Recent research underscored the resurgence of webcam-based eye tracking as a cost-effective, ecologically valid, and scalable choice, attributed to advancements in machine learning-assisted calibration methods. Online gambling poses a public health concern, necessitating innovative research methods and evidence-based strategies for harm reduction. This study investigates users’ fixation patterns on responsible gambling (RG) information in different formats on gambling websites using webcam-based eye tracking. Participants browsed six gambling websites to search for RG information. 19 participants’ data were analyzed on Areas of Interest (AOIs) categorized based on RG formats. The study has found that usability varied across RG formats and also highlights both feasibility and challenges of webcam-based eye tracking for online behavior research, with practical implications to gambling stakeholders.

In the world of information technology and the Internet, which has become a part of human life today and is constantly expanding, Attention to the users' requirements such as information security, fast processing, dynamic and instant access, and costs savings has become essential. The solution that is proposed for such problems today is a technology that is called cloud computing. Today, cloud computing is considered one of the most essential distributed tools for processing and storing data on the Internet. With the increasing using this tool, the need to schedule tasks to make the best use of resources and respond appropriately to requests has received much attention, and in this regard, many efforts have been made and are being made. To this purpose, various algorithms have been proposed to calculate resource allocation, each of which has tried to solve equitable distribution challenges while using maximum resources. One of these calculation methods is the DRF algorithm. Although it offers a better approach than previous algorithms, it faces challenges, especially with time-consuming resource allocation computing. These challenges make the use of DRF more complex than ever in the low number of requests with high resource capacity as well as the high number of simultaneous requests. This study tried to reduce the computations costs associated with the DRF algorithm for resource allocation by introducing a new approach to using this DRF algorithm to automate calculations by machine learning and artificial intelligence algorithms (Autonomic Dominant Resource Fairness or A-DRF).

The allocation of multiple types of resources fairly and efficiently has become a substantial concern in state-of-the-art computing systems. Accordingly, the rapid growth of cloud computing has highlighted the importance of resource management as a complicated and NP-hard problem. Unlike traditional frameworks, in modern data centers, incoming jobs pose demand profiles, including diverse sets of resources such as CPU, memory, and bandwidth across multiple servers. Accordingly, the fair distribution of resources, respecting such heterogeneity appears to be a challenging issue. Furthermore, the efficient use of resources as well as fairness, establish trade-off that renders a higher degree of satisfaction for both users and providers. Dominant Resource Fairness (DRF) has been introduced as an initial attempt to address fair resource allocation in multi-resource cloud computing infrastructures. Dozens of approaches have been proposed to overcome existing shortcomings associated with DRF. Although all those developments have satisfied several desirable fairness features, there are still substantial gaps. Firstly, it is not clear how to measure the fair allocation of resources among users. Secondly, no particular trade-off considers non-dominant resources in allocation decisions. Thirdly, those allocations are not intuitively fair as some users are not able to maximize their allocations. In particular, the recent approaches have not considered the aggregate resource demands concerning dominant and non-dominant resources across multiple servers. These issues lead to an uneven allocation of resources over numerous servers which is an obstacle against utility maximization for some users with dominant resources. Correspondingly, in this paper, a resource allocation algorithm called H-FFMRA is proposed to distribute resources with fairness across servers and users, considering dominant and non-dominant resources. The experiments show that H-FFMRA achieves approximately %20 improvements on fairness as well as full utilization of resources compared to DRF in multi-server settings.

Task scheduling in cloud computing is considered as a significant issue that has attracted much attention over the last decade. In cloud environments, users expose considerable interest in submitting tasks on multiple Resource types. Subsequently, finding an optimal and most efficient server to host users' tasks seems a fundamental concern. Several attempts have suggested various algorithms, employing Swarm optimization and heuristics methods to solve the scheduling issues associated with cloud in a multi-resource perspective. However, these approaches have not considered the equalization of dominant resources on each specific resource type. This substantial gap leads to unfair allocation, SLA degradation and resource contention. To deal with this problem, in this paper we propose a novel task scheduling mechanism called MRFS. MRFS employs Lagrangian multipliers to locate tasks in suitable servers with respect to the number of dominant resources and maximum resource availability. To evaluate MRFS, we conduct time-series experiments in the cloudsim driven by randomly generated workloads. The results show that MRFS maximizes per-user utility function by %15-20 in FFMRA compared to FFMRA in absence of MRFS. Furthermore, the mathematical proofs confirm that the sharingincentive, and Pareto-efficiency properties are improved under MRFS.

