Reviewerly/exHarmony huggingface dataset:
Dual-Task Self-supervision for Cross-modality Domain Adaptation

Data annotation is always an expensive and time-consuming issue for deep learning based medical image analysis. To ease the need of annotations, domain adaptation is recently introduced to generalize neural networks from a labeled source domain to unlabeled target domain without much performance degradation. In this paper, we propose a novel target domain self-supervision for domain adaptation by constructing an edge generation auxiliary task to assist primary segmentation task so as to extract better target representation and improve target segmentation performance. Besides, in order to leverage detailed information contained in low-level features, we propose a hierarchical low-level adversarial learning mechanism to encourage low-level features domain uninformative in a hierarchical way, so that the segmentation performance can benefit from low-level features without being affected by domain shift. Following these two proposed approach, we develop a cross-modality domain adaptation framework which employs the dual-task collaboration for target domain self-supervision, and encourages low-level detailed features domain uninformative for better alignment. Our proposed framework achieves state-of-the-art results on public cross-modality segmentation datasets.
An explainable attention network for fraud detection in claims management

Insurance companies must manage millions of claims per year. While most of these are not fraudulent, those that are nevertheless cost insurance companies and those they insure vast amounts of money. The ultimate goal is to develop a predictive model that can single out fraudulent claims and pay out non-fraudulent ones automatically. Health care claims have a peculiar data structure, comprising inputs of varying length and variables with a large number of categories. Both issues are challenging for traditional econometric methods. We develop a deep learning model that can handle these challenges by adapting methods from text classification. Using a large dataset from a private health insurer in Germany, we show that the model we propose outperforms a conventional machine learning model. With the rise of digitalization, unstructured data with characteristics similar to ours will become increasingly common in applied research, and methods to deal with such data will be needed.
Uncertainty quantification for a global imaging spectroscopy surface composition investigation

Airborne and orbital imaging spectroscopy can facilitate the quantification of chemical and physical attributes of surface materials through analysis of spectral signatures. Prior to analysis, estimates of surface reflectance must be inferred from radiance measurements in a process known as atmospheric correction, which compensates for the distortion of the electromagnetic signal by the atmosphere. Inaccuracies in the correction process can alter characteristic spectral signatures, leading to subsequent mischaracterization of surface properties. Global observations pose new challenges for mapping surface composition, as varied atmospheric conditions and surface biomes challenge traditional atmospheric correction methods. Recent work adopted an optimal estimation (OE) approach for retrieving surface reflectance from observed radiance measurements, providing the reflectance estimates with a posterior probability. This work incorporates these input probabilities to improve the accuracy of surface feature measurements. We demonstrate this using a generic feature-fitting method that is applicable to a wide range of Earth surface studies including geology, ecosystem studies, hydrology and urban studies. Specifically, we use a probabilistic framework based on generalized Tikhonov-regularized least squares, a rigorous formulation for appropriate weighting of features by their observation uncertainty and leveraging of prior knowledge of material abundance for improving estimation accuracy. We demonstrate the validity of this procedure and quantify the increase in model performance by simulating expected accuracies in the reflectance estimation. To evaluate global uncertainties in mineral estimation, we simulate observations representative of the expected global range of atmospheric water vapor and aerosol levels, and characterize the sensitivity of our procedure to those quantities.
High-Dimensional Semiquantum Cryptography

A semiquantum key distribution (SQKD) protocol allows two users, one of whom is restricted in their quantum capabilities to being nearly classical, to establish a shared secret key, secure against an all-powerful adversary. The study of such protocols helps to answer the fundamental question of “how quantum” must a protocol be to gain an advantage over classical communication. In this article, we design a new SQKD protocol using high-dimensional quantum states and conduct an information theoretic security analysis. We show that, similar to the fully quantum key distribution case, high-dimensional systems can increase the noise tolerance in the semiquantum case. Furthermore, we prove several general security results which are applicable to other (S)QKD protocols (both high-dimensional ones and standard qubit-based protocols) utilizing a two-way quantum channel.
Non-Prehensile Manipulation in Clutter with Human-In-The-Loop

