Reviewerly/exHarmony huggingface dataset:
Effect of a Real-Time Risk Score on 30-day Readmission Reduction in Singapore

To develop a risk score for the real-time prediction of readmissions for patients using patient specific information captured in electronic medical records (EMR) in Singapore to enable the prospective identification of high-risk patients for enrolment in timely interventions.Machine-learning models were built to estimate the probability of a patient being readmitted within 30 days of discharge. EMR of 25,472 patients discharged from the medicine department at Ng Teng Fong General Hospital between January 2016 and December 2016 were extracted retrospectively for training and internal validation of the models. We developed and implemented a real-time 30-day readmission risk score generation in the EMR system, which enabled the flagging of high-risk patients to care providers in the hospital. Based on the daily high-risk patient list, the various interfaces and flow sheets in the EMR were configured according to the information needs of the various stakeholders such as the inpatient medical, nursing, case management, emergency department, and postdischarge care teams.Overall, the machine-learning models achieved good performance with area under the receiver operating characteristic ranging from 0.77 to 0.81. The models were used to proactively identify and attend to patients who are at risk of readmission before an actual readmission occurs. This approach successfully reduced the 30-day readmission rate for patients admitted to the medicine department from 11.7% in 2017 to 10.1% in 2019 (p < 0.01) after risk adjustment.Machine-learning models can be deployed in the EMR system to provide real-time forecasts for a more comprehensive outlook in the aspects of decision-making and care provision.
The Royal Game of Ur: Virtual Reality Prototype of the Board Game Played in Ancient Mesopotamia

We present a virtual reality implementation of the "Royal Game of Ur", an ancient board game played by the people of Akkadian empire since around 3000 BC. The game's rules recently deciphered from the cuneiform's tablets have been implemented using MinMax approach with alpha-beta pruning optimization, allowing for a highly competitive game-play against even the most skilled human players. The game utilizes freehand interaction and, experimentally, a brain computer interface to roll the dice and move the pieces.
Long Text QA Matching Model Based on BiGRU–DAttention–DSSM

QA matching is a very important task in natural language processing, but current research on text matching focuses more on short text matching rather than long text matching. Compared with short text matching, long text matching is rich in information, but distracting information is frequent. This paper extracted question-and-answer pairs about psychological counseling to research long text QA-matching technology based on deep learning. We adjusted DSSM (Deep Structured Semantic Model) to make it suitable for the QA-matching task. Moreover, for better extraction of long text features, we also improved DSSM by enriching the text representation layer, using a bidirectional neural network and attention mechanism. The experimental results show that BiGRU–Dattention–DSSM performs better at matching questions and answers.
Co-assemble: A collaborative AR cross-devices teaching system for assemble practice courses

Assembly trainings in engineering drawing courses mainly rely on physical models, revealing many limitations. We use augmented reality and Azure Spatial Anchors to design "Co-assemble," a multi-user cross-device collaborative system for the assembly practice on mobile devices. There are three modes in the system: single, collaboration, and class. It will help students understanding the models' structure and assembly activities and help teachers easily teach or monitor classes. We also presented and discussed the results of the preliminary user study evaluating the system.
NNStreamer: Efficient and Agile Development of On-Device AI Systems

We propose NNStreamer, a software system that handles neural networks as filters of stream pipelines, applying the stream processing paradigm to deep neural network applications. A new trend with the wide-spread of deep neural network applications is on-device AI. It is to process neural networks on mobile devices or edge/IoT devices instead of cloud servers. Emerging privacy issues, data transmission costs, and operational costs signify the need for on-device AI, especially if we deploy a massive number of devices. NNStreamer efficiently handles neural networks with complex data stream pipelines on devices, significantly improving the overall performance with minimal effort. Besides, NNStreamer simplifies implementations and allows reusing off-the-shelf media filters directly, which reduces developmental costs significantly. We are already deploying NNStreamer for a wide range of products and platforms, including the Galaxy series and various consumer electronic devices. The experimental results suggest a reduction in developmental costs and enhanced performance of pipeline architectures and NNStreamer. It is an open-source project incubated by Linux Foundation AI & Data, available to the public and applicable to various hardware and software platforms.
Estimating a 2D pose from a tiny person image with super-resolution reconstruction

Human pose estimation has achieved tremendous advances in accuracy with the emergence of various deep neural network architectures. However, for low-resolution (LR) images, we are far from achieving an acceptable accuracy. Deep learning based super-resolution (SR) has been proven to be helpful for addressing the challenges of face recognition and object detection in LR images. Following this idea, we integrate SR into existing human pose estimation networks to increase accuracy for LR images. In this work, we propose a novel end-to-end network architecture for the effective combination of SR and human pose estimation. Moreover, an approach is presented to guide the SR network to generate intermediate high-resolution (HR) images that contribute to pose estimation, rather than simply taking SR as an upstream task. The experimental results show that the accuracy of our approach has over 20% improved to that of the interpolation-assisted pose estimation network on the downsampled MPII dataset.
CSPN: Multi-Scale Cascade Spatial Pyramid Network for Object Detection

