User-centric Information Research Articles

A Shark Inspired Ensemble Deep Learning Stacks for Ensuring the Security in Internet of Things (IoT)-Based Smart City Infrastructure

Recent advancements in the Internet of Things (IoT) have paved the way for intelligent and sustainable solutions in smart city environments. However, despite these advantages, IoT-connected devices present significant privacy and security risks, as network attacks increasingly exploit user-centric information. To protect the network against the rapidly growing number of cyber-attacks, it is essential to employ a cognitive intrusion detection system (CIDS) capable of handling complex and voluminous network data. This research presents a novel ensemble deep learning framework designed to enhance cybersecurity in IoT-based smart city ecosystems. The proposed architecture integrates Self-Attention Convolutional Neural Networks, Bidirectional Gated Recurrent Units, and Shark Smell Optimized Feed Forward Networks to create a robust, adaptive system for detecting and mitigating cyber threats. By leveraging fog computing, the model significantly reduces latency and computational overhead, making it highly suitable for large-scale IoT deployments. Extensive experimentation using the ToN-IoT dataset demonstrates the framework's exceptional performance, achieving a 99.78% detection rate across various attack types and an AUC of 0.989. The proposed model outperforms existing state-of-the-art approaches, achieving a mean fitness function value of 0.85640 and a standard deviation of 0.037630 in binary classification outcomes. In multi-class classification, the model maintains a mean fitness function value of 0.8230 and a variance of 2.28930 × 104, significantly outperforming other meta-heuristic algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The model exhibits superior accuracy and efficiency compared to existing state-of-the-art approaches, particularly in identifying complex and emerging threats. This research makes significant contributions by introducing innovative feature extraction techniques, optimizing model performance for resource-constrained environments, and providing a scalable solution for securing smart city infrastructure. The findings highlight the potential of ensemble deep learning approaches to fortify IoT networks against cyberattacks, paving the way for more resilient and secure smart cities.

Adoption of Human Resource Information Systems

This research investigated the critical factors influencing the successful adoption of E-HRM systems. Leveraging an updated DeLone and McLean IS Success Model, the impact of system quality, information quality, and service quality on system usage and user satisfaction were explored, ultimately linking these factors to E-HRM adoption success. A quantitative approach was employed, with data collected through a meticulously designed survey distributed to employees. Structural equation modeling (SEM) facilitated a rigorous analysis of the relationships between variables, while descriptive statistics painted a clear picture of the data landscape. SmartPLS 4.0 served as a robust tool for data analysis. The findings revealed a compelling narrative. Six of the nine hypotheses achieved significance, highlighting the direct and indirect influences of system quality, information quality, and service quality on system usage and user satisfaction. Notably, system usage emerged as a vital mediating variable, bridging the gap between system attributes and successful adoption. This underscores the pivotal role of encouraging user engagement and positive system experiences. Furthermore, the analysis unveiled a fascinating interconnectedness among all six variables. Each element, like a meticulously interwoven thread, contributes to the tapestry of successful E-HRM adoption. This interplay emphasizes the importance of a holistic approach, where no single factor reigns supreme but rather a harmonious synergy propels the system toward success. In conclusion, this study offers valuable insights for optimizing E-HRM implementation. High-quality systems with accurate and user-centric information, coupled with exceptional service, pave the way for a thriving E-HRM environment. By fostering user engagement and nurturing a holistic approach that recognizes the interconnectedness of key factors, organizations can unlock the true potential of their E-HRM systems, transforming them into catalysts for success.
 Keywords: E-HRM, system quality, system use, user and system satisfaction

Building Advanced Web Applications Using Data Ingestion and Data Processing Tools

Today, advanced websites serve as robust data repositories that constantly collect various user-centered information and prepare it for subsequent processing. The data collected can include a wide range of important information from email addresses, usernames, and passwords to demographic information such as age, gender, and geographic location. User behavior metrics are also collected, including browsing history, click patterns, and time spent on pages, as well as different preferences like product selection, language preferences, and individual settings. Interactions, device information, transaction history, authentication data, communication logs, and various analytics and metrics contribute to the comprehensive range of user-centric information collected by websites. A method to systematically ingest and transfer such differently structured information to a central message broker is thoroughly described. In this context, a novel tool—Dataphos Publisher—for the creation of ready-to-digest data packages is presented. Data acquired from the message broker are employed for data quality analysis, storage, conversion, and downstream processing. A brief overview of the commonly used and freely available tools for data ingestion and processing is also provided.