We propose a human-operator guided planning approach to pushing-based manipulation in clutter. Most recent approaches to manipulation in clutter employs randomized planning. The problem, however, remains a challenging one where the planning times are still in the order of tens of seconds or minutes, and the success rates are low for difficult instances of the problem. We build on these control-based randomized planning approaches, but we investigate using them in conjunction with human-operator input. In our framework, the human operator supplies a high-level plan, in the form of an ordered sequence of objects and their approximate goal positions. We present experiments in simulation and on a real robotic setup, where we compare the success rate and planning times of our human-in-the-loop approach with fully autonomous sampling-based planners. We show that with a minimal amount of human input, the low-level planner can solve the problem faster and with higher success rates.
The effectiveness of physical education teaching in college based on Artificial intelligence methods

Traditional physical education in colleges and universities is difficult to arouse students’ interest in sports, resulting in low activity participation rate and inability to exercise the body. How to effectively improve the effectiveness of physical education in colleges and universities has become one of the hot topics of most concern from all walks of life. In physical education, innovative teaching concepts and methods, teaching methods and processes, and teaching evaluation methods are all conducive to improving the classroom atmosphere of physical education and successfully improve the effectiveness of physical education. This article focuses on analyzing the current status of physical education in colleges and universities. Based on the rapid development of artificial intelligence technology, how to improve the effectiveness of physical education is studied, and an experimental method is used to compare and analyze physical education in a college. The analysis results show that artificial intelligence-based physical education can obviously improve students’ strength quality, speed quality, endurance quality, and agility quality, which provides a more important reference and reference for improving the effectiveness of college physical education.
Robust Medical Image Segmentation from Non-expert Annotations with Tri-network

Deep convolutional neural networks (CNNs) have achieved commendable results on a variety of medical image segmentation tasks. However, CNNs usually require a large amount of training samples with accurate annotations, which are extremely difficult and expensive to obtain in medical image analysis field. In practice, we notice that the junior trainees after training can label medical images in some medical image segmentation applications. These non-expert annotations are more easily accessible and can be regarded as a source of weak annotation to guide network learning. In this paper, we propose a novel Tri-network learning framework to alleviate the problem of insufficient accurate annotations in medical segmentation tasks by utilizing the non-expert annotations. To be specific, we maintain three networks in our framework, and each pair of networks alternatively select informative samples for the third network learning, according to the consensus and difference between their predictions. The three networks are jointly optimized in such a collaborative manner. We evaluated our method on real and simulated non-expert annotated datasets. The experiment results show that our method effectively mines informative information from the non-expert annotations for improved segmentation performance and outperforms other competing methods.
Computing Optimal Decision Sets with SAT

As machine learning is increasingly used to help make decisions, there is a demand for these decisions to be explainable. Arguably, the most explainable machine learning models use decision rules. This paper focuses on decision sets, a type of model with unordered rules, which explains each prediction with a single rule. In order to be easy for humans to understand, these rules must be concise. Earlier work on generating optimal decision sets first minimizes the number of rules, and then minimizes the number of literals, but the resulting rules can often be very large. Here we consider a better measure, namely the total size of the decision set in terms of literals. So we are not driven to a small set of rules which require a large number of literals. We provide the first approach to determine minimum-size decision sets that achieve minimum empirical risk and then investigate sparse alternatives where we trade accuracy for size. By finding optimal solutions we show we can build decision set classifiers that are almost as accurate as the best heuristic methods, but far more concise, and hence more explainable.
Identifying Children with Autism Spectrum Disorder Based on Gaze-Following

This paper presents a novel method to identify children with Autism Spectrum Disorder (ASD) based on the stimuli with gaze-following. Individuals with ASD are characterized by having atypical visual attention patterns, especially in social scenes. Gaze-following is considered to be a key element in understanding social scenarios, and it is reasonable to use stimuli with gaze-following to identify the children with ASD. Thus in this paper, we first construct a dataset of eye movements in gaze-following scenes for children with ASD (i.e., GazeFollow4ASD dataset), including 300 images with gaze-following information inside them and the corresponding eye movement data collected from 8 children with ASD and 10 healthy controls. We propose a novel deep neural network (DNN) model to extract discriminative features and classify children with ASD and healthy controls on single images. The proposed model shows the best performance among all compared methods on all datasets.
A deep neural network compression algorithm based on knowledge transfer for edge devices

