Reviewerly/exHarmony huggingface dataset:
Fizeau interferometry-based, motion-free characterization of surface radius of curvature using actively-controlled varifocal optics

In this paper, we present an optical setup which estimates the radius of curvature of spherical surfaces with the aid of Fizeau interferometry. While the use of Fizeau interferometry to achieve these measurements is wellunderstood in prior art and commercially deployed, we propose a variant of Fizeau interferometry for the same measurements. Our proposed method deploys electronically-controlled tunable focus lenses to perform cats-eye and confocal beam scans with a motion-free scanning system as opposed to a motion-based laser scanning and repositioning system. Eliminating motion from a surface scanning system mitigates system breakdowns related to bulk mechanical motion of optical elements. It also promises to reduce the system cost as well as bulkiness of such interferometric systems. We show the proposed system improvement via the use of a standard tunable focus lens on a legacy commercial Zygo surface curvature measuring system. We demonstrate the operation of the proposed system with experimental data and results using lenses and curved mirrors as samples. For all samples, we compare our measurements from the actively-tunable Fizeau interferometer to baseline measurements from the same original Zygo system using its own zoom lens. The experimental results show an excellent agreement between measurements from the motion-based legacy commercial system and the actively-tunable bulk motionfree system. Future work would focus on characterizing sample surface aberrations by subtracting wavefront aberrations imparted by the tunable focus lens piece.
Are We Equal Online?: An Investigation of Gendered Language Patterns and Message Engagement on Enterprise Communication Platforms

It was previously hypothesized that gender differences -- and thus gender discrimination -- would disappear if communication was no longer in person, and instead was transmitted and received in the same format for all. Yet, even online, researchers have identified gendered language styles in written communication that reveal gender cues and can lead to unequal treatment. In this work, we revisit these past findings and ask whether the same gendered patterns can be found on modern communication platforms, which present a new set of engagement features and mixed synchronous capabilities. We quantitatively analyze 335,000 Slack messages sent by 845 individuals as part of 46 teams, collected over six years of a product design capstone course. We found little evidence of traditionally gendered communication styles (characterized as elaborate, uncertain, and supportive) from the minority-gender participants. We did identify relationships between message author gender, communication style, and message engagement --- women and minority genders were more likely to have their messages engaged with, but only when using certain communication styles --- suggesting complex power dynamics exist on these platforms. We contribute the first study of gendered language styles on Enterprise Communication Platforms, adding to the community's understanding of how new settings and emerging technology relate to team collaborative dynamics, and motivating future tool development to support collaboration in diverse teams.
Curiosity-tuned experience replay for wargaming decision modeling without reward-engineering

Reinforcement Learning (RL) has become a promising technique to deal with the tough decision modeling problem in the wargaming field. However, to deploy current RL algorithms requires reward-engineering scenario by scenario, which is laborious for massive wargaming scenarios. To tackle this issue, this paper proposes an improved RL method, curiosity-tuned experience replay (CTER), which allows the RL-driven decision model to achieve a relatively effective policy under the sparse reward. CTER uses the curiosity mechanism to regulate the three critical procedures during learning with experience replay: the exploration, storage, and revisitation of the experiences. Based on the prediction-based curiosity, CTER generates an intrinsic reward to fill the sparse reward space, and further provides an adaptive exploration strategy to collect more informative experiences. Moreover, CTER develops a novel prioritized replay and memory updating mechanism to reuse experiences more efficiently. Through the systematic evaluation and comparison on typical game tasks and wargaming tasks, CTER shows its effectiveness and generalization in different scenarios without reward-engineering. Especially, the policy performance of CTER-based RL with the sparse reward is almost equivalent to that of ordinary RL with dense engineered rewards. Our work may offer a relatively universal approach for wargaming decision modeling, which can free the RL-based decision modelers from the laborious reward-engineering.
Machine learning prediction models for clinical management of blood-borne viral infections: a systematic review of current applications and future impact

