In Codice Ratio is a research project to study techniques for analyzing the contents of historical documents conserved in the Vatican Apostolic Archives. In this paper, we present our efforts to develop a system to support the automatic transcription of medieval manuscripts, while maintaining the training data collection effort minimal. We focus on crowdsourcing as a means for scalable, expertless

Texts are the major information carrier for internet users, from which learning the latent representations has important research and practical value. Neural topic models have been proposed and have great performance in extracting interpretable latent topics and representations of texts. However, there remain two major limitations: (1) these methods generally ignore the contextual information of texts

During the outbreak of the new Coronavirus (2019-nCoV) in 2020, the spread of fake news has caused serious social panic. Fake news often uses multimedia information such as text and image to mislead readers, spreading and expanding its influence. One of the most important problems in fake news detection based on multimodal data is to extract the general features as well as to fuse the intrinsic characteristics

Predicting emerging research topics is important to researchers and policymakers. In this study, we propose a two-step solution to the problem of emerging topic prediction. The first step forecasts the future popularity score, a novel indicator reflecting the impact and growth, of candidate topics in a time-series manner. The second step selects novel topics from the candidates predicted to be popular

Popularity bias is an undesirable phenomenon associated with recommendation algorithms where popular items tend to be suggested over long-tail ones, even if the latter would be of reasonable interest for individuals. Such intrinsic tendencies of the recommenders may lead to producing ranked lists, in which items are not equally covered along the popularity tail. Although some recent studies aim to

Controllable response generation is an attractive and valuable task to the success of conversational systems. However, controlling both pattern and content of the response has not been well studied in existing models since they are mainly based on matching mechanisms. To tackle the problem, we first design a pattern model to automatically learn and extract speech patterns from words. The pattern is

Many information needs cannot be resolved with a single query, and instead lead naturally to a sequence of queries, issued as a search session. In a session test collection, each topic has an associated query sequence, with users assumed to follow that sequence when reformulating their queries. Here we propose a session-based offline evaluation framework as an extension to the existing query-based

Stance detection on social media is an emerging opinion mining paradigm for various social and political applications in which sentiment analysis may be sub-optimal. There has been a growing research interest for developing effective methods for stance detection methods varying among multiple communities including natural language processing, web science, and social computing, where each modeled stance

Electronic voting systems face many challenges, including authentication, privacy, data integrity, transparency and verifiability. However, the developed over 10 years ago blockchain technology provides an out-of-the-box solution for many of those challenges. Despite that, some issues are still to be solved, like remote authentication, anonymity and end-to-end verifiability. For this reason, research

The sharing of electronic medical records (EMRs) has great positive significance for research on disease and epidemic prevention. Recently, blockchain-based eHealth systems have achieved great success in terms of EMRs sharing and management, but there still remain some challenges. Permissioned blockchain-based solutions provide high throughput and scalability, but may suffer from rollback attacks and

We propose a hybrid context based topic model with an adaptive context window length for word sense disambiguation in document representation. Document representation is an essential part of various document based tasks, and word sense disambiguation is to capture the distinctions of word senses in the representation. Traditional methods mainly rely on knowledge libraries for data enrichment; however

Most existing methods for relation extraction tasks depend heavily on large-scale annotated data; they cannot learn from existing knowledge and have low generalization ability. It is urgent for us to solve the above problems by further developing few-shot learning methods. Because of the limitations of the most commonly used CNN model which is not good at sequence labeling and capturing long-range

Blockchain is a distributed digital ledger with features such as tamper-proof and privacy protection, which can provide reliable security solutions for the Internet of Things, smart home, and other scenarios. However, most of the devices in these scenarios only have limited computing, storage, bandwidth, and other resources, which makes it difficult to bear the burden of the blockchain consensus process

With the frequent occurrence of extreme natural phenomena, news about meteorological disasters has increased. As a timely and effective social sensor, social networks have gradually become an important data source for the perception of extreme meteorological events. Meteorological briefing refers to screening valuable knowledge from massive data to provide decision-makers with efficient situational

Industry 4.0-based oil and gas supply-chain (OaG-SC) industry automates and efficiently executes most of the processes by using cloud computing (CC), artificial intelligence (AI), Internet of things (IoT), and industrial Internet of things (IIoT). However, managing various operations in OaG-SC industries is a challenging task due to the involvement of various stakeholders. It includes landowners, Oil

