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In the age of a knowledge-based economy and following extensive socio-economic changes, the success of organizations is not limited to gaining financial and material resources. Instead, it depends on the acquisition of intangible assets that can be used to achieve a sustainable competitive advantage. In the new strategic environment, organizations will thrive when they see themselves as a learning organization whose goal is to improve intellectual capital continually; an organization that cannot increase its intellectual capital cannot survive. The term intellectual capital is used in the overlap of all assets, intangible resources and non-physical resources of an organization, including processes, innovation capacity and implicit and explicit knowledge of its members and partner network. However, despite the growing importance of intellectual capital and cloud computing as vital resources for organizations' competitive advantage, there is a limited understanding of them. Simultaneously, the management of intellectual capital enables organizational managers to create, nurture, control and preserves a strong competitive advantage source, the advantage that competitors will not easily capture. So, the main objective of the present investigation is to check out the factors affecting the adoption of intellectual capital management systems based on cloud computing in hospitals.

In the last two decades, we have moved toward economics, where investment in Information Technology (IT), human resources, development, research and advertising is essential to maintain competitive advantage and certify the sustainability of organizations. Therefore, it can be stated that the economic value is the creation and management of intangible assets, which are referred to as intellectual capital. On the other hand, cloud computing is presented as a new paradigm for hosting and providing services through the Internet. Cloud computing can lead to too many benefits to organizations, including cost reduction, flexibility and improved performance. The present article examines how optimal intellectual capital management can be achieved using cloud computing. So, seven hypotheses were developed through the dimensions of technology, environment, organization and innovation. In this study, the path analysis was performed using Analytic Hierarchy Process (AHP) and Partial Least Squares (PLS). By reviewing the literature related to the model of technology, organization, environment and innovation dissemination theory, four main criteria, and 15 sub-criteria were identified based on the opinions of specialists, professors and IT experts based on AHP and PLS methods.

The results of this investigation confirmed all the hypotheses. The results illustrated that environmental and technological factors should be regarded more when adopting intellectual capital management systems based on cloud computing. The results also indicated that intellectual capital highly influences improving performance. Furthermore, cloud apps, like other disruptive technology, deliver superior benefits while still presenting a slew of realistic challenges that must be tackled. In order to draw a growing customer base to this business model, software vendors should resolve these concerns. The literature revealed that the computing industry is making tremendous strides around the world. Nevertheless, in order to achieve a faster and softer adoption, newer and more advanced techniques are still required.

The research outcomes can significantly impact a wide range of organizations, such as health-related organizations. However, there are some limitations; for example, the sample is limited to one country. Therefore, future studies can measure the data of this study in different samples in different countries. Future researchers can also boost the model's predictive capability to adopt cloud computing in other organizations by adding environmental, organizational, innovation and other technical factors.

Managers will use these emerging innovations to minimize costs and maximize profits in the intellectual capital management competition. An effective cloud computing based on an electronic human resource management system can significantly increase system performance in industries. The investigators expect that the results will direct clinicians and scholars into a more advanced and developed age of cloud-based apps.

Investigations on the impact of cloud computing on intellectual capital management are rare. Accordingly, this investigation provided a new experience in terms of intellectual capital in the field of cloud computing. This study filled the scientific research gap to understand the factors affecting intellectual capital management systems based on cloud computing. This study provides a better insight into the power of organizational and environmental structure to adopt this technology in hospitals.

The subject of this paper is wearable computing technology. The purpose of this paper is to determine its potential in libraries to meet the needs of both users and librarians. The specific goals include: description of the main features of wearable computing from the perspective of its potential usefulness in libraries; analysis of areas of wearable computing use in public institutions; discussion of the potential of wearable computing for library users; and discussion of the potential of wearable computing for librarians to determine its potential in libraries to meet the needs of users and librarians.

The method of subject literature overview was used. The state of research from the period 2008 to 2018 on the use of wearable computing in libraries was established based on a search of selected sources Web of Science, Scopus, Elsevier and Emerald databases and LISTA database.

Wearable computing can be used in many areas of library activities to serve the needs of users and librarians. In the context of services for users, wearable computing can be used, among others, to help users navigate the library, to provide resources in new and interesting forms, and in education and entertainment. For the needs of librarians, additional data provided via wearable devices can accelerate the process of cataloguing resources and increase motivation to work via gamification options and features that make it possible to measure personal work efficiency. Librarians can also use the data obtained wearable computing devices to more quickly track, identify, select and organize resources. As with users, librarians can also use wearable computing in their education.

The paper is a viewpoint based on subject literature. It presents only a sketch of potential wearable computing applications in libraries. The aim of the paper is to initiate further discussion on the possibilities of using wearable devices to improve both services for users and internal library processes. The author’s hope is that the concepts presented here will be tested in practice by librarians, which will allow further development of research on this subject.

The results can be widely used in practice as a framework for the implementation of wearable computing solutions in libraries.

The paper can help to facilitate the debate on the role of the implementation of new technologies in libraries.

