Privacy Protection in COVID Data Tracking: Textual Analysis of the Literature

The literary review presented in the following paper aims to analyze the tracking tools used in different countries during the period of the COVID-19 pandemic. Tracking apps that have been adopted in many countries to collect data in a homogeneous and immediate way have made up for the difficulty of collecting data and standardizing evaluation criteria. However, the regulation on the protection of personal data in the health sector and the adoption of the new General Data Protection Regulation in European countries has placed a strong limitation on their use. This has not been the case in non-European countries, where monitoring methodologies have become widespread. The textual analysis presented is based on co-occurrence and multiple correspondence analysis to show the contact tracing methods adopted in different countries in the pandemic period by relating them to the issue of privacy. It also analyzed the possibility of applying Blockchain technology in applications for tracking contagions from COVID-19 and managing health data to provide a high level of security and transparency, including through anonymization, thus increasing user trust in using the apps.

An Analysis of A Rose for Emily from the Perspective of Spatial Narrative

The novel A Rose for Emily is one of the early works of William Faulkner,a famous American southern writer who has won The Nobel Prize in Literature in 1950.Spatial narrative theory analyzes and summarizes the spatial characteristics of modern and postmodern novels,showing the narrative characteristics of the works that the time is disrupted and suspended,and the space is extended indefinitely.This paper intends to interpret A Rose for Emily from the perspective of spatial narrative theory,and analyze the geological space,social space and text space in the novel,so as to reveal the important role of spatial structure in creating atmosphere and promoting narrative process,in order to better grasp the spatial metaphors in Faulkner’s novels.

Study on the Textual Coherence Function of Conjunctions in Political Texts and Their Translation Reconstruction

The assessment of translation quality in political texts is primarily based on achieving effective communication.Throughout the translation process,it is essential to not only accurately convey the original content but also effectively transform the structural mechanisms of the source language.In the translation reconstruction of political texts,various textual cohesion methods are often employed,with conjunctions serving as a primary means for semantic coherence within text units.

Genetic-Based Keyword Matching DBSCAN in IoT for Discovering Adjacent Clusters

As location information of numerous Internet of Thing(IoT)devices can be recognized through IoT sensor technology,the need for technology to efficiently analyze spatial data is increasing.One of the famous algorithms for classifying dense data into one cluster is Density-Based Spatial Clustering of Applications with Noise(DBSCAN).Existing DBSCAN research focuses on efficiently finding clusters in numeric data or categorical data.In this paper,we propose the novel problem of discovering a set of adjacent clusters among the cluster results derived for each keyword in the keyword-based DBSCAN algorithm.The existing DBSCAN algorithm has a problem in that it is necessary to calculate the number of all cases in order to find adjacent clusters among clusters derived as a result of the algorithm.To solve this problem,we developed the Genetic algorithm-based Keyword Matching DBSCAN(GKM-DBSCAN)algorithm to which the genetic algorithm was applied to discover the set of adjacent clusters among the cluster results derived for each keyword.In order to improve the performance of GKM-DBSCAN,we improved the general genetic algorithm by performing a genetic operation in groups.We conducted extensive experiments on both real and synthetic datasets to show the effectiveness of GKM-DBSCAN than the brute-force method.The experimental results show that GKM-DBSCAN outperforms the brute-force method by up to 21 times.GKM-DBSCAN with the index number binarization(INB)is 1.8 times faster than GKM-DBSCAN with the cluster number binarization(CNB).

Multimodal sentiment analysis for social media contents during public emergencies

Purpose:Nowadays,public opinions during public emergencies involve not only textual contents but also contain images.However,the existing works mainly focus on textual contents and they do not provide a satisfactory accuracy of sentiment analysis,lacking the combination of multimodal contents.In this paper,we propose to combine texts and images generated in the social media to perform sentiment analysis.Design/methodology/approach:We propose a Deep Multimodal Fusion Model(DMFM),which combines textual and visual sentiment analysis.We first train word2vec model on a large-scale public emergency corpus to obtain semantic-rich word vectors as the input of textual sentiment analysis.BiLSTM is employed to generate encoded textual embeddings.To fully excavate visual information from images,a modified pretrained VGG16-based sentiment analysis network is used with the best-performed fine-tuning strategy.A multimodal fusion method is implemented to fuse textual and visual embeddings completely,producing predicted labels.Findings:We performed extensive experiments on Weibo and Twitter public emergency datasets,to evaluate the performance of our proposed model.Experimental results demonstrate that the DMFM provides higher accuracy compared with baseline models.The introduction of images can boost the performance of sentiment analysis during public emergencies.Research limitations:In the future,we will test our model in a wider dataset.We will also consider a better way to learn the multimodal fusion information.Practical implications:We build an efficient multimodal sentiment analysis model for the social media contents during public emergencies.Originality/value:We consider the images posted by online users during public emergencies on social platforms.The proposed method can present a novel scope for sentiment analysis during public emergencies and provide the decision support for the government when formulating policies in public emergencies.

