394 research outputs found
Mobile heritage practices. Implications for scholarly research, user experience design, and evaluation methods using mobile apps.
Mobile heritage apps have become one of the most popular means for audience
engagement and curation of museum collections and heritage contexts. This
raises practical and ethical questions for both researchers and practitioners, such
as: what kind of audience engagement can be built using mobile apps? what are
the current approaches? how can audience engagement with these experience
be evaluated? how can those experiences be made more resilient, and in turn
sustainable? In this thesis I explore experience design scholarships together with
personal professional insights to analyse digital heritage practices with a view to
accelerating thinking about and critique of mobile apps in particular. As a result,
the chapters that follow here look at the evolution of digital heritage practices,
examining the cultural, societal, and technological contexts in which mobile
heritage apps are developed by the creative media industry, the academic
institutions, and how these forces are shaping the user experience design
methods. Drawing from studies in digital (critical) heritage, Human-Computer
Interaction (HCI), and design thinking, this thesis provides a critical analysis of
the development and use of mobile practices for the heritage. Furthermore,
through an empirical and embedded approach to research, the thesis also
presents auto-ethnographic case studies in order to show evidence that mobile
experiences conceptualised by more organic design approaches, can result in
more resilient and sustainable heritage practices. By doing so, this thesis
encourages a renewed understanding of the pivotal role of these practices in the
broader sociocultural, political and environmental changes.AHRC REAC
A Critical Review Of Post-Secondary Education Writing During A 21st Century Education Revolution
Educational materials are effective instruments which provide information and report new discoveries uncovered by researchers in specific areas of academia. Higher education, like other education institutions, rely on instructional materials to inform its practice of educating adult learners. In post-secondary education, developmental English programs are tasked with meeting the needs of dynamic populations, thus there is a continuous need for research in this area to support its changing landscape. However, the majority of scholarly thought in this area centers on K-12 reading and writing. This paucity presents a phenomenon to the post-secondary community. This research study uses a qualitative content analysis to examine peer-reviewed journals from 2003-2017, developmental online websites, and a government issued document directed toward reforming post-secondary developmental education programs. These highly relevant sources aid educators in discovering informational support to apply best practices for student success. Developmental education serves the purpose of addressing literacy gaps for students transitioning to college-level work. The findings here illuminate the dearth of material offered to developmental educators. This study suggests the field of literacy research is fragmented and highlights an apparent blind spot in scholarly literature with regard to English writing instruction. This poses a quandary for post-secondary literacy researchers in the 21st century and establishes the necessity for the literacy research community to commit future scholarship toward equipping college educators teaching writing instruction to underprepared adult learners
A Survey on Deep Multi-modal Learning for Body Language Recognition and Generation
Body language (BL) refers to the non-verbal communication expressed through
physical movements, gestures, facial expressions, and postures. It is a form of
communication that conveys information, emotions, attitudes, and intentions
without the use of spoken or written words. It plays a crucial role in
interpersonal interactions and can complement or even override verbal
communication. Deep multi-modal learning techniques have shown promise in
understanding and analyzing these diverse aspects of BL. The survey emphasizes
their applications to BL generation and recognition. Several common BLs are
considered i.e., Sign Language (SL), Cued Speech (CS), Co-speech (CoS), and
Talking Head (TH), and we have conducted an analysis and established the
connections among these four BL for the first time. Their generation and
recognition often involve multi-modal approaches. Benchmark datasets for BL
research are well collected and organized, along with the evaluation of SOTA
methods on these datasets. The survey highlights challenges such as limited
labeled data, multi-modal learning, and the need for domain adaptation to
generalize models to unseen speakers or languages. Future research directions
are presented, including exploring self-supervised learning techniques,
integrating contextual information from other modalities, and exploiting
large-scale pre-trained multi-modal models. In summary, this survey paper
provides a comprehensive understanding of deep multi-modal learning for various
BL generations and recognitions for the first time. By analyzing advancements,
challenges, and future directions, it serves as a valuable resource for
researchers and practitioners in advancing this field. n addition, we maintain
a continuously updated paper list for deep multi-modal learning for BL
recognition and generation: https://github.com/wentaoL86/awesome-body-language
Graphonomics and your Brain on Art, Creativity and Innovation : Proceedings of the 19th International Graphonomics Conference (IGS 2019 – Your Brain on Art)
[Italiano]: “Grafonomia e cervello su arte, creatività e innovazione”.
