Reviewerly/exHarmony huggingface dataset:
Recognition of Handwritten Medical Prescription Using Signature Verification Techniques

Patient record keeping plays a vital role in diagnoses and cures. Due to a shortage of time, most doctors write prescriptions manually in Pakistan. At times, it becomes difficult for pharmacists to read prescriptions properly. As a result, they may dispense the wrong medicine. This might cause risky and deadly effects on the patient's health. This paper proposes an online handwritten medical prescription recognition system that lets doctors write prescriptions on a tablet using a stylus and automatically recognizes the medicine. We use signature verification techniques to recognize the doctor's handwriting to overcome the problem of misinterpretation of the medicine name by the pharmacist. The proposed system stores different features like the pen coordinates, time, and several pen-ups and pen-downs. Besides using features already proposed in the literature for signature verification, we propose some new features that greatly enhance recognition accuracy. We built a dataset of 24 medicine names from two users and compared results using newly proposed features. We have obtained 84%, 78%, 77.47% 77.31%, 74.17%, 60%, 38.5%, 68%, and 61.64% accuracies for 9 users using SVM classifier.
Robust contrast enhancement method using a retinex model with adaptive brightness for detection applications

Low light image enhancement with adaptive brightness, color and contrast preservation in degraded visual conditions (e.g., extreme dark background, lowlight, back-light, mist. etc.) is becoming more challenging for machine cognition applications than anticipated. A realistic image enhancement framework should preserve brightness and contrast in robust scenarios. The extant direct enhancement methods amplify objectionable structure and texture artifacts, whereas network-based enhancement approaches are based on paired or large-scale training datasets, raising fundamental concerns about their real-world applicability. This paper presents a new framework to get deep into darkness in degraded visual conditions following the fundamental of retinex-based image decomposition. We separate the reflection and illumination components to perform independent weighted enhancement operations on each component to preserve the visual details with a balance of brightness and contrast. A comprehensive weighting strategy is proposed to constrain image decomposition while disrupting the irregularities of high frequency reflection and illumination to improve the contrast. At the same time, we propose to guide the illumination component with a high-frequency component for structure and texture preservation in degraded visual conditions. Unlike existing approaches, the proposed method works regardless of the training data type (i.e., low light, normal light, or normal and low light pairs). A deep into darkness network (D2D-Net) is proposed to maintain the visual balance of smoothness without compromising the image quality. We conduct extensive experiments to demonstrate the superiority of the proposed enhancement. We test the performance of our method for object detection tasks in extremely dark scenarios. Experimental results demonstrate that our method maintains the balance of visual smoothness, making it more viable for future interactive visual applications.
Lamarckian Platform: Pushing the Boundaries of Evolutionary Reinforcement Learning Towards Asynchronous Commercial Games

Despite the emerging progress of integrating evolutionary computation into reinforcement learning, the absence of a high-performance platform endowing composability and massive parallelism causes non-trivial difficulties for research and applications related to asynchronous commercial games. Here we introduce Lamarckian <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><sup>1</sup></xref> <fn id="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><label><sup>1</sup></label> The code and demonstrational setup of Lamarckian are publicly available at <uri>https://github. com/lamarckian/lamarckian</uri>. </fn> – an open-source platform featuring support for evolutionary reinforcement learning scalable to distributed computing resources. To improve the training speed and data efficiency, Lamarckian adopts optimized communication methods and an asynchronous evolutionary reinforcement learning workflow. To meet the demand for an asynchronous interface by commercial games and various methods, Lamarckian tailors an asynchronous Markov Decision Process interface and designs an object-oriented software architecture with decoupled modules. In comparison with the state-of-the-art RLlib, we empirically demonstrate the unique advantages of Lamarckian on benchmark tests with up to 6000 CPU cores: i) both the sampling efficiency and training speed are doubled when running PPO on Google football game; ii) the training speed is 13 times faster when running PBT+PPO on Pong game. Moreover, we also present two use cases: i) how Lamarckian is applied to generating behavior-diverse game AI; ii) how Lamarckian is applied to game balancing tests for an asynchronous commercial game.
A Virtual Fence for Drones: Efficiently Detecting Propeller Blades with a DVXplorer Event Camera

In previous work, we prototyped a portable drone detection system using a DAVIS 346 event camera and a Raspberry Pi 4, running in 5.14 W. Here, we expand on this work by switching to the higher-resolution DVXplorer and by including a small neural network classifier system. The resulting system improves the range at which drones can be recognized (from 9m to 19m). We also demonstrate our novel in-lab test system, capable of generating controlled training data across a wide variety of lighting and optical conditions. The new 100-neuron classification system runs at 100Hz with an accuracy of 98% on our field test and 96% on the in-lab test suite.
Vector Representations of Idioms in Conversational Systems

