Mood-tracking application as persuasive technology for reduction of occupational stress

Occupational stress is a major concern in the job performance of employees. The use of persuasive technologies is one way to prevent or reduce this stress. The present study developed a mood tracking application and tested its effect on stress reduction of employees. Twenty-six employees of a petrochemical company were divided into equally-sized groups of married and single participants. Employees' accommodations are different based on their marital status. Other variables effecting stress were same for both groups. An OSI-R questionnaire was used to collect data before and after four weeks of application use. In present pilot study, data analysis shows that this kind of persuasive technology can have a significant positive effect on single employees and help them to decrease and manage their stress; however, no meaningful results were recorded for the married group because they did not use the application as much as participants in the single group

Forensically ready digital identity management systems, issues of digital identity life cycle and context of usage

Collecting necessary digital and network forensics to prove the identity of an individual who is responsible for a crime, or suspected of a malicious attack, or has used a device during an incident, with minimum doubt to the court or other legitimate organisations based on the digital forensic investigation model is one of the most important legal and security issues of digital identity management systems (DIMSs). Without a good understanding and identification of the most important parameters of DIMS based on the digital forensic investigation model, it is not possible to do digital forensic investigation and provide required evidence. Therefore, the main goal of this paper is to identify and prioritise DIMS parameters by considering a user’s digital identity lifecycle, the contexts of usage challenges, and constraints that should be considered in a digital forensic readiness model

Identification and classification of organizational level competencies for BI success

Business intelligence is a technology-oriented solution that businesses need to survive in today’s competitive and constantly changing market. To gain the benefits of BI systems, it is important to evaluate, assess, and improve factors that have an influence on BI success. Organizational competencies can provide answers to the question of how companies could gain more benefits from BI systems. While investment in BI systems is increasingly growing, measures to evaluate effective organizational competencies leading to BI success are gaining more importance. Therefore, this research identified a number of effective organizational competencies that contribute to BI success. Using the developed questionnaire for determining the effect of organizational level success on BI success, the research data was gathered for the study. A chi-square test confirmed the effectiveness of all nineteen identified competencies. Then, an exploratory factor analysis (EFA) was carried out on the data in order to identify the underlying dimensions. In addition, competencies were grouped into six categories, namely data management, information system/information technology (IS/IT) development, financial resources, relationship management, IS strategy and human capital policies. As a result, these competencies can be used as a measure to evaluate an organization’s status in holding some of the effective factors for BI success

A novel granular approach for detecting dynamic online communities in social network

The great surge in the research of community discovery in complex network is going on due to its challenging aspects. Dynamicity and overlapping nature are among the common characteristics of these networks which are the main focus of this paper. In this research, we attempt to approximate the granular human-inspired viewpoints of the networks. This is especially helpful when making decisions with partial knowledge. In line with the principle of granular computing, in which precision is avoided, we define the micro- and macrogranules in two levels of nodes and communities, respectively. The proposed algorithm takes microgranules as input and outputs meaningful communities in rough macrocommunity form. For this purpose, the microgranules are drawn toward each other based on a new rough similarity measure defined in this paper. As a result, the structure of communities is revealed and adapted over time, according to the interactions observed in the network, and the number of communities is extracted automatically. The proposed model can deal with both the low and the sharp changes in the network. The algorithm is evaluated in multiple dynamic datasets and the results confirm the superiority of the proposed algorithm in various measures and scenarios

Haringey employment gap research project: summary and full report

The Council is developing ‘Opportunity Haringey’ - an inclusive economic framework. The aim is to shape and drive forward the Council’s ambitions for a local economy that moves from reacting and responding to the challenges of the pandemic to a pro-active partnership approach that unlocks the creativity of residents and businesses to harness Haringey’s strengths to secure economic prosperity for all that is fairer and greener. Opportunity Haringey is being developed by undertaking economic analysis and engaging and listening to businesses, residents and a range of local and strategic partners in order to understand the range of needs, demands and aspirations across the many diverse communities in the borough. The Haringey Employment Gap research project, a key part of Opportunity Haringey, was carried out during 2023 by London Metropolitan University in partnership with the London Borough of Haringey. The project is part of London Met Lab Empowering London initiative, which aims to engage the University with its community partners. The aim of the project is to identify those groups in Haringey who suffer the highest levels of unemployment and to highlight the barriers they face in accessing jobs. It is well known that Black, Asian and minority ethnic residents, people with disabilities, young people and lone parents experience higher levels of unemployment and this research project seeks to identify which particular groups are most affected and what the main barriers are in accessing work. The research project looks at how well existing employment advisory services in Haringey support Black, Asian and minority ethnic residents, residents with disabilities, lone parents and young people to get into work. This Summary Report sets out the key findings and recommendations from the Haringey Employment Gap research project. It should be noted that while there are key differences in terms of the barriers to accessing work experienced by the different target groups in this research, such as race discrimination and discrimination against people with disabilities, there are also many common barriers that are experienced across all the target groups. Some of the key common barriers include low expectations and confidence to attain meaningful employment, poor experiences of employment support services, particularly of the Job Centres and for many women with children, especially single parents, unaffordable childcare. It should also be noted that many of the key findings and recommendations echo those of the recent No Wrong Door report by City Hall and The Young Foundation (2023) and The Commission on the Future of Employment Support interim report (2023) reports, particularly on how important it is to join up skills and employment support to deliver an accessible offer for job seekers

Opportunity Haringey: Haringey employment gap research report. Summary Report.