Improving Targeted Multimodal Sentiment Classification with Semantic Description of Images

Targeted multimodal sentiment classification(TMSC)aims to identify the sentiment polarity of a target mentioned in a multimodal post.The majority of current studies on this task focus on mapping the image and the text to a high-dimensional space in order to obtain and fuse implicit representations,ignoring the rich semantic information contained in the images and not taking into account the contribution of the visual modality in the multimodal fusion representation,which can potentially influence the results of TMSC tasks.This paper proposes a general model for Improving Targeted Multimodal Sentiment Classification with Semantic Description of Images(ITMSC)as a way to tackle these issues and improve the accu-racy of multimodal sentiment analysis.Specifically,the ITMSC model can automatically adjust the contribution of images in the fusion representation through the exploitation of semantic descriptions of images and text similarity relations.Further,we propose a target-based attention module to capture the target-text relevance,an image-based attention module to capture the image-text relevance,and a target-image matching module based on the former two modules to properly align the target with the image so that fine-grained semantic information can be extracted.Our experimental results demonstrate that our model achieves comparable performance with several state-of-the-art approaches on two multimodal sentiment datasets.Our findings indicate that incorporating semantic descriptions of images can enhance our understanding of multimodal content and lead to improved sentiment analysis performance.

Active Learning Strategies for Textual Dataset-Automatic Labelling

The Internet revolution has resulted in abundant data from various sources,including social media,traditional media,etcetera.Although the availability of data is no longer an issue,data labelling for exploiting it in supervised machine learning is still an expensive process and involves tedious human efforts.The overall purpose of this study is to propose a strategy to automatically label the unlabeled textual data with the support of active learning in combination with deep learning.More specifically,this study assesses the performance of different active learning strategies in automatic labelling of the textual dataset at sentence and document levels.To achieve this objective,different experiments have been performed on the publicly available dataset.In first set of experiments,we randomly choose a subset of instances from training dataset and train a deep neural network to assess performance on test set.In the second set of experiments,we replace the random selection with different active learning strategies to choose a subset of the training dataset to train the same model and reassess its performance on test set.The experimental results suggest that different active learning strategies yield performance improvement of 7% on document level datasets and 3%on sentence level datasets for auto labelling.

Attack Behavior Extraction Based on Heterogeneous Cyberthreat Intelligence and Graph Convolutional Networks

The continuous improvement of the cyber threat intelligence sharing mechanism provides new ideas to deal with Advanced Persistent Threats(APT).Extracting attack behaviors,i.e.,Tactics,Techniques,Procedures(TTP)from Cyber Threat Intelligence(CTI)can facilitate APT actors’profiling for an immediate response.However,it is difficult for traditional manual methods to analyze attack behaviors from cyber threat intelligence due to its heterogeneous nature.Based on the Adversarial Tactics,Techniques and Common Knowledge(ATT&CK)of threat behavior description,this paper proposes a threat behavioral knowledge extraction framework that integrates Heterogeneous Text Network(HTN)and Graph Convolutional Network(GCN)to solve this issue.It leverages the hierarchical correlation relationships of attack techniques and tactics in the ATT&CK to construct a text network of heterogeneous cyber threat intelligence.With the help of the Bidirectional EncoderRepresentation fromTransformers(BERT)pretraining model to analyze the contextual semantics of cyber threat intelligence,the task of threat behavior identification is transformed into a text classification task,which automatically extracts attack behavior in CTI,then identifies the malware and advanced threat actors.The experimental results show that F1 achieve 94.86%and 92.15%for the multi-label classification tasks of tactics and techniques.Extend the experiment to verify the method’s effectiveness in identifying the malware and threat actors in APT attacks.The F1 for malware and advanced threat actors identification task reached 98.45%and 99.48%,which are better than the benchmark model in the experiment and achieve state of the art.The model can effectivelymodel threat intelligence text data and acquire knowledge and experience migration by correlating implied features with a priori knowledge to compensate for insufficient sample data and improve the classification performance and recognition ability of threat behavior in text.

The Loss of Textual Function Caused by Passive Voice Translation

Passive voice is an important grammatical phenomenon,and the translation of English passive voice is a hot issue in translation research.In translation,some linguistic phenomena of the source language cannot be expressed in the target language,and the translation has limits of translatability.However,fewer scholars have studied the loss of textual function caused by the translation of passive voice.The authors argue that the conversion of voice in the translation process can,to a certain extent,cause the loss of meaning.In this paper,the authors analyze the loss of textual function caused by the conversion of English passive voice to Chinese active voice from the perspective of the limits of translatability.The authors believe that this phenomenon is common and unavoidable.Therefore,when dealing with the passive voice,the translator should preserve its discourse function as much as possible,rather than just“converting passive to active voice”.

