Combining content and social features in a deep learning approach to Vietnamese email prioritization

The email overload problem has been discussed in numerous email-related studies. One of the possible solutions to this problem is email prioritization, which is the act of automatically predicting the importance levels of received emails and sorting the user’s inbox accordingly. Several learning-based methods have been proposed to address the email prioritization problem using content features as well as social features. Although these methods have laid the foundation works in this field of study, the reported performance is far from being practical. Recent works on deep neural networks have achieved good results in various tasks. In this paper, the authors propose a novel email prioritization model which incorporates several deep learning techniques and uses a combination of both content features and social features from email data. This method targets Vietnamese emails and is tested against a self-built Vietnamese email corpus. Conducted experiments explored the effects of different model configurations and compared the effectiveness of the new method to that of a previous work

The Impact of Digital Transformation on Customer Satisfaction to Digital Banking Service of Commercial Banks in Vietnam

The present study focuses on examining the interplay and correlation between the digital transformation process as assessed by the digital banking service quality components (Ease of use, Effectiveness, Interoperability, Privacy/ Security, Empathy, Responsiveness, Reliability, Service portfolios, Service charge) and customer satisfaction for digital banking services at commercial banks in Vietnam. The predictors (independent variables) for this study are the aforementioned service quality aspects and moderator is Service charge. The outcome variable (dependent variable) is customer satisfaction. The authors combined qualitative and quantitative research techniques to develop observed variables and assess the model's fit. This study can help banking leaders evaluate and improve the quality of digital banking services in the context of financial liberalization and globalization. Keywords: Digital transformation, Digital Banking, Banking service quality, Customer satisfaction DOI: 10.7176/EJBM/15-6-04 Publication date:March 31st 202

Anti-DreamBooth: Protecting users from personalized text-to-image synthesis

Text-to-image diffusion models are nothing but a revolution, allowing anyone, even without design skills, to create realistic images from simple text inputs. With powerful personalization tools like DreamBooth, they can generate images of a specific person just by learning from his/her few reference images. However, when misused, such a powerful and convenient tool can produce fake news or disturbing content targeting any individual victim, posing a severe negative social impact. In this paper, we explore a defense system called Anti-DreamBooth against such malicious use of DreamBooth. The system aims to add subtle noise perturbation to each user's image before publishing in order to disrupt the generation quality of any DreamBooth model trained on these perturbed images. We investigate a wide range of algorithms for perturbation optimization and extensively evaluate them on two facial datasets over various text-to-image model versions. Despite the complicated formulation of DreamBooth and Diffusion-based text-to-image models, our methods effectively defend users from the malicious use of those models. Their effectiveness withstands even adverse conditions, such as model or prompt/term mismatching between training and testing. Our code will be available at \href{https://github.com/VinAIResearch/Anti-DreamBooth.git}{https://github.com/VinAIResearch/Anti-DreamBooth.git}.Comment: Project page: https://anti-dreambooth.github.io

Self-adaptive Controllers for Renewable Energy Communities Based on Transformer Loading Estimation

In this paper, self-adaptive controllers for renewable energy communities based on data-driven approach are proposed to mitigate the voltage rise and transformer congestion at the community level. In the proposed approach, the transformer loading percentage is estimated by the trained data-driven model, which uses the extreme gradient boosting regression algorithm based on a measurement set acquired from critical coupling points of the communities. To avoid voltage rise issues, the droop control parameters (i.e., voltage threshold for P - V, Q - V curves) are adaptively tuned based on the solar irradiance availability and estimated transformer loading. The proposed approach has been tested in the IEEE European LV distribution network. Results showed that the control approach could effectively reduce 22.2 % of the total overloaded instances, while still keeping voltage magnitude in the operation range. This method can help DSOs manage voltage violation and congestion without further communication

An Empirical Research on Customers’ Awareness of E-Commerce in the Context of Vietnamese Developing Economies

Many research studies have been conducted in the field of e-commerce, which can contribute for academic background and development of e-commerce around the world. In Vietnam, the awareness of customers toward e-commerce is limited for the emerging of this industry in Vietnam recently. This study tries to figure out research model explains the factors and to study the awareness of customers that influence the behaviors and acceptance of users buying online in the developing economy. According to the research, buying online is quite risky. Therefore, product safety would be the purchasing decision's priority. Online sales providers need to provide a safe way of ensuring consumers ' purchasing process. Through hard qualification process, they need to shortlist the good quality product. The sale would grow dramatically as consumers gained confidence. Not only should they care about the short-term profit in order to get the products of low quality

Abietane diterpenoids and neolignans from the roots of Pinus kesiya

The phytochemical investigation of the ethyl acetate extract of Pinus kesiya Royle ex Gordon roots led to the isolation of two abietane diterpenes, 7-oxo-15-hydroxy-dehydroabietic acid (1) and dehydroabietic acid (2) as well as two neolignans, cedrusin (3) and cedrusin-4-O-β-D-glucopyranoside (4). Their structures were determined by combination of spectral analysis and comparison with reported data. Among them, compound 1 was isolated from the genus Pinus for the first time. Keywords. Pinus kesiya, abietane diterpenes, neolignans, dehydroabietic acid, cedrusin

Class based Influence Functions for Error Detection

Influence functions (IFs) are a powerful tool for detecting anomalous examples in large scale datasets. However, they are unstable when applied to deep networks. In this paper, we provide an explanation for the instability of IFs and develop a solution to this problem. We show that IFs are unreliable when the two data points belong to two different classes. Our solution leverages class information to improve the stability of IFs. Extensive experiments show that our modification significantly improves the performance and stability of IFs while incurring no additional computational cost.Comment: Thang Nguyen-Duc, Hoang Thanh-Tung, and Quan Hung Tran are co-first authors of this paper. 12 pages, 12 figures. Accepted to ACL 202

Electrosynthesis of a poly(1,5-diaminonaphthalene) - polypyrrole nanowire bilayer for trichlorfon insecticide biosensing

In this study, an acetylcholinesterase enzyme biosensor was developed based on a bilayer of poly(1,5-diaminonaphthalene) and polypyrrole nanowire structures modifying carbon screen-printed electrodes (SPEs). A polypyrrole nanowire inner layer was electrodeposited on the surface of SPEs to enhance conductivity and specific areas. A poly(1,5-diaminonaphthalene) outer layer was used for immobilizing acetylcholinesterase through glutaraldehyde agent. On the basis of the inhibition of organophosphate pesticides on the enzymatic activity of acetylcholinesterase enzyme, the acetylcholinesterase-immobilized bilayer of the conductive polymer electrode was designed for electrochemical determination of trichlorfon insecticide, one of the popular organophosphate pesticides. Keywords. Acetylcholinesterase, biosensors, poly(1,5-diaminonaphthalene), polypyrrole nanowire, trichlorfon

Chemical constituents of Chirita drakei Burtt collected in Ha Long bay, Quang Ninh province, Viet Nam. Part 1. Compounds isolated from the n-hexane and ethyl acetate extracts.

Two triterpenes, an anthraquinone, two lignans and a phenolic compound were isolated from the n-hexane and ethyl acetate extracts of the aerial part of Chirita drakei Burtt collected in islands, on mountain slopes of Ha Long bay, Quang Ninh province. Their structures have been elucidated by mass, NMR spectroscopy and comparison with published data. There are no report on the chemical constituents of Chirita drakei before our study. Keywords. Chirita drakei, triterpene, anthraquinone, lignin