In the digital era, innovations in business intelligence are critical to staying competitive and popular across the growing business trends. Businesses have begun to investigate the next stage of data analytics and business intelligence solutions. On the other hand, Customer Churn Prediction (CCP) is a crucial process in business decision making, which properly identifies the churn users and takes

Recently, various Cross-Domain Collaborative Filtering (CDCF) algorithms are presented to address the sparsity problem, leveraging ratings of auxiliary domains to improve target domain’s recommendation performance. Therein, two-sided CDCF algorithms have shown better performance, given the fact that they can extract both user and item information. However, as the auxiliary domains are not all related

Text understanding from natural scene images has progressively gained much interest in computer vision due to the frequent emergence of handheld or wearable devices. Sometimes, these devices capture low-quality images. As a result, the image can be disrupted with unpredictable blur caused by text movement and camera shake. Further, the problem is integrated with edge intelligence, an emerging paradigm

Relevance is a key concept in information retrieval and widely used for the evaluation of search systems using test collections. We present a comprehensive study of the effect of the choice of relevance scales on the evaluation of information retrieval systems. Our work analyzes and compares four crowdsourced scales (2-levels, 4-levels, and 100-levels ordinal scales, and a magnitude estimation scale)

For more than a decade now, academicians and online platform administrators have been studying solutions to the problem of bot detection. Bots are computer algorithms whose use is far from being benign: malicious bots are purposely created to distribute spam, sponsor public characters and, ultimately, induce a bias within the public opinion. To fight the bot invasion on our online ecosystem, several

Non-negative matrix factorization (NMF) is a dimension reduction method that extracts semantic features from high-dimensional data. Most of the developed optimization methods for NMF only pay attention to how each feature vector of factorized matrices should be modeled, and ignore the relationships among feature vectors. Such a relationship among documents’ feature vectors provides better factorization

The text lines of ancient Tibetan books are skewed and distorted, strokes are broken, and complex adjacent text lines touch each other, which makes text line segmentation extremely challenging. In this paper, a text line segmentation method based on local baselines and connected component allocation is proposed. First, the pseudotext line is detected by analyzing the horizontal projection, straight

Danmaku is an emerging comment design for videos that allows real-time, interactive comments from viewers. Danmaku increases viewers’ interaction with other viewers and streamers, thereby raising viewers’ loyalty and sense of belonging. Sending Danmaku comments demonstrates a higher degree of viewer involvement than traditional static comments below the videos. Therefore, it is necessary and meaningful

With accelerating global economic integration processes, the financial risk early warning requirements for companies are becoming higher and higher. This study discusses the financial risk evaluation Z-Score Model for intelligent IoT-based enterprises. The financial risk early warning system of an IoT company is made up of four parts: alarm source analysis, Z-Score Model analysis, and main financial

In a conversational context, a user converses with a system through a sequence of natural-language questions, i.e., utterances. Starting from a given subject, the conversation evolves through sequences of user utterances and system replies. The retrieval of documents relevant to an utterance is difficult due to informal use of natural language in speech and the complexity of understanding the semantic

In recent years, there has been a rise in incorrect information, or misinformation, being shared on social media. Such misinformation tends to be more eye-catching and misleading. Previous studies on detecting misinformation online have focused mainly on linguistic characteristics; however, the role of linguistic characteristics in misinformation dissemination has not yet been thoroughly researched

Ethnicity-targeted hate speech has been widely shown to influence on-the-ground inter-ethnic conflict and violence, especially in such multi-ethnic societies as Russia. Therefore, ethnicity-targeted hate speech detection in user texts is becoming an important task. However, it faces a number of unresolved problems: difficulties of reliable mark-up, informal and indirect ways of expressing negativity

Depression is a widespread and intractable problem in modern society, which may lead to suicide ideation and behavior. Analyzing depression or suicide based on the posts of social media such as Twitter or Reddit has achieved great progress in recent years. However, most work focuses on English social media and depression prediction is typically formalized as being present or absent. In this paper,

Cloud datacenter (Dc) have become popular in recent years with the rising popularity and high performance of cloud computing. The multi-step of data computation and diverse task dependencies fail in the task, energy consumption, overloading of Virtual Machines (VMs), and violation of the agreement. To overcome these challenges, we propose a genetic algorithm (GA) based multiphase fault tolerance (MFTGA)