The issue of the use of wearable computing has not yet been widely discussed in library and information science journals. In the very few publications on similar topics, only the perspective on the application of new solutions in services for users is presented. This paper also shows the potential for improving libraries’ internal processes with the use of wearable computing.

This study aims to identify the social representation of cloud computing from the perspective of Information Technology (IT) professionals in emerging countries, comparing it with the extant literature on this subject.

Data were collected from IT professionals in Brazil, which was used as a proxy for the emerging countries’ context related to cloud computing. Social Representation Theory was then applied to analyze the data.

Mismatches between theory and practice on cloud computing make it clear that most of the current scientific literature on cloud computing is, to a great extent, based on the context of developed countries rather than on the context of emerging ones.

Errors of inference may have been made during the categorization of the words evoked. Furthermore, Brazil was used as a proxy for the emerging countries’ context related to cloud computing.

IT professionals in emerging countries have quite an operational view of cloud computing. Thus, companies in these countries have to align cloud computing better with new business models and corporate strategies in order to take advantage of the transformational impacts of cloud computing.

IT professionals in emerging countries have failed to notice the strategic value of cloud computing, the new business models enabled by same, the privacy issues related to it and the impact cloud computing adoption can have on the IT costs of an organization. Moreover, mobility can be a paramount issue related to cloud computing in emerging countries – a fact thus far overlooked by academia.

This study aims to introduce an affective computing‐based method of identifying student understanding throughout a distance learning course.

The study proposed a learning emotion recognition model that included three phases: feature extraction and generation, feature subset selection and emotion recognition. Features are extracted from facial images and transform a given measument of facial expressions to a new set of features defining and computing by eigenvectors. Feature subset selection uses the immune memory clone algorithms to optimize the feature selection. Emotion recognition uses a classifier to build the connection between facial expression and learning emotion.

Experimental results using the basic expression of facial expression recognition research database, JAFFE, show that the proposed facial expression recognition method has high classification performance. The experiment results also show that the recognition of spontaneous facial expressions is effective in the synchronous distance learning courses.

The study shows that identifying student comprehension based on facial expression recognition in synchronous distance learning courses is feasible. This can help instrutors understand the student comprehension real time. So instructors can adapt their teaching materials and strategy to fit with the learning status of students.

This paper aims to explore the potential capabilities of the application of computing features (CFs) in resolving the most common issues that adversely affect performance in architecture, engineering and construction (AEC) projects.

Through a comprehensive review of the literature, 10 most significant CFs were identified. Also, the 15 most prevalent issues in AEC projects (AECIs), for which computers can provide remedial solutions, were determined and categorized into three phases of design, construction and operation. Potential impacts of each CF on handling AECIs in each of the three stages were assessed based on experts’ perceptions.

CFs are ranked based on their potential on solving the identified AECIs. So too, the order of the most common AECIs is provided in terms of their disposition for being solved by CFs. In this regard, findings reveal that the most effective CF in addressing AECIs is “artificial intelligence” and the most solvable AECI by using CFs is “increased costs and poor budgeting.” Furthermore, the most appropriate CF to handle each AECI is specified, as a result of which, it is inferred CFs are more effective in handling operation-related issues, compared to design, construction phases.

The results can provide a profound insight into software/tool selection based on features that enable technological tools and programs to handle AECIs in the three major phases of projects. Moreover, technology providers and vendors are provided with a benchmark to supply products according to the demands of the AEC industry. The study provides a readily available point of reference for practitioners in selecting various CFs for handling common AECIs that affect their projects.

A large body of research is available on the evaluation of various computer-based technologies and tools. Nevertheless, little, if any, study exists that explores the potential of underlying CFs of these technologies in addressing endemic problems across various stages of AEC projects. This study is one of the first in its kind that shifts to exploring various CFs, as the main enablers of computer technologies, establish links between these CFs and common issues of projects and assesses the potential of various CFs in addressing common problems of construction projects.

The purpose of this paper is to study heterogeneous transfer learning for activity recognition using heuristic search techniques. Many pervasive computing applications require information about the activities currently being performed, but activity recognition algorithms typically require substantial amounts of labeled training data for each setting. One solution to this problem is to leverage transfer learning techniques to reuse available labeled data in new situations.

This paper introduces three novel heterogeneous transfer learning techniques that reverse the typical transfer model and map the target feature space to the source feature space and apply them to activity recognition in a smart apartment. This paper evaluates the techniques on data from 18 different smart apartments located in an assisted-care facility and compares the results against several baselines.

The three transfer learning techniques are all able to outperform the baseline comparisons in several situations. Furthermore, the techniques are successfully used in an ensemble approach to achieve even higher levels of accuracy.

The techniques in this paper represent a considerable step forward in heterogeneous transfer learning by removing the need to rely on instance – instance or feature – feature co-occurrence data.

The authorship authentication (AA) problem is concerned with correctly attributing a text document to its corresponding author. Historically, this problem has been the focus of various studies focusing on the intuitive idea that each author has a unique style that can be captured using stylometric features (SF). Another approach to this problem, known as the bag-of-words (BOW) approach, uses keywords occurrences/frequencies in each document to identify its author. Unlike the first one, this approach is more language-independent. This paper aims to study and compare both approaches focusing on the Arabic language which is still largely understudied despite its importance.