Investigating Driver Preferences for Traffic Information Using Digital Signage and Road Surface Holograms

This study investigates road users' information preferences to enhance traffic information provision strategies. Traditional road signs primarily convey static information like speed limits and restricted areas. However, smart street poles now offer the capability to collect real-time road conditions and deliver dynamic traffic updates promptly. Leveraging these smart poles allows for more effective and tailored information delivery strategies. The research examines three types of media for information dissemination: digital signage, road surface hologram, and a combination of both. Additionally, it surveys road users' preferences for information delivery methods, including text, graphics, or a mixture, based on the type of information. Conjoint analysis of 220 responses reveals essential attributes influencing users when they receive information. The findings indicate that a majority of road users prefer a blend of digital signage and road surface holograms, with a notable preference among younger individuals for digital signage. For road surface holograms, the favored approach involves a mix of text and graphics. This study lays the foundation for user-centric information strategies, offering insights into tailoring approaches based on the type of information and user preferences.

A Proposed Model for Improving the Performance of Knowledge Bases in Real-World Applications by Extracting Semantic Information

Knowledge Bases are information resources that convert factual knowledge to machine-readable formats to allow users to extract their desired data from multiple sources. The objective of knowledge base population frameworks is to extend KBs with semantic information to solve fundamental artificial intelligence problems such as understanding human knowledge. Information extraction entails the discovery of critical knowledge facts from unstructured text, which is important in the population of knowledge bases. The objective of this paper is to explore the concept of information extraction as a technique for accelerating the performance of knowledge bases with minimal annotation efforts for real-world applications such as content recommendation during a web search. This entails performing slot filling operations for data collection from large KBs and applying probabilistic estimations to determine the accuracy of the new information. The results are then used to explore the feasibility of applying knowledge bases to real-world tasks such as user-centric information access by encoding entities with deep semantic knowledge.

Empowered consumers as data providers in the user-centric personal information ecosystem

Empowered consumers as data providers in the user-centric personal information ecosystem

A Deep Learning Architecture for Psychometric Natural Language Processing

Psychometric measures reflecting people’s knowledge, ability, attitudes, and personality traits are critical for many real-world applications, such as e-commerce, health care, and cybersecurity. However, traditional methods cannot collect and measure rich psychometric dimensions in a timely and unobtrusive manner. Consequently, despite their importance, psychometric dimensions have received limited attention from the natural language processing and information retrieval communities. In this article, we propose a deep learning architecture, PyNDA, to extract psychometric dimensions from user-generated texts. PyNDA contains a novel representation embedding, a demographic embedding, a structural equation model (SEM) encoder, and a multitask learning mechanism designed to work in unison to address the unique challenges associated with extracting rich, sophisticated, and user-centric psychometric dimensions. Our experiments on three real-world datasets encompassing 11 psychometric dimensions, including trust, anxiety, and literacy, show that PyNDA markedly outperforms traditional feature-based classifiers as well as the state-of-the-art deep learning architectures. Ablation analysis reveals that each component of PyNDA significantly contributes to its overall performance. Collectively, the results demonstrate the efficacy of the proposed architecture for facilitating rich psychometric analysis. Our results have important implications for user-centric information extraction and retrieval systems looking to measure and incorporate psychometric dimensions.

