Combination of Domain Knowledge and Deep Learning for Sentiment Analysis of Short and Informal Messages on Social Media

Sentiment analysis has been emerging recently as one of the major natural language processing (NLP) tasks in many applications. Especially, as social media channels (e.g. social networks or forums) have become significant sources for brands to observe user opinions about their products, this task is thus increasingly crucial. However, when applied with real data obtained from social media, we notice that there is a high volume of short and informal messages posted by users on those channels. This kind of data makes the existing works suffer from many difficulties to handle, especially ones using deep learning approaches. In this paper, we propose an approach to handle this problem. This work is extended from our previous work, in which we proposed to combine the typical deep learning technique of Convolutional Neural Networks with domain knowledge. The combination is used for acquiring additional training data augmentation and a more reasonable loss function. In this work, we further improve our architecture by various substantial enhancements, including negation-based data augmentation, transfer learning for word embeddings, the combination of word-level embeddings and character-level embeddings, and using multitask learning technique for attaching domain knowledge rules in the learning process. Those enhancements, specifically aiming to handle short and informal messages, help us to enjoy significant improvement in performance once experimenting on real datasets.Comment: A Preprint of an article accepted for publication by Inderscience in IJCVR on September 201

Fairness Continual Learning Approach to Semantic Scene Understanding in Open-World Environments

Continual semantic segmentation aims to learn new classes while maintaining the information from the previous classes. Although prior studies have shown impressive progress in recent years, the fairness concern in the continual semantic segmentation needs to be better addressed. Meanwhile, fairness is one of the most vital factors in deploying the deep learning model, especially in human-related or safety applications. In this paper, we present a novel Fairness Continual Learning approach to the semantic segmentation problem. In particular, under the fairness objective, a new fairness continual learning framework is proposed based on class distributions. Then, a novel Prototypical Contrastive Clustering loss is proposed to address the significant challenges in continual learning, i.e., catastrophic forgetting and background shift. Our proposed loss has also been proven as a novel, generalized learning paradigm of knowledge distillation commonly used in continual learning. Moreover, the proposed Conditional Structural Consistency loss further regularized the structural constraint of the predicted segmentation. Our proposed approach has achieved State-of-the-Art performance on three standard scene understanding benchmarks, i.e., ADE20K, Cityscapes, and Pascal VOC, and promoted the fairness of the segmentation model

FedDCT: Federated Learning of Large Convolutional Neural Networks on Resource Constrained Devices using Divide and Co-Training

We introduce FedDCT, a novel distributed learning paradigm that enables the usage of large, high-performance CNNs on resource-limited edge devices. As opposed to traditional FL approaches, which require each client to train the full-size neural network independently during each training round, the proposed FedDCT allows a cluster of several clients to collaboratively train a large deep learning model by dividing it into an ensemble of several small sub-models and train them on multiple devices in parallel while maintaining privacy. In this co-training process, clients from the same cluster can also learn from each other, further improving their ensemble performance. In the aggregation stage, the server takes a weighted average of all the ensemble models trained by all the clusters. FedDCT reduces the memory requirements and allows low-end devices to participate in FL. We empirically conduct extensive experiments on standardized datasets, including CIFAR-10, CIFAR-100, and two real-world medical datasets HAM10000 and VAIPE. Experimental results show that FedDCT outperforms a set of current SOTA FL methods with interesting convergence behaviors. Furthermore, compared to other existing approaches, FedDCT achieves higher accuracy and substantially reduces the number of communication rounds (with $4-8$ times fewer memory requirements) to achieve the desired accuracy on the testing dataset without incurring any extra training cost on the server side.Comment: Under review by the IEEE Transactions on Network and Service Managemen

A Multitask Data-Driven Model for Battery Remaining Useful Life Prediction

Lithium-ion batteries (LIBs) have recently been used widely in moving devices. Understand status of the batteries can help to predict the failure and improve the effectiveness of using them. There are some lithium-ion information that define the battery health over time. These are state-of-charge (SOC), state-of-health (SOH), and remaining-useful-life (RUL). Normally, a LIB is working under charging and discharging cycles continuously. In this paper, we will focus on the data dependency of different time-slots in a cycle and in a sequence of cycles to retrieve RUL. We leverage multi-channel inputs such as temperature, voltage, current and the nature of peaks cross the cycles to improve our prediction. Comparing to existing methods, the experiments show that we can improve from 0.040 to 0.033 (reduce 17.5%) in RMSE loss, which is significant

Building a Chatbot System to Analyze Opinions of English Comments

Chatbot research has advanced significantly over the years. Enterprises have been investigating how to improve these tools’ performance, adoption, and implementation to communicate with customers or internal teams through social media. Besides, businesses also want to pay attention to quality reviews from customers via social networks about products available in the market. From there, please select a new method to improve the service quality of their products and then send it to publishing agencies to publish based on the needs and evaluation of society. Although there have been numerous recent studies, not all of them address the issue of opinion evaluation on the chatbot system. The primary goal of this paper’s research is to evaluate human comments in English via the chatbot system. The system’s documents are preprocessed and opinion-matched to provide opinion judgments based on English comments. Based on practical needs and social conditions, this methodology aims to evolve chatbot content based on user inter-actions, allowing for a cyclic and human-supervised process with the following steps to evaluate comments in English. First, we preprocess the input data by collecting social media comments, and then our system parses those comments according to the rating views for each topic covered. Finally, our system will give a rating and comment result for each comment entered into the system. Experiments show that our method can improve accuracy better than the referenced methods by 78.53%

