Reviewerly/exHarmony huggingface dataset:
QNLP in Practice: Running Compositional Models of Meaning on a Quantum Computer

Quantum Natural Language Processing (QNLP) deals with the design and implementation of NLP models intended to be run on quantum hardware. In this paper, we present results on the first NLP experiments conducted on Noisy Intermediate-Scale Quantum (NISQ) computers for datasets of size greater than 100 sentences. Exploiting the formal similarity of the compositional model of meaning by Coecke, Sadrzadeh, and Clark (2010) with quantum theory, we create representations for sentences that have a natural mapping to quantum circuits. We use these representations to implement and successfully train NLP models that solve simple sentence classification tasks on quantum hardware. We conduct quantum simulations that compare the syntax-sensitive model of Coecke et al. with two baselines that use less or no syntax; specifically, we implement the quantum analogues of a “bag-of-words” model, where syntax is not taken into account at all, and of a word-sequence model, where only word order is respected. We demonstrate that all models converge smoothly both in simulations and when run on quantum hardware, and that the results are the expected ones based on the nature of the tasks and the datasets used. Another important goal of this paper is to describe in a way accessible to AI and NLP researchers the main principles, process and challenges of experiments on quantum hardware. Our aim in doing this is to take the first small steps in this unexplored research territory and pave the way for practical Quantum Natural Language Processing.
Application of Face Recognition in Tracing COVID-19 Fever Patients and Close Contacts

The outbreak of COVID-19 corona virus in late 2019 has put a tremendous threat to the whole world.Driven by the mission to save lives, we develop a face recognition system to detect patients with fever symptom and to trace close contacts.A real-time alert is sent to the account manager on web or mobile app to enable further actions to quarantine the patients and close contacts.This provides a way to control the spread of the virus.
Progressive Transformer-Based Generation of Radiology Reports

Inspired by Curriculum Learning, we propose a consecutive (i.e., image-to-text-to-text) generation framework where we divide the problem of radiology report generation into two steps.Contrary to generating the full radiology report from the image at once, the model generates global concepts from the image in the first step and then reforms them into finer and coherent texts using a transformer architecture.We follow the transformer-based sequence-tosequence paradigm at each step.We improve upon the state-of-the-art on two benchmark datasets.
A Model for Examining Challenges and Opportunities in Use of Cloud Computing for Health Information Systems

Health Information Systems (HIS) are becoming crucial for health providers, not only for keeping Electronic Health Records (EHR) but also because of the features they provide that can be lifesaving, thanks to the advances in Information Technology (IT). These advancements have led to increasing demands for additional features to these systems to improve their intelligence, reliability, and availability. All these features may be provisioned through the use of cloud computing in HIS. This study arrives at three dimensions pertinent to adoption of cloud computing in HIS through extensive interviews with experts, professional expertise and knowledge of one of the authors working in this area, and review of academic and practitioner literature. These dimensions are financial performance and cost; IT operational excellence and DevOps; and security, governance, and compliance. Challenges and drivers in each of these dimensions are detailed and operationalized to arrive at a model for HIS adoption. This proposed model detailed in this study can be employed by executive management of health organizations, especially senior clinical management positions like Chief Technology Officers (CTOs), Chief Information Officers (CIOs), and IT managers to make an informed decision on adoption of cloud computing for HIS. Use of cloud computing to support operational and financial excellence of healthcare organizations has already made some headway in the industry, and its use in HIS would be a natural next step. However, due to the mission′s critical nature and sensitivity of information stored in HIS, the move may need to be evaluated in a holistic fashion that can be aided by the proposed dimensions and the model. The study also identifies some issues and directions for future research for cloud computing adoption in the context of HIS.
Public key encryption supporting equality test and flexible authorization without bilinear pairings

Public key encryption with equality test (PKEET) is an important cryptographic primitive for protecting users’ outsourced data in the cloud-based email systems. Due to the fact that it is required to deploy different authorization policies for the demand of dynamic privacy protection in the cloud-based email systems, Ma et al. recently presented a new related primitive, called public key encryption with equality test supporting flexible authorization (PKEET-FA). In their proposal, four types of authorization were considered to support different authorization policies. Their proposal is based on the bilinear pairings. However, as the basic operation of equality test, bilinear pairings are expensive operations compared with modular multiplications and modular inverses. Hence, it is desired to propose a new method for constructing efficient equality test with modular multiplications and modular inverses. In this paper, we present such a PKEET-FA scheme. Compared with Ma et al.’s, our proposal is (surprisingly) more efficient, especially in terms of equality test. Moreover, our proposal supports an additional type of authorization, called user-specific ciphertext-to-user (or user-specific user-to-ciphertext) level authorization. Hence, ours is more flexible than Ma et al.’s.
Towards Causal Representation Learning

