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Regular Expressions (RegEx) can be employed as a technique for supervised learning to define and search for specific patterns inside text. This work devised a method that utilizes regular expressions to convert the reference style of academic papers into several styles, dependent on the specific needs of the target publication or conference. Our research aimed to detect distinctive patterns of reference styles using RegEx and compare them with a dataset including various reference styles. We gathered a diverse range of reference format categories, encompassing seven distinct classes, from various sources such as academic papers, journals, conference proceedings, and books. Our approach involves employing RegEx to convert one referencing format to another based on the user's specific preferences. The proposed model demonstrated an accuracy of 57.26% for book references and 57.56% for journal references. We used the similarity ratio and Levenshtein distance to evaluate the dataset's performance. The model achieved a 97.8% similarity ratio with a Levenshtein distance of 2. Notably, the APA style for journal references yielded the best results. However, the effectiveness of the extraction function varies depending on the reference style. For APA style, the model showed a 99.97% similarity ratio with a Levenshtein distance of 1. Overall, our proposed model outperforms baseline machine learning models in this task. This study introduces an automated program that utilizes regular expressions to modify academic reference formats. This will enhance the efficiency, precision, and adaptability of academic publishing. [ABSTRACT FROM AUTHOR]

In the recent scenario, the most challenging requirements are to handle the massive generation of multimedia data from the Internet of Things (IoT) devices which becomes very difficult to handle only through the cloud. Fog computing technology emerges as an intelligent solution and uses a distributed environment to operate. The objective of the paper is latency minimization in e-healthcare through fog computing. Therefore, In IoT multimedia data transmission, the parameters such as transmission delay, network delay, and computation delay must be reduced as there is a high demand for healthcare multimedia analytics. Fog computing provides processing, storage, and analyze the data nearer to IoT and end-users to overcome the latency. In this paper, the novel Intelligent Multimedia Data Segregation (IMDS) scheme using Machine learning (k-fold random forest) is proposed in the fog computing environment that segregates the multimedia data and the model used to calculate total latency (transmission, computation, and network). With the simulated results, we achieved 92% as the classification accuracy of the model, an approximately 95% reduction in latency as compared with the pre-existing model, and improved the quality of services in e-healthcare. [ABSTRACT FROM AUTHOR]

Incunabula are the texts printed mainly during the second half of 15th century that are a key cultural element in a revolutionary period of the history and evolution of the book and the printing. In these books, the identification of their origin largely affects its academic, cultural, patrimonial, and economical value. This paper proposes a process to automate the identification of the origin of a digitised incunable document using the Proctor/Haebler method, a commonly established procedure in the field. This process has been validated with a selected dataset obtained from the incunabula collection at the digital repository of the University of Zaragoza. [ABSTRACT FROM AUTHOR]

Nowadays, the majority of online book retailers utilize their own recommendation engines to suggest books to their customers. Nonetheless, the majority of the times, the books that are suggested to users are irrelevant. This system aims to create a new strategy by utilizing the content-based filtering capability. This technique will result in a more refined and useful recommendation for the user. For testing reasons, a webbased prototype will be developed, and the system will be taught by feeding it data. Users will benefit from our recommendation system by having easier access to library materials and reduced resource waste. [ABSTRACT FROM AUTHOR]