This paper considers a group decision-making mechanism for a group of social agents with ambiguous interactions. First, a fuzzy inference approach is introduced to describe bounded confidence based interaction rules among social agents for a certain object in a social network platform. Second, an influence graph is introduced to model the communication network topology associated with the group of

Sharing government data is of great significance to social development, but insecure and inappropriate sharing may lead to privacy breaches. Data sharing in consortium blockchain has provided a promising direction for efficient privacy preserving government data sharing. However, since government data is not owned by a single person or any government employers, it is hard to attribute participants’

Financial text mining has been widely viewed as a promising approach to analyzing questions related to financial issues. However, only a few studies have ever emphasized constructing financial domain sentiment lexicons, especially in a Chinese financial context. In light of this gap in research, the purpose of this study is to generate a Chinese financial domain sentiment lexicon (CFDSL). Then, that

Researchers have exerted tremendous effort in designing ways to detect and identify rumors automatically. Traditional approaches focus on feature engineering. They require lots of human actions and are difficult to generalize. Deep learning solutions come to help. However, they usually fail to capture the underlying structure of the rumor propagation and the influence of all participants involved in

Natural language processing (NLP) based approaches have recently received attention for legal systems of several countries. It is of interest to study the wide variety of legal systems that have so far not received any attention. In particular, for the legal system of the Republic of Turkey, codified in Turkish, no works have been published. We first review the state-of-the-art of NLP in law, and then

Semantic segmentation is the important task of assigning a semantic label to each pixel. However, semantic segmentation based on the deep neural network usually requires massive annotations consumption to acquire better performance. To avoid the problem, some algorithms based on weakly-supervised and semi-supervised conditions have been proposed and achieved gradually improving performance in recent

In this paper, we discuss how research methods are used in the Chinese library and information science (LIS) community, a fundamental question in the pursuit of a global overview of this interdisciplinary research domain. We used manual coding to identify research methods mentioned in 2,421 Chinese-language papers written by 53 prestigious Chinese LIS researchers across different generations. For all

Social media users were found to be persuaded by misinformation and contribute to the propagation of misinformation. It is important and urgent to understand the mechanisms behind the presentation and diffusion of misinformation-containing posts in social media. However, majority of previous studies on online misinformation focused on the non-content or surface-linguistic features, which omitted the

When ranking a list of documents relative to a given query, the vocabulary mismatches could compromise the performance, as a result of the different language used in the queries and the documents. Though the BERT-based re-ranker have significantly advanced the state-of-the-art, such mismatch still exist. Moreover, recent works demonstrated that it is non-trivial to use the established query expansion

Although recommender systems (RSs) play a crucial role in our society, previous studies have revealed that the performance of RSs may considerably differ between groups of individuals with different characteristics or from different demographics. In this case, a RS is considered to be unfair when it does not perform equally well for different groups of users. Considering the importance of RSs in the

Immense use of social media platforms results in quicker and wider dissemination of not only legitimate information, but also rumors which may cause mental stress and reduce the productivity of the society. One of the important ways to counteract the rumors in online social networks (OSNs) is to spread counter-rumors. In this work, we propose a Precedence based Competitive Cascade (PCC) model for the

The Covid-19 pandemic caused substantial changes, particularly concerning marketing, which led to high digital use. Social networking enables people to communicate easily with others and provides marketers with many ways to interact with consumers. As a consequence of the lockdown, economic activity is declining dramatically. The response of policymakers, the government, and industry to resolving the

The impact of data characteristics on the performance of classical recommender systems has been recently investigated and produced fruitful results about the relationship they have with recommendation accuracy. This work provides a systematic study on the impact of broadly chosen data characteristics (DCs) of recommender systems. This is applied to the accuracy and fairness of several variations of

Blockchain is a disruptive technique that finds many applications in FinTech, IoT, and token economy. Because of the asynchrony, the competitive mining, and the indeterministic block propagation delay in networks, forks in the blockchain occur frequently, which not only waste a lot of computing resources but also result in potential security issues. This issue will greatly affect the efficiency of

Recommender systems are typically designed to fulfill end user needs. However, in some domains the users are not the only stakeholders in the system. For instance, in a news aggregator website users, authors, magazines as well as the platform itself are potential stakeholders. Most of the collaborative filtering recommender systems suffer from popularity bias. Therefore, if the recommender system only

