A word-building method based on neural network for text classification

Text classification is a foundational task in many natural language processing applications. All traditional text classifiers take words as the basic units and conduct the pre-training process (like word2vec) to directly generate word vectors at the first step. However, none of them have considered the information contained in word structure which is proved to be helpful for text classification. In this paper, we propose a word-building method based on neural network model that can decompose a Chinese word to a sequence of radicals and learn structure information from these radical level features which is a key difference from the existing models. Then, the convolutional neural network is applied to extract structure information of words from radical sequence to generate a word vector, and the long short-term memory is applied to generate the sentence vector for the prediction purpose. The experimental results show that our model outperforms other existing models on Chinese dataset. Our model is also applicable to English as well where an English word can be decomposed down to character level, which demonstrates the excellent generalisation ability of our model. The experimental results have proved that our model also outperforms others on English dataset

AELA-DLSTMs: Attention-enabled and location-aware double LSTMs for aspect-level sentiment classification

Aspect-level sentiment classification, as a fine-grained task in sentiment classification, aiming to extract sentiment polarity from opinions towards a specific aspect word, has been made tremendous improvements in recent years. There are three key factors for aspect-level sentiment classification: contextual semantic information towards aspect words, correlations between aspect words and their context words, and location information of context words with regard to aspect words. In this paper, two models named AE-DLSTMs (Attention-Enabled Double LSTMs) and AELA-DLSTMs (Attention-Enabled and Location-Aware Double LSTMs) are proposed for aspect-level sentiment classification. AE-DLSTMs takes full advantage of the DLSTMs (Double LSTMs) which can capture the contextual semantic information in both forward and backward directions towards aspect words. Meanwhile, a novel attention weights generating method that combines aspect words with their contextual semantic information is designed so that those weights can make better use of the correlations between aspect words and their context words. Besides, we observe that context words with different distances or different directions towards aspect words have different contributions in sentiment polarity. Based on AE-DLSTMs, the location information of context words by assigning different weights is incorporated in AELA-DLSTMs to improve the accuracy. Experiments are conducted on two English datasets and one Chinese dataset. The experimental results have confirmed that our models can make remarkable improvements and outperform all the baseline models in all datasets, improving the accuracy of 1.67 percent to 4.77 percent in different datasets compared with baseline models

Convolution-deconvolution word embedding: an end-to-end multi-prototype fusion embedding method for natural language processing

Existing unsupervised word embedding methods have been proved to be effective to capture latent semantic information on various tasks of Natural Language Processing (NLP). However, existing word representation methods are incapable of tackling both the polysemousunaware and task-unaware problems that are common phenomena in NLP tasks. In this work, we present a novel Convolution-Deconvolution Word Embedding (CDWE), an end-to-end multi-prototype fusion embedding that fuses context-specific information and taskspecific information. To the best of our knowledge, we are the first to extend deconvolution (e.g. convolution transpose), which has been widely used in computer vision, to word embedding generation. We empirically demonstrate the efficiency and generalization ability of CDWE by applying it to two representative tasks in NLP: text classification and machine translation. The models of CDWE significantly outperform the baselines and achieve state-of-the-art results on both tasks. To validate the efficiency of CDWE further, we demonstrate how CDWE solves the polysemous-unaware and task-unaware problems via analyzing the Text Deconvolution Saliency, which is an existing strategy for evaluating the outputs of deconvolution

Scale-balanced loss for object detection

Object detection is an important field in computer vision. Nevertheless, a research area that has so far not received much attention is the study into the effectiveness of anchor matching strategy and imbalance in anchor-based object detection, in particular small object detection. It is clear that the objects with larger sizes tend to match more anchors than smaller ones. This matching imbalance may result in poor performance in detecting small objects. It can be alleviated by paying more attention to the objects that match with fewer anchors. We propose an innovative flexible loss function for object detection, which is compatible with popular anchor-based detection methods. The proposed method, called the scale-balanced loss, does not add any extra computational cost to the original pipelines. By re-weighting strategy, the proposed method significantly improves the accuracy of multi-scale object detection, especially for small objects. Comprehensive experiments indicate that the scale-balanced loss achieved excellent generalization performance when separately applied to some popular detection methods. The scale-balanced loss attained up to 15% improvements on recall rates of small and medium objects in both the PASCAL VOC and MS COCO dataset. It is also beneficial to the AP result on MS COCO with an improvement of more than 1.5%

A sentiment information collector–extractor architecture based neural network for sentiment analysis