Purpose: Using the decision tree model, this study aims to understand the online travelers booking behaviors on Expedia.com, by examining influential determinants of online hotel booking, especially for longer-stay travelers. The geographical distance is also considered in understanding the booking behaviors trisecting travel destinations (i.e. Americas, Europe and Asia). Design/methodology/approach: The data were obtained from American Statistical Association DataFest and Expedia.com. Based on the US travelers who made hotel reservation on the website, the study used a machine learning algorithm, decision tree, to analyze the influential determinants on hotel booking considering the geographical distance between origin and destination. Findings: The results of the findings demonstrate that the choice of package product is the prioritized determinant for longer-stay hotel guests. Several similarities and differences were found from the significant determinants of the decision tree, in accordance with the geographic distance among the Americas, Europe and Asia. Research limitations/implications: This paper presents the extension to an existing machine learning environment, and especially to the decision tree model. The findings are anticipated to expand the understanding of online hotel booking and apprehend the influential determinants toward consumers' decision-making process regarding the relationship between geographical distance and traveler's hotel staying duration. Originality/value: This research brings a meaningful understanding of the hospitality and tourism industry, especially to the realm of machine learning adapted to an online booking website. It provides a unique approach to comprehend and forecast consumer behavior with data mining. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to construct a multi-relational network for an online sharing platform in the age of the sharing economy, to identify the factors impacting users' product adoption behavior and to predict consumers' purchases of user-generated products on the platform. Design/methodology/approach: The study conducted multi-relational network analyses of five different sub-networks in identifying influential factors for e-book adoption. Meanwhile, the study adopted machine learning methods with different classification algorithms and feature sets to predict users' purchasing behaviors. Findings: The authors found that an individual's adoption of a product was correlated with his or her purchasing habits and collaboration with others on the online sharing platform. Through the inclusion of network features, the authors were able to build a predictive model that forecasted consumers' purchases of user-generated e-books with reasonable accuracy. Research limitations/implications: The interdisciplinary approach used in the study can serve as a good reference for identifying factors impacting the product adoption behavior of users in the online sharing platform, through employing different sociological and computational methods. Practical implications: The outcome of the study has provided important managerial implications, especially for the design of social commerce platform in the age of the sharing economy. Social implications: The authors verified the social influence impacting consumers' product adoption behavior and shed light on the value of collaboration in the age of the sharing economy. Originality/value: The study was the first to identify user-generated e-book adoption on an online sharing platform from a multi-relational network perspective. The idea and the approach supplied a new method of behavioral analysis in the context of a sharing economy. [ABSTRACT FROM AUTHOR]

Purpose -- The purpose of this paper is to develop a novel feature selection approach for automatic text classification of large digital documents -- e-books of online library system. The main idea mainly aims on automatically identifying the discourse features in order to improving the feature selection process rather than focussing on the size of the corpus. Design/methodology/approach -- The proposed framework intends to automatically identify the discourse segments within e-books and capture proper discourse subtopics that are cohesively expressed in discourse segments and treating these subtopics as informative and prominent features. The selected set of features is then used to train and perform the e-book classification task based on the support vector machine technique. Findings -- The evaluation of the proposed framework shows that identifying discourse segments and capturing subtopic features leads to better performance, in comparison with two conventional feature selection techniques: TFIDF and mutual information. It also demonstrates that discourse features play important roles among textual features, especially for large documents such as e-books. Research limitations/implications -- Automatically extracted subtopic features cannot be directly entered into FS process but requires control of the threshold. Practical implications -- The proposed technique has demonstrated the promised application of using discourse analysis to enhance the classification of large digital documents -- e-books as against to conventional techniques. Originality/value -- A new FS technique is proposed which can inspect the narrative structure of large documents and it is new to the text classification domain. The other contribution is that it inspires the consideration of discourse information in future text analysis, by providing more evidences through evaluation of the results. The proposed system can be integrated into other library management systems. [ABSTRACT FROM AUTHOR]

The Microbiome Protocols eBook (MPB) is a valuable resource that provides standardized wet-lab protocols for microbiome research. The first edition, launched in 2020, received widespread acclaim and featured 152 meticulously curated protocols. The MPB aims to bridge the gaps in microbiome protocols and facilitate progress in the field by addressing challenges in wet experiments and data analysis. The upcoming second edition will incorporate updates from the first edition and expand into new areas, covering innovative methodologies and emerging technologies. The MPB is an open-access resource that is easily accessible to all researchers. [Extracted from the article]

Purpose: The authors' goal is to investigate variations in the writing style of book reviews published on different social reading platforms and referring to books of different genres, which enables acquiring insights into communication strategies adopted by readers to share their reading experiences. Design/methodology/approach: The authors propose a corpus-based study focused on the analysis of A Good Review, a novel corpus of online book reviews written in Italian, posted on Amazon and Goodreads, and covering six literary fiction genres. The authors rely on stylometric analysis to explore the linguistic properties and lexicon of reviews and the authors conducted automatic classification experiments using multiple approaches and feature configurations to predict either the review's platform or the literary genre. Findings: The analysis of user-generated reviews demonstrates that language is a quite variable dimension across reading platforms, but not as much across book genres. The classification experiments revealed that features modelling the syntactic structure of the sentence are reliable proxies for discerning Amazon and Goodreads reviews, whereas lexical information showed a higher predictive role for automatically discriminating the genre. Originality/value: The high availability of cultural products makes information services necessary to help users navigate these resources and acquire information from unstructured data. This study contributes to a better understanding of the linguistic characteristics of user-generated book reviews, which can support the development of linguistically-informed recommendation services. Additionally, the authors release a novel corpus of online book reviews meant to support the reproducibility and advancements of the research. [ABSTRACT FROM AUTHOR]

