Reviewerly/exHarmony huggingface dataset:
MedAccess: A Scalable Architecture for Blockchain-based Health Record Management

Electronic Health Record is a digital version of a patient's medical history maintained by a healthcare provider over the course of their visits. Unfortunately, medical records of one patient is fragmented across many hospitals, private clinics, labs, pharmacies and personal health records from wearables. Healthcare providers hesitate to share “proprietary” data. This is where the blockchain technology comes in use. Moreover, blockchain opens the opportunity to record patients' records as blocks, encrypt and make it impossible (immutable) to proceed with any changes for the information stored. The main goal would be to take advantage of the blockchain technology provide immutability, data integrity where each record is privately encrypted and publicly viewed. Nevertheless, the overall cost to store information in a practical blockchain itself is very expensive. This is where a decentralized P2P network that can store the data off-the-chain would assist in storing the actual content of each medical record and only the identifier is sent and stored on the blockchain. Any individual would be able to publicly view the medical record, however, only patients possess the private key and hence share it with desired individuals e.g. physicians or any medical practitioner. In this paper, we propose an architecture that can be used to develop scalable blockchain applications using an off-chain solution that will allow physicians, lab technicians and patients to manage the medical records in a secure manner.
Proposal for a Deterministic Single-Atom Source of Quasisuperradiant <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>N</mml:mi></mml:math> -Photon Pulses

We propose a single-atom, cavity quantum electrodynamics system, compatible with recently demonstrated, fiber-integrated micro- and nano-cavity setups, for the on-demand production of optical number-state, $0N$-state, and binomial-code-state pulses. The scheme makes use of Raman transitions within an entire atomic ground-state hyperfine level and operates with laser and cavity fields detuned from the atomic transition by much more than the excited-state hyperfine splitting. This enables reduction of the dynamics to that of a simple, cavity-damped Tavis-Cummings model with the collective spin determined by the total angular momentum of the ground hyperfine level.
Matchgate benchmarking: Scalable benchmarking of a continuous family of many-qubit gates

We propose a method to reliably and efficiently extract the fidelity of many-qubit quantum circuits composed of continuously parametrized two-qubit gates called matchgates. This method, which we call matchgate benchmarking, relies on advanced techniques from randomized benchmarking as well as insights from the representation theory of matchgate circuits. We argue the formal correctness and scalability of the protocol, and moreover deploy it to estimate the performance of matchgate circuits generated by two-qubit XY spin interactions on a quantum processor.
CARE: Commonsense-Aware Emotional Response Generation with Latent Concepts

Rationality and emotion are two fundamental elements of humans. Endowing agents with rationality and emotion has been one of the major milestones in AI. However, in the field of conversational AI, most existing models only specialize in one aspect and neglect the other, which often leads to dull or unrelated responses. In this paper, we hypothesize that combining rationality and emotion into conversational agents can improve response quality. To test the hypothesis, we focus on one fundamental aspect of rationality, i.e., commonsense, and propose CARE, a novel model for commonsense-aware emotional response generation. Specifically, we first propose a framework to learn and construct commonsense-aware emotional latent concepts of the response given an input message and a desired emotion. We then propose three methods to collaboratively incorporate the latent concepts into response generation. Experimental results on two large-scale datasets support our hypothesis and show that our model can produce more accurate and commonsense-aware emotional responses and achieve better human ratings than state-of-the-art models that only specialize in one aspect.
Impact of Encoding of High Cardinality Categorical Data to Solve Prediction Problems

The prediction problem in any domain is very important to assess the prices and preferences among people. This issue varies for different kinds of data. Data may be nominal or ordinal, it may involve more categories or less. For any category to be considered by a machine learning algorithm, it needs to be encoded before any other operation can be further performed. There are various encoding schemes available like label encoding, count encoding and one hot encoding. This paper aims to understand the impact of various encoding schemes and the accuracy among the prediction problems of high cardinality categorical data. The paper also proposes an encoding scheme based on curated strings. The domain chosen for this purpose is predicting doctors’ fees in various cities having different profiles and qualification.
Cryptocurrencies and Hyperinflation

This discussion paper considers the monetary role that cryptocurrencies can play as hedges against the crises stemming from hyperinflation. It also briefly examines such a relationship vis-a-vis currency devaluation and monetary defaults. The findings suggest that, for all the legitimate criticisms volleyed against cryptocurrencies (governance, legal, security), there are indeed certain macroeconomic issues which can draw upon the decentralized, apolitical, and deflationary monetary nature of cryptocurrencies. These advantages must then be weighted by monetary policy practitioners against the risks that still linger in the cryptocurrency space.
Structured Prediction as Translation between Augmented Natural Languages