Scale variation is one of the key challenges in object detection. One solution is Image Pyramid, which employs images of multiple resolutions for training. Another solution is Feature Pyramid, which uses multi-scale features for prediction and is widely used in current object detectors due to its high efficiency. However, the representational power of each scale in Feature Pyramid is inconsistent, which makes the performance lower than Image Pyramid. To solve this problem and obtain better detection performance, we propose a novel net-work named Multi-Scale Cascade Spatial Pyramid Network (MS-CSPN) to strengthen Feature Pyramid. First, we de-sign CSPN to expand the receptive field in a cascade form to detect objects of different scales. Secondly, we propose a Cross-Scale Sharing Strategy, which shares the parameters of CSPN at all scales. Finally, we introduce global context information to enhance MS-CSPN. Experimental results on the MS-COCO benchmark show that the proposed MS-CSPN improves the mAP by a large margin compared to previous related works.
Domain Generalized Person Re-Identification via Cross-Domain Episodic Learning

Aiming at recognizing images of the same person across distinct camera views, person re-identification (re-ID) has been among active research topics in computer vision. Most existing re-ID works require collection of a large amount of labeled image data from the scenes of interest. When the data to be recognized are different from the source-domain training ones, a number of domain adaptation approaches have been proposed. Nevertheless, one still needs to collect labeled or unlabelled target-domain data during training. In this paper, we tackle an even more challenging and practical setting, domain generalized (DG) person re-ID. That is, while a number of labeled source-domain datasets are available, we do not have access to any target-domain training data. In order to learn domain-invariant features without knowing the target domain of interest, we present an episodic learning scheme which advances meta learning strategies to exploit the observed source-domain labeled data. The learned features would exhibit sufficient domain-invariant properties while not overfitting the source-domain data or ID labels. Our experiments on four benchmark datasets confirm the superiority of our method over the state-of-the-arts.
Tackling Occlusion in Siamese Tracking with Structured Dropouts

Occlusion is one of the most difficult challenges in object tracking to model. This is because unlike other challenges, where data augmentation can be of help, occlusion is hard to simulate as the occluding object can be anything in any shape. In this paper, we propose a simple solution to simulate the effects of occlusion in the latent space. Specifically, we present structured dropout to mimick the change in latent codes under occlusion. We present three forms of dropout (channel dropout, segment dropout and slice dropout) with the various forms of occlusion in mind. To demonstrate its effectiveness, the dropouts are incorporated into two modern Siamese trackers (SiamFC and SiamRPN++). The outputs from multiple dropouts are combined using an encoder network to obtain the final prediction. Experiments on several tracking benchmarks show the benefits of structured dropouts, while due to their simplicity requiring only small changes to the existing tracker models.
The Effect of Tilting a PV Array by Monthly or Seasonal Optimal Tilt Angles on Energy Yield of a Solar PV System

The energy yield from a photovoltaic (PV) array is highly dependent on the array angle of inclination with respect to the horizontal plane. For a fixed mounted PV array, the yearly optimal tilt angle is the angle that maximizes the solar radiation falling on the array surface throughout the year and thereby maximizes the electrical energy production. This paper discusses the impact on the energy yield when tilting the PV array by monthly or seasonal optimal tilt angles rather than by the yearly optimal tilt angle. The gain percentage of energy yield from a PV array was calculated when the array was tilted by the monthly optimal tilt angles and also when the array was tilted by the seasonal optimal tilt angles. The geographic location of the University College of Science and Technology located in Gaza Strip - Palestine was selected in this study. Photovoltaic Geographical Information System (PVGIS) software was used to produce the results. The results showed that the produced energy increased by 5.82% and 4.77% using monthly and seasonal optimal tilt angles respectively. In order to confirm the results, System Advisor Model (SAM) software developed by the National Renewable Energy Laboratory (NREL) was used. The results obtained from SAM software were close to those obtained from PVGIS software tool.
Adversarial Examples Detection Beyond Image Space

Deep neural networks have been proved that they are vulnerable to adversarial examples, which are generated by adding human-imperceptible perturbations to images. To defend these adversarial examples, various detection based methods have been proposed. However, most of them perform poorly on detecting adversarial examples with extremely slight perturbations. By exploring these adversarial examples, we find that there exists compliance between perturbations and prediction confidence, which guides us to detect few-perturbation attacks from the aspect of prediction confidence. To detect both few-perturbation attacks and large-perturbation attacks, we propose a method beyond image space by a two-stream architecture, in which the image stream focuses on the pixel artifacts and the gradient stream copes with the confidence artifacts. The experimental results show that the proposed method outperforms the existing methods under oblivious attacks and is verified effective to defend omniscient attacks as well.
E-Commerce Product Image Classification using Transfer Learning

