The dissemination and the reach of scientific knowledge have increased at a blistering pace. In this context, e-print servers have played a central role by providing scientists with a rapid and open mechanism for disseminating research without having to wait for the (lengthy) peer-review process. While helping the scientific community in several ways, e-print servers also provide scientific communicators and the general public with access to a wealth of knowledge without having to pay hefty subscription fees. Arguably, e-print servers' value has never been so evident, for better or worse, as during the COVID-19 pandemic. This motivates us to study how e-print servers are positioned within the greater Web, and how they are "used" on Web communities. Using data from Reddit (2005-2021) and 4chan's Politically Incorrect board (2016--2021), we uncover a surprisingly diverse set of communities discussing e-print papers. We find that real-world events and distinct factors influence the e-prints people are talking about. For instance, there was a sudden increase in the discussion of e-prints, corresponding to a surge in COVID-19 related research, in the first phase of the pandemic. We find a substantial difference in the conversation around e-prints and their actual content; in fact, e-prints are often being exploited to further conspiracy theories and/or extremist ideology. Overall, our work further highlights the need to quickly and effectively validate non peer-reviewed e-prints that get substantial press/social media coverage, as well as mitigate wrongful interpretations of scientific outputs.
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Reminiscence therapy is an inexpensive non-pharmacological therapy commonly used due to its therapeutic value for PwD, as it can be used to promote independence, positive moods and behavior, and improve their quality of life. Caregivers are one of the main pillars in the adoption of digital technologies for reminiscence therapy, as they are responsible for its administration. Despite their comprehensive understanding of the needs and difficulties associated with the therapy, their perspective has not been fully taken into account in the development of existing technological solutions. To inform the design of technological solutions within dementia care, we followed a user-centered design approach through worldwide surveys, follow-up semi-structured interviews, and focus groups. Seven hundred and seven informal and 52 formal caregivers participated in our study. Our findings show that technological solutions must provide mechanisms to carry out the therapy in a simple way, reducing the amount of work for caregivers when preparing and conducting therapy sessions. They should also diversify and personalize the current session (and following ones) based on both the biographical information of the PwD and their emotional reactions. This is particularly important since the PwD often become agitated, aggressive or angry, and caregivers might not know how to properly deal with this situation (in particular, the informal ones). Additionally, formal caregivers need an easy way to manage information of the different PwD they take care of, and consult the history of sessions performed (in particular, to identify images that triggered negative emotional reactions, and consult any notes taken about them). As a result, we present a list of validated functional requirements gathered for the PwD and both formal and informal caregivers, as well as the corresponding expected primary and secondary outcomes.
Information technology and software services are pervasive, occupying the centre of most aspects of contemporary societies. This has given rise to commonly expected norms and expectations around how such systems should work, appropriate penalties for violating these expectations, and more importantly, indicators of how to reduce the consequences of violations and sanctions. Evidence for expectation violations and ensuing sanctions exists in a range of portals used by individuals and groups to start new friendships, explore new ideas, and provide feedback for products and services. Therein lies insights that could lead to functional socio-technical systems, and general awareness and anticipations of human actions (and interactions) when using information technology and software services. However, limited previous work has examined such artifacts to provide these understandings. To contribute to such understandings and theoretical advancement we study expectation violations in mobile apps, considered among the most engaging socio-technical systems. We used content analysis and expectancy violation theory (EVT) and expectation confirmation theory (ECT) to explore the evidence and nature of sanctions in app reviews for a specific domain of apps. Our outcomes show that users respond to expectation violation with sanctions when their app does not work as anticipated, developers seem to target specific market niches when providing services in an app domain, and users within an app domain respond with similar sanctions. We contribute to the advancement of expectation violation theories, and we provide practical insights for the mobile app community.
The Internet of Behaviors (IoB) puts human behavior at the core of engineering intelligent connected systems. IoB links the digital world to human behavior to establish human-driven design, development, and adaptation processes. This paper defines the novel concept by an IoB model based on a collective effort interacting with software engineers, human-computer interaction scientists, social scientists, and cognitive science communities. The model for IoB is created based on an exploratory study that synthesizes state-of-the-art analysis and experts interviews. The architecture of a real industry 4.0 manufacturing infrastructure helps to explain the IoB model and it's application. The conceptual model was used to successfully implement a socio-technical infrastructure for a crowd monitoring and queue management system for the Uffizi Galleries, Florence, Italy. The experiment, which started in the fall of 2016 and was operational in the fall of 2018, used a data-driven approach to feed the system with real-time sensory data. It also incorporated prediction models on visitors' mobility behavior. The system's main objective was to capture human behavior, model it, and build a mechanism that considers changes, adapts in real-time, and continuously learns from repetitive behaviors. In addition to the conceptual model and the real-life evaluation, this paper provides recommendations from experts and gives future directions for IoB to become a significant technological advancement in the coming few years.
