1,313 research outputs found
Challenging Social Media Threats using Collective Well-being Aware Recommendation Algorithms and an Educational Virtual Companion
Social media (SM) have become an integral part of our lives, expanding our
inter-linking capabilities to new levels. There is plenty to be said about
their positive effects. On the other hand however, some serious negative
implications of SM have repeatedly been highlighted in recent years, pointing
at various SM threats for society, and its teenagers in particular: from common
issues (e.g. digital addiction and polarization) and manipulative influences of
algorithms to teenager-specific issues (e.g. body stereotyping). The full
impact of current SM platform design -- both at an individual and societal
level -- asks for a comprehensive evaluation and conceptual improvement. We
extend measures of Collective Well-Being (CWB) to SM communities. As users'
relationships and interactions are a central component of CWB, education is
crucial to improve CWB. We thus propose a framework based on an adaptive
"social media virtual companion" for educating and supporting the entire
students' community to interact with SM. The virtual companion will be powered
by a Recommender System (CWB-RS) that will optimize a CWB metric instead of
engagement or platform profit, which currently largely drives recommender
systems thereby disregarding any societal collateral effect. CWB-RS will
optimize CWB both in the short term, by balancing the level of SM threat the
students are exposed to, as well as in the long term, by adopting an
Intelligent Tutor System role and enabling adaptive and personalized sequencing
of playful learning activities. This framework offers an initial step on
understanding how to design SM systems and embedded educational interventions
that favor a more healthy and positive society
Online Game Level Generation from Music
Game consists of multiple types of content, while the harmony of different
content types play an essential role in game design. However, most works on
procedural content generation consider only one type of content at a time. In
this paper, we propose and formulate online level generation from music, in a
way of matching a level feature to a music feature in real-time, while adapting
to players' play speed. A generic framework named online player-adaptive
procedural content generation via reinforcement learning, OPARL for short, is
built upon the experience-driven reinforcement learning and controllable
reinforcement learning, to enable online level generation from music.
Furthermore, a novel control policy based on local search and k-nearest
neighbours is proposed and integrated into OPARL to control the level generator
considering the play data collected online. Results of simulation-based
experiments show that our implementation of OPARL is competent to generate
playable levels with difficulty degree matched to the ``energy'' dynamic of
music for different artificial players in an online fashion
Semantic Communications using Foundation Models: Design Approaches and Open Issues
Foundation models (FMs), including large language models, have become
increasingly popular due to their wide-ranging applicability and ability to
understand human-like semantics. While previous research has explored the use
of FMs in semantic communications to improve semantic extraction and
reconstruction, the impact of these models on different system levels,
considering computation and memory complexity, requires further analysis. This
study focuses on integrating FMs at the effectiveness, semantic, and physical
levels, using universal knowledge to profoundly transform system design.
Additionally, it examines the use of compact models to balance performance and
complexity, comparing three separate approaches that employ FMs. Ultimately,
the study highlights unresolved issues in the field that need addressing.Comment: This work has been submitted to the IEEE for possible publication.
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Maturity and Future of Artificial Intelligence
Artificial Intelligence (AI) is one of the most important technology in the world today. She has completely matured in the end of 20 century and revolutionized the 21st century. In general, the field of artificial intelligence seeks to advance the science and engineering of intelligence, with the goal of creating machines with human-like characteristics. This includes developing machines with a wide range of human-inspired capabilities, including communication, perception, planning, reasoning, knowledge representation, the ability to move and manipulate objects, and learning. AI approaches problems using tools and techniques from a wide variety of other fields, including probability and statistics, symbolic computation, search and optimization, game theory, information engineering, mathematics, psychology, linguistics, and philosophy. Throughout this paper we will develop all concepts behind IA maturity and how it will impact our future daily live. A case of how will this technology affect our justice and education in the future? Will be provided
Reinforcement Learning for Generative AI: A Survey
Deep Generative AI has been a long-standing essential topic in the machine
learning community, which can impact a number of application areas like text
generation and computer vision. The major paradigm to train a generative model
is maximum likelihood estimation, which pushes the learner to capture and
approximate the target data distribution by decreasing the divergence between
the model distribution and the target distribution. This formulation
successfully establishes the objective of generative tasks, while it is
incapable of satisfying all the requirements that a user might expect from a
generative model. Reinforcement learning, serving as a competitive option to
inject new training signals by creating new objectives that exploit novel
signals, has demonstrated its power and flexibility to incorporate human
inductive bias from multiple angles, such as adversarial learning,
hand-designed rules and learned reward model to build a performant model.