Un forum internazionale per discutere sui recenti progressi nell'interazione tra arti creative, neuroscienze, ingegneria, comunicazione, tecnologia, industria, istruzione, design, applicazioni forensi e mediche. I contributi hanno esaminato lo stato dell'arte, identificando sfide e opportunità , e hanno delineato le possibili linee di sviluppo di questo settore di ricerca. I temi affrontati includono: strategie integrate per la comprensione dei sistemi neurali, affettivi e cognitivi in ambienti realistici e complessi; individualità e differenziazione dal punto di vista neurale e comportamentale; neuroaesthetics (uso delle neuroscienze per spiegare e comprendere le esperienze estetiche a livello neurologico); creatività e innovazione; neuro-ingegneria e arte ispirata dal cervello, creatività e uso di dispositivi di mobile brain-body imaging (MoBI) indossabili; terapia basata su arte creativa; apprendimento informale; formazione; applicazioni forensi. / [English]: “Graphonomics and your brain on art, creativity and innovation”.
A single track, international forum for discussion on recent advances at the intersection of the creative arts, neuroscience, engineering, media, technology, industry, education, design, forensics, and medicine.
The contributions reviewed the state of the art, identified challenges and opportunities and created a roadmap for the field of graphonomics and your brain on art.
The topics addressed include: integrative strategies for understanding neural, affective and cognitive systems in realistic, complex environments; neural and behavioral individuality and variation; neuroaesthetics (the use of neuroscience to explain and understand the aesthetic experiences at the neurological level); creativity and innovation; neuroengineering and brain-inspired art, creative concepts and wearable mobile brain-body imaging (MoBI) designs; creative art therapy; informal learning; education; forensics
DCCRN-KWS: an audio bias based model for noise robust small-footprint keyword spotting
Real-world complex acoustic environments especially the ones with a low
signal-to-noise ratio (SNR) will bring tremendous challenges to a keyword
spotting (KWS) system. Inspired by the recent advances of neural speech
enhancement and context bias in speech recognition, we propose a robust audio
context bias based DCCRN-KWS model to address this challenge. We form the whole
architecture as a multi-task learning framework for both denosing and keyword
spotting, where the DCCRN encoder is connected with the KWS model. Helped with
the denoising task, we further introduce an audio context bias module to
leverage the real keyword samples and bias the network to better iscriminate
keywords in noisy conditions. Feature merge and complex context linear modules
are also introduced to strength such discrimination and to effectively leverage
contextual information respectively. Experiments on the internal challenging
dataset and the HIMIYA public dataset show that our DCCRN-KWS system is
superior in performance, while ablation study demonstrates the good design of
the whole model.Comment: Accepted by INTERSPEECH202
Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology
The great behavioral heterogeneity observed between individuals with the same
psychiatric disorder and even within one individual over time complicates both
clinical practice and biomedical research. However, modern technologies are an
exciting opportunity to improve behavioral characterization. Existing
psychiatry methods that are qualitative or unscalable, such as patient surveys
or clinical interviews, can now be collected at a greater capacity and analyzed
to produce new quantitative measures. Furthermore, recent capabilities for
continuous collection of passive sensor streams, such as phone GPS or
smartwatch accelerometer, open avenues of novel questioning that were
previously entirely unrealistic. Their temporally dense nature enables a
cohesive study of real-time neural and behavioral signals.
To develop comprehensive neurobiological models of psychiatric disease, it
will be critical to first develop strong methods for behavioral quantification.
There is huge potential in what can theoretically be captured by current
technologies, but this in itself presents a large computational challenge --
one that will necessitate new data processing tools, new machine learning
techniques, and ultimately a shift in how interdisciplinary work is conducted.
In my thesis, I detail research projects that take different perspectives on
digital psychiatry, subsequently tying ideas together with a concluding
discussion on the future of the field. I also provide software infrastructure
where relevant, with extensive documentation.
