Character-level Convolutional Networks for Text Classification

This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF variants, and deep learning models such as word-based ConvNets and recurrent neural networks.Comment: An early version of this work entitled "Text Understanding from Scratch" was posted in Feb 2015 as arXiv:1502.01710. The present paper has considerably more experimental results and a rewritten introduction, Advances in Neural Information Processing Systems 28 (NIPS 2015

Decentralised Distributed Massive MIMO

In this thesis, decentralised distributed massive multiple-input multiple-output (DD-MaMIMO) is considered for providing high spectral efficiency (SE) per user. In the DD-MaMIMO system, a large number of access points (APs) within a coordination region are connected to an edge processing unit (EPU) via fronthaul links, serving the users within a service region. Initially, we investigate a DD-MaMIMO system with perfect fronthaul links and assume that the processing takes place in the EPU. To demonstrate the improved SE, we compare our proposed architecture to cell-free MaMIMO. Furthermore, we discuss the scalability of DD-MaMIMO and give its definition. Secondly, we extend our research to the limited-capacity fronthaul links which is essential in practice. To model the limited-capacity fronthaul links, we adopt the Bussgang decomposition to express the quantisation. We propose two strategies for obtaining channel state information (CSI): estimate-and-quantise (EQ) and quantise-and-estimate (QE). Particularly, in the QE scheme, we derive the closed-form expressions of Bussgang decomposition coefficients for the non-Gaussian distribution input of the quantiser, as the elements of pilots follow complex Gaussian distribution. Both CSI acquisition strategies are analysed with respect to the mean square error (MSE) of channel estimation. Finally, we explore the processing which happens at the AP which is the local estimation in DD-MaMIMO. Here, two approaches are exploited for data decoding at the EPU: simply averaging decoding and large scale fading decoding. We further compare the local estimation scheme with the decentralised processing scheme. The scalability is also discussed as the channel estimation and data detection happens at the AP

IMPROVED DESIGN OF DTW AND GMM CASCADED ARABIC SPEAKER

In this paper, we discuss about the design, implementation and assessment of a two-stage Arabic speaker recognition system, which aims to recognize a target Arabic speaker among several people. The first stage uses improved DTW (Dynamic Time Warping) algorithm and the second stage uses SA-KM-based GMM (Gaussian Mixture Model). MFCC (Mel Frequency Cepstral Coefficients) and its differences form, as acoustic feature, are extracted from the sample speeches. DTW provides three most possible speakers and then the recognition results are conveyed to GMM training processes. A specified similarity assessment algorithm, KL distance, is applied to find the best match with the target speaker. Experimental results show that text-independent recognition rate of the cascaded system reaches 90 percent

Distill the Image to Nowhere: Inversion Knowledge Distillation for Multimodal Machine Translation

Past works on multimodal machine translation (MMT) elevate bilingual setup by incorporating additional aligned vision information. However, an image-must requirement of the multimodal dataset largely hinders MMT's development -- namely that it demands an aligned form of [image, source text, target text]. This limitation is generally troublesome during the inference phase especially when the aligned image is not provided as in the normal NMT setup. Thus, in this work, we introduce IKD-MMT, a novel MMT framework to support the image-free inference phase via an inversion knowledge distillation scheme. In particular, a multimodal feature generator is executed with a knowledge distillation module, which directly generates the multimodal feature from (only) source texts as the input. While there have been a few prior works entertaining the possibility to support image-free inference for machine translation, their performances have yet to rival the image-must translation. In our experiments, we identify our method as the first image-free approach to comprehensively rival or even surpass (almost) all image-must frameworks, and achieved the state-of-the-art result on the often-used Multi30k benchmark. Our code and data are available at: https://github.com/pengr/IKD-mmt/tree/master..Comment: Long paper accepted by EMNLP2022 main conferenc

Better Sign Language Translation with Monolingual Data

Sign language translation (SLT) systems, which are often decomposed into video-to-gloss (V2G) recognition and gloss-to-text (G2T) translation through the pivot gloss, heavily relies on the availability of large-scale parallel G2T pairs. However, the manual annotation of pivot gloss, which is a sequence of transcribed written-language words in the order in which they are signed, further exacerbates the scarcity of data for SLT. To address this issue, this paper proposes a simple and efficient rule transformation method to transcribe the large-scale target monolingual data into its pseudo glosses automatically for enhancing the SLT translation. Empirical results show that the proposed approach can significantly improve the performance of SLT, especially achieving state-of-the-art results on two SLT benchmark datasets PHEONIX-WEATHER 2014T and ASLG-PC12. Our code has been released at: https://github.com/pengr/Mono\_SLT

The Influencing Factors Model of Cross-Border E-commerce Development: A Theoretical Analysis

Cross-border e-commerce (CBEC) is the future trend of cross-border trade. Although China is at the forefront of CBEC development, its transaction volume is still not satisfactory. The purpose of this paper is to study factors influencing the development of CBEC industry from the macro-environment perspective. First, we commented and summarized relevant literature at home and abroad about business ecosystem and factors determining the development of CBEC, then proposed a model of factors influencing CBEC development by combining business ecosystem theory with PEST framework, followed by interpretation and discussion. The model consists of core species, key species, supporting species, parasitic species in the CBEC ecosystem, and they are affected by external environmental factors from political, economic, social and technological perspectives