Citizen science involves non-expert volunteers collaborating to investigate scientific problems. Volunteers’ knowledge contribution is a key premise to the success of citizen science projects. Despite previous research on the type and valence of feedback information, little is known about the main and interactive effects of these two facets of feedback information on volunteers’ knowledge contribution

This paper focuses on an important problem of detecting offensive analogy meme on online social media where the visual content and the texts/captions of the meme together make an analogy to convey the offensive information. Existing offensive meme detection solutions often ignore the implicit relation between the visual and textual contents of the meme and are insufficient to identify the offensive

Fact verification aims to retrieve related evidence from raw text to verify the correctness of a given claim. Existing works mainly leverage the single-granularity features for the representation learning of evidences, i.e., sentence features, ignoring other features like entity-level and context-level features. In addition, they usually focus on improving the prediction accuracy while lacking the

KeyWords Plus and Medical Subject Headings (MeSH) are widely used in bibliometric studies for topic mapping. The objective of this study is to compare the two description systems in documents about cannabis research to find the concordance between systems and establish whether there is neutrality in topic mapping. A total of 25,593 articles from 1970 to 2019 were drawn from Web of Science's Core Collection

During times of crisis, information access is crucial. Given the opaque processes behind modern search engines, it is important to understand the extent to which the “picture” of the Covid-19 pandemic accessed by users differs. We explore variations in what users “see” concerning the pandemic through Google image search, using a two-step approach. First, we crowdsource a search task to users in four

The pervasive application of algorithmic decision-making is raising concerns on the risk of unintended bias in AI systems deployed in critical settings such as healthcare. The detection and mitigation of model bias is a very delicate task that should be tackled with care and involving domain experts in the loop. In this paper we introduce FairLens, a methodology for discovering and explaining biases

Crucial business data is an essential asset for each business establishment. Data security is vital when sensitive data are transmitted over the Internet in a business environment. Steganography is the art of obscuring data inside a regular file of similar or different forms. For digital forensics, hiding data has always been necessary. The current information hiding method based on deep learning models

Scientists’ leaving scientific work at an age when they should have continued generating more scientific output inevitably leads to limited success in their careers as scientists. However, this phenomenon has received relatively little attention in science of science and scientometrics, relative to how frequently it occurs in academia. Motivated by this gap, this study seeks to identify and characterize

As the novel coronavirus (COVID-19) continues to ravage the world at an unprecedented rate, formal recommendations from medical experts are becoming muffled by the avalanche of toxic content posted on social media platforms. This high level of toxic content prevents the dissemination of important and time-sensitive information and jeopardizes the sense of community that online social networks (OSNs)

Recently, online shopping has turned into a mainstream means for users to purchase as well as consume with the upsurge development of Internet technology. User satisfaction can be improved effectively by doing Sentiment Analysis (SA) of a large quantity of user reviews on e-commerce platforms. It is still challenging to envisage the accurate sentiment polarities of the user reviews because of the changes

Recommender Systems are decision support tools that adopt advanced algorithms in order to help users to find less-explored items that can be interesting for them. While recommender systems may offer a range of attractive benefits, they may also intensify undesired effects, such as the Popularity Bias, where a few popular users/items get more popular and many unpopular users/items get more unpopular

Despite the prevalent use of gamification in the health management context, its “dark side” for users remains unknown. To fill this research gap, we used the person–environment fit (P-E fit) model to build a theoretical model to explore the influences of gamification characteristics on stressors (privacy invasion and social overload) and strain (gamification exhaustion) for individuals. A survey was

Dubious credibility of online news has become a major problem with negative consequences for both readers and the whole society. Despite several efforts in the development of automatic methods for measuring credibility in news stories, there has been little previous work focusing on providing explanations that go beyond a black-box decision or score. In this work, we use two machine learning approaches

Algorithm fairness is an established line of research in the machine learning domain with substantial work while the equivalent in the recommender system domain is relatively new. In this article, we consider rating-based recommender systems which model the recommendation process as a prediction problem. We consider different types of biases that can occur in this setting, discuss various fairness

This paper is our attempt at answering a twofold question covering the areas of ethics and authorship analysis solutions. Firstly, since the methods used for performing authorship analysis imply that an author can be recognized by the content he or she creates, we were interested in finding out whether it would be possible for an author identification system to correctly attribute works to authors

Previous studies have concluded that there are significant differences in travelers’ preferences depending on the trip type. The problem of extracting users’ preferences from a corpus of text can be solved by using traditional clustering algorithms, which work quite well when there is no predefined data structure. However, in this paper, we consider the problem of extracting users’ preferences when

