ORCID Profile
0000-0002-5414-8276
Current Organisation
University of Queensland
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Database Management | Crop and pasture production | Animal reproduction and breeding | Horticultural crop improvement (incl. selection and breeding) | Crop and pasture improvement (incl. selection and breeding) | Artificial Intelligence and Image Processing | Information Systems | Knowledge representation and reasoning | Pattern Recognition and Data Mining | Pattern recognition | Database systems | Knowledge Representation and Machine Learning | Research, Science and Technology Policy | Business Information Systems | Data management and data science
ANZSRC Socio-Economic Objective (SEO)
Technological and Organisational Innovation | Health and Support Services not elsewhere classified | Expanding Knowledge in Technology | Electronic Information Storage and Retrieval Services | Expanding Knowledge in the Information and Computing Sciences |
Related Links
Publications
Deep dynamic imputation of clinical time series for mortality prediction
Publisher: Elsevier BV
Date: 11-2021
Connecting Social Media Data with Observed Hybrid Data for Environment Monitoring
Publisher: No publisher found
Date: 2017
Unsupervised 2D Dimensionality Reduction with Adaptive Structure Learning
Publisher: No publisher found
Date: 2017
Fuzzy Integral Optimization with Deep Q-Network for EEG-Based Intention Recognition
Publisher: Springer International Publishing
Date: 2018
A Multiview Learning Framework With a Linear Computational Cost
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2018
Fuzzy Integral Optimization with Deep Q-Network for EEG-Based Intention Recognition
Publisher: No publisher found
Date: 2018
An Embedded Feature Selection Framework for Hybrid Data
Publisher: Springer International Publishing
Date: 2017
Provenance-Based Rumor Detection
Publisher: Springer International Publishing
Date: 2017
Multi-factors based sentence ordering for cross-document fusion from multimodal content
Publisher: Elsevier BV
Date: 08-2017
Connecting social media data with observed hybrid data for environment monitoring
Publisher: Springer International Publishing
Date: 05-10-2017
Answering why-not questions on semantic multimedia queries
Publisher: Springer Science and Business Media LLC
Date: 02-09-2017
CausalRec: Causal Inference for Visual Debiasing in Visually-Aware Recommendation
Publisher: ACM
Date: 17-10-2021
Sequential Diagnosis Prediction with Transformer and Ontological Representation
Publisher: IEEE
Date: 12-2021
Efficient search over incomplete knowledge graphs in binarized embedding space
Publisher: Elsevier BV
Date: 10-2021
Multi-label classification via learning a unified object-label graph with sparse representation
Publisher: Springer Science and Business Media LLC
Date: 27-11-2016
Computing Unrestricted Synopses Under Maximum Error Bound
Publisher: Springer Science and Business Media LLC
Date: 05-10-2011
Semi-supervised Feature Analysis for Multimedia Annotation by Mining Label Correlation
Publisher: No publisher found
Date: 2014
Learning Graph-based POI Embedding for Location-based Recommendation
Publisher: ACM
Date: 24-10-2016
Deep Interpretable Mortality Model for Intensive Care Unit Risk Prediction
Publisher: No publisher found
Date: 2019
Mining Personal Health Index from Annual Geriatric Medical Examinations
Publisher: IEEE
Date: 12-2014
DOI: 10.1109/ICDM.2014.32
Zero-Shot Learning by Harnessing Adversarial Samples
Publisher: ACM
Date: 26-10-2023
Structured Streaming Skeleton - A New Feature for Online Human Gesture Recognition
Publisher: Association for Computing Machinery (ACM)
Date: 10-2014
DOI: 10.1145/2648583
Abstract: Online human gesture recognition has a wide range of applications in computer vision, especially in human-computer interaction applications. The recent introduction of cost-effective depth cameras brings a new trend of research on body-movement gesture recognition. However, there are two major challenges: (i) how to continuously detect gestures from unsegmented streams, and (ii) how to differentiate different styles of the same gesture from other types of gestures. In this article, we solve these two problems with a new effective and efficient feature extraction method—Structured Streaming Skeleton (SSS)—which uses a dynamic matching approach to construct a feature vector for each frame. Our comprehensive experiments on MSRC-12 Kinect Gesture, Huawei/3DLife-2013, and MSR-Action3D datasets have demonstrated superior performances than the state-of-the-art approaches. We also demonstrate model selection based on the proposed SSS feature, where the classifier of squared loss regression with l 2,1 norm regularization is a recommended classifier for best performance.
