Safeguarding security and privacy remains a major challenge with regards to the Internet of Things (IoT) primarily due to the large scale and distribution of IoT networks. The information systems in Smart Homes are mainly based on sharing information through smart devices (IoT) and embedded sensors. Each sensor generates data to be processed or assembled by a central system. This data, while being

Deduplication scheme based on convergent encryption (CE) is widely-used in cloud storage system to eliminate redundant data. However, the adversaries can obtain the data by the brute-force attack, if the data belongs to a predictable set for CE. In addition, previous works usually introduce the third-party auditors to execute the data integrity verification, suffering from data disclosure by the auditors

Blockchain has received a lot of attention recently for its characteristics of decentralization, immutability, traceability, etc., making it a promising technology for the development of various applications, especially the management of various digital information. However, most current blockchain systems exhibit problems such as high computational overhead and centralization of power. Reliance on

A large volume of patients’ opinions—as online doctor reviews (ODRs)—are available online in order to access, analyze, and improve patients’ perceptions about the quality of care; however, this development needs to be explored further. Drawing on the two-factor theory, this paper aims to mine ODRs to explore the different determinants of patient satisfaction (PS) and patient dissatisfaction (PD) toward

Meeting the information needs of cancer patients’ family members is critical for improving care quality and family members’ well-being. An online health community (OHC) can be an effective channel to provide information support, thus attracting many cancer patients’ family members. Several studies have examined the information needs of cancer patients’ family members in OHCs. However, most of them

Linking textual content to entities from the knowledge graph has received increasing attention in the context of which surface form representations of entities, e.g., terms or phrases, are disambiguated and linked to appropriate entities. This allows textual content, e.g., social user-generated content, to be interpreted and reasoned on at a higher semantic level. However, recent research has shown

Automatic summarization systems provide an effective solution to today's unprecedented growth of textual data. For real-world tasks, such as data mining and information retrieval, the factual correctness of generated summary is critical. However, existing models usually focus on improving the informativeness rather than optimizing factual correctness. In this work, we present a Fact-Aware Reinforced

Being one of the most common empirical regularities, the Zipf’s law for word frequencies is a power law relation between word frequencies and frequency ranks of words. We quantitatively study semantic uncertainty of words through non-point distribution-based word embeddings and reveal the Zipfian regularities. Uncertainty of a word can increase due to polysemy, the word having “broad” meaning (such

With the increasing number of open-source software projects, code plagiarism has become one of the threats to the software industry. However, current research on code copyright protection mostly focuses on the approach for code plagiarism detection, failing to fundamentally solve the problem of copyright confirmation and protection. This paper proposes a blockchain-based code copyright management system

The domain feature retrieval has potential applications in text summarization. However, it is challenging to mine domain features from the user reviews. In this paper, a novel Domain Feature Miner (DOFM) is designed by (i) formulating the feature mining problem as a clustering problem and (ii) engaging three newly conceived empirical observations such as frequency count, grouping semantics, and distributional

Blockchain technologies and distributed ledgers enable the design and implementation of trustable data logging systems that can be used by multiple parties to produce a non-repudiable database. The case of Internet of Vehicles may greatly benefit of such a possibility to track the chain of responsibility in case of accidents or damages due to bad or omitted maintenance, improving the safety of circulation

The surveillance and forecast of newly confirmed cases are important to mobilize medical resources and facilitate policymaking during a public health emergency. Digital surveillance using data available online has increasingly become a trend with the advancement of the Internet. In this study, we assessed the predictive value of multiple online medical behavioral data, including online medical consultation

The ubiquity of user-item interactions makes it essential and challenging to utilize the rich variety of hidden structural and temporal information for effective and efficient recommendation. In this work, our goal is to address the limitations of existing research: (i) inadequacy of popularity trend prediction and temporal recommendation (ii) failure to clarify the influence and mechanism of structural

Although the sharing of knowledge in online health communities (OHCs) has been explored in recent years, little research has been done to explore the relationship between general and specific knowledge-sharing. Based on the literature on knowledge-sharing in OHCs, this study developed a research model to explore how physicians’ general knowledge-sharing behaviors influence their specific knowledge-sharing

Conventionally, it is common that video retrieval methods aggregate the visual feature representations from every frame as the feature of the video, where each frame is treated as an isolated, static image. Such methods lack the power of modeling the intra-frame and inter-frame relationships for the local regions, and are often vulnerable to the visual redundancy and noise caused by various types of