The Design and Evaluation of a User-Centric Information Security Risk Assessment and Response Framework

The risk of sensitive information disclosure and modification through the use of online services has increased considerably and may result in significant damage. As the management and assessment of such risks is a well-known discipline for organizations, it is a challenge for users from the general public. Users have difficulties in using, understanding and reacting to security-related threats. Moreover, users only try to protect themselves from risks salient to them. Motivated by the lack of risk assessment solutions and limited impact of awareness programs tailored for users of the general public, this paper aims to develop a structured approach to help in protecting users from threats and vulnerabilities and, thus, reducing the overall information security risks. By focusing on the user and that different users react differently to the same stimuli, the authors developed a user-centric risk assessment and response framework that assesses and communicates risk on both user and system level in an individualized, timely and continuous way. Three risk assessment models were proposed that depend on user-centric and behavior-related factors when calculating risk. This framework was evaluated using a scenario-based simulation of a number of users and results analyzed. The analysis demonstrated the effectiveness and feasibility of the proposed approach. Encouragingly, this analysis provided an indication that risk can be assessed differently for the same behavior based upon a number of user-centric and behavioral-related factors resulting in an individualized granular risk score/level. This granular risk assessment, provided a more insightful evaluation of both risk and response. The analysis of results was also useful in demonstrating how risk is not the same for all users and how the proposed model is effective in adapting to differences between users offering a novel approach to assessing information security risks.

Patient Health Record Systems Scope and Functionalities: Literature Review and Future Directions.

BackgroundA new generation of user-centric information systems is emerging in health care as patient health record (PHR) systems. These systems create a platform supporting the new vision of health services that empowers patients and enables patient-provider communication, with the goal of improving health outcomes and reducing costs. This evolution has generated new sets of data and capabilities, providing opportunities and challenges at the user, system, and industry levels.ObjectiveThe objective of our study was to assess PHR data types and functionalities through a review of the literature to inform the health care informatics community, and to provide recommendations for PHR design, research, and practice.MethodsWe conducted a review of the literature to assess PHR data types and functionalities. We searched PubMed, Embase, and MEDLINE databases from 1966 to 2015 for studies of PHRs, resulting in 1822 articles, from which we selected a total of 106 articles for a detailed review of PHR data content.ResultsWe present several key findings related to the scope and functionalities in PHR systems. We also present a functional taxonomy and chronological analysis of PHR data types and functionalities, to improve understanding and provide insights for future directions. Functional taxonomy analysis of the extracted data revealed the presence of new PHR data sources such as tracking devices and data types such as time-series data. Chronological data analysis showed an evolution of PHR system functionalities over time, from simple data access to data modification and, more recently, automated assessment, prediction, and recommendation.ConclusionsEfforts are needed to improve (1) PHR data quality through patient-centered user interface design and standardized patient-generated data guidelines, (2) data integrity through consolidation of various types and sources, (3) PHR functionality through application of new data analytics methods, and (4) metrics to evaluate clinical outcomes associated with automated PHR system use, and costs associated with PHR data storage and analytics.

Returning information back to fishers: Graphical and numerical literacy of small-scale Indonesian tuna fishers

Developing world fishers are often assumed unable to comprehend fisheries management information because of their poor numerical and graphical literacy. This study questions this assumption by assessing the extent to which small-scale tuna fishers in Indonesia engaged in enumeration programs are able to understand, interpret and find value in the data collected from them when presented in graphical and numerically-aggregated forms. The analysis was based on structured interviews held with twenty tuna hand-line fishers on Buru Island, Maluku, Indonesia. We found that scientific displays such as graphs, tables and maps are understandable even for semi-literate fishers. Different forms of displays have more or less relevance and value for them in reflexive way. The sequence in which scientific displays are presented also matters, indicating that displays should be presented and explained in gradations from simple to more complex forms. Overall, however, the results show that face-to-face explanation remains necessary when communicating graphical and numerical information to fishers. Further attention should be given to forms of communication with fishers that allow for more reflexive decision-making and a shift to user-centric information systems. The ongoing development of mobile technologies aimed at incentivizing fishers to engage with and contribute to data and information collection, would benefit from selecting suitable information displays, presenting these in a guided sequence, and monitoring how fishers use this information to make decisions on the water.