Freedom: Online Activity Recognition via Dictionary-Based Sparse Representation of RFID Sensing Data
Publisher: IEEE
Date: 11-2015
AI 2021: Advances in Artificial Intelligence - 34th Australasian Joint Conference, AI 2021, Sydney, NSW, Australia, February 2-4, 2022, Proceedings
Publisher: Springer International Publishing
Date: 2022
Semi-Supervised Multiple Feature Analysis for Action Recognition
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2014
Learning Multiple Diagnosis Codes for ICU Patients with Local Disease Correlation Mining
Publisher: Association for Computing Machinery (ACM)
Date: 06-03-2017
DOI: 10.1145/3003729
Abstract: In the era of big data, a mechanism that can automatically annotate disease codes to patients’ records in the medical information system is in demand. The purpose of this work is to propose a framework that automatically annotates the disease labels of multi-source patient data in Intensive Care Units (ICUs). We extract features from two main sources, medical charts and notes. The Bag-of-Words model is used to encode the features. Unlike most of the existing multi-label learning algorithms that globally consider correlations between diseases, our model learns disease correlation locally in the patient data. To achieve this, we derive a local disease correlation representation to enrich the discriminant power of each patient data. This representation is embedded into a unified multi-label learning framework. We develop an alternating algorithm to iteratively optimize the objective function. Extensive experiments have been conducted on a real-world ICU database. We have compared our algorithm with representative multi-label learning algorithms. Evaluation results have shown that our proposed method has state-of-the-art performance in the annotation of multiple diagnostic codes for ICU patients. This study suggests that problems in the automated diagnosis code annotation can be reliably addressed by using a multi-label learning model that exploits disease correlation. The findings of this study will greatly benefit health care and management in ICU considering that the automated diagnosis code annotation can significantly improve the quality and management of health care for both patients and caregivers.
Characterising the immune response in braf V600E and BRAF Wild type melanoma
Publisher: Elsevier BV
Date: 02-2020
On the Prediction of Re-tweeting Activities in Social Networks - A Report on WISE 2012 Challenge
Publisher: No publisher found
Date: 2012
Learning from less for better
Publisher: ACM
Date: 12-09-2016
Editorial for application-driven knowledge acquisition
Publisher: Springer Science and Business Media LLC
Date: 25-06-2020
Unsupervised 2D Dimensionality Reduction with Adaptive Structure Learning
Publisher: MIT Press - Journals
Date: 05-2017
DOI: 10.1162/NECO_A_00950
Abstract: In recent years, unsupervised two-dimensional (2D) dimensionality reduction methods for unlabeled large-scale data have made progress. However, performance of these degrades when the learning of similarity matrix is at the beginning of the dimensionality reduction process. A similarity matrix is used to reveal the underlying geometry structure of data in unsupervised dimensionality reduction methods. Because of noise data, it is difficult to learn the optimal similarity matrix. In this letter, we propose a new dimensionality reduction model for 2D image matrices: unsupervised 2D dimensionality reduction with adaptive structure learning (DRASL). Instead of using a predetermined similarity matrix to characterize the underlying geometry structure of the original 2D image space, our proposed approach involves the learning of a similarity matrix in the procedure of dimensionality reduction. To realize a desirable neighbors assignment after dimensionality reduction, we add a constraint to our model such that there are exact [Formula: see text] connected components in the final subspace. To accomplish these goals, we propose a unified objective function to integrate dimensionality reduction, the learning of the similarity matrix, and the adaptive learning of neighbors assignment into it. An iterative optimization algorithm is proposed to solve the objective function. We compare the proposed method with several 2D unsupervised dimensionality methods. K-means is used to evaluate the clustering performance. We conduct extensive experiments on Coil20, AT& T, FERET, USPS, and Yale data sets to verify the effectiveness of our proposed method.