Digital motivation refers to the use of software-based solutions to change, enhance, or maintain people’s attitude and behaviour towards specific tasks, policies, and regulations. Gamification, persuasive technology, and entertainment computing are example strands of such a paradigm. Digital motivation has unique properties which necessitate careful consideration of its analysis design methods. This stems from the strong human factor involvement, and if it is not implemented effectively, it can result in digital motivation being perceived negatively or leading to reduced motivation. The emerging literature on the topic includes approaches for creating digital motivation solutions. However, their primary focus is on specifying its operation, for example, the design of feedback, rewards and levels. In this paper, we propose a novel modelling language which enables capturing digital motivation as an integral part of the organisational and social structure of a business, captured via goal models. We also demonstrate how modelling of motivational techniques at this level, the goal level, enables a more powerful analysis that informs the introduction, design and management of digital motivation. Finally, we evaluate the language and its analysis using different perspectives and quality measures and report the results.

Procrastination refers to a voluntary postponement that prevents people from performing their tasks and can hurt productivity and wellbeing. Procrastination might occur due to a lack of motivation to perform tasks or due to the low self-control that people might have over their time and task management. Social Networking Sites (hereafter SNS) are designed to enable their users to engage in online interaction for different purposes such as increasing popularity or exploring information. SNS embed influence and persuasion techniques to attract users which can make them a medium for procrastination where some users fail to maintain a desirable level of self-control over their usage. However, we argue that advances in persuasive technology and gamification techniques can be utilised to augment SMS and help users to regain self-control over their procrastination. Implementing these techniques correctly means that users can still enjoy accessing SNS while maintaining a desirable level of control over their procrastination. Building these anti-procrastination tools, however, is a challenging design activity due to their potential of triggering negative side-effects such as reactance and workarounds, and affecting the overall user experience. In this paper, we conduct user studies, consisting of an exploratory stage using focus groups, diary study and interviews and followed by a design stage based mainly on co-design sessions. Our studies' participants self-declared having a problematic degree of procrastination on SNS, to explore procrastination countermeasure techniques that can augment the future designs of SNS and how best to apply them.

Social Network Sites (SNSs) are meant to facilitate interaction between people. The design of SNSs employs persuasive techniques with the aim of enhancing the user experience but also increasing interaction and user retention.Examples include the personalisation of content, temporarily available feeds, and notification and alert features. Socialness is now being embedded in new paradigms such as the Internet of Things and cyber-physical systems where devices can link people to each other and increase relatedness and group creation. One of the phenomena associated with such persuasion techniques is the experience of Fear of Missing Out (FoMO). FoMO typically refers to the preoccupation of SNS users with being deprived of interaction while offline. The salience, mood modification and conflict typically experienced as part of FoMO, are symptoms of digital addiction (DA). Despite recognition of the widespread experience of FoMO, existing research focuses on user psychology to interpret it. The contribution of SNS design in triggering FoMO remains largely unexplored. In this paper, we conduct a multi-stage qualitative research including interviews, a diary study and three focus group sessions to explore the relationship between SNS features and FoMO. Our findings demonstrate how the different SNS features act as persuasion triggers for certain kinds of FoMO. Also, we suggest features that could be introduced to social network sites to allow individuals to manage FoMO and identify the principles and challenges associated with engineering them.

Online gambling, unlike other mediums of problem-atic and addictive behaviours, such as tobacco and alcohol, offers unprecedented opportunities for building information systems that are able to monitor and understand a user's behaviour in real-time and adapt persuasive messages and interactions that would fit their personal profile and usage context. Online gambling industry usually provides Application Programming Interfaces (APIs) meant mainly to enable third-party applications to network with their gambling services and enhance a user's gambling experience. In this industrial practice and experience paper, we advocate that such API's can also be used to retrieve gamblers' online data, such as browsing and betting history, promotions and available offers and use it to build more intel-ligent and proactive responsible gambling information systems. We report on our industrial experience in this field and make the argument that data available for persuasive marketing and usability should, under specific usage conditions, also be made available for responsible gambling information systems. This principle would provide equal opportunities for both directions. We discuss the psychological foundations of our proposed solution and the risks and challenges typically found when building such a software-assisted intervention, persuasion and emotion regulation technology. We also shed light on its potential implications from the perspectives of social corporate responsibility and data protection. We finally propose a conceptual architecture to demonstrate our vision and explain how it can be implemented. In the wider context, the paper is meant to provide insights on building behavioural awareness and regulation information systems in relation to problematic digital media usage.