Customizing content for rural mobile phones: a study to understand the user needs of rural India

Mobile phones have initiated affordable, need-based and user-centric information services in rural India. Though rural India majorly comprises illiterate, semi-literate and literate population, they have access to mobile phones and are familiar with various social media functions of these phones. This paper has explored how semi-literate and illiterate rural people use mobile phones to enhance their day-to-day activities, in spite of being severely handicapped due to low/no literacy and by limited understanding of the functions of the mobile phone. The research follows a grounded theory design. Data were collected from the unskilled, semi-skilled and skilled labour class (migratory workers), living as tenants in the village of Sikandarpur Ghosi and Sukhrali in the district of Gurgaon, Haryana, India. The paper suggests how a planned and proactive approach towards content customization should be considered by the policy makers and industry experts to optimize mobile phone use in the rural areas of India through (a) language, (b) vernacular keypads, (c) mobile content and (d) (if possible) through mobile phone design according to the rural needs. This would serve twofold purposes of increasing digital literacy and strengthening the information communication network within the village. Suggestions on funding for development of such initiatives have also been made in order to ensure that the approach be more proactive and implementable.

The PeRvasive Environment Sensing and Sharing Solution

To stimulate better user behavior and improve environmental and economic sustainability, it is of paramount importance to make citizens effectively aware of the quality of the environment in which they live every day. In particular, we claim that users could significantly benefit from cost-effective efficient Internet-of-Things (IoT) solutions that provide them with up-to-date live information about air pollution in the areas where they live, suitably adapted to different situations and with different levels of dynamically selected granularities (e.g., at home/district/city levels). Our PeRvasive Environment Sensing and Sharing (PRESS) project has the ambition of increasing users’ awareness of the natural environment they live in, as a first step towards improved sustainability; the primary target is the efficient provisioning of real-time user-centric information about environmental conditions in the surroundings, and in particular about air pollution. To this purpose, we have designed, implemented, and thoroughly evaluated the PRESS framework, which is capable of achieving good flexibility and scalability while integrating heterogeneous monitoring data, ranging from sensed air pollution to user-provided quality perceptions. Among the elements of technical originality, PRESS exploits extended Kura IoT gateways with novel congestion detection and recovery mechanisms that allow us to optimize bandwidth allocation between in-the-field PRESS components and the cloud. The reported performance results show the feasibility of the proposed solution, by pointing out not only the scalability and efficiency of the adopted message-based solution that uses Message Queue Telemetry Transport (MQTT) and WebSockets, but also the capability of PRESS to quickly identify and manage traffic congestions, thus, ensuring good quality levels to final users.

Cognition to Collaboration: User-Centric Approach and Information Behaviour Theories/Models

Aim/Purpose: The objective of this paper is to review the vast literature of user-centric in-formation science and inform about the emerging themes in information behaviour science. Background: The paradigmatic shift from system-centric to user-centric approach facilitates research on the cognitive and individual information processing. Various information behaviour theories/models emerged. Methodology: Recent information behaviour theories and models are presented. Features, strengths and weaknesses of the models are discussed through the analysis of the information behaviour literature. Contribution: This paper sheds light onto the weaknesses in earlier information behaviour models and stresses (and advocates) the need for research on social information behaviour. Findings: Prominent information behaviour models deal with individual information behaviour. People live in a social world and sort out most of their daily or work problems in groups. However, only seven papers discuss social information behaviour (Scopus search). Recommendations for Practitioners : ICT tools used for inter-organisational sharing should be redesigned for effective information-sharing during disaster/emergency times. Recommendation for Researchers: There are scarce sources on social side of the information behaviour, however, most of the work tasks are carried out in groups/teams. Impact on Society: In dynamic work contexts like disaster management and health care settings, collaborative information-sharing may result in decreasing the losses. Future Research: A fieldwork will be conducted in disaster management context investigating the inter-organisational information-sharing.

