Abstract
With the increasing development of human-AI teaming structures within and across geographies, the time is ripe for a continuous and objective look at the predictors, barriers, and facilitators of human-AI scientific collaboration from a multidisciplinary point of view. This paper aims at contributing to this end by exploiting a set of factors affecting attitudes towards the adoption of human-AI interaction into scientific work settings. In particular, we are interested in identifying the determinants of trust and acceptability when considering the combination of hybrid human-AI approaches for improving research practices. This includes the way as researchers assume human-centered artificial intelligence (AI) and crowdsourcing as valid mechanisms for aiding their tasks. Through the lens of a unified theory of acceptance and use of technology (UTAUT) combined with an extended technology acceptance model (TAM), we pursue insights on the perceived usefulness, potential blockers, and adoption drivers that may be representative of the intention to use hybrid intelligence systems as a way of unveiling unknown patterns from large amounts of data and thus enabling novel scientific discoveries.

Abstract
The study of data quality in crowdsourcing campaigns is currently a prominent research topic, given the diverse range of participants involved. A potential solution to enhancing data quality processes in crowdsourcing is cognitive personalization, which involves appropriately adapting or assigning tasks based on a crowd worker's cognitive profile. There are two common methods for assessing a crowd worker's cognitive profile: administering online cognitive tests, and inferring behavior from task fingerprinting based on user interaction log events. This article presents the findings of a study that investigated the complementarity of both approaches in a microtask scenario, focusing on personalizing task design. The study involved 134 unique crowd workers recruited from a crowdsourcing marketplace. The main objective was to examine how the administration of cognitive ability tests can be used to allocate crowd workers to microtasks with varying levels of difficulty, including the development of a deep learning model. Another goal was to investigate if task fingerprinting can be used to allocate crowd workers to different microtasks in a personalized manner. The results indicated that both objectives were accomplished, validating the usage of cognitive tests and task fingerprinting as effective mechanisms for microtask personalization, including the development of a deep learning model with 95% accuracy in predicting the accuracy of the microtasks. While we achieved an accuracy of 95%, it is important to note that the small dataset size may have limited the model's performance.

Abstract
With the widespread availability and pervasiveness of artificial intelligence (AI) in many application areas across the globe, the role of crowdsourcing has seen an upsurge in terms of importance for scaling up data-driven algorithms in rapid cycles through a relatively low-cost distributed workforce or even on a volunteer basis. However, there is a lack of systematic and empirical examination of the interplay among the processes and activities combining crowd-machine hybrid interaction. To uncover the enduring aspects characterizing the human-centered AI design space when involving ensembles of crowds and algorithms and their symbiotic relations and requirements, a Computer-Supported Cooperative Work (CSCW) lens strongly rooted in the taxonomic tradition of conceptual scheme development is taken with the aim of aggregating and characterizing some of the main component entities in the burgeoning domain of hybrid crowd-AI centered systems. The goal of this article is thus to propose a theoretically grounded and empirically validated analytical framework for the study of crowd-machine interaction and its environment. Based on a scoping review and several cross-sectional analyses of research studies comprising hybrid forms of human interaction with AI systems and applications at a crowd scale, the available literature was distilled and incorporated into a unifying framework comprised of taxonomic units distributed across integration dimensions that range from the original time and space axes in which every collaborative activity take place to the main attributes that constitute a hybrid intelligence architecture. The upshot is that when turning to the challenges that are inherent in tasks requiring massive participation, novel properties can be obtained for a set of potential scenarios that go beyond the single experience of a human interacting with the technology to comprise a vast set of massive machine-crowd interactions.

Abstract

Abstract
The internet in the last few years has changed the way people interact with each other. In the past, users were just passive actors, consuming the information available on the web. Nowadays, their behavior is the opposite. With the so-called web 2.0, internet users became active agents and are now responsible for the creation of the content in web sites like MySpace, Wikipedia, YouTube, Yahoo! Answers and many more. Likewise, the way people buy a product or service has changed considerably. Thousands of online communities have been created on the internet, where users can share opinions and ideas about an electronic device, a medical service or a restaurant. An increasing number of consumers use this kind of online communities as information source before buying a product or service. This article describes a web system with the goal of creating an online community, where users could share their knowledge about local services, writing reviews and answering questions made by other members of the community regarding those services. The system will provide means for synchronous and asynchronous communication between users so that they can share their knowledge more easily.

Abstract
To improve their efficiency and competitiveness, organizations are increasingly interested in applications that support team work, usually know as groupware. Beside interoperability, familiarity with the application and users' mobility support, a feature that is of outmost importance in groupware is the notification of events produced by cooperative activities. Web Services have emerged recently to support the exchange of data in distributed environments using common Internet technologies and have been used mainly to build business-to-business applications. However, Web Services have capabilities that make them suitable to meet the requirements posed by groupware applications, a field where little work has been carried out. This article describes a model for developing cooperative applications based on Web Services technology and using asynchronous notification of events, and presents a brief description of the implementation of the support services for that model and of a prototype application that uses them.