Being a supervised learning problem, the authors start by collecting a very large data set of Arabic documents to be used for training and testing purposes. For the SF approach, they compute hundreds of SF, whereas, for the BOW approach, the popular term frequency-inverse document frequency technique is used. Both approaches are compared under various settings.

The results show that the SF approach, which is much cheaper to train, can generate more accurate results under most settings.

Numerous advantages of efficiently solving the AA problem are obtained in different fields of academia as well as the industry including literature, security, forensics, electronic markets and trading, etc. Another practical implication of this work is the public release of its sources. Specifically, some of the SF can be very useful for other problems such as sentiment analysis.

This is the first study of its kind to compare the SF and BOW approaches for authorship analysis of Arabic articles. Moreover, many of the computed SF are novel, while other features are inspired by the literature. As SF are language-dependent and most existing papers focus on English, extra effort must be invested to adapt such features to Arabic text.

The purpose of this paper is to investigate the government roles in cloud computing assimilation along two dimensions: government regulation and government support.

A research model was developed to depict the dual roles of government regulation and government support in cloud computing assimilation as well as the mediating effect of top management support (TMS). Using survey data collected from 376 Chinese firms that have already adopted cloud services, the authors tested the research model.

The impacts of both government regulation and government support on cloud computing assimilation are partially mediated by TMS. Government support exerts stronger impacts on TMS than government regulation.

This study extends the current information systems literature by highlighting the specific mechanisms through which governments influence firms’ assimilation of cloud computing.

Governments in developing countries could actively allocate funds or enact policies to effectively encourage cloud computing assimilation.

This study would complement previous findings about government regulation, and develop a more holistic understanding about the dual roles of governments in information technology innovation assimilation.

Road accidents, an inadvertent mishap can be detected automatically and alerts sent instantly with the collaboration of image processing techniques and on-road video surveillance systems. However, to rely exclusively on visual information especially under adverse conditions like night times, dark areas and unfavourable weather conditions such as snowfall, rain, and fog which result in faint visibility lead to incertitude. The main goal of the proposed work is certainty of accident occurrence.

The authors of this work propose a method for detecting road accidents by analyzing audio signals to identify hazardous situations such as tire skidding and car crashes. The motive of this project is to build a simple and complete audio event detection system using signal feature extraction methods to improve its detection accuracy. The experimental analysis is carried out on a publicly available real time data-set consisting of audio samples like car crashes and tire skidding. The Temporal features of the recorded audio signal like Energy Volume Zero Crossing Rate 28ZCR2529 and the Spectral features like Spectral Centroid Spectral Spread Spectral Roll of factor Spectral Flux the Psychoacoustic features Energy Sub Bands ratio and Gammatonegram are computed. The extracted features are pre-processed and trained and tested using Support Vector Machine (SVM) and K-nearest neighborhood (KNN) classification algorithms for exact prediction of the accident occurrence for various SNR ranges. The combination of Gammatonegram with Temporal and Spectral features of the validates to be superior compared to the existing detection techniques.

Temporal, Spectral, Psychoacoustic features, gammetonegram of the recorded audio signal are extracted. A High level vector is generated based on centroid and the extracted features are classified with the help of machine learning algorithms like SVM, KNN and DT. The audio samples collected have varied SNR ranges and the accuracy of the classification algorithms is thoroughly tested.

Denoising of the audio samples for perfect feature extraction was a tedious chore.

The existing literature cites extraction of Temporal and Spectral features and then the application of classification algorithms. For perfect classification, the authors have chosen to construct a high level vector from all the four extracted Temporal, Spectral, Psycho acoustic and Gammetonegram features. The classification algorithms are employed on samples collected at varied SNR ranges.

The purpose of this paper is to analyze the various features and functions of Koha, Libsys, NewGenLib and Virtua with the help of specially designed evaluation checklist and rank them based on features/functions of integrated library management system (ILMS).

The evaluation approach taken in this paper is similar to that of Singh and Sanaman (2012) and Madhusudhan and Shalini (2014) with minor modifications, comprising 306 features/functions and categorized as ten broad categories.

The paper explores different features of open source (OS) and commercial ILMS, which reveals that Virtua got the highest total score of 218 (77.86 per cent), followed by Koha ILMS with 204 score (72.86 per cent). Interestingly, NewGenLib got the lowest total score, that is, 163 (58.21 per cent). ILMS under study are lagging behind in exploiting the full potential of the Web 2.0 features, including cloud computing features, and needs to be addressed in their future development.

It is hoped that both the OS and commercial software will attend to the lacunae and soon develop fully functional Web 2.0/3.0 and cloud-based technologies.

The findings of this paper will not only guide the librarians in the selection of a good ILMS, which can cater to the needs of their libraries, but also abreast the knowledge of evaluation of ILMS for the students of Library and Information Science. And the findings will help the ILMS vendors to know the limitations of their ILMS, so that they can overcome the limitations faced by users and improve their products.