Bitcoin introduced delegation of control over a monetary system from a select few to all who participate in that system. This delegation is known as the decentralization of controlling power and is a powerful security mechanism for the ecosystem. After the introduction of Bitcoin, the field of cryptocurrency has seen widespread attention from industry and academia, so much so that the original novel

Sarcasm target detection (identifying the target of mockery in a sarcastic sentence) is an emerging field in computational linguistics. Although there has been some research in this field, accurately identifying the target still remains problematic especially when the target of mockery is not presented in the text. In this paper, we propose a combination of a machine learning classifier and a deep

In many cases, our decision-making process is closely related to online reviews. However, there have been threats of opinion spams by hired reviewers increasingly, which aim to mislead potential customers by hiding genuine consumers’ opinions. Opinion spams should be filed up collectively to falsify true information. Fortunately, we can spot the possibility to detect them from their collusiveness.

Detecting research trends helps researchers and decision makers to promptly identify and analyze research topics. However, due to citation and publication delay, previous studies on trend analysis are more likely to identify ex-post trends. In this study, we employ author-defined keywords to represent topics and propose a simple, effective, and ex-ante approach, called author-defined keyword frequency

Recency ranking, in Community-based Question Answering (CQA), would refer to put recent answers in a list’s top positions. To be recent is not related to how new is the date of creation or editing of a given answer, but how current is the content of the answer. A good ranking should also consider the answers’ quality since a current but no quality answer may be useless. Similarly, a high-quality answer

Smart contracts are more sensitive from a security perspective than other software due to several reasons. First, smart contracts are immutable thus cannot be easily patched once deployed. Second, smart contracts are directly tied to payments and can hold millions of dollars’ worth of digital currencies. Third, smart contracts are still a new practice thus do not have best coding practices and development

We introduce the task of changing the narrative point of view, where characters are assigned a narrative perspective that is different from the one originally used by the writer. The resulting shift in the narrative point of view alters the reading experience and can be used as a tool in fiction writing or to generate types of text ranging from educational to self-help and self-diagnosis. We introduce

Misinformation of COVID-19 is prevalent on social media as the pandemic unfolds, and the associated risks are extremely high. Thus, it is critical to detect and combat such misinformation. Recently, deep learning models using natural language processing techniques, such as BERT (Bidirectional Encoder Representations from Transformers), have achieved great successes in detecting misinformation. In this

This paper presents DWIE, the ‘Deutsche Welle corpus for Information Extraction’, a newly created multi-task dataset that combines four main Information Extraction (IE) annotation subtasks: (i) Named Entity Recognition (NER), (ii) Coreference Resolution, (iii) Relation Extraction (RE), and (iv) Entity Linking. DWIE is conceived as an entity-centric dataset that describes interactions and properties

The sudden outbreak of coronavirus disease 2019 (COVID-19) in early 2020 has dramatically changed people's lives. Some countries have taken mass home quarantine to control the virus. However, the side effects of quarantine have rarely been interrogated by current COVID-19 research. This study thus investigates the effects of online social support on the public's beliefs in overcoming COVID-19 by embracing

A group trip entails collaborative information behaviour (CIB) of multiple actors seeking, sharing, and using travel-related information. However, there is a lack of investigation on how people choose to assume or be appointed different CIB roles during such leisure projects. Thus, limited information support is provided to travellers involved in group trips. This article investigates role adoption

Blockchain provides a decentralized environment for applications and information systems in various fields. It is an innovative revolution for the traditional Internet. However, without proper regulatory mechanisms, the blockchain technology has gradually become a hotbed of criminal activities, such as Ponzi scheme that brings huge economic losses to people. To maintain the security of the blockchain

Collaborative filtering based recommender systems typically suffer from scalability issues when new users and items join the system at a very rapid rate. We tackle this concerning issue by employing a decomposition based recommendation approach. We partition the users in the recommendation domain with respect to location using a Voronoi Diagram and execute the recommender algorithm individually in

We are presenting a continuously adaptive approach that significantly reduces the storage requirements of smart-contracts, an essential part of programmable blockchains, aimed towards increasing the throughput of the blockchain overall. Our approach proposes a semantically-different blockchain architecture, which introduces a new type of nodes whose purpose is to enhance the storage used by smart-contracts

Innovation-driven development worldwide is driving scientific and technological (Sci-Tech) progress. As an important form of Sci-Tech achievements, patents provide effective protection and transformation opportunities for Sci-Tech innovations; this includes using and exchanging of patents to realize their value. However, realizing patent value involves some challenges: (1) discovering the transformation