We propose a new framework, Translation between Augmented Natural Languages (TANL), to solve many structured prediction language tasks including joint entity and relation extraction, nested named entity recognition, relation classification, semantic role labeling, event extraction, coreference resolution, and dialogue state tracking. Instead of tackling the problem by training task-specific discriminative classifiers, we frame it as a translation task between augmented natural languages, from which the task-relevant information can be easily extracted. Our approach can match or outperform task-specific models on all tasks, and in particular achieves new state-of-the-art results on joint entity and relation extraction (CoNLL04, ADE, NYT, and ACE2005 datasets), relation classification (FewRel and TACRED), and semantic role labeling (CoNLL-2005 and CoNLL-2012). We accomplish this while using the same architecture and hyperparameters for all tasks, and even when training a single model to solve all tasks at the same time (multi-task learning). Finally, we show that our framework can also significantly improve the performance in a low-resource regime, thanks to better use of label semantics.
Digital Contact Tracing: Large-Scale Geolocation Data as an Alternative to Bluetooth-Based Apps Failure

The currently deployed contact-tracing mobile apps have failed as an efficient solution in the context of the COVID-19 pandemic. None of them has managed to attract the number of active users required to achieve an efficient operation. This urges the research community to re-open the debate and explore new avenues that lead to efficient contact-tracing solutions. This paper contributes to this debate with an alternative contact-tracing solution that leverages already available geolocation information owned by BigTech companies with very large penetration rates in most countries adopting contact-tracing mobile apps. Moreover, our solution provides sufficient privacy guarantees to protect the identity of infected users as well as precluding Health Authorities from obtaining the contact graph from individuals.
Experimental realization of nonadiabatic holonomic single‐qubit quantum gates with two dark paths in a trapped ion

For circuit-based quantum computation, experimental implementation of universal set of quantum logic gates with high-fidelity and strong robustness is essential and central. Quantum gates induced by geometric phases, which depend only on global properties of the evolution paths, have built-in noise-resilience features. Here, we propose and experimentally demonstrate nonadiabatic holonomic single-qubit quantum gates on two dark paths in a trapped $^{171}\mathrm{Yb}^{+}$ ion based on four-level systems with resonant drives. We confirm the implementation with measured gate fidelity through both quantum process tomography and randomized benchmarking methods. Meanwhile, we find that nontrivial holonomic two-qubit quantum gates can also be realized within current experimental technologies. Compared with previous implementations on three-level systems, our experiment share both the advantage of fast nonadiabatic evolution and the merit of robustness against systematic errors, and thus retains the main advantage of geometric phases. Therefore, our experiment confirms a promising method for fast and robust holonomic quantum computation.
Score-Level Multi Cue Fusion for Sign Language Recognition

Sign Languages are expressed through hand and upper body gestures as well as facial expressions. Therefore, Sign Language Recognition (SLR) needs to focus on all such cues. Previous work uses hand-crafted mechanisms or network aggregation to extract the different cue features, to increase SLR performance. This is slow and involves complicated architectures. We propose a more straightforward approach that focuses on training separate cue models specializing on the dominant hand, hands, face, and upper body regions. We compare the performance of 3D Convolutional Neural Network (CNN) models specializing in these regions, combine them through score-level fusion, and use the weighted alternative. Our experimental results have shown the effectiveness of mixed convolutional models. Their fusion yields up to $$19\%$$ accuracy improvement over the baseline using the full upper body. Furthermore, we include a discussion for fusion settings, which can help future work on Sign Language Translation (SLT).
Agile and Versatile Quantum Communication: Signatures and Secrets

Agile cryptography allows for a resource-efficient swap of a cryptographic core in case the security of an underlying classical cryptographic algorithm becomes compromised. Conversely, versatile cryptography allows the user to switch the cryptographic task without requiring any knowledge of its inner workings. In this paper, we suggest how these related principles can be applied to the field of quantum cryptography by explicitly demonstrating two quantum cryptographic protocols, quantum digital signatures (QDS) and quantum secret sharing (QSS), on the same hardware sender and receiver platform. Crucially, the protocols differ only in their classical postprocessing. The system is also suitable for quantum key distribution (QKD) and is highly compatible with deployed telecommunication infrastructures, since it uses standard quadrature phase-shift keying encoding and heterodyne detection. For the first time, QDS protocols are modified to allow for postselection at the receiver, enhancing protocol performance. The cryptographic primitives QDS and QSS are inherently multipartite, and we prove that they are secure not only when a player internal to the task is dishonest, but also when (external) eavesdropping on the quantum channel is allowed. In our first proof-of-principle demonstration of an agile and versatile quantum communication system, the quantum states are distributed at GHz rates. A 1-bit message may be securely signed using our QDS protocols in less than 0.05 ms over a 2-km fiber link and in less than 0.2 s over a 20-km fiber link. To our knowledge, this also marks the first demonstration of a continuous-variable direct QSS protocol.1 MoreReceived 27 January 2020Revised 24 November 2020Accepted 18 December 2020Corrected 9 March 2021DOI:https://doi.org/10.1103/PhysRevX.11.011038Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasCommunication schemesInformation & communication theoryQuantum communicationQuantum cryptographyQuantum information processingQuantum networksQuantum InformationInterdisciplinary PhysicsNetworks
COVID-19 Contact Tracing Conundrums: Insights From the Front Lines