In this study, we demonstrate that an open-domain conversational system trained on idioms or figurative language generates more fitting responses to prompts containing idioms. Idioms are a part of everyday speech in many languages and across many cultures, but they pose a great challenge for many natural language processing (NLP) systems that involve tasks such as information retrieval (IR), machine translation (MT), and conversational artificial intelligence (AI). We utilized the Potential Idiomatic Expression (PIE)-English idiom corpus for the two tasks that we investigated: classification and conversation generation. We achieved a state-of-the-art (SoTA) result of a 98% macro F1 score on the classification task by using the SoTA T5 model. We experimented with three instances of the SoTA dialogue model—the Dialogue Generative Pre-trained Transformer (DialoGPT)—for conversation generation. Their performances were evaluated by using the automatic metric, perplexity, and a human evaluation. The results showed that the model trained on the idiom corpus generated more fitting responses to prompts containing idioms 71.9% of the time in comparison with a similar model that was not trained on the idiom corpus. We have contributed the model checkpoint/demo/code to the HuggingFace hub for public access.
An Entity Linking Algorithm Derived from Graph Convolutional Network and Contextualized Semantic Relevance

In the era of big data, a large amount of unstructured text data springs up every day. Entity linking involves relating the mentions found in the texts to the corresponding entities, which stand for objective things in the real world, in a knowledge base. This task can help computers understand semantics in the texts correctly. Although there have been numerous approaches employed in research such as this, some challenges are still unresolved. Most current approaches utilize neural models to learn important features of the entity and mention context. However, the topic coherence among the referred entities is frequently ignored, which leads to a clear preference for popular entities but poor accuracy for less popular ones. Moreover, the graph-based models face much noise information and high computational complexity. To solve the problems above, the paper puts forward an entity linking algorithm derived from the asymmetric graph convolutional network and the contextualized semantic relevance, which can make full use of the neighboring node information as well as deal with unnecessary noise in the graph. The semantic vector of the candidate entity is obtained by continuously iterating and aggregating the information from neighboring nodes. The contextualized relevance model is a symmetrical structure that is designed to realize the deep semantic measurement between the mentions and the entities. The experimental results show that the proposed algorithm can fully explore the topology information of the graph and dramatically improve the effect of entity linking compared with the baselines.
A New Regularization for Retinex Decomposition of Low-Light Images

We study unsupervised Retinex decomposition for low light image enhancement. Being an underdetermined problem with infinite solutions, well-suited priors are required to reduce the solution space. In this paper, we analyze the characteristics of low-light images and their illumination component and identify a trivial solution not taken into consideration by the previous unsupervised state-of-the-art methods. The challenge comes from the fact that the trivial solution cannot be completely eliminated from the feasible set as it corresponds to the true solution when the low-light image contains a light source or an overexposed area. To address this issue, we propose a new regularization term which only remove absurd solutions and keep plausible ones in the set. To demonstrate the efficiency of the proposed prior, we conduct our experiments using deep image priors in a framework similar to the recent work RetinexDIP and an in-depth ablation study. Finally, we observe no more halo artefacts in the restored image. For all-but-one metrics, our unsupervised approach gives results as good as the supervised state-of-the-art indicating the potential of this framework for low-light image enhancement.
Tsunami Early Warning From Global Navigation Satellite System Data Using Convolutional Neural Networks

Abstract We investigate the potential of using Global Navigation Satellite System (GNSS) observations to directly forecast full tsunami waveforms in real time. We train convolutional neural networks to use less than 9 min of GNSS data to forecast the full tsunami waveforms over 6 hr at select locations, and obtain accurate forecasts on a test data set. Our training and test data consists of synthetic earthquakes and associated GNSS data generated for the Cascadia Subduction Zone using the MudPy software, and corresponding tsunami waveforms in Puget Sound computed using GeoClaw. We use the same suite of synthetic earthquakes and waveforms as in earlier work where tsunami waveforms were used for forecasting, and provide a comparison. We also explore varying the number of GNSS stations, their locations, and their observation durations.
Thumbnail-preserving encryption based on improved logistic system