Advanced Data Mining and Applications - 15th International Conference, ADMA 2019, Dalian, China, November 21-23, 2019, Proceedings
Publisher: Springer International Publishing
Date: 2019
Multi-modality Sensor Data Classification with Selective Attention
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2018
Abstract: Multimodel wearable sensor data classificationplays an important role in ubiquitous computingand has a wide range of applications in variousscenarios from healthcare to entertainment. How-ever, most of the existing work in this field em-ploys domain-specific approaches and is thus inef-fective in complex situations where multi-modalitysensor data is collected. Moreover, the wearablesensor data is less informative than the conven-tional data such as texts or images. In this paper,to improve the adaptability of such classificationmethods across different application contexts, weturn this classification task into a game and applya deep reinforcement learning scheme to dynami-cally deal with complex situations. We also intro-duce a selective attention mechanism into the rein-forcement learning scheme to focus on the crucialdimensions of the data. This mechanism helps tocapture extra information from the signal, and canthus significantly improve the discriminative powerof the classifier. We carry out several experimentson three wearable sensor datasets, and demonstratecompetitive performance of the proposed approachcompared to several state-of-the-art baselines.
Object Detection Difficulty: Suppressing Over-aggregation for Faster and Better Video Object Detection
Publisher: ACM
Date: 26-10-2023
Learning Attentional Temporal Cues of Brainwaves with Spatial Embedding for Motion Intent Detection
Publisher: IEEE
Date: 11-2019
Semi-supervised Feature Analysis for Multimedia Annotation by Mining Label Correlation
Publisher: Springer International Publishing
Date: 2014
Online Action Recognition by Template Matching
Publisher: Springer Berlin Heidelberg
Date: 2013
Detecting Suicidal Ideation with Data Protection in Online Communities
Publisher: Springer International Publishing
Date: 2019
Temporal Self-Attention Network for Medical Concept Embedding
Publisher: IEEE
Date: 11-2019
Rethinking Generative Zero-Shot Learning: An Ensemble Learning Perspective for Recognising Visual Patches
Publisher: ACM
Date: 12-10-2020
PDD Graph: Bridging Electronic Medical Records and Biomedical Knowledge Graphs via Entity Linking
Publisher: No publisher found
Date: 2017
Detecting Suicidal Ideation with Data Protection in Online Communities
Publisher: No publisher found
Date: 2019
Global Convolutional Neural Processes
Publisher: IEEE
Date: 12-2021
BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes
Publisher: IEEE
Date: 11-2021
Collaborative text categorization via exploiting sparse coefficients
Publisher: Springer Science and Business Media LLC
Date: 28-04-2018
Deep Interpretable Mortality Model for Intensive Care Unit Risk Prediction
Publisher: Springer International Publishing
Date: 2019
Collective Protection: Preventing Sensitive Inferences via Integrative Transformation
Publisher: IEEE
Date: 11-2019
Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2018
Abstract: Many natural language processing tasks solely rely on sparse dependencies between a few tokens in a sentence. Soft attention mechanisms show promising performance in modeling local/global dependencies by soft probabilities between every two tokens, but they are not effective and efficient when applied to long sentences. By contrast, hard attention mechanisms directly select a subset of tokens but are difficult and inefficient to train due to their combinatorial nature. In this paper, we integrate both soft and hard attention into one context fusion model, "reinforced self-attention (ReSA)", for the mutual benefit of each other. In ReSA, a hard attention trims a sequence for a soft self-attention to process, while the soft attention feeds reward signals back to facilitate the training of the hard one. For this purpose, we develop a novel hard attention called "reinforced sequence s ling (RSS)", selecting tokens in parallel and trained via policy gradient. Using two RSS modules, ReSA efficiently extracts the sparse dependencies between each pair of selected tokens. We finally propose an RNN/CNN-free sentence-encoding model, "reinforced self-attention network (ReSAN)", solely based on ReSA. It achieves state-of-the-art performance on both the Stanford Natural Language Inference (SNLI) and the Sentences Involving Compositional Knowledge (SICK) datasets.
Behavior-Interior-Aware User Preference Analysis Based on Social Networks
Publisher: Hindawi Limited
Date: 09-10-2018
DOI: 10.1155/2018/7371209
Abstract: There is a growing trend recently in big data analysis that focuses on behavior interiors, which concern the semantic meanings (e.g., sentiment, controversy, and other state-dependent factors) in explaining the human behaviors from psychology, sociology, cognitive science, and so on, rather than the data per se as in the case of exterior dimensions. It is more intuitive and much easier to understand human behaviors with less redundancy in concept by exploring the behavior interior dimensions, compared with directly using behavior exteriors. However, they usually approach from a unidimensional perspective with a lack of a sense of interrelatedness. Thus, integrating multiple behavior dimensions together into some numerical measures to form a more comprehensive view for subsequent prediction processes becomes a pivotal issue. Moreover, these studies usually focus on the magnitude but neglect the associated temporal features. In this paper, we propose a behavior interior dimension-based neighborhood collaborative filtering method for the top- N hashtag adoption frequency prediction that takes into account the interdependence in temporal dynamics. Our proposed approach couples the similarity in user preference and their impact propagation, by integrating the linear threshold model and the enhanced CF model based on behavior interiors. Experiments on Twitter demonstrate that the behavior-interior-aware CF models achieve better adoption prediction results than the state-of-the-art methods, and the joint consideration of similarity in user preference and their impact propagation results in a significant improvement than treating them separately.