Sentiment analysis, also known as opinion mining is a key natural language processing (NLP) task that receives much attention these years, where deep learning based neural network models have achieved great success. However, the existing deep learning models cannot effectively make use of the sentiment information in the sentence for sentiment analysis. In this paper, we propose a Sentiment Information Collector–Extractor architecture based Neural Network (SICENN) for sentiment analysis consisting of a Sentiment Information Collector (SIC) and a Sentiment Information Extractor (SIE). The SIC based on the Bi-directional Long Short Term Memory structure aims at collecting the sentiment information in the sentence and generating the information matrix. The SIE takes the information matrix as input and extracts the sentiment information precisely via three different sub-extractors. A new ensemble strategy is applied to combine the results of different sub-extractors, making the SIE more universal and outperform any single sub-extractor. Experiments results show that the proposed architecture outperforms the state-of-the-art methods on three datasets of different language

Why gadget usage among preschoolers should matter to teachers? a pilot study

Undoubtedly, gadgets are devices for communication, entertainment and education as well for younger children. Children play with gadgets sitting or lying down at home and worse of all, apart from lacking in physical activity, they only communicate with the apps screen rather than with their parents. Besides parents, teachers are crucial in nurturing preschoolers during the early stage of their development. As a result, the current qualitative study interviewed 14 teachers to perceive their knowledge in gadget usage, sedentary behaviour and social skills among preschoolers. Besides that, teaching methods were also uncovered to understand how the teachers enhance the social skills of preschoolers and reduce their sedentary behaviour. Inductive Analysis (IA) revealed that most teachers reported that, “Parents always give their mobile phones” to the preschoolers and interestingly, the preschoolers said, “Tell the teachers that they are playing gadgets at home.” Moreover, some of the teachers reported that gadgets are not safe for preschoolers if too much time is spent on them. As predicted, all the teachers were apparently unaware of the detriment of gadget usage on sedentary behaviour and social skills, especially for four-year-old children as most of them were quiet in preschool. The teachers’ attitude and habit were found to be moderate in lesson planning and improving the social skills of preschoolers but minimal for addressing their sedentary behaviour

Numerical analysis with experimental verification to predict outdoor power conversion efficiency of inverted organic solar devices

Inverted organic solar cell (IOSC) devices with different volume ratios of In₂S₃ nanoparticles have been studied under local spectral irradiances in Malaysia with respect to that of AM1.5G. The J-V curves of encapsulated IOSC devices were measured outdoor using an Ivium Potentiostat and local spectral irradiances were acquired using an AVANTES spectrometer concurrently. All of the IOSC devices experienced significant improvement in power conversion efficiency (PCE) under the both local sunny and cloudy conditions with respect to the AM 1.5G, by 22–35% and 31–65%, respectively. From spectral analysis, the area under the graph of spectral irradiance in UV–visible region is significantly higher compared to infrared region for both local sunny and cloudy conditions, by 44.6% and 55.9%, respectively, while it is only recorded as 12.9% for AM 1.5G. Last but not the least, we have successfully verified the numerical analysis to predict device performance by comparing the simulated and measured PCE values for different irradiance intensities whereby the prediction of PCE is better under sunny condition with a deviation of 3.4–10.8% compared to cloudy conditions, with deviation of 28.9–30.5%

Authentication of Smartphone Users Based on Activity Recognition and Mobile Sensing

Smartphones are context-aware devices that provide a compelling platform for ubiquitous computing and assist users in accomplishing many of their routine tasks anytime and anywhere, such as sending and receiving emails. The nature of tasks conducted with these devices has evolved with the exponential increase in the sensing and computing capabilities of a smartphone. Due to the ease of use and convenience, many users tend to store their private data, such as personal identifiers and bank account details, on their smartphone. However, this sensitive data can be vulnerable if the device gets stolen or lost. A traditional approach for protecting this type of data on mobile devices is to authenticate users with mechanisms such as PINs, passwords, and fingerprint recognition. However, these techniques are vulnerable to user compliance and a plethora of attacks, such as smudge attacks. The work in this paper addresses these challenges by proposing a novel authentication framework, which is based on recognizing the behavioral traits of smartphone users using the embedded sensors of smartphone, such as Accelerometer, Gyroscope and Magnetometer. The proposed framework also provides a platform for carrying out multi-class smart user authentication, which provides different levels of access to a wide range of smartphone users. This work has been validated with a series of experiments, which demonstrate the effectiveness of the proposed framework