Analysis of subjective texts like offensive content or hate speech is a great challenge, especially regarding annotation process. Most of current annotation procedures are aimed at achieving a high level of agreement in order to generate a high quality reference source. However, the annotation guidelines for subjective content may restrict the annotators’ freedom of decision making. Motivated by a

Hashing has been shown to be successful in a number of Approximate Nearest Neighbor (ANN) domains, ranging from medicine, computer vision to information retrieval. However, current deep hashing methods either ignore both rich information of labels and visual linkages of image pairs, or leverage relaxation-based algorithms to address discrete problems, resulting in a large information loss. To address

Fairness emerged as an important requirement to guarantee that Machine Learning (ML) predictive systems do not discriminate against specific individuals or entire sub-populations, in particular, minorities. Given the inherent subjectivity of viewing the concept of fairness, several notions of fairness have been introduced in the literature. This paper is a survey that illustrates the subtleties between

The authors conducted the first study to investigate the types of coping tactics that blind and visually impaired (BVI) users applied when they encountered difficulties interacting with digital libraries (DLs). Coping tactics are defined as diverse action choices used by BVI users to accomplish specific goals in response to different types of help-seeking situations. Sixty-four participants were recruited

Group service in online health communities (OHCs) is a novel form of service where physicians can share knowledge and collaborate with others to provide better services to patients through the Internet. Although many literatures have explored the impact of individual group joining behavior on group performance, few researches have been done on the relationship between individual group joining behavior

To study the fuzzy logical relationship between the cause event and result event in the system fault evolution process, a method of forming the expression of the fuzzy logical relationship by superposition of the basic logical relationships and fuzzy membership degree is proposed. It is called the fuzzy logical structure function. Two problems need to be solved to determine the fuzzy logical structure

Fine-grained sentiment analysis has shown great benefits to real-word applications, such as for social media texts and product reviews. While the current state-of-the-art methods employ external syntactic dependency knowledge and enhance the task performances, most of them make use of merely the dependency edges, leaving the dependency labels unexploited, which the work presented here shows to be also

Fake news or misinformation is the information or stories intentionally created to deceive or mislead the readers. Nowadays, social media platforms have become the ripe grounds for misinformation, spreading them in a few minutes, which led to chaos, panic, and potential health hazards among people. The rapid dissemination and a prolific rise in the spread of fake news and misinformation create the

Observational learning (OL) and word-of-mouth learning (WOML), two main types of social learning, can influence online consumer decisions. The consumer decision process is not limited to consumption decisions; it may be viewed as a problem-solving process that includes three stages: search, evaluation, and purchase. To date, the effects and the mechanisms of OL and WOML on the purchase process remain

Suggesting new venues to be visited by a user in a specific city remains an interesting but challenging problem, partly because of the inherent high sparsity of the data available in location-based social networks (LSBNs). At the same time, in traditional recommender systems, in order to improve their performance in these sparse situations, different techniques have been proposed mainly by augmenting

We study the task of on-line scientific resource profiling, which aims at better understanding and summarizing on-line scientific resources to promote resource search and recommendation systems. To this end we propose to exploit the resource citation information in scientific literature by extracting the fine-grained relations between the cited on-line resources and other resource-related scientific

The dynamics of transformations that the world is experiencing at global dimensions due to the intensity of technological changes demand sophisticated management tools to assess risks in the business and industrial sectors, aimed at ensuring investment security. The objective of this article is to analyze and propose the technical Due Diligence as a methodology to assess risks in Start-up ecosystems

Extracting events from texts using neural networks has gained increasing research focus in recent years. However, existing methods prepare candidate arguments in a separate classifier suffering from the error propagation problem and fail to model correlations between entity mentions and event structures. To improve the performance of both entity recognition and event extraction, we propose a transition-based

We study the effectiveness of Feature Density (FD) using different linguistically-backed feature preprocessing methods in order to estimate dataset complexity, which in turn is used to comparatively estimate the potential performance of machine learning (ML) classifiers prior to any training. We hypothesize that estimating dataset complexity allows for the reduction of the number of required experiments

Many recent studies have demonstrated that the propagation patterns of news on social media can facilitate the detection of fake news. Most of these studies rely on the complete propagation networks to build their model, which is not fully available in the early stages and may take a long time to complete. Hence, relying on the complete propagation network is not ideal for fake news early detection