Lightweight Privacy-Preserving Task Assignment in Skill-Aware Crowdsourcing: [Full Version]

Crowdsourcing platforms dedicated to work, be it paid or voluntary, essentially consist in intermediating between tasks—sent by requesters—and workers. They are used by a growing number of individuals and organizations, for tasks that are more and more diverse, complex , and that require specific skills, availabilities, experiences, or even devices. On the one hand, these highly detailed task specifications and worker profiles enable high-quality task assignments. On the other hand, detailed worker profiles may disclose a large amount of personal information to the central platform (e.g., personal preferences, availabilities, wealth, occupations), jeopardizing the privacy of workers. In this paper, we propose a lightweight approach to protect workers privacy against the platform along the current crowdsourcing task assignment process. Our approach (1) satisfies differential privacy by building on the well-known randomized response technique, applied by each worker to perturb locally her profile before sending it to the platform, and (2) copes with the resulting perturbation by benefiting from a taxonomy defined on workers profiles. We describe the lightweight upgrades to be brought to the workers, to the platform, and to the requesters. We show formally that our approach satisfies differential privacy, and empirically, through experiments performed on various synthetic datasets, that it is a promising research track for coping with realistic cost and quality requirements

Integration of Blockchain and Auction Models: A Survey, Some Applications, and Challenges

In recent years, blockchain has gained widespread attention as an emerging technology for decentralization, transparency, and immutability in advancing online activities over public networks. As an essential market process, auctions have been well studied and applied in many business fields due to their efficiency and contributions to fair trade. Complementary features between blockchain and auction models trigger a great potential for research and innovation. On the one hand, the decentralized nature of blockchain can provide a trustworthy, secure, and cost-effective mechanism to manage the auction process; on the other hand, auction models can be utilized to design incentive and consensus protocols in blockchain architectures. These opportunities have attracted enormous research and innovation activities in both academia and industry; however, there is a lack of an in-depth review of existing solutions and achievements. In this paper, we conduct a comprehensive state-of-the-art survey of these two research topics. We review the existing solutions for integrating blockchain and auction models, with some application-oriented taxonomies generated. Additionally, we highlight some open research challenges and future directions towards integrated blockchain-auction models

Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services

Artificial Intelligence-Generated Content (AIGC) is an automated method for generating, manipulating, and modifying valuable and diverse data using AI algorithms creatively. This survey paper focuses on the deployment of AIGC applications, e.g., ChatGPT and Dall-E, at mobile edge networks, namely mobile AIGC networks, that provide personalized and customized AIGC services in real time while maintaining user privacy. We begin by introducing the background and fundamentals of generative models and the lifecycle of AIGC services at mobile AIGC networks, which includes data collection, training, finetuning, inference, and product management. We then discuss the collaborative cloud-edge-mobile infrastructure and technologies required to support AIGC services and enable users to access AIGC at mobile edge networks. Furthermore, we explore AIGCdriven creative applications and use cases for mobile AIGC networks. Additionally, we discuss the implementation, security, and privacy challenges of deploying mobile AIGC networks. Finally, we highlight some future research directions and open issues for the full realization of mobile AIGC networks

Framing the User Experience in Mobile Newsmaking with Smartphones

Mobile handheld devices are changing the practices of newsmaking, the roles of journalists and readers in it, and the published news in profound ways. The activity of mobile newsmaking aims at a tangible outcome, the news, which are consumed by an audience. Relatively little research exists in HCI (Human-Computer Interaction) that explores what is user experience of mobile systems in goal-oriented creative activity in organizational settings and especially in the natural contexts of use. This thesis addresses this gap by focusing on user experience, which arises when smartphones are used in mobile newsmaking to create and publish online and print news in the newspaper industry. This thesis has two main goals. First, it aims to gain a holistic understanding of user experience in mobile newsmaking with smartphones from the viewpoint of mobile reporters as users. Second, it explores how mobile and location-based assignments assigned by the newsroom can support cooperative newsmaking. This thesis contains nine scientific publications based on twelve case studies. The research approach of the studies is primarily qualitative. Seven of the studies included the usage of a mobile service client for newsmaking in the mobile context of use. Two of the twelve studies concentrated on reader participation in newsmaking as a form of mobile crowdsourcing. The rest of the studies focused on professional use. Over one hundred participants participated in the studies, of which a majority were students of visual journalism with prior work experience in journalism. The empirical findings are synthesized in the thesis summary. The model of user experience in mobile newsmaking with smartphones and the process model for mobile assignment-based processes summarize the thesis work on user experience and cooperative processes. User experience in mobile newsmaking is constructed in a process of using the mobile system in a goal-oriented and creative activity in the mobile context of use. The activity of mobile newsmaking consists of several subactivities starting from encountering a newsworthy event to the publishing of the news. It may include mobile reporter’s cooperation with others, who are in the field or in the newsroom. The constructed model of user experience has seven main components: user, system, the context of use, tangible outcome, descriptive attributes, overall evaluative judgments, and consequences. The model emphasizes the characteristics of the tangible outcome of system use (news material, news) as a fourth component that can contribute to user experience in addition to the characteristics of the user, system and the context of use. User’s experienced quality of the system is described by verbally expressible descriptive attributes divided to four components. The components of the descriptive attributes are the quality of the outcome (technical and content-based quality) and the perceived impacts (benefits and costs) that complement instrumental (pragmatic) and non-instrumental (hedonic) qualities from prior models of user experience. Ease-of-use, speed, light weight, small-size, unobtrusiveness, reliability, connectivity, controllability, being always along, and multifunctionality are key attributes for positive user experience. For users, pride of the outcome, fit with needs, motivations and goals, feeling of being in control, mastery of the system and activity, and the fit of the system to user’s role and situation are important. The process model for mobile assignment-based processes illustrates the coordination and cooperation related information and communication needs of the mobile reporter and the newsroom at differenct phases of newsmaking. The constructed models and synthesized results can aid academics and practitioners when designing, studying, and evaluating solutions for mobile work that can be complex, cooperative and creative and which aims at a perceivable or tangible outcome. They can also aid in recognizing the critical success factors of the solutions for different types of users and circumstances of the context of use. Further, results can aid when selecting and planning ICT solutions for media organizations and when planning the related editorial processes, workflows, and work roles. Finally, the constructed models can be used and validated in future research in other fields of mobile work and crowdsourcing