Thereby, reinforcement learning has become a trending research field and has
stretched the limits of generative AI in both model design and application. It
is reasonable to summarize and conclude advances in recent years with a
comprehensive review. Although there are surveys in different application areas
recently, this survey aims to shed light on a high-level review that spans a
range of application areas. We provide a rigorous taxonomy in this area and
make sufficient coverage on various models and applications. Notably, we also
surveyed the fast-developing large language model area. We conclude this survey
by showing the potential directions that might tackle the limit of current
models and expand the frontiers for generative AI
Natural Language Generation for Advertising: A Survey
Natural language generation methods have emerged as effective tools to help
advertisers increase the number of online advertisements they produce. This
survey entails a review of the research trends on this topic over the past
decade, from template-based to extractive and abstractive approaches using
neural networks. Additionally, key challenges and directions revealed through
the survey, including metric optimization, faithfulness, diversity,
multimodality, and the development of benchmark datasets, are discussed
Exploring Virtual Reality and Doppelganger Avatars for the Treatment of Chronic Back Pain
Cognitive-behavioral models of chronic pain assume that fear of pain and subsequent avoidance behavior contribute to pain chronicity and the maintenance of chronic pain. In chronic back pain (CBP), avoidance of movements often plays a major role in pain perseverance and interference with daily life activities. In treatment, avoidance is often addressed by teaching patients to reduce pain behaviors and increase healthy behaviors. The current project explored the use of personalized virtual characters (doppelganger avatars) in virtual reality (VR), to influence motor imitation and avoidance, fear of pain and experienced pain in CBP. We developed a method to create virtual doppelgangers, to animate them with movements captured from real-world models, and to present them to participants in an immersive cave virtual environment (CAVE) as autonomous movement models for imitation.
Study 1 investigated interactions between model and observer characteristics in imitation behavior of healthy participants. We tested the hypothesis that perceived affiliative characteristics of a virtual model, such as similarity to the observer and likeability, would facilitate observers’ engagement in voluntary motor imitation. In a within-subject design (N=33), participants were exposed to four virtual characters of different degrees of realism and observer similarity, ranging from an abstract stickperson to a personalized doppelganger avatar designed from 3d scans of the observer. The characters performed different trunk movements and participants were asked to imitate these. We defined functional ranges of motion (ROM) for spinal extension (bending backward, BB), lateral flexion (bending sideward, BS) and rotation in the horizontal plane (RH) based on shoulder marker trajectories as behavioral indicators of imitation. Participants’ ratings on perceived avatar appearance were recorded in an Autonomous Avatar Questionnaire (AAQ), based on an explorative factor analysis. Linear mixed effects models revealed that for lateral flexion (BS), a facilitating influence of avatar type on ROM was mediated by perceived identification with the avatar including avatar likeability, avatar-observer-similarity and other affiliative characteristics. These findings suggest that maximizing model-observer similarity may indeed be useful to stimulate observational modeling.
Study 2 employed the techniques developed in study 1 with participants who suffered from CBP and extended the setup with real-world elements, creating an immersive mixed reality. The research question was whether virtual doppelgangers could modify motor behaviors, pain expectancy and pain. In a randomized controlled between-subject design, participants observed and imitated an avatar (AVA, N=17) or a videotaped model (VID, N=16) over three sessions, during which the movements BS and RH as well as a new movement (moving a beverage crate) were shown. Again, self-reports and ROMs were used as measures. The AVA group reported reduced avoidance with no significant group differences in ROM. Pain expectancy increased in AVA but not VID over the sessions. Pain and limitations did not significantly differ. We observed a moderation effect of group, with prior pain expectancy predicting pain and avoidance in the VID but not in the AVA group. This can be interpreted as an effect of personalized movement models decoupling pain behavior from movement-related fear and pain expectancy by increasing pain tolerance and task persistence. Our findings suggest that personalized virtual movement models can stimulate observational modeling in general, and that they can increase pain tolerance and persistence in chronic pain conditions. Thus, they may provide a tool for exposure and exercise treatments in cognitive behavioral treatment approaches to CBP
Rapid prototyping for biomedical engineering: current capabilities and Challenges
A new set of manufacturing technologies has emerged in the past decades to address market requirements in a customized way and to provide support for research tasks that require prototypes. These new techniques and technologies are usually referred to as rapid prototyping and manufacturing technologies, and they allow prototypes to be produced in a wide range of materials with remarkable precision in a couple of hours. Although they have been rapidly incorporated into product development methodologies, they are still under development, and their applications in bioengineering are continuously evolving. Rapid prototyping and manufacturing technologies can be of assistance in every stage of the development process of novel biodevices, to address various problems that can arise in the devices' interactions with biological systems and the fact that the design decisions must be tested carefully. This review focuses on the main fields of application for rapid prototyping in biomedical engineering and health sciences, as well as on the most remarkable challenges and research trends
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