Machine Learning systems rely heavily on annotated instances. Such annotations are frequently done by human experts, or by tools developed by experts, and so the central message of this book, Noise: A Flaw in Human Judgment (Kahneman, Sibony, and Sunstein 2021) is of considerable importance to AI/Machine Learning community. The core message is that if a number of experts are asked to annotate tasks that involve judgments, these responses will frequently differ. This observation poses a problem for how analysts choose a particular annotated dataset (from the group), or process the set of responses to give a "balanced" response, or whether to reject all the annotated datasets. A further important aspect of this book is the case studies which demonstrate that differences in judgments between fellow experts have been reported in a significant number of disciplines including, business, the law, government, and medicine. Kahneman, Sibony and Sunstein (2021), referred to as KSS subsequently, discuss how Expert Biases can be reduced, but the main focus of this book is a discussion of Noise, that is, differences that often occur between fellow experts, and how Noise can often be reduced. To address the last point KSS have formulated a set of six decision hygiene principles which include the recommendation that complex tasks should be subdivided, and then each subtask should be solved separately. A further principle is that each task should be solved by individual experts before the various judgments are discussed with fellow experts. Effectively, the book being reviewed covers three main topics: First, it reports several motivating studies that show how judgments of fellow experts varied significantly in the pricing of insurance premiums, and in setting the lengths of custodial sentences. These motivating studies very effectively illustrate the central concepts of Judgment, Noise, and Bias; that section also provides definitions of these core concepts and discusses how Noise is often amplified in group meetings. Secondly, the authors provide detailed discussion of further studies, in a variety of domains, which report the levels of disagreement between experts. Thirdly, KSS discusses how to reduce the levels of Noise between experts, as noted above, the authors refer to these as Principles of Noise Hygiene. These three parts are interwoven in a complex way throughout the book; in our view, the best overview of the book is given in the section Review and Conclusions: Taking Noise Seriously (KSS, p. 361). [ABSTRACT FROM AUTHOR]

The concept "cultures of reading" should be expanded to include machines that read. Machine reading is exempliied by the computer system called Never- Ending Language Learning (NELL). Because NELL lacks real- world experience, its semantic comprehension is limited to forming categories of words. his process illustrates a major diference between human and machine reading: whereas human reading involves causal reasoning, machine reading relies more on correlations. Human- machine hybrid reading, for example the kind done with an e-book, can be understood as a cognitive assemblage through which information, interpretations, and meanings circulate. he introduction of mechanical cognition into printing can be seen in the Paige Compositor, from 1878. he transition from electromechanical cognition to more lexible digital and electronic computational media marks the movement from print, understood as a technology involving the arrangement of type pieces to impress ink on paper, to postprint, in which inked products originate as computer iles. his change, which signals an ontological rupture in writing and reading practices, is addressed through a cognitive- assemblage approach emphasizing the distribution of cognition among technical and human actors. [ABSTRACT FROM AUTHOR]

It could do software grunt work, freeing up developers to work at a higher, or more abstract level, or it could help noncoders create simple programs. David Choi, another study author at DeepMind, imagines running the model in reverse: translating code into explanations of what it's doing, which could benefit programmers trying to understand others' code. But real-world programming often requires managing large code packages in multiple places, which requires a more holistic understanding of the software, Solar-Lezama says. [Extracted from the article]

Few of previous reading studies comprehensively examined the contributing factors of students' digital reading literacy. To fill this gap, based upon the ecological perspective, this study aims to investigate which factors from the student, home, and school context are more important in discriminating high-performing digital readers from non–high-performing digital readers. The data of the Progress in International Reading Literacy Study 2016 with 74,692 Grade 4 students from 14 countries and economies was analyzed using the machine learning approach of support vector machine with recursive feature elimination. Results showed that except print reading levels, students' reading self-efficacy, home resources for learning, talking about what have read in class, and the number of books in the home are the most influential contextual factors contributing to the high performance of digital readers. The selected 20 key contextual factors render a high prediction power for discriminating digital readers. Our findings show that, in general, home-related factors have overarching influences on children's digital reading development; at the school level, instruction-related features are more influential than school characteristics. [ABSTRACT FROM AUTHOR]