A Comparative Study of Transactional and Semantic Approaches for Predicting Cascades on Twitter
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2018
Abstract: The availability of massive social media data has enabled the prediction of people’s future behavioral trends at an unprecedented large scale. Information cascades study on Twitter has been an integral part of behavior analysis. A number of methods based on the transactional features (such as keyword frequency) and the semantic features (such as sentiment) have been proposed to predict the future cascading trends. However, an in-depth understanding of the pros and cons of semantic and transactional models is lacking. This paper conducts a comparative study of both approaches in predicting information diffusion with three mechanisms: retweet cascade, url cascade, and hashtag cascade. Experiments on Twitter data show that the semantic model outperforms the transactional model, if the exterior pattern is less directly observable (i.e. hashtag cascade). When it becomes more directly observable (i.e. retweet and url cascades), the semantic method yet delivers approximate accuracy (i.e. url cascade) or even worse accuracy (i.e. retweet cascade). Further, we demonstrate that the transactional and semantic models are not independent, and the performance gets greatly enhanced when combining both.
Uncovering Locally Discriminative Structure for Feature Analysis
Publisher: No publisher found
Date: 2016
Multi-task support vector machines for feature selection with shared knowledge discovery
Publisher: Elsevier BV
Date: 03-2016
Unsupervised Feature Analysis with Class Margin Optimization
Publisher: No publisher found
Date: 2015
Explaining similarity for SPARQL queries
Publisher: Springer Science and Business Media LLC
Date: 12-06-2021
ZSTAD: Zero-Shot Temporal Activity Detection
Publisher: IEEE
Date: 06-2020
Exploring BCI Control in Smart Environments: Intention Recognition Via EEG Representation Enhancement Learning
Publisher: Association for Computing Machinery (ACM)
Date: 29-05-2021
DOI: 10.1145/3450449
Abstract: The brain–computer interface (BCI) control technology that utilizes motor imagery to perform the desired action instead of manual operation will be widely used in smart environments. However, most of the research lacks robust feature representation of multi-channel EEG series, resulting in low intention recognition accuracy. This article proposes an EEG2Image based Denoised-ConvNets (called EID) to enhance feature representation of the intention recognition task. Specifically, we perform signal decomposition, slicing, and image mapping to decrease the noise from the irrelevant frequency bands. After that, we construct the Denoised-ConvNets structure to learn the colorspace and spatial variations of image objects without cropping new training images precisely. Toward further utilizing the color and spatial transformation layers, the colorspace and colored area of image objects have been enhanced and enlarged, respectively. In the multi-classification scenario, extensive experiments on publicly available EEG datasets confirm that the proposed method has better performance than state-of-the-art methods.