Plaintext images and images encrypted using traditional encryption schemes in cloud storage represent the extremes of usability and privacy. Recently, thumbnail-preserving encryption (TPE) has been proposed to balance the usability and privacy of images in the cloud. An ideal TPE scheme should achieve lossless decryption, the same thumbnail, low time complexity, and resistance to certain attacks, which are missing in existing TPE schemes. Motivated by this idea, we propose a new TPE scheme based on an improved logistic chaotic system, which takes advantage of the good dynamics of the chaotic system and withstands the experimental evaluations in terms of privacy and usability. In the encryption mechanism, we design the TPE scheme from the binary and rank-then-encrypt perspectives by randomly selecting pixels. Compared with the existing schemes, the proposed scheme can reach the ideal state with only one round of encryption. Meanwhile, the experimental results such as time cost, image visual quality and security analysis also show that the scheme has strong competitiveness.
Experimental validation of the Kibble-Zurek mechanism on a digital quantum computer

The Kibble-Zurek mechanism (KZM) captures the essential physics of nonequilibrium quantum phase transitions with symmetry breaking. KZM predicts a universal scaling power law for the defect density which is fully determined by the system’s critical exponents at equilibrium and the quenching rate. We experimentally tested the KZM for the simplest quantum case, a single qubit under the Landau-Zener evolution, on an open access IBM quantum computer (IBM-Q). We find that for this simple one-qubit model, experimental data validates the central KZM assumption of the adiabatic-impulse approximation for a well isolated qubit. Furthermore, we report on extensive IBM-Q experiments on individual qubits embedded in different circuit environments and topologies, separately elucidating the role of crosstalk between qubits and the increasing decoherence effects associated with the quantum circuit depth on the KZM predictions. Our results strongly suggest that increasing circuit depth acts as a decoherence source, producing a rapid deviation of experimental data from theoretical unitary predictions.
Anomalous energy exchanges and Wigner-function negativities in a single-qubit gate

Anomalous weak values and Wigner function's negativity are well known witnesses of quantum contextuality. We show that these effects occur when analyzing the energetics of a single qubit gate generated by a resonant coherent field traveling in a waveguide. The buildup of correlations between the qubit and the field is responsible for bounds on the gate fidelity, but also for a nontrivial energy balance recently observed in a superconducting setup. In the experimental scheme, the field is continuously monitored through heterodyne detection and then post-selected over the outcomes of a final qubit's measurement. The post-selected data can be interpreted as field's weak values and can show anomalous values in the variation of the field's energy. We model the joint system dynamics with a collision model, gaining access to the qubit-field entangled state at any time. We find an analytical expression of the quasi-probability distribution of the post-selected heterodyne signal, i.e. the conditional Husimi-Q function. The latter grants access to all the field's weak values: we use it to obtain that of the field's energy change and display its anomalous behaviour. Finally, we derive the field's conditional Wigner function and show that anomalous weak values and Wigner function's negativities arise for the same values of the gate's angle.
The relationship between physical activity changes and mental health factors in adolescents due to COVID-19

The purpose of this study is to provide basic data to prepare countermeasures for the physical and mental health of adolescents in Korea by analyzing the relationship between mental health according to changes in physical activity due to COVID-19 for adolescents. To achieve the purpose of this study, 52,602 adolescents were analyzed using raw data from the 2021 Korea Youth Risk Behavior Web-based Surveys. Looking at the results of the major studies, changes in physical activity due to COVID-19 and subjective health perception(p=.000), depression(p=.000), GAD(generalized anxiety disorder)(p=.000), and stress perception(p=.000) was found to have a statistically significant relationship. In addition, as a result of examining the effects of changes in physical activity on subjective health cognition, depression, generalized anxiety disorder, and stress cognition, subjective health cognition had a positive relationship, and changes in depression, generalized anxiety disorder, and stress cognition had a negative relationship. Therefore, to summarize the results of this study, it was found that there was a change in the physical activity of adolescents due to COVID-19, and the decrease in physical activity had a negative effect on mental health. In order to respond to the re-spread of COVID-19 or the emergence of new infectious diseases in the future, it is necessary to develop sports activities and educational programs and application strategies that take into account the life patterns of adolescents.
XR technologies for self-regulated student exhibitions in art education: survey and first design considerations

This paper provides a survey of the first results of an EU-funded research project, namely CREAMS that aims to design, develop and evaluate a framework and open-source tools based on virtual, augmented, and mixed reality for the creation of virtual exhibitions for art students. The virtual exhibitions will exploit the potential of digital technology to provide rich, multilayered, multimodal content such as digital narratives, that will contextualize the artworks, offer additional material, and engage audiences in more meaningful ways. Specifically, in this paper, we present a literature review concerning system designs, implementations, and best practices of XR technologies usage in art education for exhibition purposes. Our findings are then filtered based on experts' feedback for triangulation purposes. Based on the finding of this preliminary user requirement analysis, we propose a first conceptual design of the envisioned system, along with a number of motivating scenarios emphasizing the main system functionalities. This is the first step of a user-centered design methodology we aim to apply throughout the building of the CREAMS framework.
Trust Metrics for Medical Deep Learning Using Explainable-AI Ensemble for Time Series Classification