The two fields of machine learning and graphical causality arose and developed separately. However, there is now cross-pollination and increasing interest in both fields to benefit from the advances of the other. In the present paper, we review fundamental concepts of causal inference and relate them to crucial open problems of machine learning, including transfer and generalization, thereby assaying how causality can contribute to modern machine learning research. This also applies in the opposite direction: we note that most work in causality starts from the premise that the causal variables are given. A central problem for AI and causality is, thus, causal representation learning, the discovery of high-level causal variables from low-level observations. Finally, we delineate some implications of causality for machine learning and propose key research areas at the intersection of both communities.
Research on the Application of Computer Multimedia Music System in College Music Teaching

Abstract With the rapid development of China’s economy, computers have been widely used. We can see it in people’s daily life and teaching. Computers have been widely used in teaching. Music courses in colleges and universities are essential and a basic content in aesthetic education, which can not only cultivate students’ aesthetic ability, but also cultivate students’ sentiment, and have very important teaching significance. At present, the traditional music teaching method has been gradually eliminated, which can not meet the needs of today’s teaching. Therefore, to further improve the efficiency of music in colleges and universities is a problem that all colleges and universities in our country have to face, and it must also be widely valued. Multimedia technology is widely used in teaching, which points out a new direction for the development of music teaching in colleges and universities in China. At present, due to a lot of focus on professional education in colleges and universities, neglect the cultivation of the students’ basic quality, at the same time, the traditional music appreciation teaching mode cannot achieve the result of comprehensive and rapid improve students’ music quality, therefore, the current college students’ music quality is generally low in common colleges and universities, the basic music knowledge, skills, master the situation is grim, such as is the weakest link in the whole education. With the popularity of personal computers among students, the effective application of computers and the Internet is a very good entry point to improve music education. The potential of computers and Internet music in colleges and universities. The computer is an indispensable tool in our life and work today.
A Novel Plaintext-Related Color Image Encryption Scheme Based on Cellular Neural Network and Chen’s Chaotic System

To address the problem that traditional stream ciphers are not sensitive to changes in the plaintext, a novel plaintext-related color image encryption scheme is proposed in this paper, which combines the 6-dimensional cellular neural network (CNN) and Chen’s chaotic system. This encryption scheme belongs to symmetric cryptography. In the proposed scheme, the initial key and switching function generated by the plaintext image are first utilized to control the CNN to complete the scrambling process. Then, Chen’s chaotic system is used to diffuse the scrambled image for realizing higher security. Finally, extensive performance evaluation is undertaken to validate the proposed scheme’s ability to offer the necessary security. Furthermore, the scheme is compared alongside state-of-the-art algorithms to establish its efficiency.
COVID-19 Case Investigation and Contact Tracing in Central Washington State, June–July 2020

To evaluate participation in COVID-19 case investigation and contact tracing in central Washington State between June 15 and July 12, 2020.In this retrospective observational evaluation we combined SARS-CoV-2 RT-PCR and antigen test reports from the Washington Disease Reporting System with community case investigation and contact tracing data for 3 health districts (comprising 5 counties) in central Washington State. All 3 health districts have large Hispanic communities disproportionately affected by COVID-19.Investigators attempted to call all referred individuals with COVID-19 (n = 4,987); 71% were interviewed. Of those asked about close contacts (n = 3,572), 68% reported having no close contacts, with similar proportions across ethnicity, sex, and age group. The 968 individuals with COVID-19 who named specific contacts (27% of those asked) reported a total of 2,293 contacts (mean of 2.4 contacts per individual with COVID-19); 85% of listed contacts participated in an interview.Most individuals with COVID-19 reported having no close contacts. Increasing community engagement and public messaging, as well as understanding and addressing barriers to participation, are crucial for CICT to contribute meaningfully to controlling the SARS-CoV-2 pandemic.
Audio-Visual Biometric Recognition and Presentation Attack Detection: A Comprehensive Survey

