Simulation analysis of environmental pollution based on parallel data model

Through model verification and algorithm correction, many factors unfavorable to environmental protection existing in the production of export products abroad are analyzed. The growth trend of export trade volume of industrial industries and regions with a high degree of openness is generally consistent with the emission trend of environmental pollutants. Environmental pollution restricts China's economic growth and brings certain negative effects on China's economic growth, The relevant data analysis suggestions for improving the impact of export product production on environmental pollution are put forward.

Applications of Big Data and Green IoT-Enabling Technologies for Smart Cities

In recent years, enormous amounts of digital data have been generated. In parallel, data collection, storage, and analysis technologies have developed. Recently, there has been an increasing trend of people moving towards urban areas. By 2030 more than 60% of the world's population will live in an urban environment. Urban areas are big data resource because they include millions of citizens, technological devices, and vehicles which generate data continuously. Besides, rapid urbanization brings many challenges, such as environmental pollution, traffic congestion, health problems, energy management, etc. Some policies for countries are required to cope with urbanization problems. One of these policies is to build smart cities. Smart cities integrate information and communication technology and various physical devices connected to the network (the internet of things or IoT) to both improve the quality of government services and citizen welfare. This chapter presents a literature review of big data, smart cities, IoT, green-IoT concepts, using technology and methods, and applications worldwide.

Block Storage Optimization and Parallel Data Processing and Analysis of Product Big Data Based on the Hadoop Platform

The traditional distributed database storage architecture has the problems of low efficiency and storage capacity in managing data resources of seafood products. We reviewed various storage and retrieval technologies for the big data resources. A block storage layout optimization method based on the Hadoop platform and a parallel data processing and analysis method based on the MapReduce model are proposed. A multireplica consistent hashing algorithm based on data correlation and spatial and temporal properties is used in the parallel data processing and analysis method. The data distribution strategy and block size adjustment are studied based on the Hadoop platform. A multidata source parallel join query algorithm and a multi-channel data fusion feature extraction algorithm based on data-optimized storage are designed for the big data resources of seafood products according to the MapReduce parallel frame work. Practical verification shows that the storage optimization and data-retrieval methods provide supports for constructing a big data resource-management platform for seafood products and realize efficient organization and management of the big data resources of seafood products. The execution time of multidata source parallel retrieval is only 32% of the time of the standard Hadoop scheme, and the execution time of the multichannel data fusion feature extraction algorithm is only 35% of the time of the standard Hadoop scheme.

Improved neural machine translation for low-resource English–Assamese pair

Language translation is essential to bring the world closer and plays a significant part in building a community among people of different linguistic backgrounds. Machine translation dramatically helps in removing the language barrier and allows easier communication among linguistically diverse communities. Due to the unavailability of resources, major languages of the world are accounted as low-resource languages. This leads to a challenging task of automating translation among various such languages to benefit indigenous speakers. This article investigates neural machine translation for the English–Assamese resource-poor language pair by tackling insufficient data and out-of-vocabulary problems. We have also proposed an approach of data augmentation-based NMT, which exploits synthetic parallel data and shows significantly improved translation accuracy for English-to-Assamese and Assamese-to-English translation and obtained state-of-the-art results.

Deep Learning for Text Style Transfer: A Survey

Text style transfer is an important task in natural language generation, which aims to control certain attributes in the generated text, such as politeness, emotion, humor, and many others. It has a long history in the field of natural language processing, and recently has re-gained significant attention thanks to the promising performance brought by deep neural models. In this paper, we present a systematic survey of the research on neural text style transfer, spanning over 100 representative articles since the first neural text style transfer work in 2017. We discuss the task formulation, existing datasets and subtasks, evaluation, as well as the rich methodologies in the presence of parallel and non-parallel data. We also provide discussions on a variety of important topics regarding the future development of this task.

Developing Core Technologies for Resource-Scarce Nguni Languages

The creation of linguistic resources is crucial to the continued growth of research and development efforts in the field of natural language processing, especially for resource-scarce languages. In this paper, we describe the curation and annotation of corpora and the development of multiple linguistic technologies for four official South African languages, namely isiNdebele, Siswati, isiXhosa, and isiZulu. Development efforts included sourcing parallel data for these languages and annotating each on token, orthographic, morphological, and morphosyntactic levels. These sets were in turn used to create and evaluate three core technologies, viz. a lemmatizer, part-of-speech tagger, morphological analyzer for each of the languages. We report on the quality of these technologies which improve on previously developed rule-based technologies as part of a similar initiative in 2013. These resources are made publicly accessible through a local resource agency with the intention of fostering further development of both resources and technologies that may benefit the NLP industry in South Africa.