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

ĐÁNH GIÁ ĐẶC ĐIỂM THỦY HÓA TRONG MÔ HÌNH TÔM SINH THÁI TẠI XÃ TAM GIANG, HUYỆN NĂM CĂN, TỈNH CÀ MAU

This paper clarifies the hydrochemical characteristics of the organic shrimp model certified by Naturland at Tam Giang commune, Nam Can district, Ca Mau province. Pond water was sampled in 8 ponds at 3 points of time of distinct precipitation fluctuations during the year (March, July and November 2015) and hydrochemical parameters related to shrimp were analysed. Results show that salinity, total alkalinity and total hardness are largely dependent on the seasonal precipitation and evapotranspiration, highest in the middle of the dry season (March) and gradually reduced until the wet season-dry season transition (November). Most of the parameters fluctuate within the sampling times but still remain suitable for shrimp. The major constraints include oxygen supersaturation during daylight hours (7.48 ± 0.45 mg/l), low concentrations of soluble nutrients (NH4-N 0.21 ± 0.05 mg/l, NO3-N 0.06 ± 0.02 mg/l, PO4-P 0.02 ± 0.01 mg/l), and high iron contents (Fe2+ 0.08 ± 0.01 mg/l, Fe3+ 0.64 ± 0.14 mg/l) as compared to limits for shrimp aquaculture. The total shrimp yields are very low (355.4 kg/ha water surface/year in which wild shrimp species have accounted for 55%), positively correlated with water depth and temperature but negatively correlated with Fe2+. To improve pond water quality and shrimp yield, it is recommended to increase water depth, nutrient concentrations and restrict the effects of runoff at the beginning of the wet season.Bài báo này làm sáng tỏ các đặc điểm thủy hóa trong mô hình tôm sinh thái được Naturland công nhận tại xã Tam Giang, huyện Năm Căn, tỉnh Cà Mau. Mẫu nước mặt được lấy tại 8 ao nuôi, tại 3 thời điểm có sự khác biệt rõ về lượng mưa trong năm (tháng 3, tháng 7 và tháng 11/2015) và phân tích các thông số thủy hóa liên quan. Kết quả cho thấy độ mặn, độ kiềm tổng số và độ cứng toàn phần phụ thuộc rất lớn vào lượng mưa và lượng bốc hơi theo mùa, cao nhất giữa mùa khô (tháng 3) và giảm dần đến cuối mùa mưa-đầu mùa khô (tháng 11). Phần lớn các thông số dao động trong năm nhưng vẫn phù hợp cho nuôi tôm. Các hạn chế chủ yếu gồm hiện tượng quá bão hòa oxy vào ban ngày (7,48 ± 0,45 mg/l), nghèo dưỡng chất hòa tan (NH4-N 0,21 ± 0,05 mg/l, NO3-N 0,06 ± 0,02 mg/l, PO4-P 0,02 ± 0,01 mg/l) và hàm lượng sắt cao (Fe2+ 0,08 ± 0,01 mg/l, Fe3+ 0,64 ± 0,14 mg/l) so với tiêu chuẩn cho phép trong nước nuôi tôm. Tổng năng suất tôm rất thấp (355,4 kg/ha mặt nước/năm, trong đó tôm tự nhiên chiếm 55%), có tương quan thuận với độ sâu mực nước và nhiệt độ nhưng tương quan nghịch với Fe2+. Để cải thiện chất lượng nước và tăng năng suất tôm cần tăng độ sâu mực nước, gia tăng hàm lượng dưỡng chất trong ao nuôi và hạn chế ảnh hưởng của sự chảy tràn bề mặt vào đầu mùa mưa

Analysis on the performance of reconfigurable intelligent surface-aided free-space optical link under atmospheric turbulence and pointing errors