E-commerce is the platform where it provisions the online businesses for selling and buying. Organizing and searching for products is a cumbersome process for service providers and customers. The considerable time are getting wasted in organizing and labeling the products. The most commonly used algorithm for image classification is the convolutional neural network (CNN). Training a huge dataset consumes a lot of computational resources and time. Image search is good when we don't have many details about the product. We have proposed a transfer learning approach based on visual geometry group-19 (VGG-19) and Inception V3 to overcome the issues related to classification, product identification, product suggestion, and image-based search. We have taken a Kaggle dataset of 70000, 28x28 labeled fashion images. The dataset is split into 85% training and 15% validation. The training and validation accuracy of the proposed transfer learning approach is better whereas the loss is lower as compared to CNN based approach.
Designing for New Forms of Vulnerability

Our workshop will concentrate on vulnerability of specific social groups due to various reasons, including COVID-19, and the potential for technology design to result in empowerment. We want to address issues of what new forms of vulnerabilities emerge and how we can design digital environments in a way that acknowledges vulnerability but also has the potential to empower people in ways that are meaningful for them. When planning the workshop, we will also reflect on social situations that can result in vulnerabilities for participants. Therefore, we will ensure that interested participants will experience low barriers to participation include a variety of people with different backgrounds and ensure that interaction happens based on equality principles and in an atmosphere of solidarity. Participants can exchange ideas and thoughts without worrying about being exposed to biased assumptions. The workshop will allow for non-hierarchical and cooperative discussion and collaboration through interactive online exercises, resulting in a collaboratively developed zine. Finally, the social sustainability of the workshop will be ensured through a website, mailing lists, joint publications and continuous contact.
The DEVIL is in the Details: A Diagnostic Evaluation Benchmark for Video Inpainting

Quantitative evaluation has increased dramatically among recent video inpainting work, but the video and mask content used to gauge performance has received relatively little attention. Although attributes such as camera and background scene motion inherently change the difficulty of the task and affect methods differently, existing evaluation schemes fail to control for them, thereby providing minimal insight into inpainting failure modes. To address this gap, we propose the Diagnostic Evaluation of Video Inpainting on Landscapes (DEVIL) benchmark, which consists of two contributions: (i) a novel dataset of videos and masks labeled according to several key inpainting failure modes, and (ii) an evaluation scheme that samples slices of the dataset characterized by a fixed content attribute, and scores performance on each slice according to reconstruction, realism, and temporal consistency quality. By revealing systematic changes in performance induced by particular characteristics of the input content, our challenging benchmark enables more insightful analysis into video inpainting methods and serves as an invaluable diagnostic tool for the field. Our code and data are available at github.com/MichiganCOG/devil.
Long live the data! Embedded data management at a long‐term ecological research site

Abstract Open‐access data associated with research efforts depend upon managing, packaging, and preserving data for sharing with collaborators and the public. The U.S. Long‐Term Ecological Research (LTER) Network, established in 1980, provides an early example of embedded data management supporting long‐term, place‐based research and contributes to our understanding of the enactment of open data access within scientific research arenas. Here, we examine collective data activities enabled by embedding data management within the Shortgrass Steppe (SGS) research site. Study of the SGS LTER, a member of the U.S. LTER Network for more than three decades, provided a unique opportunity to investigate data management practices and challenges during the life cycle of a long‐term project. It illustrates how a continuous, uninterrupted focus on data management positioned in dynamic interaction with researchers at a site as well as with an active network‐wide data management committee can stimulate the growth of both data expertise and data infrastructure. We report on an ethnographic study by a collaborative team of researchers, all having been involved with the LTER network and well‐positioned for investigating data management challenges faced during the periods of activation, maturation, and decommissioning of a project at a research site. Termination of the SGS site's membership in the U.S. LTER Network prompted rethinking about long‐term data management. During the decommissioning phase, we document how views on temporality and data management strategies shift from planning for a longitudinal, ongoing site to wrapping up a long‐term project. Striving to ensure “long live the data” at the end, novel data arrangements, such as development of a digital legacy project collection, contribute to data stewardship. Lastly, from this study of a long‐term research site, we offer five recommendations about data management and describe strategies pertinent to planning for data management and open access for other research projects.
PoseCVAE: Anomalous Human Activity Detection