Databases Theory and Applications
Publisher: Springer International Publishing
Date: 2021
Self-attention Enhanced Patient Journey Understanding in Healthcare System
Publisher: Springer International Publishing
Date: 2021
Human action recognition based on semi-supervised discriminant analysis with global constraint
Publisher: Elsevier BV
Date: 04-2013
Dynamic illness severity prediction via multi-task RNNs for Intensive Care Unit
Publisher: IEEE
Date: 11-2018
Local Graph Convolutional Networks for Cross-Modal Hashing
Publisher: ACM
Date: 17-10-2021
PDD Graph: Bridging Electronic Medical Records and Biomedical Knowledge Graphs via Entity Linking
Publisher: Springer International Publishing
Date: 2017
Compressive representation for device-free activity recognition with passive RFID signal strength
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2017
Making Sense of Spatio-Temporal Preserving Representations for EEG-Based Human Intention Recognition
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2020
Uncovering Locally Discriminative Structure for Feature Analysis
Publisher: Springer International Publishing
Date: 2016
A multi-level neural network for implicit causality detection in web texts
Publisher: Elsevier BV
Date: 04-2022
Hybrid Collaborative Recommendation via Semi-AutoEncoder
Publisher: Springer International Publishing
Date: 2017
Is Visual Context Really Helpful for Knowledge Graph? A Representation Learning Perspective
Publisher: ACM
Date: 17-10-2021
Towards universal health coverage for people with stroke in South Africa: a scoping review
Publisher: BMJ
Date: 11-2021
DOI: 10.1136/BMJOPEN-2021-049988
Abstract: To explore the opportunities and challenges within the health system to facilitate the achievement of universal health coverage (UHC) for people with stroke (PWS) in South Africa (SA). SA. Scoping review. We conducted a scoping review of opportunities and challenges to achieve UHC for PWS in SA. Global and Africa-specific databases and grey literature were searched in July 2020. We included studies of all designs that described the healthcare system for PWS. Two frameworks, the Health Systems Dynamics Framework and WHO Framework, were used to map data on governance and regulation, resources, service delivery, context, reorientation of care and community engagement. A narrative approach was used to synthesise results. Fifty-nine articles were included in the review. Over half (n=31, 52.5%) were conducted in Western Cape province and most (n=41, 69.4%) were conducted in urban areas. Studies evaluated a erse range of health system categories and various outcomes. The most common reported component was service delivery (n=46, 77.9%), and only four studies (6.7%) evaluated governance and regulation. Service delivery factors for stroke care were frequently reported as poor and compounded by context-related limiting factors. Governance and regulations for stroke care in terms of government support, investment in policy, treatment guidelines, resource distribution and commitment to evidence-based solutions were limited. Promising supporting factors included adequately equipped and staffed urban tertiary facilities, the emergence of Stroke units, prompt assessment by health professionals, positive staff attitudes and care, two clinical care guidelines and educational and information resources being available. This review fills a gap in the literature by providing the range of opportunities and challenges to achieve health for all PWS in SA. It highlights some health system areas that show encouraging trends to improve service delivery including comprehensiveness, quality and perceptions of care.
InDISP: An Interpretable Model for Dynamic Illness Severity Prediction
Publisher: Springer International Publishing
Date: 2022
Learning to Hash for Efficient Search Over Incomplete Knowledge Graphs
Publisher: IEEE
Date: 11-2019
Self-Supervised Adversarial Distribution Regularization for Medication Recommendation
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 08-2021
Abstract: Medication recommendation is a significant healthcare application due to its promise in effectively prescribing medications. Avoiding fatal side effects related to Drug-Drug Interaction (DDI) is among the critical challenges. Most existing methods try to mitigate the problem by providing models with extra DDI knowledge, making models complicated. While treating all patients with different DDI properties as a single cohort would put forward strict requirements on models' generalization performance. In pursuit of a valuable model for a safe recommendation, we propose the Self-Supervised Adversarial Regularization Model for Medication Recommendation (SARMR). SARMR obtains the target distribution associated with safe medication combinations from raw patient records for adversarial regularization. In this way, the model can shape distributions of patient representations to achieve DDI reduction. To obtain accurate self-supervision information, SARMR models interactions between physicians and patients by building a key-value memory neural network and carrying out multi-hop reading to obtain contextual information for patient representations. SARMR outperforms all baseline methods in the experiment on a real-world clinical dataset. This model can achieve DDI reduction when considering the different number of DDI types, which demonstrates the robustness of adversarial regularization for safe medication recommendation.
Unsupervised feature analysis with class margin optimization
Publisher: Springer International Publishing
Date: 2015
Memory-Based Network for Scene Graph with Unbalanced Relations
Publisher: ACM
Date: 12-10-2020
Distributionally Robust Semi-Supervised Learning for People-Centric Sensing
Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
Date: 17-07-2019
DOI: 10.1609/AAAI.V33I01.33013321
Abstract: Semi-supervised learning is crucial for alleviating labelling burdens in people-centric sensing. However, humangenerated data inherently suffer from distribution shift in semi-supervised learning due to the erse biological conditions and behavior patterns of humans. To address this problem, we propose a generic distributionally robust model for semi-supervised learning on distributionally shifted data. Considering both the discrepancy and the consistency between the labeled data and the unlabeled data, we learn the latent features that reduce person-specific discrepancy and preserve task-specific consistency. We evaluate our model in a variety of people-centric recognition tasks on real-world datasets, including intention recognition, activity recognition, muscular movement recognition and gesture recognition. The experiment results demonstrate that the proposed model outperforms the state-of-the-art methods.