Haringey Council is developing ‘Opportunity Haringey’ – an inclusive economic framework. The aim is to shape and drive forward the Council’s ambitions for a local economy that moves from reacting and responding to the challenges of the pandemic to a pro-active partnership approach that unlocks the creativity of residents and businesses to harness Haringey’s strengths to secure economic prosperity for all that is fairer and greener. Opportunity Haringey is being developed by undertaking economic analysis and engaging and listening to businesses, residents and a range of local and strategic partners in order to understand the range of needs, demands and aspirations across the many diverse communities in the borough. The Haringey Employment Gap research project, a key part of Opportunity Haringey, was carried out during 2023 by London Metropolitan University in partnership with Haringey Council. The project is part of London Met Lab Empowering London initiative, which aims to engage the University with its community partners. The aim of the project is to identify those groups in Haringey who experience the highest levels of unemployment and to highlight the barriers they face in accessing jobs. It is well known that Black, Asian and Minority Ethnic residents, people with disabilities, young people and lone parents experience higher levels of unemployment and this research project seeks to identify which particular groups are most affected and what the main barriers are in accessing work. The research project looks at how well existing employment advisory services in Haringey support Black, Asian and Minority Ethnic residents, residents with disabilities, lone parents and young people to get into work. This Summary Report sets out the key findings and recommendations from the Haringey Employment Gap research project. It should be noted that while there are key differences in terms of the barriers to accessing work experienced by the different target groups in this research, such as race discrimination and discrimination against people with disabilities, there are also many common barriers that are experienced across all the target groups. Some of the key common barriers include low expectations and confidence to attain meaningful employment, poor experiences of employment support services, particularly of the Job Centres and for many women with children, especially single parents, unaffordable chilcare

A Novel Smartphone Application for Indoor Positioning of Users based on Machine Learning

Smartphones are linked with individuals and are valuable and yet easily available sources for characterising users’ behaviour and activities. User’s location is among the characteristics of each individual that can be utilised in the provision of location-based services (LBs) in numerous scenarios such as remote health-care and interactive museums. Mobile phone tracking and positioning techniques approximate the position of a mobile phone and thereby its user, by disclosing the actual coordinate of a mobile phone. Considering the advances in positioning techniques, indoor positioning is still a challenging issue, because the coverage of satellite signals is limited in indoor environments. One of the promising solutions for indoor positioning is fingerprinting in which the signals of some known transmitters are measured in several reference points (RPs). This measured data, which is called dataset is stored and used to train a mathematical model that relates the received signal from the transmitters (model input) and the location of that user (the output of the model). Considering all the improvements in indoor positioning, there is still a gap between practical solutions and the optimal solution that provides near theoretical accuracy for positioning. This accuracy directly impacts the level of usability and reliability in corresponding LBSs. In this paper, we develop a smartphone app with the ability to be trained and detect users’ location, accurately. We use Gaussian Process Regression (GPR) as a probabilistic method to find the parameters of a non-linear and non-convex indoor positioning model. We collect a dataset of received signals’ strength (RSS) in several RPs by using a software which is prepared and installed on an Android smartphone.We also find the accurate 2σ confidence interval in the presented GPR method and evaluate the performance of the proposed method by measured data in a realistic scenario. The measurements confirm that our proposed method outperforms some conventional methods including KNN, SVR and PCA-SVR in terms of accuracy

User preferences for adaptive user interfaces in health information systems

An adaptive user interface requires identification of user requirements. Interface designers and engineers must understand end-user interaction with the system to improve user interface design. A combination of interviews and observations is applied for user requirement analysis in health information systems (HIS). Then, user preferences are categorized in this paper as either data entry, language and vocabulary, information presentation, or help, warning and feedback. The user preferences in these categories were evaluated using the focus group method. Focus group sessions with different types of HIS users comprising medical staff (with and without computer skills) and system administrators identified each user group’s preference for the initial adaptation of the HIS user interface. User needs and requirements must be identified to adapt the interface to users during data entry into the system. System designers must understand user interactions with the system to identify their needs and preferences. Without this, interface design cannot be adapted to users and users will not be comfortable using the system and eventually abandon its use

Organizational Knowledge Mapping Based on Library Information System

One of the most popular techniques for identifying knowledge in organizations is knowledge mapping. It can help decision makers to better understand the knowledge flow within the organizations. Mapping organizations knowledge, especially in research institutes, has attracted much attention from senior managements in recent years. Libraries, among the most important parts of research institutes, have a significant role in scientific advances. Due to this important role, many knowledge operations take place in collaboration with libraries. All of library transactions including users borrowing and returning logs and also books metadata are recorded in library information systems. Users' transaction logs are rich resources to extract information about knowledge operations in an organization. In this paper we propose a new methodology for drawing knowledge map, based on library information system logs. Our proposed methodology contains five steps including data collection and making data warehouse, data preprocessing and refinement, applying knowledge mapping algorithm for extracting input data for mapping, drawing knowledge map and finally analyzing the results. According to this methodology, we have drawn the IRANDOC knowledge map emphasizing interdisciplinary domains based on library information system users’ logs. IRANDOC knowledge map shows most studied subjects and also interrelation between them which are invaluable source of knowledge for IRANDOC decision makers in order to initiate research projects

Personalisation : the application of personal assistant agents to future mobile communications services

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