Online reviews are a crucial asset for e-commerce platforms as they provide consumers with valuable insights into products. It is important to note that these reviews are subjective and may contain biases. Therefore, it is essential to approach them with a critical eye. Despite this, online reviews remain a valuable tool for consumers when making purchasing decisions. This study focuses on developing web-based mini-games that target cognitive biases. The games are specifically designed to enhance the perception of e-commerce online reviews. A pilot study involving 85 participants was conducted to explore the potential of integrating these cognitive bias games into web platforms. The findings indicate promising avenues for leveraging these games to enhance cognitive personalization and improve the quality of e-commerce online reviews.

Accessibility in digital labor is a research line that has been trending over the last few years. The usage of crowdsourcing, especially in the form of microtasks, can become an inclusive solution to support accessible digital work. Integrating cognitive abilities tests and task fingerprinting has proven to be effective mechanisms for microtask personalization when considering neurotypical people. In this article, we report the elaboration of usability tests on microtask personalization with neurodivergent people. The preliminary study recruited six participants with autism, attention deficit hyperactivity disorder, and dyslexia. The results obtained indicate that this solution can be inclusive and increase the accessibility of crowdsourcing tasks and platforms. One limitation of this study is that it is essential to evaluate this solution on a large scale to ensure the identification of errors and/or features of cognitive personalization in microtask crowdsourcing.

Abstract Online microtask labor has increased its role in the last few years and has provided the possibility of people who were usually excluded from the labor market to work anytime and without geographical barriers. While this brings new opportunities for people to work remotely, it can also pose challenges regarding the difficulty of assigning tasks to workers according to their abilities. To this end, cognitive personalization can be used to assess the cognitive profile of each worker and subsequently match those workers to the most appropriate type of work that is available on the digital labor market. In this regard, we believe that the time is ripe for a review of the current state of research on cognitive personalization for digital labor. The present study was conducted by following the recommended guidelines for the software engineering domain through a systematic literature review that led to the analysis of 20 primary studies published from 2010 to 2020. The results report the application of several cognition theories derived from the field of psychology, which in turn revealed an apparent presence of studies indicating accurate levels of cognitive personalization in digital labor in addition to a potential increase in the worker’s performance, most frequently investigated in crowdsourcing settings. In view of this, the present essay seeks to contribute to the identification of several gaps and opportunities for future research in order to enhance the personalization of online labor, which has the potential of increasing both worker motivation and the quality of digital work.

When crowd workers provide their contributions in a shared working environment, they may be influenced by the inputs of other contributors in implicit ways. Stigmergy in crowdsourcing consists of tracking changes in work activities to guide crowd workers based on the digital traces left by other workers. In such scenarios, there is no direct communication between the contributors. Still, the traceable changes they left during their actions act as a mediating element that clearly affects the final work product. From a behavior analysis perspective, the properties recorded in event logs can be of practical value in observing the behavioral traces produced by crowd workers when performing microtasks. This form of task fingerprinting has been explored for over a decade to better understand performance-related data and user navigational behavior in crowdsourcing markets. In line with this, the goal of this paper is to study the feasibility of task fingerprinting alongside the stigmergic effect occurring in a crowdsourcing setting through a user event logger. To this end, a case study was conducted using a real-world scenario of extreme weather phenomena represented on interactive maps. Each user could observe the traces of other crowd members while providing annotations. Twelve experts in weather forecasting were recruited to participate in this study to annotate extreme weather events. The results indicate that it is possible to use task fingerprinting for tracking the stigmergic effect in such activities with gains in terms of implicit coordination. Furthermore, the task fingerprinting allowed to map participants with similar behavioral traces, suggesting an increase in the accuracy of annotation clusters.

Crowdsourcing has received considerable attention over the last fifteen years and has been the subject of several experiments that demonstrate its large potential for use in real-world situations. With the rapid growth of and access to crowd work environments, there is a need for new ways to ensure more equitable access for all people. Task design is one of the core aspects of the crowdsourcing process and its optimization is a priority for many requesters that want to have their tasks solved in short times and with high levels of accuracy. Aligned with this goal, a cognitive personalization framework can make it feasible to assess the information processing preferences of crowd workers in order to provide a useful user interface (UI) adaptation. In an effort to address this issue, this study recruited a total of 64 crowd workers to take cognitive style tests and perform prototypical tasks. The results indicate that it is possible to apply short tests and then obtain some useful indicators for better matching tasks to workers with implications for improving the general outcomes and acceptance rates in crowdsourcing.