Biometric recognition is a trending technology that uses unique characteristics data to identify or verify/authenticate security applications. Amidst the classically used biometrics, voice and face attributes are the most propitious for prevalent applications in day-to-day life because they are easy to obtain through restrained and user-friendly procedures. The pervasiveness of low-cost audio and face capture sensors in smartphones, laptops, and tablets has made the advantage of voice and face biometrics more exceptional when compared to other biometrics. For many years, acoustic information alone has been a great success in automatic speaker verification applications. Meantime, the last decade or two has also witnessed a remarkable ascent in face recognition technologies. Nonetheless, in adverse unconstrained environments, neither of these techniques achieves optimal performance. Since audio-visual information carries correlated and complementary information, integrating them into one recognition system can increase the system's performance. The vulnerability of biometrics towards presentation attacks and audio-visual data usage for the detection of such attacks is also a hot topic of research. This paper made a comprehensive survey on existing state-of-the-art audio-visual recognition techniques, publicly available databases for benchmarking, and Presentation Attack Detection (PAD) algorithms. Further, a detailed discussion on challenges and open problems is presented in this field of biometrics.
ennemi: Non-linear correlation detection with mutual information

We present ennemi, a Python package for correlation analysis based on mutual information (MI). MI is a measure of relationship between variables. Unlike Pearson correlation it is valid also for non-linear relationships, yet in the linear case the two are equivalent. The effect of other variables can be removed like with partial correlation, with the same equivalence. These features make MI a better correlation measure for exploratory analysis of many variable pairs. Our package provides methods for common correlation analysis tasks using MI. It is scalable, integrated with the Python data science ecosystem, and requires minimal configuration.
Modeling extra-deep electromagnetic logs using a deep neural network

Modern geosteering is heavily dependent on real-time interpretation of deep electromagnetic (EM) measurements. We present a methodology to construct a deep neural network (DNN) model trained to reproduce a full set of extra-deep EM logs consisting of 22 measurements per logging position. The model is trained in a 1D layered environment consisting of up to seven layers with different resistivity values. A commercial simulator provided by a tool vendor is used to generate a training dataset. The dataset size is limited because the simulator provided by the vendor is optimized for sequential execution. Therefore, we design a training dataset that embraces the geological rules and geosteering specifics supported by the forward model. We use this dataset to produce an EM simulator based on a DNN without access to the proprietary information about the EM tool configuration or the original simulator source code. Despite employing a relatively small training set size, the resulting DNN forward model is quite accurate for the considered examples: a multi-layer synthetic case and a section of a published historical operation from the Goliat Field. The observed average evaluation time of 0.15 ms per logging position makes it also suitable for future use as part of evaluation-hungry statistical and/or Monte-Carlo inversion algorithms within geosteering workflows.
MRI Brain Tumor Segmentation and Uncertainty Estimation Using 3D-UNet Architectures

Automation of brain tumor segmentation in 3D magnetic resonance images (MRIs) is key to assess the diagnostic and treatment of the disease. In recent years, convolutional neural networks (CNNs) have shown improved results in the task. However, high memory consumption is still a problem in 3D-CNNs. Moreover, most methods do not include uncertainty information, which is especially critical in medical diagnosis. This work studies 3D encoder-decoder architectures trained with patch-based techniques to reduce memory consumption and decrease the effect of unbalanced data. The different trained models are then used to create an ensemble that leverages the properties of each model, thus increasing the performance. We also introduce voxel-wise uncertainty information, both epistemic and aleatoric using test-time dropout (TTD) and data-augmentation (TTA) respectively. In addition, a hybrid approach is proposed that helps increase the accuracy of the segmentation. The model and uncertainty estimation measurements proposed in this work have been used in the BraTS’20 Challenge for task 1 and 3 regarding tumor segmentation and uncertainty estimation.
Automatic question-answer pairs generation and question similarity mechanism in question answering system

Abstract With the swift growth of the information over the past few years, taking full benefit is increasingly essential. Question Answering System is one of the promising methods to access this much information. The Question Answering System lacks humans’ common sense and reasoning power and cannot identify unanswerable questions and irrelevant questions. These questions are answered by making unreliable and incorrect guesses. In this paper, we address this limitation by proposing a Question Similarity mechanism. Before a question is posed to a Question-Answering system, it is compared with possible generated questions of the given paragraph, and then a Question Similarity Score is generated. The Question Similarity mechanism effectively identifies the unanswerable and irrelevant questions. The proposed Question Similarity mechanism incorporates a human way of reasoning to identify unanswerable and irrelevant questions. This mechanism can avoid the unanswerable and irrelevant questions altogether from being posed to the Question Answering system. It helps the Question Answering Systems to focus only on the answerable questions to improve their performance. Along with this, we introduce an application of the Question Answering System that generates the question-answer pairs given a passage and is useful in several fields.
FakeBuster: A DeepFakes Detection Tool for Video Conferencing Scenarios