Trustworthiness is a roadblock in mass adoption of artificial intelligence (AI) in medicine. This research developed a framework to explore the trustworthiness as it applies to AI in medicine with respect to common stakeholders in medical device development. Within this framework the element of explainability of AI models was explored by evaluating an ensemble of explainable AI (XAI) methods. In current literature a litany of XAI methods are available that provide a variety of insights into the learning and function of AI models. XAI methods provide a human readable output for the AI’s learned processes. These XAI methods provide very subjective outputs with varying degrees of quality. Currently, there are no metrics or methods of objectively evaluating XAI generated explanations against outputs from other XAI methods or to test repeatable consistency of explanations from a single XAI method. This research presents two constituent elements, similarity and stability, to explore the concept of explainability for time series medical data (ECG). Thus providing a repeatable and testable framework to evaluate XAI methods and their generated explanations. This is accomplished using subject matter expert (SME) annotated ECG signals (time-series signals) represented as images to AI models and XAI methods. The XAI methods of Vanilla Saliency, SmoothGrad, GradCAM and GradCAM++ were used to generate XAI outputs for a VGG-16 based deep learning classification model. The framework provides insights about XAI method generated explanations for the AI and how closely that learning corresponds to SME decision making. It also objectively evaluates how closely explanations generated by any XAI method resemble outputs from other XAI methods. Lastly, the framework provides insights about possible enhancements to XAI to go beyond what was identified by the SMEs in their decision making.
Classification of neurologic outcomes from medical notes using natural language processing

Neurologic disability level at hospital discharge is an important outcome in many clinical research studies. Outside of clinical trials, neurologic outcomes must typically be extracted by labor intensive manual review of clinical notes in the electronic health record (EHR). To overcome this challenge, we set out to develop a natural language processing (NLP) approach that automatically reads clinical notes to determine neurologic outcomes, to make it possible to conduct larger scale neurologic outcomes studies. We obtained 7314 notes from 3632 patients hospitalized at two large Boston hospitals between January 2012 and June 2020, including discharge summaries (3485), occupational therapy (1472) and physical therapy (2357) notes. Fourteen clinical experts reviewed notes to assign scores on the Glasgow Outcome Scale (GOS) with 4 classes, namely ‘good recovery’, ‘moderate disability’, ‘severe disability’, and ‘death’ and on the Modified Rankin Scale (mRS), with 7 classes, namely ‘no symptoms’, ‘no significant disability’, ‘slight disability’, ‘moderate disability’, ‘moderately severe disability’, ‘severe disability’, and ‘death’. For 428 patients’ notes, 2 experts scored the cases generating interrater reliability estimates for GOS and mRS. After preprocessing and extracting features from the notes, we trained a multiclass logistic regression model using LASSO regularization and 5-fold cross validation for hyperparameter tuning. The model performed well on the test set, achieving a micro average area under the receiver operating characteristic and F-score of 0.94 (95% CI 0.93–0.95) and 0.77 (0.75–0.80) for GOS, and 0.90 (0.89–0.91) and 0.59 (0.57–0.62) for mRS, respectively. Our work demonstrates that an NLP algorithm can accurately assign neurologic outcomes based on free text clinical notes. This algorithm increases the scale of research on neurological outcomes that is possible with EHR data.
DeMFI: Deep Joint Deblurring and Multi-frame Interpolation with Flow-Guided Attentive Correlation and Recursive Boosting

We propose a novel joint deblurring and multi-frame interpolation (DeMFI) framework in a two-stage manner, called DeMFI-Net, which converts blurry videos of lower-frame-rate to sharp videos at higher-frame-rate based on flow-guided attentive-correlation-based feature bolstering (FAC-FB) module and recursive boosting (RB), in terms of multi-frame interpolation (MFI). Its baseline version performs feature-flow-based warping with FAC-FB module to obtain a sharp-interpolated frame as well to deblur two center-input frames. Its extended version further improves the joint performance based on pixel-flow-based warping with GRU-based RB. Our FAC-FB module effectively gathers the distributed blurry pixel information over blurry input frames in feature-domain to improve the joint performances. RB trained with recursive boosting loss enables DeMFI-Net to adequately select smaller RB iterations for a faster runtime during inference, even after the training is finished. As a result, our DeMFI-Net achieves state-of-the-art (SOTA) performances for diverse datasets with significant margins compared to recent joint methods. All source codes, including pretrained DeMFI-Net, are publicly available at https://github.com/JihyongOh/DeMFI .