Search Engines for User Centric Information Retrieval and Scholarly Communication

Search Engine (SE) is a software system that is designed to search for information on the World Wide Web. Search Engine helps users to find rapidly relevant information. Search Engines were established based on the traditional database and information retrieval methods and also many other algorithms have been added them to improve their search. The Interconnection of world through the use of Internet and Search Engines has changed the profession of Librarianship. The paper describes the Search Engine Architecture, Working Process, Information Retrieval Features, Scholarly Communication and different types of search engines and their usefulness in the field of Science, Arts, Social Sciences, Engineering, Medical, Legal other aspects. Search engines such as, Scirus, Infomine, Refseek, TechXtra, PubMed, Catalaw, Google, Bing, and Yahoo, are presented with their usefulness and the disciplines in which they are used. Search Engines are very important for retrieving information and scholarly communication in achieving academic excellence as per the changing needs of users in Higher Education. Search engines create an environment whereby scholars and researchers can easily share, publish their research findings and be able to locate and also retrieve the required information. Thus, the paper explains the usefulness of SEs to the wider academic community that prepares academicians to face challenges in Higher Education.

Capabilities, Costs, Networks and Innovations: Impact of Mobile Phones in Rural India

India did not participate in the landline phone revolution but has seen an unprecedented growth in mobile phones, with over 919 million subscribers by the end of March 2012, making it second only people face several developmental constraints, such as low literacy, poor healthcare facilities, low per capita income, a high degree of poverty and problems related to poor infrastructure. Mobile phones can facilitate need-based and user-centric information and services at a cost that is affordable to India’s rural population, which was hitherto unreachable. Given this context, this study explores the socioeconomic impact of mobile phone usage in rural areas on the basis of a field survey conducted in two states of India: Punjab – a relatively developed state – and Bihar – a relatively underdeveloped state. The field survey revealed that mobile phones helped users gather information for both agricultural and non-agricultural purposes, as well as enabling them to keep in touch with their relatives and migrant family members. Mobile users benefit by obtaining timely information on a variety of subjects, including on employment opportunities and higher education for their children, by transferring funds and even by calling family members during emergencies. However, there is a marked difference in the usage of mobile phones among those in Punjab compared with those in Bihar. In developed areas, people were found to be early users of new technologies, reflected also in high usage of mobile value-added services (MVAS) and innovative uses like money transfer and agricultural information in Punjab. Meanwhile, a major and somewhat neglected dimension of mobile phone usage is that making communication substantially cheaper promotes social interaction. Multi-locational households with at least one migrant worker are increasing in numbers. Cheap mobile phones help such households keep in touch and remit money. Social relations can also cross traditional boundaries. The study also reveals that there are many innovations in the use of mobile phones, often carried out by users of different kinds, pointing to the importance of users in innovation processes.

Personalized Information Seeking Powered by Mobile Ontology for Sightless Residents of under Developing States

 Abstract—Nowadays internet is considered as one of the prevalent sources of information. Due to the unimpeded augmentation of the internet, billions of bytes of data are becoming the part of the internet on a daily basis. You can hardly find a topic about which you cannot get information from internet. But at the same time, it's extremely difficult to find the accurate personalized information from a large pool of data. Several conventional information fetching tools are currently working on the World Wide Web (WWW) but they are not powerful enough to search out user centric information. On the other hand, the sightless residents of underdeveloped countries are deprived from getting the required information from the internet due to unavailability of internet and special technical aid which is required for them to get along with any computer system. In the past no serious measures were taken to facilitate the special citizens and the research work which we found were mostly for academic purpose. So, there has been a strong impetus in the area of information engineering to develop and improve techniques to extract precise information from huge volumes of extraneous data. Ontology is one of the emerging solutions which can intelligently express and fetch the domain knowledge. The proposed architecture introduces an intelligent mechanism for the extraction of highly precise information for the visually impaired people through voice SMS. The architectural simplicity also does the proposed solution prominent from the available solutions.

A novel semantic web browser for user centric information retrieval: PERSON

As semantic web technologies mature and consequently more semantic web data is published on the web, there is a growing need for tools to access and use this data in user friendly ways. The most generic tool to access semantic web data is the common web browser. Developing plug-ins for advanced web browsers is one way of adding semantic information to web documents automatically or semi-automatically. To enable this enhancement, we present a novel semantic web browser called PERSON which is short for PERsonal Semantic extensiON. PERSON is an extension for Mozilla Firefox Web Browser and it adds semantics to users’ browsing experience by annotating web resources, associating them in categorical, entity-based manner and presenting more meaningful, structured documents to users. As a notable difference, semantic layer provided by PERSON works not only on web pages navigated by users but also on RSS data of user choice. Additionally, acquired content from web resources associated with linked data domains bring users together with structured information easily. In this paper, we evaluate PERSON as a new approach for semantic web browsing and present the analysis, design and implementation of PERSON and compare its features with related tools.

