Real-time Optimal Resource Allocation for Embedded UAV Communication Systems

We consider device-to-device (D2D) wireless information and power transfer systems using an unmanned aerial vehicle (UAV) as a relay-assisted node. As the energy capacity and flight time of UAVs is limited, a significant issue in deploying UAV is to manage energy consumption in real-time application, which is proportional to the UAV transmit power. To tackle this important issue, we develop a real-time resource allocation algorithm for maximizing the energy efficiency by jointly optimizing the energy-harvesting time and power control for the considered (D2D) communication embedded with UAV. We demonstrate the effectiveness of the proposed algorithms as running time for solving them can be conducted in milliseconds.Comment: 11 pages, 5 figures, 1 table. This paper is accepted for publication on IEEE Wireless Communications Letter

TEACHERS’ PERCEPTIONS ABOUT TASK-BASED LANGUAGE TEACHING AND ITS IMPLEMENTATION

Task-based language teaching has been advocated as a potentially effective approach in teaching English as a foreign language (EFL) in Asian contexts, including Vietnam. However, little is known about perceptions of tasks used by teachers in actual classroom practices at tertiary education. This article therefore examines teachers’ perceptions about task-based language teaching and its implementation in EFL classes. Questionnaire and interviews were conducted to investigate the perceptions of sixty-eight university teachers in the Mekong Delta. The findings reveal positive perceptions and understanding of teachers towards task-based language teaching (TBLT). Implications for practical applications of TBLT are also presented.  Article visualizations

Multi-Branch Network for Imagery Emotion Prediction

For a long time, images have proved perfect at both storing and conveying rich semantics, especially human emotions. A lot of research has been conducted to provide machines with the ability to recognize emotions in photos of people. Previous methods mostly focus on facial expressions but fail to consider the scene context, meanwhile scene context plays an important role in predicting emotions, leading to more accurate results. In addition, Valence-Arousal-Dominance (VAD) values offer a more precise quantitative understanding of continuous emotions, yet there has been less emphasis on predicting them compared to discrete emotional categories. In this paper, we present a novel Multi-Branch Network (MBN), which utilizes various source information, including faces, bodies, and scene contexts to predict both discrete and continuous emotions in an image. Experimental results on EMOTIC dataset, which contains large-scale images of people in unconstrained situations labeled with 26 discrete categories of emotions and VAD values, show that our proposed method significantly outperforms state-of-the-art methods with 28.4% in mAP and 0.93 in MAE. The results highlight the importance of utilizing multiple contextual information in emotion prediction and illustrate the potential of our proposed method in a wide range of applications, such as effective computing, human-computer interaction, and social robotics. Source code: https://github.com/BaoNinh2808/Multi-Branch-Network-for-Imagery-Emotion-PredictionComment: SOICT 202

Masked Face Analysis via Multi-Task Deep Learning

Face recognition with wearable items has been a challenging task in computer vision and involves the problem of identifying humans wearing a face mask. Masked face analysis via multi-task learning could effectively improve performance in many fields of face analysis. In this paper, we propose a unified framework for predicting the age, gender, and emotions of people wearing face masks. We first construct FGNET-MASK, a masked face dataset for the problem. Then, we propose a multi-task deep learning model to tackle the problem. In particular, the multi-task deep learning model takes the data as inputs and shares their weight to yield predictions of age, expression, and gender for the masked face. Through extensive experiments, the proposed framework has been found to provide a better performance than other existing methods

The effects from the United States and Japan to emerging stock markets in Asia and Vietnam

The subprime mortgage crisis in the United States (U.S.) in mid-2008 suggests that stock prices volatility do spillover from one market to another after international stock markets downturn. The purpose of this paper is to examine the magnitude of return and volatility spillovers from developed markets (the U.S. and Japan) to eight emerging equity markets (India, China, Indonesia, Korea, Malaysia, the Philippines, Taiwan, Thailand) and Vietnam. Employing a mean and volatility spillover model that deals with the U.S. and Japan shocks and day effects as exogenous variables in ARMA(1,1), GARCH(1,1) for Asian emerging markets, the study finds some interesting findings. Firstly, the day effect is present on six out of nine studied markets, except for the Indian, Taiwanese and Philippine. Secondly, the results of return spillover confirm significant spillover effects across the markets with different magnitudes. Specifically, the U.S. exerts a stronger influence on the Malaysian, Philippine and Vietnamese market compared with Japan. In contrast, Japan has a higher spillover effect on the Chinese, Indian, Korea, and Thailand than the U.S. For the Indonesian market, the return effect is equal. Finally, there is no evidence of a volatility effect of the U.S. and Japanese markets on the Asian emerging markets in this study

The Effects of Collaborative Learning on Young ESL Learners’ L2 Anxiety and Speaking Performance