Transparency is a requirement that denotes the communication of information that should help audience to take informed decisions. The existing research on transparency in information systems usually focuses on the party who provides transparency and its inter-relation with other requirements such as privacy, security and regulatory requirements. Engineering transparency, however, also requires the analysis of the information receivers’ situation and their transparency requirements and the medium used to communicate and present the information. A holistic consideration of transparency will enhance its management and increase its usefulness. In this paper, we provide a novel engineering framework, consisting of a modelling language and nine analytical reasonings, which is meant to represent transparency requirements and detect a set of possible side-effects. Examples of such detections include detecting information overload, information starvation, and transparency leading to biased decisions. We then evaluate the modelling language through a case study and report the results.

Transparency is a key emerging requirement in modern businesses and their information systems. Transparency refers to the information which flows amongst stakeholders for the purpose of informed decision-making and taking the right action. Transparency is generally associated with positive connotations such as trust and accountability. However, it has been shown that it could have adverse effects such as information overload and affecting decisions objectiveness. This calls for systematic approaches for transparency to ensure its cost-effectiveness and avoid such adverse side effects. This is especially true considering that the relatively few works in the literature on transparency requirements have focused mainly on making information available and accessible and have paid little focus on the information receivers’ side and making it meaningful for them. In this paper, we reflect on our previous research on transparency and its multi-faceted constructs and review multi-disciplinary conceptualisation and propose four reference models which are meant to form a holistic conceptual baseline for transparency requirements in information systems. These reference models cover transparency actors, transparency meaningfulness, transparency usefulness, and information quality in transparency. We also discuss the interdependencies amongst these four reference models and their implications for requirements engineers and information system analysts. As a proof of concept, we analyse a mainstream transparency document, the United Kingdom Freedom of Information Act, in the light of our reference models and demonstrate the need to consider transparency more holistically and the need to include the information receiver’s perspective and the inter-relations amongst various properties and constituents of transparency as well. We then highlight areas of improvement informed by our analysis.

Transparency is one of the main requirements in business information systems with its unique characteristics, and it requires designated engineering approaches. Despite that, transparency requirements have often been studied along other mainstream requirements relating to information, such as privacy, and seldom studied as a first-class concept. In addition, the literature on transparency is mainly driven by the perspective of information providers, and a large number of stakeholders who receive or request information are usually neglected. To achieve a holistic and more efficient management of transparency requirements, we propose a conceptual framework which integrates three mechanisms of crowdsourcing, structured feedback and social adaptation. Crowdsourcing facilitates the involvement of a large, diverse group of stakeholders in transparency engineering. The use of structured feedback helps automating the process of feedback acquisition and analysis. Eventually, as transparency requirements evolve over time, social adaptation can be applied to adapt the business information system to meet the emerging transparency requirements of the stakeholders.

Software-based motivation refers to the use of technology to enhance the engagement and efficiency of people in performing tasks and following a certain behaviour. Instances of such paradigm include gamification, persuasive technology and entertainment computing. Despite its potential, an ad-hoc introduction of software-based motivation to a business environment may lead to detrimental effects such as creating pressure and tension, and also reducing quality and authenticity. Hence, we advocate the need for a systematic engineering process to develop software solutions for motivation requirements. One of the challenges is in the high diversity in users' perception and acceptance of motivation strategies and their software-based incarnations. In this paper, we propose the use of personas as an intermediate step which increases efficiency in the engineering process for both engineers and users. We conduct an empirical research and identify elements which describe people with regard to their perception and preferences towards software-based motivational techniques and create a set of exemplary personas to aid the engineering process. We also present guidelines and challenges related to using persona-based engineering methods for software-based motivation.

Crowdsourcing is a paradigm which enables and harnesses the power and wisdom of a usually large, diverse crowd in innovation, problem solving and knowledge acquisition. The scale, benefits and application areas of this traditional model are amplified by the advances of information and communication technology such as the advent of Web 2.0, which, at the same time, has increased the complexity and, hence, the need for systematic development approaches. While crowdsourcing has been successfully applied in several projects and de-facto platforms already exist for them, the research on engineering principles, methods and tools for developing a crowdsourcing project is still in the early stages. In this paper, we study the adaptation of crowdsourcing settings to fit the nature of the problem being crowdsourced. As a method, we review the literature and complement that with an online expert survey involving practitioners and researchers active in the field of crowdsourcing. We then interpret the obtained results and identify a set of recommendations on how to set up crowdsourcing to fit each of the five common categories of problems. Our results inform future crowdsourcing developers with best practice experiences on planning and configuring their projects.