TCBR中案例相关度及案例冗余处理模型

首先介绍基于文档的案例推理技术TCBR,以及CRN、IEs等基本理论,然后详细介绍CRN的构建过程和相关的函数模型,并提出一个求和函数来计算案例与当前问题的相关值,在文章的后面阐述了TCBR的案例学习方法以及案例相似程度的一个判断函数,并提出在这种情况下对案例冗余的处理方案。

An Investigation into the Case of Multi-interpretation in Poetry Translation

In translating poems, it is very common that different people have quite different versions of the same poem. This paper therefore intends to expound upon the underlying factors from the perspective of Hermeneutics,by exploring the relationship between the textual meaning and the textual significance of a poem, as well as the relationship between the author's intention and the textual intention of a poem, aiming to explain the key element in translating poems—multi-interpretation.

Textual Function of Expletives in Godfather

This thesisanalyzes the expletives in the film Godfather with the theoretical frame of Halliday's(1985) System-Functional Linguistics Theory,especially from the perspective of the textual function of expletives,namely,we will elaborate the textual function with the film Godfather as the example. The expletives may be very appropriate and necessary in certain situation since they have a cohesive function.

On Textual Function of English Business Letters

The English business letter (EBL) is an important written text used for international business communication and it has its own features of text. This paper explores the textual function of EBL in terms of thematic structure and thematic progression and finds that EBL has its unique textual features.

A Study of Textual Metaphor in Hotel English

The theory of Grammatical Metaphor was first put forward by Halliday-the founder of systematic-functional school, whose research, however, only limited to ideational metaphor and interpersonal metaphor. Halliday took textual metaphor with a pinch of salt. This paper will focus on four representative forms of textual metaphor, that is, metaphorical thematic structure, metaphorical information structure, metaphorical cohesion and nominalization, based on Hotel English, to elaborate its factuality and theoreticality and excavate its functions in Hotel communication.

A Brief Introduction of Macrostructures Theory

In recent years,numerous textual studies have appeared.However,the majority of them concentrated on the study of cohesion and coherence of sentences.Van Dijk(1977,1980),Holland famous linguist,put forward"Macrostructures"which provide us with a theo retical basis to study macrostructure of texts.The paper aims to introduce it in very detail and make it known to all the English learners.

A Metafunctional Analysis of About Books

Since the proposal of metafunctions, they are often used to analyze the discourse. This paper is to analyze the text About Books from these three metafunctions.

Comparative Textual Studies on Bai Hua's Poem Xiatian Hai Hen Yuan and Its English Translations

Based on the poem Xiatian Hai Hen Yuan夏天还很远, one of the most characteristic works of the Chinese poet Bai Hua, along with its four English translations, this paper conducts comparative textual studies with regard to three aspects, including the translation of hidden personae, the selection of the tense and aspectas well as the rendition of poetic rhythms. The author holds the view that, on the one hand, different textual interpretations have gone a long way toward the dissemination of the original poem in the English-speaking world;on the other hand, the translator's proper understanding and rendition is of great significance with a view to a better presentation of the unique beauty of Chinese poetry both in the content and the form.

Functions of Discourse Markers

Discourse markers are pervasive in conversation, generally used to facilitate human communication. With respect to different contextual effects that discourse markers have in utterance interpretation, they may function subjectively, interactionally or textually.

Review on“Long-Dan”,one of the traditional Chinese medicinal herbs recorded in Chinese Pharmacopoeia

“Long-Dan”is an important traditional Chinese medicinal(TCM)herb used widely for the treatment of inflammation,hepatitis,rheumatism,cholecystitis,and tuberculosis.In the Chinese Pharmacopoeia,the roots and rhizomes of four species from the genus Gentiana(Gentianaceae)are recorded as the original materials of“Long-Dan”,called Gentianae Radix et Rhizoma.The species included G.manshurica,G.scabra,G.triflora and G.rigescens,which are distributed in different areas of China.Though iridoid and secoiridoid glucosides were reported as the main constituents in“Long-Dan”,these four different species also resulted in different minor components,which may related to their pharmacological activities.Herein,we summarized the herbal textual study,distribution,chemical constituents,biological investigation and quality control of the recorded“Long-Dan”origins in Chinese Pharmacopoeia during the period 1960 to 2011.

Prospects of utilization of waste dumped low-grade limestone for iron making: A case study

Low grade dumped limestone sample having high silica of 8.1%, 36.8% CaO, and 3% Al2O3 has been studied with the aim to reduce the silica level to below 3% for its utilization in iron making. Beneficiation study of the sample was initiated with desliming of the feed sample of -100 μm to remove the siliceous ultrafine particles and to improve the feed quality. Flotation study was carried out by column flotation technique varying the collector dosage, superficial air flow velocity and froth depth to assess their effect on silica reduction and CaO recovery. It was observed that increased collector dosage and superficial air velocity increases the recovery of CaO, and increase in the froth depth reduces the mass flow and silica content in the concentrate. The best result was found at 1.25 cm/sec superficial air velocity, 25 cm froth depth, 1.25 kgpt collector dosage and concentrate assayed 47.3% CaO, 2.8% silica with 72% CaO recovery.