Improving Chinese Sentiment Analysis via Segmentation-Based Representation Using Parallel CNN
Publisher: No publisher found
Date: 2017
Online Action Recognition by Template Matching
Publisher: No publisher found
Date: 2013
Scalable Estimator for Multi-task Gaussian Graphical Models Based in an IoT Network
Publisher: Association for Computing Machinery (ACM)
Date: 21-06-2021
DOI: 10.1145/3432312
Abstract: Recently, the Internet of Things (IoT) receives significant interest due to its rapid development. But IoT applications still face two challenges: heterogeneity and large scale of IoT data. Therefore, how to efficiently integrate and process these complicated data becomes an essential problem. In this article, we focus on the problem that analyzing variable dependencies of data collected from different edge devices in the IoT network. Because data from different devices are heterogeneous and the variable dependencies can be characterized into a graphical model, we can focus on the problem that jointly estimating multiple, high-dimensional, and sparse Gaussian Graphical Models for many related tasks (edge devices). This is an important goal in many fields. Many IoT networks have collected massive multi-task data and require the analysis of heterogeneous data in many scenarios. Past works on the joint estimation are non-distributed and involve computationally expensive and complex non-smooth optimizations. To address these problems, we propose a novel approach: Multi-FST. Multi-FST can be efficiently implemented on a cloud-server-based IoT network. The cloud server has a low computational load and IoT devices use asynchronous communication with the server, leading to efficiency. Multi-FST shows significant improvement, over baselines, when tested on various datasets.
Access to health care for people with stroke in South Africa: a qualitative study of community perspectives
Publisher: Springer Science and Business Media LLC
Date: 09-04-2022
DOI: 10.1186/S12913-022-07903-9
Abstract: Incidence of stroke is increasing in sub-Saharan Africa. People who survive stroke experience disability and require long-term care. Health systems in South Africa (SA) are experiencing important challenges, and services in the public health system for people with stroke (PWS) are fragmented. We aimed to explore the perspectives and experiences of PWS related to stroke care services to inform health system strengthening measures. In-depth interviews with 16 PWS in urban and rural areas in the Western and Eastern Cape Provinces of SA were conducted between August and October 2020. PWS were recruited through existing research networks, non-government organisations and organisations of persons with disabilities by snowball s ling. Interviews were transcribed, coded, and thematically analysed. We used the conceptual framework of access to health care as proposed by Levesque et al. to map and inform barriers to accessing health care from the user perspective. PWS recognised the need for health care when they experienced signs of acute stroke. Health literacy on determinants of stroke was low. Challenges to accessing stroke care include complex pathways to care, physical mobility related to stroke, long travel distances and limited transport options, waiting times and out of pocket expenses. The perceived quality of services was influenced by cultural beliefs, attitudinal barriers, and information challenges. Some PWS experienced excellent care and others particularly poor care. Positive staff attitude, perceived competence and trustworthiness went in hand with many technical and interpersonal deficits, such as long waiting times and poor staff attitude that resulted in poor satisfaction and reportedly poor outcomes for PWS. Strategic leadership, governance and better resources at multiple levels are required to address the unmet demands and needs for health care of PWS. Stroke care could be strengthened by service providers routinely providing information about prevention and symptoms of stroke, treatment, and services to patients and their social support network. The role of family members in continuity of care could be strengthened by raising awareness of existing resources and referral pathways, and facilitating connections within services.
EEG-based Motion Intention Recognition via Multi-task RNNs
Publisher: Society for Industrial and Applied Mathematics
Date: 07-05-2018
SMR: Medical Knowledge Graph Embedding for Safe Medicine Recommendation
Publisher: Elsevier BV
Date: 02-2021
Hybrid Collaborative Recommendation via Semi-AutoEncoder
Publisher: No publisher found
Date: 2017
Ready for Use: Subject-Independent Movement Intention Recognition via a Convolutional Attention Model
Publisher: ACM
Date: 17-10-2018
Diagnosis Code Assignment Using Sparsity-Based Disease Correlation Embedding
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2016
Improving Chinese Sentiment Analysis via Segmentation-Based Representation Using Parallel CNN
Publisher: Springer International Publishing
Date: 2017
NeuRec: On Nonlinear Transformation for Personalized Ranking
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2018
Abstract: Modeling user-item interaction patterns is an important task for personalized recommendations. Many recommender systems are based on the assumption that there exists a linear relationship between users and items while neglecting the intricacy and non-linearity of real-life historical interactions. In this paper, we propose a neural network based recommendation model (NeuRec) that untangles the complexity of user-item interactions and establish an integrated network to combine non-linear transformation with latent factors. We further design two variants of NeuRec: user-based NeuRec and item-based NeuRec, by focusing on different aspects of the interaction matrix. Extensive experiments on four real-world datasets demonstrated their superior performances on personalized ranking task.