Free-space optical (FSO) communication can provide the cost-efficient, secure, high data-rate communication links required for applications. For example, it provides broadband Internet access and backhauling for the fifth-generation (5G) and the sixth-generation (6G) communication networks. However, previous solutions to deal with signal loss caused by obstructions and atmospheric turbulence. In these solutions, reconfigurable intelligent surfaces (RISs) are considered hardware technology to improve the performance of optical wireless communication systems. This study investigates the pointing error effects for RIS-aided FSO links under atmospheric turbulence channels. We analyze the performance of RIS-aided FSO links influenced by pointing errors, atmospheric attenuation, and turbulence for the subcarrier quadrature amplitude modulation (SC-QAM) technique. Atmospheric turbulence is modeled using log-normal distribution for weak atmospheric turbulence. Several numerical outcomes obtained for different transmitter beam waist radius and pointing error displacement standard deviation are shown to quantitatively illustrate the average symbol error rate (ASER)

Neural Collapse in Deep Linear Networks: From Balanced to Imbalanced Data

Modern deep neural networks have achieved impressive performance on tasks from image classification to natural language processing. Surprisingly, these complex systems with massive amounts of parameters exhibit the same structural properties in their last-layer features and classifiers across canonical datasets when training until convergence. In particular, it has been observed that the last-layer features collapse to their class-means, and those class-means are the vertices of a simplex Equiangular Tight Frame (ETF). This phenomenon is known as Neural Collapse ($\mathcal{NC}$). Recent papers have theoretically shown that $\mathcal{NC}$ emerges in the global minimizers of training problems with the simplified ``unconstrained feature model''. In this context, we take a step further and prove the $\mathcal{NC}$ occurrences in deep linear networks for the popular mean squared error (MSE) and cross entropy (CE) losses, showing that global solutions exhibit $\mathcal{NC}$ properties across the linear layers. Furthermore, we extend our study to imbalanced data for MSE loss and present the first geometric analysis of $\mathcal{NC}$ under bias-free setting. Our results demonstrate the convergence of the last-layer features and classifiers to a geometry consisting of orthogonal vectors, whose lengths depend on the amount of data in their corresponding classes. Finally, we empirically validate our theoretical analyses on synthetic and practical network architectures with both balanced and imbalanced scenarios.Comment: 93 pages, 20 figures, 4 tables. Hien Dang and Tho Tran contributed equally to this wor

Molecular Histopathology and Cytopathology in Cardiovascular Diseases

In this chapter, we describe the most deadly heart diseases, including the fourth parts: Anatomy of the heart, chronic coronary syndrome and acute coronary syndrome and STEMI, Cardiomyopathy, and Pulmonary embolism. The written structure of a component includes Abstract, Pathophysiology, Clinical diagnostic criteria, histopathology, and cytopathology. The content is summarized based on the recommendations of the American Heart Association and the European Society of Cardiology. All images in this chapter are data at our center. In the chapter, we will see the relationship between histopathology and cytopathology and pathophysiology, which will serve as a basis for us to have more studies in the future

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

Average symbol error rate analysis of reconfigurable intelligent surfaces based free-space optical link over Weibull distribution channels

Optical wireless communication (OWC) enables wireless connectivity using ultraviolet bands, infrared or visible. With its advantages features as high bandwidth, low cost, and operation in an unregulated spectrum. Free-space optical (FSO) communication systems are near terrestrial as a communication link between transceivers, the link is line-of-sight and successfully transmitted optical signals. Nevertheless, the optical signals transmissions over the FSO channels bring challenges to the system. To overcome the challenges posed by the FSO channels, the most common technique is to use relay stations, the most recent is the reconfigurable intelligent surfaces (RISs) technique. This study introduces a Weibull distribution model for a free-space optical communication link with RISs assisted, the parameter used to evaluate the performance of the system is the average symbol error rate (ASER). The RISs effect is examined by considering the influence of the transmitter beam waist radius, shape parameter, aperture radius, scale parameter, and signal-to-noise ratio on the ASER

BISMUTH FILM ELECTRODE FOR STRIPPING VOLTAMMETRIC DETERMINATION OF BLOOD LEAD AND PRELIMINARY ASSESSMENT OF BLOOD LEAD LEVEL IN THE RESIDENTS AT CANH DUONG VILLAGE, THUA THIEN HUE PROVINCE

Joint Research on Environmental Science and Technology for the Eart