Examining the effects of lead on the life of larval zebrafish (1-7 days old)

Lead (Pb) is a toxic metal and and can cause variety of disorders and effect on neu-ronal function and neurodevelopment. Using zebrafish as a model, the aim of this study was to evaluate the effects of concentrations of Pb2+ on the life of zebrafish larvae (from 1 to 7 days old)yesBelgorod State Universit

Does the informal economy mitigate poverty and how does it work? : the case of Vietnam

Countries with lower quality institutions or heavier burden of regulation are associated with a larger informal sector. In addition, other studies show that low startup costs are a key determinant in entering the informal economy. The paper investigates the linkage between the informal economy and poverty reduction based on the 2010 Vietnam Household Living Standard Surveys. Among low income households, those with members involved in informal economic activities have a higher per capita income than those with no members in the informal economy, and informal wage workers earn more than informal self-employed workers on average. Meanwhile, among non-poor households an inverse trend is observed

Universality in odd-even harmonic generation and application in terahertz waveform sampling

Odd-even harmonics emitted from a laser-target system imprint rich, subtle information characterizing the system's dynamical asymmetry, which is desirable to decipher. In this Letter, we discover a simple universal relation between the odd-even harmonics and the asymmetry of the THz-assisted laser-atomic system -- atoms in a fundamental mid-IR laser pulse combined with a THz laser. First, we demonstrate numerically and then analytically formulize the harmonic even-to-odd ratio as a function of the THz electric field, the source of the system's asymmetry. Notably, we suggest a scaling that makes the obtained rule universal, independent of the parameters of both the fundamental pulse and atomic target. This universality facilitates us to propose a general pump-probe scheme for THz waveform sampling from the even-to-odd ratio, measurable within a conventional compact setup

Impact of climate change on meteorological, hydrological and agricultural droughts in the Lower Mekong River Basin: a case study of the Srepok Basin, Vietnam

peer reviewedThe objective of this study is to assess future changes in meteorological, hydrology and agricultural droughts under the impact of changing climate in the Srepok River Basin, a subbasin of LMB, using three drought indices; standardized precipitation index (SPI), standardized runoff index (SRI) and standardized soil moisture index (SSWI). The well-calibrated Soil and Water Assessment Tool (SWAT) is used as a simulation tool to estimate the features of meteorological, hydrological and agricultural droughts. The climate data for the 2016–2040 period is obtained from four different regional climate models; HadGEM3-RA, SNU-MM5, RegCM4 and YSU-RSM, which are downscaled from the HadGEM2-AO GCM. The results show that the severity, duration and frequency of droughts are predicted to increase in the near future for this region. Moreover, the meteorological drought is less sensitive to climate change than the hydrological and agricultural droughts; however, it has a stronger correlation with the hydrological and agricultural droughts as the accumulation period is increased. These findings may be useful for water resources management and future planning for mitigation and adaptation to the climate change impact in the Srepok River Basin

ẢNH HƯỞNG CỦA CÔNG NGHỆ THỰC TẾ ẢO TĂNG CƯỜNG ĐẾN Ý ĐỊNH MUA HÀNG TRỰC TUYẾN CỦA NGƯỜI TIÊU DÙNG

The study shows the effects of factors affecting consumers' purchase intention after experiencing Virtual Try-on (VTO) in Vietnam. The study is carried out by quantitative method through data from 408 subjects of different ages in Hanoi and Northern provinces. The findings of the study demonstrate that perceived usefulness, perceived ease of use, perceived enjoyment, and perceived privacy risk, have a significant impact on users' attitudes toward Virtual Try-On (VTO). Consequently, these factors increase their influence on customers' purchase intentions. Based on these results, the research group recommends that, in practical settings, enterprises concentrate on providing high-quality services, promoting their products to augment the aforementioned factors, and simultaneously addressing users' attitudes to enhance the overall customer experience. Therefore, this study provides valuable insights into the factors that influence customers' attitudes toward VTO technology, thereby contributing to the existing literature on the topic.Nghiên cứu chỉ ra những tác động của các nhân tố ảnh hưởng tới ý định mua hàng của người tiêu dùng qua hành vi sử dụng công nghệ trải nghiệm sản phẩm trực tuyến (Virtual Try-on - VTO) tại Việt Nam. Phương pháp định lượng được sử dụng để phân tích dữ liệu từ 408 đối tượng trong nhiều độ tuổi khác nhau trên địa bàn thành phố Hà Nội và các tỉnh thành phố miền Bắc. Kết quả cho thấy rằng các nhân tố như cảm nhận tính hữu ích, cảm nhận tính dễ sử dụng, cảm nhận tính thích thú, và cảm nhận rủi ro về quyền riêng tư có tác động đến thái độ của người dùng với công nghệ VTO từ đó gia tăng ảnh hưởng tới ý định mua sắm của khách hàng. Nhóm nghiên cứu đề xuất rằng, trong bối cảnh thực tiễn, doanh nghiệp cần tập trung vào cung cấp dịch vụ chất lượng, quảng bá sản phẩm để nâng cao các yếu tố đã được đề cập, đồng thời quan tâm đến thái độ của người dùng để cải thiện trải nghiệm tích cực cho khách hàng