COVID-19 contact tracing is an induction social network intervention in which the structure of the social network is leveraged to deploy proven COVID-19 interventions such as testing and social distancing. The Howard Brown Health organization has rapidly expanded to include COVID-19 testing, contact tracing, and linkage to resources since the first cases were identified in Chicago, Illinois. COVID-19 is penetrating the most vulnerable networks in the United States; existing inequities are widening as community resources and organizations have had to place services on hold.Here we address several questions that arise as organizations build capacity for contact tracing, including questions involving the potential impact of contact tracing, stakeholders who could be involved, the timing of contact tracing deployment, and the impact potential for digital technology.Contact tracing is critical at later stages of epidemic decline given the potential for isolated outbreaks as larger events, schools, stadiums, and festivals reopen. Local contact tracing efforts can have other indirect benefits with respect to limiting transmission, such as increasing testing rates and addressing structural barriers through provision of life-saving resources and access to crucial social support.
HER2 Molecular Marker Scoring Using Transfer Learning and Decision Level Fusion

In prognostic evaluation of breast cancer, immunohistochemical (IHC) marker human epidermal growth factor receptor 2 (HER2) is used for prognostic evaluation. Accurate assessment of HER2-stained tissue sample is essential in therapeutic decision making for the patients. In regular clinical settings, expert pathologists assess the HER2-stained tissue slide under microscope for manual scoring based on prior experience. Manual scoring is time consuming, tedious, and often prone to inter-observer variation among group of pathologists. With the recent advancement in the area of computer vision and deep learning, medical image analysis has got significant attention. A number of deep learning architectures have been proposed for classification of different image groups. These networks are also used for transfer learning to classify other image classes. In the presented study, a number of transfer learning architectures are used for HER2 scoring. Five pre-trained architectures viz. VGG16, VGG19, ResNet50, MobileNetV2, and NASNetMobile with decimating the fully connected layers to get 3-class classification have been used for the comparative assessment of the networks as well as further scoring of stained tissue sample image based on statistical voting using mode operator. HER2 Challenge dataset from Warwick University is used in this study. A total of 2130 image patches were extracted to generate the training dataset from 300 training images corresponding to 30 training cases. The output model is then tested on 800 new test image patches from 100 test images acquired from 10 test cases (different from training cases) to report the outcome results. The transfer learning models have shown significant accuracy with VGG19 showing the best accuracy for the test images. The accuracy is found to be 93%, which increases to 98% on the image-based scoring using statistical voting mechanism. The output shows a capable quantification pipeline in automated HER2 score generation.
Smartphone Addiction and Cybercrime Victimization in the Context of Lifestyles Routine Activities and Self-Control Theories: The User’s Dual Vulnerability Model of Cybercrime Victimization

(1) Background: This paper combines lifestyle-routine activities (L-RAT) and self-control (SCT) theories along with the literature on smartphone addiction in a joint model that addresses the multiple vulnerabilities that make the smartphone user a potential victim of cybercrime. This model, which we call the dual vulnerability model of cybercrime victimization, was subjected to empirical testing on a nationally representative sample of smartphone users. (2) Methods: Data from 2837 participants from a nationally representative sample of Spanish smartphone users were modeled using Mplus causal modeling software. (3) Results: The results of the study confirm the predictions of L-RAT and SCT in explaining cybercrime victimization (higher cybercrime victimization under conditions of high exposure, proximity, and suitability, relative absence of capable guardian, and low self-control). A significant effect of smartphone addiction on cybercrime victimization was also observed above and beyond L-RAT and SCT predictors. (4) Conclusions: The potential victim of cybercrime presents a double vulnerability, on the one hand, those identified by criminological theories such as L-RAT and SCT, and on the other hand, those derived from the deregulated-addicted use of the Internet access device (smartphone in our work).
Visually Grounded Models of Spoken Language: A Survey of Datasets, Architectures and Evaluation Techniques