Compound Rank-k Projections for Bilinear Analysis
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2016
Semantics Disentangling for Generalized Zero-Shot Learning
Publisher: IEEE
Date: 10-2021
Action recognition by exploring data distribution and feature correlation
Publisher: IEEE
Date: 06-2012
Unobtrusive posture recognition via online learning of multi-dimensional RFID received signal strength
Publisher: IEEE
Date: 12-2016
Provenance-Based Rumor Detection
Publisher: No publisher found
Date: 2017
An Embedded Feature Selection Framework for Hybrid Data
Publisher: No publisher found
Date: 2017
Quadratic Sparse Gaussian Graphical Model Estimation Method for Massive Variables
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2020
Abstract: We consider the problem of estimating a sparse Gaussian Graphical Model with a special graph topological structure and more than a million variables. Most previous scalable estimators still contain expensive calculation steps (e.g., matrix inversion or Hessian matrix calculation) and become infeasible in high-dimensional scenarios, where p (number of variables) is larger than n (number of s les). To overcome this challenge, we propose a novel method, called Fast and Scalable Inverse Covariance Estimator by Thresholding (FST). FST first obtains a graph structure by applying a generalized threshold to the s le covariance matrix. Then, it solves multiple block-wise subproblems via element-wise thresholding. By using matrix thresholding instead of matrix inversion as the computational bottleneck, FST reduces its computational complexity to a much lower order of magnitude (O(p2)). We show that FST obtains the same sharp convergence rate O(√(log max{p, n}/n) as other state-of-the-art methods. We validate the method empirically, on multiple simulated datasets and one real-world dataset, and show that FST is two times faster than the four baselines while achieving a lower error rate under both Frobenius-norm and max-norm.
On the Prediction of Re-tweeting Activities in Social Networks – A Report on WISE 2012 Challenge
Publisher: Springer Berlin Heidelberg
Date: 2012
Compact representation for large-scale unconstrained video analysis
Publisher: Springer Science and Business Media LLC
Date: 10-06-2015
Total IgE as a Marker for Chronic Spontaneous Urticaria
Publisher: XMLink
Date: 2021
Self-attention Enhanced Patient Journey Understanding in Healthcare System
Publisher: No publisher found
Date: 2020
Achieving universal health coverage for people with stroke in South Africa: protocol for a scoping review
Publisher: BMJ
Date: 10-2020
DOI: 10.1136/BMJOPEN-2020-041221
Abstract: Stroke is the second most common cause of death after HIV/AIDS and a significant health burden in South Africa. The extent to which universal health coverage (UHC) is achieved for people with stroke in South Africa is unknown. Therefore, a scoping review to explore the opportunities and challenges within the South African health system to facilitate the achievement of UHC for people with stroke is warranted. The scoping review will follow the approach recommended by Levac, Colquhoun and O’Brien, which includes five steps: (1) identifying the research question, (2) identifying relevant studies, (3) selecting the studies, (4) charting the data, and (5) collating, summarising and reporting the results. Health Systems Dynamics Framework and WHO Framework on integrated people-centred health services will be used to map, synthesise and analyse data thematically. Ethical approval is not required for this scoping review, as it will only include published and publicly available data. The findings of this review will be published in an open-access, peer-reviewed journal and we will develop an accessible summary of the results for website posting and stakeholder meetings.
Fall detection in multi-camera surveillance videos
Publisher: ACM
Date: 22-10-2013
Cal-SFDA: Source-Free Domain-adaptive Semantic Segmentation with Differentiable Expected Calibration Error
Publisher: ACM
Date: 26-10-2023
Mitigating Generation Shifts for Generalized Zero-Shot Learning
Publisher: ACM
Date: 17-10-2021
Graph-based clustering and ranking for diversified image search
Publisher: Springer Science and Business Media LLC
Date: 24-09-2017
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