With the applications of blockchain technology in various fields, the research on blockchain has attracted much attention. Different from the researches focusing on specific applications of blockchain technology in a certain field, this study devotes to capturing the attitudes of investors regarding different risk criteria in blockchain technology investment decision making. We use personalized quantifiers

Multi-view data contains a set of features representing different perspectives associated with the same data and this phenomenon can be commonly observed in real-world applications. Multi-view clustering in terms of text and image data faces substantial challenges such as Structure-preserving and Sparsity. Existing methods do not conserve the structure of data space and the recent improvements have

The spread of the COVID-19 virus continues to increase fatality rates and exhaust the capacity of healthcare providers. Efforts to prevent transmission of the virus among humans remains a high priority. The current efforts to quarantine involve social distancing, monitoring and tracking the infected patients. However, the spread of the virus is too rapid to be contained only by manual and inefficient

Although blockchains are systems that are intended to survive on their own, they still may need human intervention like other Parallel and Distributed Computing (PDC) architectures. The reasons for this intervention are twofold: management of blockchain nodes and governance of blockchain. The second focuses on sustaining blockchain unlike the first one concerning with operation of individual nodes

Designers of smart environments based on radio frequency identification devices have a challenging task to build secure mutual authentication protocols. These systems are classified into two major factions which are traditional closed-loop systems, and open-loop systems. To the best of our knowledge, all of the mutual authentication protocols previously introduced for these two categories rely on a

Considering that RFID technology presents a significant growth in IIoT environments, industrial manufacturing is being one of the most benefited by this growth. As growth implies increased security risks, access control systems have emerged as an essential solution for IIoT environments and particularly in RFID systems. Considering Hyperledger Fabric Blockchain as a modular project oriented to environments

The recent introduction of thousands of cryptocurrencies in an unregulated environment has created many opportunities for unscrupulous traders to profit from price manipulation. We quantify the scope of one widespread tactic, the “pump and dump”, in which actors coordinate to bid up the price of coins before selling at a profit. We joined all relevant channels on two popular group-messaging platforms

Secure semantic searching provides privacy-preserving information retrieval for arbitrary queries semantically related to keywords. However, current verification mechanisms cannot verify the correctness of search results for semantically related queries in mutual distrust scenarios. Moreover, most secure semantic searching schemes still perform exact matching on ciphertext after query expansion, then

Since the beginning of 2020, the Chinese government has implemented substantial policies to prevent and control the COVID-19 epidemic. This research attempts to reveal and characterize the patterns of China's policy against COVID-19. Bibliometric methods are applied for studying policy evolution, with the aim of discovering the transitions of the policies over time, the collaborations among policy

The global pandemic of COVID-19 has brought significant attentions to three important features of disease direct reporting systems: traceability, reliability, and effectiveness. A traditional disease direct reporting system has a central node of control, with a hierarchical structure that goes up from locals (cities and counties) to regions and eventually reaches a central data repository. Such systems

Predicting the success of a business venture has always been a struggle for both practitioners and researchers. However, thanks to companies that aggregate data about other firms, it has become possible to create and validate predictive models based on an unprecedented amount of real-world examples. In this study, we use data obtained from one of the largest platforms integrating business information

In this paper, we introduce a novel knowledge-based word-sense disambiguation (WSD) system. In particular, the main goal of our research is to find an effective way to filter out unnecessary information by using word similarity. For this, we adopt two methods in our WSD system. First, we propose a novel encoding method for word vector representation by considering the graphical semantic relationships

In this paper, we propose a dialogue-based navigation system for narrow areas (e.g., buildings, etc.). In particular, we develop Campus Navigation Partner System (CNPS), intended for the use by freshmen (first-year students) who are likely to get lost in the new environment on the university campus (with the campus of Kitami Institute of Technology set as the experimental environment). The CNPS operates

The dissemination of misinformation in health emergencies poses serious threats to public health and increases health anxiety. To understand the underlying mechanism of the dissemination of misinformation regarding health emergencies, this study creatively draws on social support theory and text mining. It also explores the roles of different types of misinformation, including health advice and caution

Blockchain plays a vital task in cybersecurity. With the exerted efforts for realising large-scale quantum computers, most current cryptographic mechanisms may be hacked. Accordingly, we need a quantum tool utilised for designing blockchain frameworks to have the ability to be executed in the level of digital computers and resist the probable attacks from both digital and quantum computers. Quantum

