Accurately mapping medical procedure names from healthcare providers to standardized terminology used by insurance companies is a crucial yet complex task. Inconsistencies in naming conventions lead missclasified procedures, causing administrative inefficiencies and claim problems private settings. Many still use human resources for manual mapping, while there clear opportunity automation. This paper proposes retrieval-based architecture leveraging sentence embeddings name matching the...

Psychological trait estimation from external factors such as movement and appearance is a challenging longstanding problem in psychology, principally based on the psychological theory of embodiment. To date, attempts to tackle this have utilized private small-scale datasets with intrusive body-attached sensors. Potential applications an automated system for include occupational fatigue marketing advertisement. In work, we propose PsyMo (Psychological traits Motion), novel, multi-purpose...

We study the task of replicating functionality black-box neural models, for which we only know output class probabilities provided a set input images. assume back-propagation through model is not possible and its training images are available, e.g. could be exposed an API. In this context, present teacher-student framework that can distill (teacher) into student with minimal accuracy loss. To generate useful data samples student, our (i) learns to on proxy (with classes different from those...

Early risk detection of mental illnesses has a massive positive impact upon the well-being people. The eRisk workshop been at forefront enabling interdisciplinary research in developing computational methods to automatically estimate early factors for issues such as depression, self-harm, anorexia and pathological gambling. In this paper, we present contributions BLUE team 2021 edition workshop, which tackle problems gambling addiction, self-harm estimating depression severity from social...

The manner of walking (gait) is a powerful biometric that used as unique fingerprinting method, allowing unobtrusive behavioral analytics to be performed at distance without subject cooperation. As opposed more traditional authentication methods, gait analysis does not require explicit cooperation the and can in low-resolution settings, requiring subject's face unobstructed/clearly visible. Most current approaches are developed controlled setting, with clean, gold-standard annotated data,...

Advances in hardware and algorithms are driving the exponential growth of Internet Things (IoT), with increasingly more pervasive computations being performed near data generation sources. With this wave technology, a range intelligent devices can perform local inferences (activity recognition, fitness monitoring, etc.), which have obvious advantages: reduced inference latency for interactive (real-time) applications better privacy by processing user locally. Video benefit many labelling...

Current benchmark tasks for natural language processing contain text that is qualitatively different from the used in informal day to digital communication. This discrepancy has led severe performance degradation of state-of-the-art NLP models when fine-tuned on real-world data. One way resolve this issue through lexical normalization, which process transforming non-standard text, usually social media, into a more standardized form. In work, we propose sentence-level sequence-to-sequence...

Gait analysis is proven to be a reliable way perform person identification without relying on subject cooperation. Walking biometric that does not significantly change in short periods of time and can regarded as unique each person. So far, the study gait focused mostly demographics estimation, considering many pedestrian attributes appearance-based methods rely on. In this work, alongside gait-based identification, we explore attribute solely from movement patterns. We propose DenseGait,...

The use of gait for person identification has important advantages such as being non-invasive, unobtrusive, not requiring cooperation and less likely to be obscured compared other biometrics. Existing methods recognition require cooperative scenarios, in which a single is walking multiple times straight line front camera. We address the challenges real-world scenarios camera feeds capture people, who most cases pass only once. privacy concerns by using motion information individuals, with no...

Memes are prevalent on the internet and continue to grow evolve alongside our culture. An automatic understanding of memes propagating can shed light general sentiment cultural attitudes people. In this work, we present team BLUE's solution for second edition MEMOTION shared task. We showcase two approaches meme classification (i.e. sentiment, humour, offensive, sarcasm motivation levels) using a text-only method BERT, Multi-Modal-Multi-Task transformer network that operates both image its...

Obtaining demographics information from video is valuable for a range of real-world applications. While approaches that leverage facial features gender inference are very successful in restrained environments, they do not work most scenarios when the subject facing camera, has face obstructed or clear due to distance camera poor resolution. We propose weakly-supervised method learning people based on their manner walking. make use state-of-the art analysis models automatically annotate...

In this work, we explore the relationship between depression and manifestations of happiness in social media. While majority works surrounding focus on symptoms, psychological research shows that there is a strong link seeking being diagnosed with depression. We make use Positive-Unlabeled learning paradigm to automatically extract happy moments from media posts both controls users depression, qualitatively analyze them linguistic tools such as LIWC keyness information. show life depressed...

This paper proposes a method for performing gait-recognition using skeletons extracted from human pose-estimation networks. Gait is powerful biometric feature that has been used successfully to identify people, even in the presence of confounding factors such as different view angles and carrying/clothing variations. While most methods make use Energy Images (GEIs), we propose MFINet, novel processing sequence an available pre-trained pose estimation network, incorporates decision process....

Depression, a prominent contributor to global disability, affects substantial portion of the population. Efforts detect depression from social media texts have been prevalent, yet only few works explored detection user-generated video content. In this work, we address research gap by proposing simple and flexible multi-modal temporal model capable discerning non-verbal cues diverse modalities in noisy, real-world videos. We show that, for in-the-wild videos, using additional high-level is...

Emotions are fundamentally integral to shaping the order and disorders in humanlives. Yet, a principled, quantitative framework explaining emotional dynamicsand their alteration mental has been elusive. This challenge arisesfrom complex multidimensional nature of emotions but also, at leastpartially, due shortage large longitudinal measurements use ofgenerative mathematical models, leading spectrum partially contrastingtheories. Our study seeks overcome these challenges by employing dynamic...

Depression has long been studied in the NLP field, with most works focusing on individuals' negative emotions. People depression experience happiness, but this not extensively studied. Previous have shown that sentiment or emotion classification approaches are unsuitable for extracting happy moments because they may be expressed only positive words. In work, we conduct a largescale study of from social media texts individuals mentioning diagnosis. We develop an extensive deep learning-based...

Natural Language Inference (NLI) evaluation is crucial for assessing language understanding models; however, popular datasets suffer from systematic spurious correlations that artificially inflate actual model performance. To address this, we propose a method the automated creation of challenging test set without relying on manual construction artificial and unrealistic examples. We categorize NLI into three difficulty levels by leveraging methods exploit training dynamics. This...