Foreign Language Anxiety (FLA) is one of the issues of interest attracting researchers in recent decades. However, while collaborative learning introduced a prospective tool for FLA, it has not been much researched in the L2 classroom context, particularly in Vietnam. This paper focuses on using collaborative learning to reduce foreign language anxiety and enhance the L2 speaking performance of young learners at an English center in Ho Chi Minh City. A combination of tools, including Aydin et al.’s (2017) Children Foreign Language Anxiety Scale (CFLAS) for the pre-tests and post-tests, the teacher’s diary, and follow-up interviews, was used to measure the changes in learner’ FLA level and speaking performance. After five-week implementations, these learners’ FLA was slightly alleviated, and their speaking performance was improved using a collaborative learning approach. Moreover, learners were found to have positive attitudes and experience with learning in the new approach. These findings implied that collaborative learning could be a potential treatment to help L2 learners uncover their anxious selves and find more confidence in using the target languag

NearbyPatchCL: Leveraging Nearby Patches for Self-Supervised Patch-Level Multi-Class Classification in Whole-Slide Images

Whole-slide image (WSI) analysis plays a crucial role in cancer diagnosis and treatment. In addressing the demands of this critical task, self-supervised learning (SSL) methods have emerged as a valuable resource, leveraging their efficiency in circumventing the need for a large number of annotations, which can be both costly and time-consuming to deploy supervised methods. Nevertheless, patch-wise representation may exhibit instability in performance, primarily due to class imbalances stemming from patch selection within WSIs. In this paper, we introduce Nearby Patch Contrastive Learning (NearbyPatchCL), a novel self-supervised learning method that leverages nearby patches as positive samples and a decoupled contrastive loss for robust representation learning. Our method demonstrates a tangible enhancement in performance for downstream tasks involving patch-level multi-class classification. Additionally, we curate a new dataset derived from WSIs sourced from the Canine Cutaneous Cancer Histology, thus establishing a benchmark for the rigorous evaluation of patch-level multi-class classification methodologies. Intensive experiments show that our method significantly outperforms the supervised baseline and state-of-the-art SSL methods with top-1 classification accuracy of 87.56%. Our method also achieves comparable results while utilizing a mere 1% of labeled data, a stark contrast to the 100% labeled data requirement of other approaches. Source code: https://github.com/nvtien457/NearbyPatchCLComment: MMM 202

Factors influencing the urge to buy impulsively of Vietnamese online buying customers towards Biti’s Hunter sport shoes

Our study investigates the factors that drive Vietnamese online shoppers in Ho Chi Minh City (HCMC) to make impulsive purchases of Biti's Hunter sports shoes (BHS), using the Stimulus-Organism-Response (S-O-R) theory and the Technology Acceptance Model (TAM). Mixed methods are applied: in-depth interviews with ten regular online shoppers and focus group discussions with e-commerce managers for qualitative data collection, and survey techniques to gather quantitative data from 319 online shoppers. Data analysis was performed using SPSS 22.0 and AMOS 22.0. Our findings reveal five factors as a stimulus - visual appeal, website ease of use, product availability, portability, and social influence – and three factors as an organism - instant gratification, impulsiveness, and trust, that lead to the response of urge to buy impulsively. Significant positive effects are found among these constructs, except the relationship between portability and impulsiveness, visual appeal, social influence, trust, instant gratification, and urge to buy impulsively

Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback

A key technology for the development of large language models (LLMs) involves instruction tuning that helps align the models' responses with human expectations to realize impressive learning abilities. Two major approaches for instruction tuning characterize supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF), which are currently applied to produce the best commercial LLMs (e.g., ChatGPT). To improve the accessibility of LLMs for research and development efforts, various instruction-tuned open-source LLMs have also been introduced recently, e.g., Alpaca, Vicuna, to name a few. However, existing open-source LLMs have only been instruction-tuned for English and a few popular languages, thus hindering their impacts and accessibility to many other languages in the world. Among a few very recent work to explore instruction tuning for LLMs in multiple languages, SFT has been used as the only approach to instruction-tune LLMs for multiple languages. This has left a significant gap for fine-tuned LLMs based on RLHF in diverse languages and raised important questions on how RLHF can boost the performance of multilingual instruction tuning. To overcome this issue, we present Okapi, the first system with instruction-tuned LLMs based on RLHF for multiple languages. Okapi introduces instruction and response-ranked data in 26 diverse languages to facilitate the experiments and development of future multilingual LLM research. We also present benchmark datasets to enable the evaluation of generative LLMs in multiple languages. Our experiments demonstrate the advantages of RLHF for multilingual instruction over SFT for different base models and datasets. Our framework and resources are released at https://github.com/nlp-uoregon/Okapi