Context. Computer workers in general, and software developers specifically, are under a high amount of stress due to continuous deadlines and, often, over-commitment. Objective. This study investigates the effects of a neuroplasticity practice, a specific breathing practice, on the attention awareness, well-being, perceived productivity, and self-efficacy of computer workers. Method. We created a questionnaire mainly from existing, validated scales as entry and exit survey for data points for comparison before and after the intervention. The intervention was a 12-week program with a weekly live session that included a talk on a well-being topic and a facilitated group breathing session. During the intervention period, we solicited one daily journal note and one weekly well-being rating. We replicated the intervention in a similarly structured 8-week program. The data was analyzed using a Bayesian multi-level model for the quantitative part and thematic analysis for the qualitative part. Results. The intervention showed improvements in participants' experienced inner states despite an ongoing pandemic and intense outer circumstances for most. Over the course of the study, we found an improvement in the participants' ratings of how often they found themselves in good spirits as well as in a calm and relaxed state. We also aggregate a large number of deep inner reflections and growth processes that may not have surfaced for the participants without deliberate engagement in such a program. Conclusion. The data indicates usefulness and effectiveness of an intervention for computer workers in terms of increasing well-being and resilience. Everyone needs a way to deliberately relax, unplug, and recover. Breathing practice is a simple way to do so, and the results call for establishing a larger body of work to make this common practice.
Fact-checking has become increasingly important due to the speed with which both information and misinformation can spread in the modern media ecosystem. Therefore, researchers have been exploring how fact-checking can be automated, using techniques based on natural language processing, machine learning, knowledge representation, and databases to automatically predict the veracity of claims. In this paper, we survey automated fact-checking stemming from natural language processing, and discuss its connections to related tasks and disciplines. In this process, we present an overview of existing datasets and models, aiming to unify the various definitions given and identify common concepts. Finally, we highlight challenges for future research.
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(e.g., model architectures, training procedures, data, systems, security, evaluation, theory) to their applications (e.g., law, healthcare, education) and societal impact (e.g., inequity, misuse, economic and environmental impact, legal and ethical considerations). Though foundation models are based on standard deep learning and transfer learning, their scale results in new emergent capabilities,and their effectiveness across so many tasks incentivizes homogenization. Homogenization provides powerful leverage but demands caution, as the defects of the foundation model are inherited by all the adapted models downstream. Despite the impending widespread deployment of foundation models, we currently lack a clear understanding of how they work, when they fail, and what they are even capable of due to their emergent properties. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration commensurate with their fundamentally sociotechnical nature.
AI in finance broadly refers to the applications of AI techniques in financial businesses. This area has been lasting for decades with both classic and modern AI techniques applied to increasingly broader areas of finance, economy and society. In contrast to either discussing the problems, aspects and opportunities of finance that have benefited from specific AI techniques and in particular some new-generation AI and data science (AIDS) areas or reviewing the progress of applying specific techniques to resolving certain financial problems, this review offers a comprehensive and dense roadmap of the overwhelming challenges, techniques and opportunities of AI research in finance over the past decades. The landscapes and challenges of financial businesses and data are firstly outlined, followed by a comprehensive categorization and a dense overview of the decades of AI research in finance. We then structure and illustrate the data-driven analytics and learning of financial businesses and data. The comparison, criticism and discussion of classic vs. modern AI techniques for finance are followed. Lastly, open issues and opportunities address future AI-empowered finance and finance-motivated AI research.
Stream processing has been an active research field for more than 20 years, but it is now witnessing its prime time due to recent successful efforts by the research community and numerous worldwide open-source communities. This survey provides a comprehensive overview of fundamental aspects of stream processing systems and their evolution in the functional areas of out-of-order data management, state management, fault tolerance, high availability, load management, elasticity, and reconfiguration. We review noteworthy past research findings, outline the similarities and differences between early ('00-'10) and modern ('11-'18) streaming systems, and discuss recent trends and open problems.
Music recommender systems (MRS) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user's fingertip. While today's MRS considerably help users to find interesting music in these huge catalogs, MRS research is still facing substantial challenges. In particular when it comes to build, incorporate, and evaluate recommendation strategies that integrate information beyond simple user--item interactions or content-based descriptors, but dig deep into the very essence of listener needs, preferences, and intentions, MRS research becomes a big endeavor and related publications quite sparse. The purpose of this trends and survey article is twofold. We first identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. We review the state of the art towards solving these challenges and discuss its limitations. Second, we detail possible future directions and visions we contemplate for the further evolution of the field. The article should therefore serve two purposes: giving the interested reader an overview of current challenges in MRS research and providing guidance for young researchers by identifying interesting, yet under-researched, directions in the field.
Conversational systems have come a long way since their inception in the 1960s. After decades of research and development, we've seen progress from Eliza and Parry in the 60's and 70's, to task-completion systems as in the DARPA Communicator program in the 2000s, to intelligent personal assistants such as Siri in the 2010s, to today's social chatbots like XiaoIce. Social chatbots' appeal lies not only in their ability to respond to users' diverse requests, but also in being able to establish an emotional connection with users. The latter is done by satisfying users' need for communication, affection, as well as social belonging. To further the advancement and adoption of social chatbots, their design must focus on user engagement and take both intellectual quotient (IQ) and emotional quotient (EQ) into account. Users should want to engage with a social chatbot; as such, we define the success metric for social chatbots as conversation-turns per session (CPS). Using XiaoIce as an illustrative example, we discuss key technologies in building social chatbots from core chat to visual awareness to skills. We also show how XiaoIce can dynamically recognize emotion and engage the user throughout long conversations with appropriate interpersonal responses. As we become the first generation of humans ever living with AI, we have a responsibility to design social chatbots to be both useful and empathetic, so they will become ubiquitous and help society as a whole.
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