A Development of Cascade Granular Neural Networks

This paper studies the design of Cascade Granular Neural Networks (CGNN) for human-centric systems. In contrast to typical rule-based systems encountered in fuzzy modeling, the proposed method consists of two-phase development for CGNN. First, we construct a Granular Neural Network (GNN) which could be treated as a preliminary design. Next, all modeling discrepancies are compensated by a second GNN with a collection of rules that become attached to the regions of the input space where the error is localized. These granular networks are constructed by building a collection of user-centric information granules through Context-based Fuzzy c-Means (CFCM) clustering. Finally, the experimental results on two examples reveal that the proposed approach shows good performance in comparison with the previous works.

Schedule-Aware Transactions for Ambient Intelligence Environments

In this paper, the authors investigate the concept of designing user-centric transaction protocols toward achieving dependable coordination in AmI environments. As a proof-of-concept, this paper presents a protocol that takes into account the schedules of roaming users, which move from one AmI environment to another, avoiding abnormal termination of transactions when users leave an environment for a short time and return later. The authors compare the proposed schedule-aware protocol against a schedule-agnostic one. Findings show that the use of user-centric information in such situations is quite beneficial.

An Emerging Theory for Evidence Based Information Literacy Instruction in School Libraries, Part 2: Building a Culture of Enquiry

Objective – The purpose of this paper is to articulate a theory for the use of action research as a tool of evidence based practice for information literacy instruction in school libraries. The emerging theory is intended to capture the complex phenomenon of information skills teaching as it is embedded in school curricula. Such a theory is needed to support research on the integrated approach to teaching information skills and knowledge construction within the framework of inquiry learning. Part 1 of this paper, in the previous issue, built a foundation for emerging theory, which established user-centric information behavior and constructivist learning theory as the substantive theory behind evidence based library instruction in schools. Part 2 continues to build on the Information Search Process and Guided Inquiry as foundational to studying the information-to-knowledge connection and the concepts of help and intervention characteristic of 21st century school library instruction. 
 
 Methods – This paper examines the purpose and methodology of action research as a tool of evidence based instruction. This is accomplished through the explication of three components of theory-building: paradigm, substantive research, and metatheory. Evidence based practice is identified as the paradigm that contributes values and assumptions about school library instruction. It establishes the role of evidence in teaching and learning, linking theory and practice. Action research, as a tool of evidence based practice is defined as the synthesis of authentic learning, or performance-based assessment practices that continuously generate evidence throughout the inquiry unit of instruction and traditional data collection methods typically used in formal research. This paper adds social psychology theory from Lewin’s work, which contributes methodology from Gestalt psychology, field theory, group dynamics, and change theory. For Lewin the purpose of action research was social reform, while action research in education targeted self through the improvement of practice. The dichotomy between purposes of self and society is resolved by the Lewin-Dewey connection, where the reiterative cycle of action and reflection is the basis for a common intent for both types of action research. Dewey’s approach comprises the metatheory for emerging theory: a philosophy of purpose and methodology that determines how the research is done.
 
 Results – The emerging theory developed in this paper postulates that evidence based information literacy instruction uses action research for two purposes. Self-oriented action research (AR(S1)) targets self-improvement on the local level of teaching and learning in school libraries; social-oriented action research (AR(S2)) targets social reform on the global level of educational improvement. Corollaries of the theory indicate a research agenda and methodologies for the research. 
 
 Conclusion – Implicit in the content of the research is methodology that evolves from the distinction between the purposes of self- and social-oriented action research. Clearly, evidence is generated in the field of teaching and learning that is situated in theory-based practices, such as user-centered information processing, constructivist learning, and a culture of inquiry that grows from social processes. Librarianship is well suited to developing practitioner-researchers who are proficient in making the information-to-knowledge connection that informs their professional performance.