dominating in number of species. Phytoplankton abundance varied from 0.18×105 to 21.2×105 cells/L during the study period, mainly due to cyanobacteria. Seven of the 23 functional groups were considered to be dominant (relative density > 5%).  The dominant functional groups were groups M and G in the dry season and groups M, G, P, and E in the rainy season. Group M (Microcystis aeruginosa) was the most common in both seasons, while group P (Closterium, Staurastrum, Aulacoseira), group E (Dinobryon, Synura), and group G (Sphaerocystis, Eudorina) were more common in the rainy season. The Shannon diversity index (H¢) showed that phytoplankton communities were relatively diverse and that most of the study sites were lightly polluted. However, the ecological status has deteriorated at some locations due to the overgrowth of group M, leading to eutrophication in this reservoir. This study highlights the usefulness of functional groups in the study of seasonal changes in phytoplankton dynamics. Functional groups are applied for the first time at Tuyen Lam Reservoir and can be used to predict early-stage cyanobacterial blooms in future studies

Graphene Effect on Efficiency of TiO2-based Dye Sensitized Solar Cells (DSSC)

Colloidal paste of TiO2 embedded with graphene (GS) was fabricated and used to spread TiO$_2$ film photo-electrode of DSSC solar cells. The dye N179 and Iodine-based electrolyte were used in the DSSC solar cells. Raman scattering, SEM images were used to identify the material phases and microstructure of the film photo-electrode. I/V characteristics of the DSSC cells were recorded at room temperature. Open-circuit voltage Voc, short-current $J_{sc}$ and efficiency η of the DSSC cells were estimated. It shows that graphene addition has affected on $V_{oc}$,$J_{sc}$ and $\eta$. The $V_{oc}$,$J_{sc}$ and $\eta$ abnormally depend on graphene content. The efficiency reached a maximal value with graphen concentration of 0.005 wt %, after that decreased. It is supposed to be related with an improving the charge transfer in the working photo-electrode of DSSC

Knowledge and attitudes about research ethics among social researchers in Vietnam: A cross-sectional study

Social research has attracted significant attention in Vietnam during recent years with more questions and discussions about how to promote the research outputs and publications in this area. However, there is limited information about the knowledge and attitudes of social researchers for research ethics in Vietnam. This paper aims to assess the knowledge and attitudes of social researchers about research ethics in Vietnam. A survey with 1200 questionnaires, through convenience sampling, was sent either printed copies or email to social researchers in the universities and research institutions in Vietnam. Our response rate was 65% (782), with mean age: 35.9 years (sd=.307). The results show that around one fifth had been trained with research ethics (23.5%), which led to significant responses to the "do not know" about the research ethics principles and research ethics committee with 14.3% and 55.3%, respectively. Despite such few experiences on the research ethics, the participants presented a positive understanding of the general principles of research ethics and positive attitudes to the importance of the related general ethics principles to social research in Vietnam. Such understandings and attitudes also led to the readiness to apply the research ethics values and principles while there are no formal ethical guidelines in Vietnam social research. These findings suggest that Vietnamese social researchers understood most critical ethics principles in social research and expect formal ethical guidelines

RMDM: A Multilabel Fakenews Dataset for Vietnamese Evidence Verification

In this study, we present a novel and challenging multilabel Vietnamese dataset (RMDM) designed to assess the performance of large language models (LLMs), in verifying electronic information related to legal contexts, focusing on fake news as potential input for electronic evidence. The RMDM dataset comprises four labels: real, mis, dis, and mal, representing real information, misinformation, disinformation, and mal-information, respectively. By including these diverse labels, RMDM captures the complexities of differing fake news categories and offers insights into the abilities of different language models to handle various types of information that could be part of electronic evidence. The dataset consists of a total of 1,556 samples, with 389 samples for each label. Preliminary tests on the dataset using GPT-based and BERT-based models reveal variations in the models' performance across different labels, indicating that the dataset effectively challenges the ability of various language models to verify the authenticity of such information. Our findings suggest that verifying electronic information related to legal contexts, including fake news, remains a difficult problem for language models, warranting further attention from the research community to advance toward more reliable AI models for potential legal applications.Comment: ISAILD@KSE 202

Label driven Knowledge Distillation for Federated Learning with non-IID Data

In real-world applications, Federated Learning (FL) meets two challenges: (1) scalability, especially when applied to massive IoT networks; and (2) how to be robust against an environment with heterogeneous data. Realizing the first problem, we aim to design a novel FL framework named Full-stack FL (F2L). More specifically, F2L utilizes a hierarchical network architecture, making extending the FL network accessible without reconstructing the whole network system. Moreover, leveraging the advantages of hierarchical network design, we propose a new label-driven knowledge distillation (LKD) technique at the global server to address the second problem. As opposed to current knowledge distillation techniques, LKD is capable of training a student model, which consists of good knowledge from all teachers' models. Therefore, our proposed algorithm can effectively extract the knowledge of the regions' data distribution (i.e., the regional aggregated models) to reduce the divergence between clients' models when operating under the FL system with non-independent identically distributed data. Extensive experiment results reveal that: (i) our F2L method can significantly improve the overall FL efficiency in all global distillations, and (ii) F2L rapidly achieves convergence as global distillation stages occur instead of increasing on each communication cycle.Comment: 28 pages, 5 figures, 10 table