Major contributions include scientific arguments and proof of concept results
for daily free-form audio journals as an underappreciated psychiatry research
datatype, as well as novel stability theorems and pilot empirical success for a
proposed multi-area recurrent neural network architecture.Comment: PhD thesis cop
Proceedings of the 8th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2023)
This volume gathers the papers presented at the Detection and Classification of Acoustic Scenes and Events 2023 Workshop (DCASE2023), Tampere, Finland, during 21–22 September 2023
Security and privacy problems in voice assistant applications: A survey
Voice assistant applications have become omniscient nowadays. Two models that provide the two most important functions for real-life applications (i.e., Google Home, Amazon Alexa, Siri, etc.) are Automatic Speech Recognition (ASR) models and Speaker Identification (SI) models. According to recent studies, security and privacy threats have also emerged with the rapid development of the Internet of Things (IoT). The security issues researched include attack techniques toward machine learning models and other hardware components widely used in voice assistant applications. The privacy issues include technical-wise information stealing and policy-wise privacy breaches. The voice assistant application takes a steadily growing market share every year, but their privacy and security issues never stopped causing huge economic losses and endangering users' personal sensitive information. Thus, it is important to have a comprehensive survey to outline the categorization of the current research regarding the security and privacy problems of voice assistant applications. This paper concludes and assesses five kinds of security attacks and three types of privacy threats in the papers published in the top-tier conferences of cyber security and voice domain
Anonymizing Speech: Evaluating and Designing Speaker Anonymization Techniques
The growing use of voice user interfaces has led to a surge in the collection
and storage of speech data. While data collection allows for the development of
efficient tools powering most speech services, it also poses serious privacy
issues for users as centralized storage makes private personal speech data
vulnerable to cyber threats. With the increasing use of voice-based digital
assistants like Amazon's Alexa, Google's Home, and Apple's Siri, and with the
increasing ease with which personal speech data can be collected, the risk of
malicious use of voice-cloning and speaker/gender/pathological/etc. recognition
has increased.
This thesis proposes solutions for anonymizing speech and evaluating the
degree of the anonymization. In this work, anonymization refers to making
personal speech data unlinkable to an identity while maintaining the usefulness
(utility) of the speech signal (e.g., access to linguistic content). We start
by identifying several challenges that evaluation protocols need to consider to
evaluate the degree of privacy protection properly. We clarify how
anonymization systems must be configured for evaluation purposes and highlight
that many practical deployment configurations do not permit privacy evaluation.
Furthermore, we study and examine the most common voice conversion-based
anonymization system and identify its weak points before suggesting new methods
to overcome some limitations. We isolate all components of the anonymization
system to evaluate the degree of speaker PPI associated with each of them.
Then, we propose several transformation methods for each component to reduce as
much as possible speaker PPI while maintaining utility. We promote
anonymization algorithms based on quantization-based transformation as an
alternative to the most-used and well-known noise-based approach. Finally, we
endeavor a new attack method to invert anonymization.Comment: PhD Thesis Pierre Champion | Universit\'e de Lorraine - INRIA Nancy |
for associated source code, see https://github.com/deep-privacy/SA-toolki
Consumer Adoption of AI-Powered Chatbots: Developing a Customized Adoption Model
Recent advancements in Artificial Intelligence have transformed the business landscape, with
AI-powered chatbots playing a crucial role in enhancing customer service and automating
tasks. As current literature seems to predominantly focus on the use of AI-powered chatbots in
organizational contexts, this study aims to fill this gap by creating an understanding of the
factors driving AI-powered chatbot adoption from a consumer perspective. To achieve this, we
utilize the Theory of Planned Behavior (TPB), the Technology Adoption Model (TAM), the
Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), and Diffusion of
Innovations (DOI), as traditional technology adoption models. Along with the constructs from
these traditional models, we add the AI-specific antecedents: Anthropomorphism, Trust,
Privacy Risk and Personalization, which were found through conducting a literature review of
AI- and chatbot adoption. This integration allowed us to develop a customized adoption model
that provides an understanding of chatbot adoption from a consumer perspective.
The study collects data through a questionnaire-based survey (n=126). Through several
multiple regression analyses, significant drivers across all the models are revealed. Subjective
Norm and Behavioral Control (TPB), Usefulness (TAM), Habit (UTAUT2) and Trialability
(DOI) were all found to have a significant positive effect on the Intention to Use AI-powered
chatbots. The Customized Model, created through stepwise estimation, includes Usefulness
(TAM), Trialability (DOI), Habit (UTAUT2), and Anthropomorphism (Model Extensions).
These four factors collectively explain 46.6% of the variance in consumers' Intention to Use
AI-powered chatbots. In terms of explaining the adoption of AI-powered chatbots, the
Customized Model outperforms traditional models by explaining the most variance while
utilizing the fewest variables. This enhanced fit may make it a more effective tool for
understanding how consumers adopt AI-powered chatbot technology. The study contributes to
businesses’ understanding of the constructs influencing chatbot adoption and implementing
effective strategies to enhance customer experiences.nhhma
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