Abstract AppVox is a mobile application that provides support for children with speech and language impairments in their speech therapy sessions, while also allowing autonomous training at home. The application simulates a vocalizer with an audio stimulus feature, which can be used to train and amend the pronunciation of specific words through repetition. In this paper, we aim to present the development of the application as an assistive technology option, by adding new features to the vocalizer as well as assessing it as a usable option for daily training interaction for children with speech and language impairments. In this regard, we invited 15 children with speech and language impairments and 20 with no impairments to perform training activities with the application. Likewise, we asked three speech therapists and three usability experts to interact, assess, and give their feedback. In this assessment, we include the following parameters: successful conclusion of the training tasks (effectiveness); number of errors made, as well as number and type of difficulties found (efficiency); and the acceptance and level of comfort in completing the requested tasks (satisfaction). Overall, the results showed that children conclude the training tasks successfully and helped to improve their language and speech capabilities. Therapists and children gave positive feedback to the AppVox interface.

This paper presents an overview of the donation based crowdfunding state of the art, establishing a classification scheme to analyze the major platforms, and discussing current research trends and future challenges of crowdfunding as a social inclusion instrument. In many social exclusion situations crowdfunding is the last stronghold to ensure the access to basic commodities, essential to the daily life and well-being of individuals. Despite the commercial success of many crowdfunding platforms, this study shows future research opportunities in the crowdfunding as an inclusion mechanism that can trigger a broader adoption for social causes. The high social impact of the research contributions in this domain can also contribute to make it a hot topic in the upcoming years.

Disability from musculoskeletal diseases and co-morbidities may lead to the worsening of social and economic well-being through a multitude of paths. Moreover since in European Union (EU) Member States it is projected that those aged 65 and over will become a much larger share (rising from 17% to 30% of the population), and those aged 80 and over (rising from 5% to 12%) will almost become as numerous as the young population in 2060, there is a great potential for Information and Communication Technologies (ICT) solutions for addressing the present and future living arrangements in older people. The UPPERCARE system is meant to affect positively both the intergenerational and partners care since it contributes to decrease usability barriers and promote collaborative environments for informal and self-care. UPPERCARE is a new approach for integrated care supported by ICT systems and services, focusing on post-operative rehabilitation of musculoskeletal pathologies, having as a case study the knee post-operative scenarios of prosthetic care. This paper presents the UPPERCARE system, that provides an integrated care solution, supported ICT, for empowering self-care and adherence to rehabilitation plans through natural interfaces, gamification and cross-modal paths for community care collaboration. The system addresses current barriers from technological, clinical, social and organisational perspectives in a multidisciplinary environment. Special attention is given to the patients' needs and behaviours entailing the participation of a wide care community, including clinical and non-clinical people, associations, institutions and authorities) through an user driven interaction within the system.

This paper presents an educational tool to be used in signal processing interpolation-related subjects. Besides the consolidation of acquired theoretical knowledge, the tool allows its users to apply three error patterns geometry to the signals and test minimum dimension and maximum dimension signal reconstruction algorithms. In the specific case of minimum dimension problems it can be solved using different solvers, iterative and direct linear equations methods. The developed tool allows the problem conditioning analysis through the spectral radius of the system matrix, the condition number and others parameters available in some specific methods. This feature gives the possibility to alter the problem definitions to the desired goal before the reconstruction begins and to choose the optimal method, depending on each problem constraints The time unit that measures the algorithms performance is expressed in terms of one Fourier Transform (FFT) calculation time. In this way the data is presented not in an absolute way but in a relative measure independent from the machine's architecture.

Wireless capsule endoscopy (WCE) is a revolutionary, patient-friendly imaging technique that enables non-invasive visual inspection of the patient's digestive tract, especially small intestine. However, the time-consuming task of reviewing the endoscopic data is a burden for the physicians. This limitation was the motive to propose a novel strategy for automatic discrimination of WCE images related to ulcer, the most common finding of digestive tract. Towards this direction, WCE data are processed with Bidimensional Ensemble Empirical Mode Decomposition to reveal their inherent structural components, and also to reconstruct a new refined image. Then, texture information is extracted by analyzing the intrinsic second/higher-order correlation of the original image and by calculating the lacunarity index of the refined image. Experimental results demonstrated promising classification accuracy (97%) exhibiting high potential towards a complete computer-aided diagnosis system.

The autonomy of blind people in their daily life depends on their knowledge of the surrounding world, and they are aided by keen senses and assistive devices that help them to deduce their surroundings. Existing solutions require that users carry a wide range of devices and, mostly, do not include mechanisms to ensure the autonomy of users in the event of system failure. This paper presents the nav4b system that combines guidance and navigation with object's recognition, extending traditional aids (white cane and smartphone). A working prototype was installed on the UTAD campus to perform experiments with blind users.

In this paper, we presented an application developed to measure the quality of a set of reconstructed high frame rate sequences. The main goal of the developed application is to allow the simultaneous visualization of both original and reconstructed sequences and measure the quality of the reconstructed frames.