The computation and storage capacity of the edge device are limited, which seriously restrict the application of deep neural network in the device. Toward to the intelligent application of the edge device, we introduce the deep neural network compression algorithm based on knowledge transfer, a three-stage pipeline: lightweight, multi-level knowledge transfer and pruning that reduce the network depth, parameter and operation complexity of the deep learning neural networks. We lighten the neural networks by using a global average pooling layer instead of a fully connected layer and replacing a standard convolution with separable convolutions. Next, the multi-level knowledge transfer minimizes the difference between the output of the ”student network” and the ”teacher network” in the middle and logits layer, increasing the supervised information when training the ”student network”. Lastly, we prune the network by cutting off the unimportant convolution kernels with a global iterative pruning strategy. The experiment results show that the proposed method improve the efficiency up to 30% than the knowledge distillation method in reducing the loss of classification performance. Benchmarked on GPU (Graphics Processing Unit) server, Raspberry Pi 3 and Cambricon-1A, the parameters of the compressed network after using our knowledge transfer and pruning method have achieved more than 49.5 times compression and the time efficiency of a single feedforward operation has been improved more than 3.2 times.
Encryption Inspired Adversarial Defense For Visual Classification

Conventional adversarial defenses reduce classification accuracy whether or not a model is under attacks. Moreover, most of image processing based defenses are defeated due to the problem of obfuscated gradients. In this paper, we propose a new adversarial defense which is a defensive transform for both training and test images inspired by perceptual image encryption methods. The proposed method utilizes a block-wise pixel shuffling method with a secret key. The experiments are carried out on both adaptive and non-adaptive maximum-norm bounded white-box attacks while considering obfuscated gradients. The results show that the proposed defense achieves high accuracy (91.55%) on clean images and (89.66%) on adversarial examples with noise distance of 8/255 on CFAR-10 dataset. Thus, the proposed defense outperforms state-of-the-art adversarial defenses including latent adversarial training, adversarial training and thermometer encoding.
A Study of Security Threats in Cloud: Passive Impact of COVID-19 Pandemic

Recently, the life in Earth becomes turbulent with the worldwide s pread of novel coronavirus (COVID-19).This outbreak has been declared as a public health emergency in the level of international concern by world health organization (WHO).To reduce the spread of COVID19 entire world has adopted social distancing, where working and learning from home is the new normal for this new world.To sustain the economical revenue and business growth companies that radically move into cloud infrastructure to support employees, who work remotely.With the unprecedented growth of cloud, data breaches and cyber security takes a huge leap.Apart from big cloud vendor small cloud startups are getting huge leap currently.Starting from enterprise solution providers, cloud supports in education, e-commerce, and healthcare also.Hackers penetrating not only the cloud resources it also hampers the hosts and device connected with it.This paper discovers several security challenges due to the sudden use of cloud platforms without adequate precautions.The aim of this paper is to highlight these areas causing security breaches and propose generic preventive measures.
Sentiment Analysis on Chinese Weibo Regarding COVID-19

The outbreak of COVID-19 has had a great impact on people's general lifestyle over the world. People express their views about COVID-19 on social media more frequently when cities are under lockdown. In this work, we are motivated to analyze the sentiments and their evolution of people in the face of this public health crisis based on Chinese Weibo, a largest social media platform in China. First, we obtained the top 50 hot searched hashtags from January 10, 2020 to May 31, 2020, and collected 1,681,265 Weibo posts associated to the hashtags regarding COVID-19. We then constructed a COVID-19 sentiment analysis dataset by annotating the related Weibo posts with 7 categories, e.g., fear, anger, disgust, sadness, gratitude, surprise, and optimism, in combination of the other two datasets. The well annotated data consists of 21,173 pieces of texts. Second, we employed three methods, i.e., LSTM, BERT, and ERNIE, to predict the sentiments of users on Weibo. Comprehensive experimental results show that ERNIE classifier has the highest accuracy and reaches 0.8837. We then analyzed the sentiment and its evolution of Weibo users to see how people respond to COVID-19 throughout the outbreak. Based on the in-depth analysis, we found that people generally felt negative (mainly fear) at early stage of the outbreak. As the pandemic situation gradually improved, people's positive sentiment began to increase. The number of cases of COVID-19, news and public events have a great influence on people's sentiments. Finally, we developed a real-time visualization system to display the trend of the user's sentiment and hot searched hashtags based on Weibo during the pandemic.
Generative transfer learning for measuring plausibility of EHR diagnosis records