Crowdsourcing is an emerging paradigm which utilises the power of the crowd in contributing information and solving problems. Crowdsourcing can support requirements elicitation, especially for systems used by a wide range of users and working in a dynamic context where requirements evolve regularly. For such systems, traditional elicitation methods are typically costly and limited in catering for the high diversity, scale and volatility of requirements. In this paper, we advocate the use of crowdsourcing for requirements elicitation and investigate ways to configure crowdsourcing to improve the quality of elicited requirements. To confirm and enhance our argument, we follow an empirical approach starting with two focus groups involving 14 participants, users and developers, followed by an online expert survey involving 34 participants from the Requirements Engineering community. We discuss our findings and present a set of challenges of applying crowdsourcing to aid requirements engineering with a focus on the elicitation stage.

The improvement and success of socio-technical systems depend on the joint optimisation of both the social and the technical parts. Improving the social part of a socio-technical system is a meticulous task, as social requirements are diverse and dynamic, and they usually evolve with time and context. Information transparency (henceforth, transparency) is one of the social requirements that can affect the overall attitude of the stakeholders present within a socio-technical system, and influence their other social requirements such as privacy, trust, collaboration and non-bias. In this paper, we advocate the need to engineer transparency as a first class requirement, propose a baseline model for transparency and show how this model can be a starting point for the analysis of transparency requirements of different stakeholders. We showcase our on-going research in the modelling and analysis of transparency as a requirement, discuss some of the challenges of transparency requirements elicitation, and present our future work.

Crowdsourcing is an emerging online paradigm for problem solving which involves a large number of people often recruited on a voluntary basis and given, as a reward, some tangible or intangible incentives. It harnesses the power of the crowd for minimizing costs and, also, to solve problems which inherently require a large, decentralized and diverse crowd. In this paper, we advocate the potential of crowdsourcing for software evaluation. This is especially true in the case of complex and highly variable software systems, which work in diverse, even unpredictable, contexts. The crowd can enrich and keep the timeliness of the developers' knowledge about software evaluation via their iterative feedback. Although this seems promising, crowdsourcing evaluation introduces a new range of challenges mainly on how to organize the crowd and provide the right platforms to obtain and process their input. We focus on the activity of obtaining evaluation feedback from the crowd and conduct two focus groups to understand the various aspects of such an activity. We finally report on a set of challenges to address and realize correct and efficient crowdsourcing mechanisms for software evaluation.

Software-mediated interactions introduce a new set of etiquettes not necessarily analogous to face-to-face interactions. Current software systems are not prepared to accommodate how users differ in their perception of the considerate nature of an interaction. Being socially inconsiderate affects the relationship between colleagues, their cooperation level, willingness to interact and, ultimately, the entire business. Hence, software acting on behalf of people or facilitating their interaction should be expected to support a rationale where both socialness and business goals are recognised. However, despite this vital role, the sociality of business interactions has rarely been considered within information systems development and research, even among areas that attempt to align the system with business needs. In this paper, we conduct an empirical study, following a mixed method approach, trying to explore the users' attitude and their perception of the elements of consideration in software-mediate interaction and what they would expect future software to provide in relation to consideration. We list a set of research challenges for information systems which embed consideration as a main social requirement.

Crowdsourcing is an emerging business model where tasks are accomplished by the general public; the crowd. Crowdsourcing has been used in a variety of disciplines, including information systems development, marketing and operationalization. It has been shown to be a successful model in recommendation systems, multimedia design and evaluation, database design, and search engine evaluation. Despite the increasing academic and industrial interest in crowdsourcing, there is still a high degree of diversity in the interpretation and the application of the concept. This paper analyses the literature and deduces a taxonomy of crowdsourcing. The taxonomy is meant to represent the different configurations of crowdsourcing in its main four pillars: the crowdsourcer, the crowd, the crowdsourced task and the crowdsourcing platform. Our outcome will help researchers and developers as a reference model to concretely and precisely state their particular interpretation and configuration of crowdsourcing.

The KDD Cup '99 data set has been widely used to evaluate intrusion detection prototypes, most based on machine learning techniques, for nearly a decade. The data set served well in the KDD Cup '99 competition to demonstrate that machine learning can be useful in intrusion detection systems. Howeve r, there are discrepancies in the findings reported in the literature. Further, some researchers have published criticisms of the data (and the DARPA data from which the KDD Cup '99 data has been derived), questioning the validity of results obtained with this data. Despite the criticisms, researchers continue to use the data due to a lack of better publicly available alternatives. Hence, it is important to identify the value of the data set and the findings from the extensive body of research based on it, which has largely been ignored by the existing critiques. This paper reports on an empirical investigation, demonstrating the impact of several methodological differences in the publicly available subsets, which uncovers several underlying causes of the discrepancy in the results reported in the literature. These findings allow us to better interpret the current body of research, and inform recommendations for future use of the data set.