LoRa serves as one of the most deployed technologies in Internet-of-Things-based information systems (IoT-IS), and self-motivated deployment is the key to the rollout of LoRa. Proper incentive can play an important role in encouraging the private deployment of LoRa, increasing coverage and promoting effective management of IoT-IS. However, existing incentive mechanisms have the vulnerabilities of insecure

Fact verification aims to retrieve relevant evidence from a knowledge base, e.g., Wikipedia, to verify the given claims. Existing methods only consider the sentence-level semantics for evidence representations, which typically neglect the importance of fine-grained features in the evidence-related sentences. In addition, the interpretability of the reasoning process has not been well studied in the

Timestamps allow us to identify a date and time when a piece of data existed or an event took place. For example, we use timestamps to establish the date when we grant a patent. Services that offer trusted timestamps on the blockchain exist, where one creates a timestamp on a value by sending the blockchain a transaction containing the value, which is eventually confirmed in a block a miner creates

Online healthcare communities (OHCs) have become producers of medical information. Solving the issue of how to effectively reuse such a large amount of medical data and discover its potential value is of the utmost importance for alleviating the shortage of medical resources. Online consultation has received widespread attention and population since its first appearance in 1999, and as a result, many

The advent of academic social networking sites (ASNS) has provided a more convenient way for users to seek out experts. This paper uses ResearchGate to explore the features of pathways through four different tasks, combined with questionnaires and interviews. These tasks are aimed at identifying the right experts with relevant expertise or experience for a given topic. We propose a pathway-based approach

Mobile health (mHealth) apps and devices are increasingly popular for health research, clinical treatment, and personal wellness, as they offer the ability to continuously monitor aspects of individuals’ health as they go about their everyday activities. Combining the data produced by these mHealth devices may give healthcare providers a more holistic view of a patient’s health, increase the level

Quality function deployment (QFD), as a widely-used quality management method, can effectively capture customers’ preferences of products and services by involving lots of evaluations including the weights of customer requirements, the performance of alternatives under different design requirements and the interactions of design requirements. However, there are challenges to use this tool to solve

The present paper focuses on semi-supervised classification problems. Semi-supervised learning is a learning task through both labeled and unlabeled samples. One of the main issues in semi-supervised learning is to use a proper selection metric for sampling from the unlabeled data in order to extract informative unlabeled data points. This is indeed vital for the semi-supervised self-training algorithms

Information behavior for health and fitness have attained increasing attention in the e-health field. However, mobile users still need exploring a largely untapped resource on fitness apps to obtain more health benefits. Regarding that prior studies seldom focused on which factor hinders fitness app exploration, this study proposes a moderated mediation model to investigate the mechanisms underlying

The platforms supporting the smart city applications are rarely implemented from scratch by a municipality and/or totally owned by a single company, but are more typically realized by integrating some existing ICT infrastructures thanks to a supporting platform, such as the well known FIWARE platform. Such a multi-tenant deployment model is required to lower the initial investment costs to implement

Medical crowdfunding is a rapidly growing healthcare practice whereby individuals leverage online platforms to assemble numerous small donations for health-related needs. In China, due to limited medical resources and disparities between urban and rural public healthcare systems, medical crowdfunding can play a complementary role in supporting the current publicly-funded medical insurance system. To

Blockchain technology brings innovation to various industries. Ethereum is currently the second blockchain platform by market capitalization, it’s also the largest smart contract blockchain platform. Smart contracts can simplify and accelerate the development of various applications, but they also bring some problems. For example, smart contracts are used to commit fraud, vulnerability contracts are

Sentiment analysis (SA) is a natural language processing (NLP) application that aims to analyse and identify sentiment within a piece of text. Arabic SA started to receive more attention in the last decade with many approaches showing some effectiveness for detecting sentiment on multiple datasets. While there have been some surveys summarising some of the approaches for Arabic SA in literature, most

Hospital information system (HIS) implementation has become an important part of health informatics construction in China. However, effective use of HIS in the Chinese context has scarcely been discussed in the academic literature. Drawing upon Burton-Jones and Grange's (2013) effective use theory, this paper investigates the antecedents of medical workers’ effective use of HIS and the relationships

Live streaming becomes very popular in recent years. An accurate live streaming recommendation system is key to enhance user experience. As both viewer and anchor change their preferences dynamically, using existing recommendation approaches cannot fully capture their preferences. In this paper, we study how to model both viewer and anchor’s dynamic behaviors and predict their behaviors from two angles