Abstract Objective Due to a complex set of processes involved with the recording of health information in the Electronic Health Records (EHRs), the truthfulness of EHR diagnosis records is questionable. We present a computational approach to estimate the probability that a single diagnosis record in the EHR reflects the true disease. Materials and Methods Using EHR data on 18 diseases from the Mass General Brigham (MGB) Biobank, we develop generative classifiers on a small set of disease-agnostic features from EHRs that aim to represent Patients, pRoviders, and their Interactions within the healthcare SysteM (PRISM features). Results We demonstrate that PRISM features and the generative PRISM classifiers are potent for estimating disease probabilities and exhibit generalizable and transferable distributional characteristics across diseases and patient populations. The joint probabilities we learn about diseases through the PRISM features via PRISM generative models are transferable and generalizable to multiple diseases. Discussion The Generative Transfer Learning (GTL) approach with PRISM classifiers enables the scalable validation of computable phenotypes in EHRs without the need for domain-specific knowledge about specific disease processes. Conclusion Probabilities computed from the generative PRISM classifier can enhance and accelerate applied Machine Learning research and discoveries with EHR data.
Optimizing multi-VM migration by allocating transfer and compression rate using Geometric Programming

Live VM migration has emerged as a core technology for resource management activities and objectives like fault tolerance, reduced energy consumption, load balancing, and system maintenance. Pre-copy migration strategy is frequently used by most of the existing hypervisors for live VM migration. However, it requires a considerable amount of data transfer. In the case of low network bandwidth availability, the migration process gets adversely affected. Thereby degrading the performance of applications running on migrated VMs. Migrating multiple VMs even requires more data to be transferred, which further faces the challenge of sharing the available network bandwidth. This motivates for designing a better strategy for the live migration of multiple VMs. Moreover, to reduce the amount of data that needs to be transferred, memory compression techniques can be utilized efficiently by providing fast and stable VM memory migration without sacrificing any service quality. This paper presents a new model using Geometric Programming that allocates transfer and compression rates to each VM to minimize the total migration time. We have found that: (a). Memory compression, along with pre-copy migration, improves the performance of the live migration of multiple virtual machines by reducing the total migration time and downtime. (b). The compression rate is dynamically allocated according to the available network bandwidth. So, in an adverse environment, when available bandwidth is less, the memory is compressed using slow compression algorithms to optimize the total migration time. In contrast, when more bandwidth is available, it uses a fast compression algorithm to transfer the data quickly. (c). With the proposed approach, more number of VMs can be migrated in parallel with considerably reduced migration time and downtime when compared with existing strategies.
Reconstructing Sinus Anatomy from Endoscopic Video – Towards a Radiation-Free Approach for Quantitative Longitudinal Assessment

Reconstructing accurate 3D surface models of sinus anatomy directly from an endoscopic video is a promising avenue for cross-sectional and longitudinal analysis to better understand the relationship between sinus anatomy and surgical outcomes. We present a patient-specific, learning-based method for 3D reconstruction of sinus surface anatomy directly and only from endoscopic videos. We demonstrate the effectiveness and accuracy of our method on in and ex vivo data where we compare to sparse reconstructions from Structure from Motion, dense reconstruction from COLMAP, and ground truth anatomy from CT. Our textured reconstructions are watertight and enable measurement of clinically relevant parameters in good agreement with CT. The source code is available at https://github.com/lppllppl920/DenseReconstruction-Pytorch.
Online Multiple Object Tracking Using Rule Distillated Siamese Random Forest

In a multiple object tracking (MOT) system, an association check between the tracker and detected objects is an important factor in determining the tracking performance.Siamese convolution neural network (CNN) is the most popular data association method in MOT owing to its good matching performance and network sharing support.However, it is unsuitable for real-time online tracking in lowend systems because numerous parameters and operations are still required.In this article, instead of a CNN, we propose using a SiameseRF algorithm which combines Siamese structure and random forest (RF), enabling high-speed learning and classification.SiameseRF has a shared-rule based Siamese structure rather than shared weight, which improves the matching performance and solves existing slow CNN-based tracking issues.During the learning process, the shared RFs consisting of tree rules are learned in the directions of increasing similarity to the positive pair {anchor, positive} and increasing difference between the negative pair {anchor, negative}.However, because many rules that make up SiameseRF remain a burden for online processing, this study proposes an additional rule distillation algorithm to effectively remove redundant and unimportant rules causing an overfitting with SiameseRF.This reduction in the number of rules reduces the processing time and number of parameters in the rule distilled SiameseRF.In experiments conducted on MOT benchmark datasets, our proposed 30% rule distilled SiameseRF achieved up to a 1.12-times faster speed and a 1.13-times higher compression rate than basic SiameseRF while maintaining a similar or somewhat better tracking performance than other state-of-the-art CNN-based MOT algorithms.