Anomalous human activity detection is the task of identifying human activities that differ from the usual. Existing techniques, in general, try to deploy some samples from an open-set (anomalous activities can not be represented as a closed set) to define the discriminator. However, it is non-trivial to obtain novel activity instances. To this end, we propose PoseCVAE, a novel anomalous human activity detection strategy using the notion of generative modeling. We adopt a hybrid training strategy comprising of self-supervised and unsupervised learning. The self-supervised learning helps the encoder and decoder to learn better latent space representation of human pose trajectories. We train our framework to predict future pose trajectory given a normal track of past poses, i.e., the goal is to learn a conditional posterior distribution that represents normal training data. To achieve this we use a novel adaptation of a conditional variational autoencoder (CVAE) and refer it as PoseCVAE. Future pose prediction will be erroneous if the given poses are sampled from a distribution different from the learnt posterior, which is indeed the case with abnormal activities. To further separate the abnormal class, we imitate abnormal poses in the encoded space by sampling from a distinct mixture of Gaussians (MoG). We use a binary cross-entropy (BCE) loss as a novel addition to the standard CVAE loss function to achieve this. We test our framework on three publicly available datasets and achieve comparable performance to existing unsupervised methods that exploit pose information.
The Roles of General Health and COVID-19 Proximity in Contact Tracing App Usage: Cross-sectional Survey Study

Background Contact tracing apps are considered useful means to monitor SARS-CoV-2 infections during the off-peak stages of the COVID-19 pandemic. Their effectiveness is, however, dependent on the uptake of such COVID-19 apps. Objective We examined the role of individuals’ general health status in their willingness to use a COVID-19 tracing app as well as the roles of socioeconomic characteristics and COVID-19 proximity. Methods We drew data from the WageIndicator Foundation Living and Working in Coronavirus Times survey. The survey collected data on labor market status as well as the potential confounders of the relationship between general health and COVID-19 tracing app usage, such as sociodemographics and regular smartphone usage data. The survey also contained information that allowed us to examine the role of COVID-19 proximity, such as whether an individual has contracted SARS-CoV-2, whether an individual has family members and colleagues with COVID-19, and whether an individual exhibits COVID-19 pandemic–induced depressive and anxiety symptoms. We selected data that were collected in Spain, Italy, Germany, and the Netherlands from individuals aged between 18 and 70 years (N=4504). Logistic regressions were used to measure individuals’ willingness to use a COVID-19 tracing app. Results We found that the influence that socioeconomic factors have on COVID-19 tracing app usage varied dramatically between the four countries, although individuals experiencing forms of not being employed (ie, recent job loss and inactivity) consistently had a lower willingness to use a contact tracing app (effect size: 24.6%) compared to that of employees (effect size: 33.4%; P&lt;.001). Among the selected COVID-19 proximity indicators, having a close family member with SARS-CoV-2 infection was associated with higher contact tracing app usage (effect size: 36.3% vs 27.1%; P&lt;.001). After accounting for these proximity factors and the country-based variations therein, we found that having a poorer general health status was significantly associated with a much higher likelihood of contact tracing app usage; compared to a self-reported “very good” health status (estimated probability of contact tracing app use: 29.6%), the “good” (estimated probability: +4.6%; 95% CI 1.2%-8.1%) and “fair or bad” (estimated probability: +6.3%; 95% CI 2.3%-10.3%) health statuses were associated with a markedly higher willingness to use a COVID-19 tracing app. Conclusions Current public health policies aim to promote the use of smartphone-based contact tracing apps during the off-peak periods of the COVID-19 pandemic. Campaigns that emphasize the health benefits of COVID-19 tracing apps may contribute the most to the uptake of such apps. Public health campaigns that rely on digital platforms would also benefit from seriously considering the country-specific distribution of privacy concerns.
Towards Improving Privacy of Synthetic DataSets

Recent growth in domain specific applications of machine learning can be attributed to availability of realistic public datasets. Real world datasets may always contain sensitive information about the users, which makes it hard to share freely with other stake holders, and researchers due to regulatory and compliance requirements. Synthesising datasets from real data by leveraging generative techniques is gaining popularity. However, the privacy analysis of these dataset is still a open research. In this work, we fill this gap by investigating the privacy issues of the generated data sets from attacker and auditor point of view. We propose instance level Privacy Score (PS) for each synthetic sample by measuring the memorisation coefficient $$\boldsymbol{\alpha _{m}}$$ per sample. Leveraging, PS we empirically show that accuracy of membership inference attacks on synthetic data drop significantly. PS is a model agnostic, post training measure, which helps data sharer with guidance about the privacy properties of a given sample but also helps third party data auditors to run privacy checks without sharing model internals. We tested our method on two real world data sets and show that attack accuracy reduced by PS based filtering.