Long short-term memory (LSTM) neural networks for short-term water level prediction in Mekong river estuaries

This study firstly adopts a state-of-the-art deep learning approach based on a Long Short-Term Memory (LSTM) neural network for predicting the hourly water level of Mekong estuaries in Vietnam. The LSTM models were developed from around 8,760 hourly data points within 2018 and were evaluated using the Nash-Sutcliffe efficiency coefficient (NSE), mean absolute error (MAE), and root mean square error (RMSE). The results showed that the NSE values for the training and testing steps were both above 0.98, which can be regarded as very good performance. Furthermore, the RMSE were between 0.09 and 0.11 m for the training and between 0.10 and 0.12 m for the testing, while MAE for the training ranged from 0.07 to 0.08 m and varied from 0.08 to 0.10 m for the testing. The LSTM networks appear to enable high precision and robustness in water level time series prediction. The outcomes of this research have crucial implications in river water level predictions, especially from the viewpoint of employing deep learning algorithms

PERSPECTIVES ON PERCEPTIONS AND PRACTICE THROUGH LEARNING CULTURES OF ENGLISH-SPEAKING COUNTRIES OF HIGH-QUALITY ENGLISH STUDIES PROGRAM STUDENTS, SCHOOL OF FOREIGN LANGUAGES, CAN THO UNIVERSITY, VIETNAM

The overall goal of the project is to understand the awareness or perceptions and practice of students of the High-quality English Studies program, School of Foreign Languages (SFL), Can Tho University (CTU), Vietnam when studying cultures of English-speaking countries (CESCs) to improve intercultural competence. The research on the perspectives or opinions of 200 High-quality English Studies students, 12 of them joining the semi-structured interview, about their awareness and practice through learning CESCs. The analysis would help the researcher understand the difficulties of students when studying cultural modules from English-speaking countries at SFL, CTU. The research results would suggest solutions to overcome the difficulties that students encounter, and at the same time provide factors that contribute to improving the intercultural competence of language students. Also, through the research results, despite many obstacles in the process of absorbing culture from cultural modules, students still retain their interest and love for the course-CESCs. However, it can be seen that the difficulty that many students often encounter is still cultural differences, thereby raising awareness of the need to learn cultures for students. Next is to design teaching materials to become more attractive and attractive, proactively find opportunities to communicate with foreigners.  Article visualizations

KIT’s IWSLT 2020 SLT Translation System

This paper describes KIT’s submissions to the IWSLT2020 Speech Translation evaluation campaign. We first participate in the simultaneous translation task, in which our simultaneous models are Transformer based and can be efficiently trained to obtain low latency with minimized compromise in quality. On the offline speech translation task, we applied our new Speech Transformer architecture to end-to-end speech translation. The obtained model can provide translation quality which is competitive to a complicated cascade. The latter still has the upper hand, thanks to the ability to transparently access to the transcription, and resegment the inputs to avoid fragmentation

Dry Eyes Status on Des Scale and Related Factors in Outpatients at Vietnam National Institute of Ophthalmology

BACKGROUND: Dry eye (DE) can effect on quality of life by pain, inability to perform certain activities that require prolonged attention (driving, reading,…) and productivity at work and finally effect to Q0L associated with DE. OSDI is scale questionnaire is created team to measure the quality of life related to ocular surface disease. AIM: To describe the dry eye disease according to OSDI scale and related factors of this disease. METHODS: A cross-sectional descriptive study was carried out on outpatients (≥ 16-year-old) who were examined and diagnosed with dry eyes at Vietnam National Institute Of Ophthalmology from April to July 2018. Data was collected using the OSDI questionnaire. RESULTS: The average age of participants was 44.6 years; 80.9% of patients were female; 39.9% were identified having mild dry eye. The related factors have been identified that associated with severe dry eye, including age OR = 1.03 (95%CI: 1.01-1.05, p = 0.005), binocular good vision OR = 0.11 (95%CI: 0.05-0.23; p < 0.0001), medical history OR = 17.09 (95%CI: 2.24-130.25; p < 0.0001), chronic conjunctivitis OR = 0.36 (95%CI: 0.14-0.91; p = 0.027), refractive errors OR = 0.14 (95%CI: 0.04-0.48; p < 0.0001), Sjogren's syndrome OR = 31.13 (95%CI: 7.08-136.76; p < 0.0001). CONCLUSION: Several related factors have been identified associated with severe dry eye, including: age, binocular good vision, medical history, chronic conjunctivitis, refractive errors, Sjogren's syndrome