This paper proposes FakeBuster, a novel DeepFake detector for (a) detecting impostors during video conferencing, and (b) manipulated faces on social media. FakeBuster is a standalone deep learning- based solution, which enables a user to detect if another person's video is manipulated or spoofed during a video conference-based meeting. This tool is independent of video conferencing solutions and has been tested with Zoom and Skype applications. It employs a 3D convolutional neural network for predicting video fakeness. The network is trained on a combination of datasets such as Deeperforensics, DFDC, VoxCeleb, and deepfake videos created using locally captured images (specific to video conferencing scenarios). Diversity in the training data makes FakeBuster robust to multiple environments and facial manipulations, thereby making it generalizable and ecologically valid.
Forecasting Future Asthma Hospital Encounters of Patients With Asthma in an Academic Health Care System: Predictive Model Development and Secondary Analysis Study

Background Asthma affects a large proportion of the population and leads to many hospital encounters involving both hospitalizations and emergency department visits every year. To lower the number of such encounters, many health care systems and health plans deploy predictive models to prospectively identify patients at high risk and offer them care management services for preventive care. However, the previous models do not have sufficient accuracy for serving this purpose well. Embracing the modeling strategy of examining many candidate features, we built a new machine learning model to forecast future asthma hospital encounters of patients with asthma at Intermountain Healthcare, a nonacademic health care system. This model is more accurate than the previously published models. However, it is unclear how well our modeling strategy generalizes to academic health care systems, whose patient composition differs from that of Intermountain Healthcare. Objective This study aims to evaluate the generalizability of our modeling strategy to the University of Washington Medicine (UWM), an academic health care system. Methods All adult patients with asthma who visited UWM facilities between 2011 and 2018 served as the patient cohort. We considered 234 candidate features. Through a secondary analysis of 82,888 UWM data instances from 2011 to 2018, we built a machine learning model to forecast asthma hospital encounters of patients with asthma in the subsequent 12 months. Results Our UWM model yielded an area under the receiver operating characteristic curve (AUC) of 0.902. When placing the cutoff point for making binary classification at the top 10% (1464/14,644) of patients with asthma with the largest forecasted risk, our UWM model yielded an accuracy of 90.6% (13,268/14,644), a sensitivity of 70.2% (153/218), and a specificity of 90.91% (13,115/14,426). Conclusions Our modeling strategy showed excellent generalizability to the UWM, leading to a model with an AUC that is higher than all of the AUCs previously reported in the literature for forecasting asthma hospital encounters. After further optimization, our model could be used to facilitate the efficient and effective allocation of asthma care management resources to improve outcomes. International Registered Report Identifier (IRRID) RR2-10.2196/resprot.5039
HonestChain: Consortium blockchain for protected data sharing in health information systems

Healthcare innovations are increasingly becoming reliant on high variety and standards-compliant (e.g., HIPAA, common data model) distributed data sets that enable predictive analytics. Consequently, health information systems need to be developed using cooperation and distributed trust principles to allow protected data sharing between multiple domains or entities (e.g., health data service providers, hospitals and research labs). In this paper, we present a novel health information sharing system viz., HonestChain that uses Blockchain technology to allow organizations to have incentive-based and trustworthy cooperation to either access or provide protected healthcare records. More specifically, we use a consortium Blockchain approach coupled with chatbot guided interfaces that allow data requesters to: (a) comply with data access standards, and (b) allow them to gain reputation in a consortium. We also propose a reputation scheme for creation and sustenance of the consortium with peers using Requester Reputation and Provider Reputation metrics. We evaluate HonestChain using Hyperledger Composer in a realistic simulation testbed on a public cloud infrastructure. Our results show that our HonestChain performs better than the state-of-the-art requester reputation schemes for data request handling, while choosing the most appropriate provider peers. We particularly show that HonestChain achieves a better tradeoff in metrics such as service time and request resubmission rate. Additionally, we also demonstrate the scalability of our consortium platform in terms of the Blockchain transaction times.
Quantifying quantum coherence of optical cat states

The optical cat state plays an essential role in quantum computation and quantum metrology. Here, we experimentally quantify quantum coherence of an optical cat state by means of relative entropy and the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m1"> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>l</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>1</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> norm of coherence in a Fock basis based on the prepared optical cat state at the rubidium D1 line. By transmitting the optical cat state through a lossy channel, we also demonstrate the robustness of quantum coherence of the optical cat state in the presence of loss, which is different from the decoherence properties of fidelity and Wigner function negativity of the optical cat state. Our results confirm that quantum coherence of optical cat states is robust against loss and pave the way for the application of optical cat states.