Social Middleware for Civic Engagement

International audienceCivic engagement refers to any collective action towards the identification and solving of public issues. Current civic technologies are traditional Web-or mobile-based platforms that make difficult, or just impossible, the participation of citizens via different communication technologies. Moreover, connected objects sensing physical-world data can nourish participatory processes by providing physical evidence to citizens; however, leveraging these data is not direct and still a time-consuming process for civic technologies developers. This paper introduces the concept of social middleware for civic engagement. Social middleware allows citizens to engage in participatory processes-supported by civic technologies-via their favorite communication tools, and to interact not only with other citizens but also with relevant connected objects and software platforms. The mission of social middleware goes beyond the connection of all these heterogeneous entities. It aims at easing the implementation of distributed applications oriented toward civic engagement by featuring dedicated built-in services

Characterizing Novelty as a Motivator in Online Citizen Science

Citizen science projects rely on the voluntary contribution of nonscientists to take part in scientific research projects. Projects taking place exclusively over the Internet face significant challenges, chief among them is the attracting and keeping the critical mass of volunteers needed to conduct the work outlined by the science team. The extent to which platforms can design experiences that positively influence volunteers’ motivation can help address the contribution challenges. Consequently, project organizers need to develop strategies to attract new participants and keep existing ones. One strategy to encourage participation is implementing features, which re-enforce motives known to change people’s attitudes towards contributing positively. The literature in psychology noted that novelty is an attribute of objects and environments that occasion curiosity in humans leading to exploratory behaviors, e.g., prolonged engagement with the object or environment. This dissertation described the design, implementation, and evaluation of an experiment conducted in three online citizen science projects. Volunteers received novelty cues when they classified data objects that no other volunteer had previously seen. The hypothesis was that exposure to novelty cues while classifying data positively influences motivational attitudes leading to increased engagement in the classification task and increased retention. The experiments resulted in mixed results. In some projects, novelty cues were universally salient, and in other projects, novelty cues had no significant impact on volunteers’ contribution behaviors. The results, while mixed, are promising since differences in the observed behaviors arise because of individual personality differences and the unique attributes found in each project setting. This research contributes to empirically grounded studies on motivation in citizen science with analyses that produce new insights and questions into the functioning of novelty and its impact on volunteers’ behaviors

Human-AI complex task planning

The process of complex task planning is ubiquitous and arises in a variety of compelling applications. A few leading examples include designing a personalized course plan or trip plan, designing music playlists/work sessions in web applications, or even planning routes of naval assets to collaboratively discover an unknown destination. For all of these aforementioned applications, creating a plan requires satisfying a basic construct, i.e., composing a sequence of sub-tasks (or items) that optimizes several criteria and satisfies constraints. For instance, in course planning, sub-tasks or items are core and elective courses, and degree requirements capture their complex dependencies as constraints. In trip planning, sub-tasks are points of interest (POIs) and constraints represent time and monetary budget, or user-specified requirements. Needless to say, task plans are to be individualized and designed considering uncertainty. When done manually, the process is human-intensive and tedious, and unlikely to scale. The goal of this dissertation is to present computational frameworks that synthesize the capabilities of human and AI algorithms to enable task planning at scale while satisfying multiple objectives and complex constraints. This dissertation makes significant contributions in four main areas, (i) proposing novel models, (ii) designing principled scalable algorithms, (iii) conducting rigorous experimental analysis, and (iv) deploying designed solutions in the real-world. A suite of constrained and multi-objective optimization problems has been formalized, with a focus on their applicability across diverse domains. From an algorithmic perspective, the dissertation proposes principled algorithms with theoretical guarantees adapted from discrete optimization techniques, as well as Reinforcement Learning based solutions. The memory and computational efficiency of these algorithms have been studied, and optimization opportunities have been proposed. The designed solutions are extensively evaluated on various large-scale real-world and synthetic datasets and compared against multiple baseline solutions after appropriate adaptation. This dissertation also presents user study results involving human subjects to validate the effectiveness of the proposed models. Lastly, a notable outcome of this dissertation is the deployment of one of the developed solutions at the Naval Postgraduate School. This deployment enables simultaneous route planning for multiple assets that are robust to uncertainty under multiple contexts

HUC-HISF: A Hybrid Intelligent Security Framework for Human-centric Ubiquitous Computing

制度:新 ; 報告番号:乙2336号 ; 学位の種類:博士(人間科学) ; 授与年月日:2012/1/18 ; 早大学位記番号:新584