Graph convolutions, in both their linear and neural network forms, have reached state-of-the-art accuracy on recommender system (RecSys) benchmarks. However, recommendation accuracy is tied with diversity in a delicate trade-off and the potential of graph convolutions to improve the latter is unexplored. Here, we develop a model that learns joint convolutional representations from a nearest neighbor

Traditional topic models are widely used for semantic discovery from long texts. However, they usually fail to mine high-quality topics from short texts (e.g. tweets) due to the sparsity of features and the lack of word co-occurrence patterns. In this paper, we propose a Variational Auto-Encoder Topic Model (VAETM for short) by combining word vector representation and entity vector representation to

Investor based social networks enable investors to share sentiments (e.g., bullish or bearish) about stock trends. Modeling and predicting the qualities of investor sentiments is a critical problem when aggregating sentiments and making investment recommendations. Most previous works relied on the overall past performance of investors to assess the quality of investor sentiments. However, we show that

Glaucoma, a group of eye diseases, damages individual eye health by injuring the optic nerve, and this leads to inevitable vision loss. Although the symptoms of glaucoma can be observed by experts, the procedure for doing so is still complex and time-consuming. This problem has become more acute in China than in other locations due to its high population and limited medical resources. With the development

Aim In a pilot study to improve detection of malignant lesions in breast mammograms, we aimed to develop a new method called BDR-CNN-GCN, combining two advanced neural networks: (i) graph convolutional network (GCN); and (ii) convolutional neural network (CNN). Method We utilised a standard 8-layer CNN, then integrated two improvement techniques: (i) batch normalization (BN) and (ii) dropout (DO).

Graph Convolutional Networks (GCNs) have been established as a fundamental approach for representation learning on graphs, based on convolution operations on non-Euclidean domain, defined by graph-structured data. GCNs and variants have achieved state-of-the-art results on classification tasks, especially in semi-supervised learning scenarios. A central challenge in semi-supervised classification consists

The aim in multi-label text classification is to assign a set of labels to a given document. Previous classifier-chain and sequence-to-sequence models have been shown to have a powerful ability to capture label correlations. However, they rely heavily on the label order, while labels in multi-label data are essentially an unordered set. The performance of these approaches is therefore highly variable

Individuals seek information for informed decision-making, and they consult a variety of information sources nowadays. However, studies show that information from multiple sources can lead to information overload, which then creates negative psychological and behavioral responses. Drawing on the Stimulus-Organism-Response (S-O-R) framework, we propose a model to understand the effect of information

Despite the computers have developed rapidly in recent years, there are still many difficulties to obtain a large number of labelled data in many practical problems, for example, medical image diagnosis, internet fraud, and pedestrian detection. To deal with learning problems with only a few labeled data, a novel semi-supervised learning method combined with dynamic graph learning with self-paced learning

Analysis of consumer reviews posted on social media is found to be essential for several business applications. Consumer reviews posted in social media are increasing at an exponential rate both in terms of number and relevance, which leads to big data. In this paper, a hybrid approach of two deep learning architectures namely Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) (RNN

With vast information that has been digitized and made available online, manually finding the answer to a question can be tedious. While search engines have emerged to facilitate information needs, users would have to manually read through the retrieved articles to locate the answer to a specific question. Therefore, the ability to automatically understand users’ natural language questions and find

Image-text retrieval task has been a popular research topic and attracts a growing interest due to it bridges computer vision and natural language processing communities and involves two different modalities. Although a lot of methods have made a great progress in image-text task, it remains challenging because of the difficulty to learn the correspondence between two heterogeneous modalities. In this

Blockchain has been one of the most attractive technologies for many modern and even future applications. Fabric, an open-source framework to implement the permissioned enterprise-grade blockchain, is getting increasing attention from innovators. The latency performance is crucial to the Fabric blockchain in assessing its effectiveness. Many empirical studies were conducted to analyze this performance

In online review sites, the analysis of user feedback for assessing its helpfulness for decision-making is usually carried out by locally studying the properties of individual reviews. However, global properties should be considered as well to precisely evaluate the quality of user feedback. In this paper we investigate the role of deviations in the properties of reviews as helpfulness determinants

Academic Social Networking Sites (ASNSs) are increasingly used by scholars to create academic profiles, share research publications, and interact with peers, among many other functions. However, it remains unclear how factors such as discipline may affect the scholarly use of such sites. This study chose ResearchGate (RG) as the sample site and gathered data from a total of 77,902 users from 61 U.S