This survey provides an overview of the evolution of visually grounded models of spoken language over the last 20 years. Such models are inspired by the observation that when children pick up a language, they rely on a wide range of indirect and noisy clues, crucially including signals from the visual modality co-occurring with spoken utterances. Several fields have made important contributions to this approach to modeling or mimicking the process of learning language: Machine Learning, Natural Language and Speech Processing, Computer Vision and Cognitive Science. The current paper brings together these contributions in order to provide a useful introduction and overview for practitioners in all these areas. We discuss the central research questions addressed, the timeline of developments, and the datasets which enabled much of this work. We then summarize the main modeling architectures and offer an exhaustive overview of the evaluation metrics and analysis techniques.
Fusing Ambient and Mobile Sensor Features Into a Behaviorome for Predicting Clinical Health Scores

Advances in machine learning and low-cost, ubiquitous sensors offer a practical method for understanding the predictive relationship between behavior and health.In this study, we analyze this relationship by building a behaviorome, or set of digital behavior markers, from a fusion of data collected from ambient and wearable sensors.We then use the behaviorome to predict clinical scores for a sample of n = 21 participants based on continuous data collected from smart homes and smartwatches and automatically labeled with corresponding activity and location types.To further investigate the relationship between domains, including participant demographics, self-report and external observation-based health scores, and behavior markers, we propose a joint inference technique that improves predictive performance for these types of high-dimensional spaces.For our participant sample, we observe correlations ranging from small to large for the clinical scores.We also observe an improvement in predictive performance when multiple sensor modalities are used and when joint inference is employed.
Spelling Correction Real-Time American Sign Language Alphabet Translation System Based on YOLO Network and LSTM

In this paper, we present a novel approach that aims to solve one of the main challenges in hand gesture recognition tasks in static images, to compensate for the accuracy lost when trained models are used to interpret completely unseen data. The model presented here consists of two main data-processing stages. A deep neural network (DNN) for performing handshape segmentation and classification is used in which multiple architectures and input image sizes were tested and compared to derive the best model in terms of accuracy and processing time. For the experiments presented in this work, the DNN models were trained with 24,000 images of 24 signs from the American Sign Language alphabet and fine-tuned with 5200 images of 26 generated signs. The system was real-time tested with a community of 10 persons, yielding a mean average precision and processing rate of 81.74% and 61.35 frames-per-second, respectively. As a second data-processing stage, a bidirectional long short-term memory neural network was implemented and analyzed for adding spelling correction capability to our system, which scored a training accuracy of 98.07% with a dictionary of 370 words, thus, increasing the robustness in completely unseen data, as shown in our experiments.
Improving radiologist's ability in identifying particular abnormal lesions on mammograms through training test set with immediate feedback

It has been shown that there are differences in diagnostic accuracy of cancer detection on mammograms, from below 50% in developing countries to over 80% in developed world. One previous study reported that radiologists from a population in Asia displayed a low mammographic cancer detection of 48% compared with over 80% in developed countries, and more importantly, that most lesions missed by these radiologists were spiculated masses or stellate lesions. The aim of this study was to explore the performance of radiologists after undertaking a training test set which had been designed to improve the capability in detecting a specific type of cancers on mammograms. Twenty-five radiologists read two sets of 60 mammograms in a standardized mammogram reading room. The first test set focused on stellate or spiculated masses. When radiologists completed the first set, the system displayed immediate feedback to the readers comparing their performances in each case with the truth of cancer cases and cancer types so that the readers could identify individual-based errors. Later radiologists were asked to read the second set of mammograms which contained different types of cancers including stellate/spiculated masses, asymmetric density, calcification, discrete mass and architectural distortion. Case sensitivity, lesion sensitivity, specificity, receiver operating characteristics (ROC) and Jackknife alternative free-response receiver operating characteristics (JAFROC) were calculated for each participant and their diagnostic accuracy was compared between two sessions. Results showed significant improvement among radiologists in case sensitivity (+ 11.4%; P < 0.05), lesion sensitivity (+ 18.7%; P < 0.01) and JAFROC (+ 11%; P < 0.01) in the second set compared with the first set. The increase in diagnostic accuracy was also recorded in the detection of stellate/spiculated mass (+ 20.6%; P < 0.05). This indicated that the performance of radiologists in detecting malignant lesions on mammograms can be improved if an appropriate training intervention is applied after the readers' weakness and strength are identified.