Hate speech is an increasingly important societal issue in the era of digital communication. Hateful expressions often make use of figurative language and, although they represent, in some sense, the dark side of language, they are also often prime examples of creative use of language. While hate speech is a global phenomenon, current studies on automatic hate speech detection are typically framed

A name resolving system services as the entrance to the IoT data, thus protecting the name resolving process and making it straight and simple are critical for the widespread application of IoT services. However, due to the centralized name management structure of the existing IoT name resolving systems, it is hard to secure the name resolving process from being tampered by the root owner of the name

In this article we extend a WordNet structure with relations linking synsets to Desikan’s brain regions. Based on lexicographer files and WordNet Domains the mapping goes from synset semantic categories to behavioural and cognitive functions and then directly to brain lobes. A human brain connectome (HBC) adjacency matrix was utilised to capture transition probabilities between brain regions. We evaluated

The task of Named Entity Recognition (NER) is aimed at identifying named entities in a given text and classifying them into pre-defined domain entity types such as persons, organizations, locations. Most of the existing NER systems make use of generic entity type classification schemas, however, the comparison and integration of (more or less) different entity types among different NER systems is a

Online and social media platforms employ automated recognition methods to presume user preferences, sensitive attributes such as race, gender, sexual orientation, and opinions. These opaque methods can predict behaviors for marketing purposes and influence behavior for profit, serving attention economics but also reinforcing existing biases such as gender stereotyping. Although two international human

The quality of obtained features by representation learning determines the performance of a learning algorithm and subsequent application tasks (e.g., high-dimensional data clustering). As an effective paradigm for learning representations, Concept Factorization (CF) has attracted a great deal of interests in the areas of machine learning and data mining for over a decade. Moreover, lots of effective

In the traditional content-based recommendation method, we usually use the movies users watched before or rated to represent their profile. However, there are many movies that users have never seen or rated. For an unrated movie, there are two possibilities: maybe the user likes it or does not like it. In this paper, we first focus on how to identify users' preferences for movies by using a collaborative

Although humor enriches human lives, some jokes fail to amuse people because of a lack of morality. In this paper, we propose a mechanism capable of selecting humor based on moral criteria. To this end, we first construct a model based on an N-gram corpus and generate joke candidates using various template patterns. We then employ a moral judgement classifier based on a recurrent neural network and

Personalized recommendation systems can help users to filter redundant information from a large amount of data. Previous relevant researches focused on learning user preferences by analyzing texts from comment communities without exploring the detailed sentiment polarity, which encountered the cold-start problem. To address this research gap, we propose a hybrid personalized recommendation model that

Media has a substantial impact on public perception of events, and, accordingly, the way media presents events can potentially alter the beliefs and views of the public. One of the ways in which bias in news articles can be introduced is by altering word choice. Such a form of bias is very challenging to identify automatically due to the high context-dependence and the lack of a large-scale gold-standard

Extracting mentions of Adverse Drug Events (ADEs) and the potential relationships among them from clinical textual data remains challenging tasks due to the following issues: (1) many ADEs mentions have multiple relations, also known as the multi-head issue, and (2) many ADEs relations contain discontinuous mentions. To deal with these problems, in this paper, we propose a Multi-Task Transfer Learning-based

A commonly used technique for improving query response time in retrieval systems is storing query-relevant information in a fast access memory storage such as a result cache. The effectiveness of the result cache heavily relies on giving correct admission and eviction decisions, which require careful analysis of query and stream characteristics. Due to anonymous and global user access patterns, search

A considerable body of research deals with the automatic identification of hate speech and related phenomena. However, cross-dataset model generalization remains a challenge. In this context, we address two still open central questions: (i) to what extent does the generalization depend on the model and the composition and annotation of the training data in terms of different categories?, and (ii) do

The primary shortcoming associated with extractive text summarization is redundancy, where more than one sentence representing a similar type of information are incorporated in summary. In the last two decades, a lot of extractive text summarization methods have been proposed, but less attention was paid to the redundancy issue. In this paper, we propose a text summarization technique that incorporates

Most of the existing log anomaly detection methods suffer from scalability and numerous false positives. Besides, they cannot rank the severity level of abnormal events. This paper proposes a log anomaly detection based on Q-learning, namely QLLog, which can detect multiple types of system anomalies and rank the severity level of abnormal events. We first build a mathematical model of log anomaly detection