An essential part of business operation for tourism industry is revenue management, i.e., how to sell the right tour package, to the right customers, at the right time, at the right price through the most appropriate and cost-effective channels. In today's world, the internet has revolutionized many business operations in the tourism industry which plays an important role in Thailand's GDP. Most tour operators utilize websites as the main channel to build relationships with customers. Thus, website performance measurement is an important strategic factor for online marketing. The objectives of this research were to identify factors contributing from Google Analytics metrics to online bookings and to forecast online bookings using those impactful factors. Several machine learning models namely artificial neural network (ANN), support vector regression, and random forest, were proposed to forecast online bookings using the mean absolute percentage error (MAPE) as the criterion for comparison. It was found that there were three Google Analytics metrics that contributed to online bookings, which were the sessions from referral, unique returning users, and the average session duration. In addition, the ANN model provided the highest accuracy result with a MAPE of 11.39%. The framework from this research can be applied to other online companies to forecast their online bookings, which is an important part of revenue management since accurate forecasts can help companies to achieve their goals. [ABSTRACT FROM AUTHOR]

As information society matures to an even higher level and as information technology becomes a necessity to our everyday lives, the needs to develop, support and satisfy personal and social needs without the limitation of time, space, and location have become a vital point to everyday lives. Smartphone users are increasing at a staggering rate but the research on mobile-Learning model and the implementation of m-Learning scenario are still behind the needs of the users. Therefore, this paper focuses on the design of 'repeatable and short-spanned m-Learning model' to meet the needs of the learners who are on the go and on the move with their smartphones. Smartphone users frequently reach out for their phones but compare to the frequencies, the actual span of time they spend per use are relatively and surprisingly short. One way to understand this phenomenon is that the users tend to immediately replace their smartphones with laptops or desktops whenever they are available. A leaning model was needed to reflect this short and frequent use, a use that is solely based on the smartphone environment. This proposed learning model first defines this particular setting and implements the model to real smartphone users over an 8 week period. To understand whether different learning backgrounds can influence this model, different schools with online and offline learning channels participated in the experiment. User survey was conducted after the experiment to geta better understanding of the smartphone users. Pretest and posttest were conducted before and after the experiment and the data were validated and analyzed using SPSS version 18.0 for PC. Preliminary descriptive statistics, multiple regression and cross validation was conducted for the analysis. The results showed that the proposed English Listening and Comprehension Mobile Digital Textbook (ELCMDT) had a positive effect on the learners in general and was more effective for learners who were already experienced with online learning. [ABSTRACT FROM AUTHOR]

In order to leverage the use of various modalities such as audio-visual materials in instilling effective learning behavior we present an intuitive approach of annotation based hapto-audio-visual interaction with the traditional digital learning materials such as e-books. By integrating the home entertainment system in the user's reading experience combined with haptic interfaces we want to examine whether such augmentation of modalities influence the user's learning patterns. The proposed Haptic E- Book (HE-Book) system leverages the haptic jacket, haptic arm band as well as haptic sofa interfaces to receive haptic emotive signals wirelessly in the form of patterned vibrations of the actuators and expresses the learning material by incorporating image, video, 3D environment based augmented display in order to pave ways for intimate reading experience in the popular mobile e-book platform. [ABSTRACT FROM AUTHOR]

Digital library as one of the most important ways in helping students acquire professional knowledge and improve their professional level has gained great attention in recent years. However, its large collection (especially the book resources) hinders students from finding the resources that they are interested in. To overcome this challenge, many researchers have already turned to recommendation algorithms. Compared with traditional recommendation tasks, in the digital library, there are two challenges in book recommendation problems. The first is that users may borrow books that they are not interested in (i.e., noisy borrowing behaviours), such as borrowing books for classmates. The second is that the number of books in a digital library is usually very large, which means one student can only borrow a small set of books in history (i.e., data sparsity issue). As the noisy interactions in students' borrowing sequences may harm the recommendation performance of a book recommender, we focus on refining recommendations via filtering out data noises. Moreover, due to the the lack of direct supervision information, we treat noise filtering in sequences as a decision-making process and innovatively introduce a reinforcement learning method as our recommendation framework. Furthermore, to overcome the sparsity issue of students' borrowing behaviours, a clustering-based reinforcement learning algorithm is further developed. Experimental results on two real-world datasets demonstrate the superiority of our proposed method compared with several state-of-the-art recommendation methods. [ABSTRACT FROM AUTHOR]