In recent years, social media has increasingly become one of the popular ways for people to consume news. As proliferation of fake news on social media has the negative impacts on individuals and society, automatic fake news detection has been explored by different research communities for combating fake news. With the development of multimedia technology, there is a phenomenon that cannot be ignored

Digital Twins are complex digital representations of assets that are used by a variety of organizations across the Industry 4.0 value chain. As the digitization of industrial processes advances, Digital Twins will become widespread. As a result, there is a need to develop new secure data sharing models for a complex ecosystem of interacting Digital Twins and lifecycle parties. Decentralized Applications

The gender information of named entities is an important prerequisite for many text analysis tasks such as gender bias detection and targeted advertising. Despite its valuable use cases, gender tagging of named entities has traditionally been database-reliant. The lack of open-source benchmarks is a major impediment to exploring the effectiveness of machine learning-based methods for this task. Towards

Responding to requests from citizens is an essential administrative service that affects the daily life of people. The drastic increase in the volume of citizen requests in recent years has necessitated on-going studies on the automatic classification of citizen requests due to the time, effort, and misclassification errors involved in manual classification. Even though there have been prior studies

Multi-turn response selection is a major task in building intelligent dialogue systems. Most existing works focus on modeling the semantic relationship between the utterances and the candidate response with neural networks like RNNs and various attention mechanisms. In this paper, we study how to leverage the advantage of pre-trained language models (PTMs) to multi-turn response selection in retrieval-based

Emoticons are popularly used to express user’s feelings in social media, blogs, and instant messaging. However, the number of emoticons existing in emoticon dictionaries which users select from is large, thus, it is difficult for users to find the desired emoticon that matches the content of their messages. In this paper, we propose a method that supports users’ emoticon selection by reordering 167

Although scientometricians have focused on the strength of the citation impact of scientific publications, only a few have paid special attention to the width of the citation impact. In this article, we aim to understand the width by establishing our empirical study on a previously built structure, namely ego-centered citation networks (ECCNs). We remove the direct links to the focal publication and

The ubiquity of connecting technologies in smart vehicles and the incremental automation of its functionalities promise significant benefits, including a significant decline in congestion and road fatalities. However, increasing automation and connectedness broadens the attack surface and heightens the likelihood of a malicious entity successfully executing an attack. In this paper, we propose a Blockchain

Phrase embedding can improve the performance of multiple NLP tasks. Most of the previous phrase-embedding methods that only use the external or internal semantic information of phrases to learn phrase embedding are challenging to solve the problem of data sparseness and have poor semantic presentation ability. To solve the above issues, in this paper, we propose an autoencoder-based method to combine

This paper introduces the perspective of dynamic citation process to identify citation patterns of scientific breakthroughs. We construct a series of citation metrics and apply them to over 100 pairs of Nobel and non-Nobel papers with millions of citations. As expected, we find that most metrics cannot distinguish the two groups under similar conditions of discipline, publication year, venue, and citation

Music has long been recognised to be able to alter people's emotions and behaviours, yet how university students use music for learning and well-being is largely unexplored. With one of the largest music user populations in the world, China has tremendous market potential for digital music. This study explores music use behaviours for learning and well-being among university students in China and how

Pneumonia is a global disease that causes high children mortality. The situation has even been worsening by the outbreak of the new coronavirus named COVID-19, which has killed more than 983,907 so far. People infected by the virus would show symptoms like fever and coughing as well as pneumonia as the infection progresses. Timely detection is a public consensus achieved that would benefit possible

The outline for the “Healthy China 2030” emphasizes social media use in health knowledge communication. Health knowledge communication enabled by social media relies heavily on recipients’ willingness to adopt and share health knowledge, which is consequently determined by their trust in content, source, and the platform. However, little is known about the relative importance and differential effects

Personalized place semantics recognition is the process of giving individual semantic labels to locations, e.g., “home” and “school”. Capturing personalized place semantics exactly is critical for location-based services. To address the problems of existing methods, i.e., the insufficient utilization of context information and the neglect of the semantic correlation across related tasks, we propose

Motivated by the practical needs of enhancing social media rumor refutation effectiveness, this paper is dedicated to develop a proper rumor refutation effectiveness index (REI), identify key factors influencing REI and propose decision making suggestions for rumor refutation platforms. 298,118 pieces of comments and 185,209 pieces of the reposters’ verification status of 248 rumor refutation microblogs