33,110 research outputs found
Machine-assisted mixed methods: augmenting humanities and social sciences with artificial intelligence
The increasing capacities of large language models (LLMs) present an
unprecedented opportunity to scale up data analytics in the humanities and
social sciences, augmenting and automating qualitative analytic tasks
previously typically allocated to human labor. This contribution proposes a
systematic mixed methods framework to harness qualitative analytic expertise,
machine scalability, and rigorous quantification, with attention to
transparency and replicability. 16 machine-assisted case studies are showcased
as proof of concept. Tasks include linguistic and discourse analysis, lexical
semantic change detection, interview analysis, historical event cause inference
and text mining, detection of political stance, text and idea reuse, genre
composition in literature and film; social network inference, automated
lexicography, missing metadata augmentation, and multimodal visual cultural
analytics. In contrast to the focus on English in the emerging LLM
applicability literature, many examples here deal with scenarios involving
smaller languages and historical texts prone to digitization distortions. In
all but the most difficult tasks requiring expert knowledge, generative LLMs
can demonstrably serve as viable research instruments. LLM (and human)
annotations may contain errors and variation, but the agreement rate can and
should be accounted for in subsequent statistical modeling; a bootstrapping
approach is discussed. The replications among the case studies illustrate how
tasks previously requiring potentially months of team effort and complex
computational pipelines, can now be accomplished by an LLM-assisted scholar in
a fraction of the time. Importantly, this approach is not intended to replace,
but to augment researcher knowledge and skills. With these opportunities in
sight, qualitative expertise and the ability to pose insightful questions have
arguably never been more critical
Chronic-Pain Protective Behavior Detection with Deep Learning
In chronic pain rehabilitation, physiotherapists adapt physical activity to
patients' performance based on their expression of protective behavior,
gradually exposing them to feared but harmless and essential everyday
activities. As rehabilitation moves outside the clinic, technology should
automatically detect such behavior to provide similar support. Previous works
have shown the feasibility of automatic protective behavior detection (PBD)
within a specific activity. In this paper, we investigate the use of deep
learning for PBD across activity types, using wearable motion capture and
surface electromyography data collected from healthy participants and people
with chronic pain. We approach the problem by continuously detecting protective
behavior within an activity rather than estimating its overall presence. The
best performance reaches mean F1 score of 0.82 with leave-one-subject-out cross
validation. When protective behavior is modelled per activity type, performance
is mean F1 score of 0.77 for bend-down, 0.81 for one-leg-stand, 0.72 for
sit-to-stand, 0.83 for stand-to-sit, and 0.67 for reach-forward. This
performance reaches excellent level of agreement with the average experts'
rating performance suggesting potential for personalized chronic pain
management at home. We analyze various parameters characterizing our approach
to understand how the results could generalize to other PBD datasets and
different levels of ground truth granularity.Comment: 24 pages, 12 figures, 7 tables. Accepted by ACM Transactions on
Computing for Healthcar
Dynamic Facial Expression Generation on Hilbert Hypersphere with Conditional Wasserstein Generative Adversarial Nets
In this work, we propose a novel approach for generating videos of the six
basic facial expressions given a neutral face image. We propose to exploit the
face geometry by modeling the facial landmarks motion as curves encoded as
points on a hypersphere. By proposing a conditional version of manifold-valued
Wasserstein generative adversarial network (GAN) for motion generation on the
hypersphere, we learn the distribution of facial expression dynamics of
different classes, from which we synthesize new facial expression motions. The
resulting motions can be transformed to sequences of landmarks and then to
images sequences by editing the texture information using another conditional
Generative Adversarial Network. To the best of our knowledge, this is the first
work that explores manifold-valued representations with GAN to address the
problem of dynamic facial expression generation. We evaluate our proposed
approach both quantitatively and qualitatively on two public datasets;
Oulu-CASIA and MUG Facial Expression. Our experimental results demonstrate the
effectiveness of our approach in generating realistic videos with continuous
motion, realistic appearance and identity preservation. We also show the
efficiency of our framework for dynamic facial expressions generation, dynamic
facial expression transfer and data augmentation for training improved emotion
recognition models
Strategic Knowledge Measurement and Management
Knowledge and intellectual capital are now recognized as vital resources for organizational survival and competitive advantage. A vast array of knowledge measures has evolved, spanning many disciplines. This chapter reviews knowledge measures focusing on groups of individuals (such as teams, business and organizations), as they reflect the stock or flow of knowledge, as well as enabling processes that enhance knowledge stocks and flows. The chapter emphasizes the importance of organizational value chains, pivotal talent pools and the link between knowledge and competitive success, in understanding the significance of today’s knowledge measures, and opportunities for future research and practice to enhance them
Single-Shot Multi-Person 3D Pose Estimation From Monocular RGB
We propose a new single-shot method for multi-person 3D pose estimation in
general scenes from a monocular RGB camera. Our approach uses novel
occlusion-robust pose-maps (ORPM) which enable full body pose inference even
under strong partial occlusions by other people and objects in the scene. ORPM
outputs a fixed number of maps which encode the 3D joint locations of all
people in the scene. Body part associations allow us to infer 3D pose for an
arbitrary number of people without explicit bounding box prediction. To train
our approach we introduce MuCo-3DHP, the first large scale training data set
showing real images of sophisticated multi-person interactions and occlusions.
We synthesize a large corpus of multi-person images by compositing images of
individual people (with ground truth from mutli-view performance capture). We
evaluate our method on our new challenging 3D annotated multi-person test set
MuPoTs-3D where we achieve state-of-the-art performance. To further stimulate
research in multi-person 3D pose estimation, we will make our new datasets, and
associated code publicly available for research purposes.Comment: International Conference on 3D Vision (3DV), 201
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