Machine learning (ML) prediction models to support clinical management of blood-borne viral infections are becoming increasingly abundant in medical literature, with a number of competing models being developed for the same outcome or target population. However, evidence on the quality of these ML prediction models are limited. This study aimed to evaluate the development and quality of reporting of ML prediction models that could facilitate timely clinical management of blood-borne viral infections. We conducted narrative evidence synthesis following the synthesis without meta-analysis guidelines. We searched PubMed and Cochrane Central Register of Controlled Trials for all studies applying ML models for predicting clinical outcomes associated with hepatitis B virus (HBV), human immunodeficiency virus (HIV), or hepatitis C virus (HCV). We found 33 unique ML prediction models aiming to support clinical decision making. Overall, 12 (36.4%) focused on HBV, 10 (30.3%) on HCV, 10 on HIV (30.3%) and two (6.1%) on co-infection. Among these, six (18.2%) addressed the diagnosis of infection, 16 (48.5%) the prognosis of infection, eight (24.2%) the prediction of treatment response, two (6.1%) progression through a cascade of care, and one (3.03%) focused on the choice of antiretroviral therapy (ART). Nineteen prediction models (57.6%) were developed using data from high-income countries. Evaluation of prediction models was limited to measures of performance. Detailed information on software code accessibility was often missing. Independent validation on new datasets and/or in other institutions was rarely done. Promising approaches for ML prediction models in blood-borne viral infections were identified, but the lack of robust validation, interpretability/explainability, and poor quality of reporting hampered their clinical relevance. Our findings highlight important considerations that can inform standard reporting guidelines for ML prediction models in the future (e.g., TRIPOD-AI), and provides critical data to inform robust evaluation of the models.
Integrated dual hyperchaotic and Josephus traversing based 3D confusion-diffusion pattern for image encryption

This paper introduces an enhanced image encryption technique that relies on 3D confusion and diffusion patterns, aiming to achieve maximum performance in terms of confusion and diffusion. The proposed encryption method combines four phases: dual hyperchaotic, improved logistic map (ILM), Josephus traversing, and a hash function. The first and second phases utilize improved Chen and Lorentz systems-based 3D patterns to enhance confusion and diffusion. In the third phase, bit-plane permutations are performed through Josephus traversing in three directions: x, y, and z. Lastly, the fourth phase implements diffusion based on ILM to optimize encryption performance. The integration of a hash function in this method serves to heighten key sensitivity. The test results demonstrate the method's exceptional performance across various analyses, including statistical, differential, brute-force, and NIST test suite assessments. Additionally, the proposed method outperforms previous approaches significantly, as indicated by the majority assessment.
Neurological Properties to Circumvent AI’s Error Reduction Impasse

Our paper proposes significant changes to AI technology. We believe this is necessary because current implementations have stagnated at average error rates of approximately 8%. Implementers hope that further improvements will lower error rates to 5% by 2025. This would require 1028 floating-point operations, which is not possible with today’s algorithms and computer technology. Even errors of 5% are excessive for many practical applications. The current AI implementations have ignominious errors. Near bankruptcy of a prominent real estate corporation, and the obligatory resignation of an elected government official resulted from AI errors. The causation errors were ludicrous and unlikely performed by humans. Applications of AI are therefore limited to those for which errors are nugatory. In contrast, the human brain’s capabilities and efficiency are astonishing. In significant contrast to current AI models, the human brain is impressive in terms of its relatively small size (adult average 79 in3), weight (approximately 4#), and power consumption (nominally 15W). We feel that this implies that AI technology needs to adopt excluded neurological properties. The current AI neuron model is an overly simplified linear model, which was proposed about 70 years ago. We propose emulating the neurological neuron’s nonlinear capabilities. The versatility of the improved AI model would be many orders of magnitude beyond that of the currently implemented linear neuron models. Also, the proposed neurological properties are of neural plasticity. Specifically, we describe the neurological associative learning aspect of neuroplasticity, partitioning associative plasticity into “inter-association” (neural network structure), and “intra-association” (neuron functioning).
Potential of Blockchain to Increase the Effectiveness of Management Accounting: A Systematic Literature Review

This comprehensive literature review examines how blockchain technology may enhance management accounting procedures. The investigation indicates that blockchain technology might change financial data administration and transmission, improving precision, effectiveness, and dependability. However, scalability, interoperability, and data protection must be addressed. The review also underlines the need for greater research to fully understand how blockchain technology affects management accounting in ever-changing organizational contexts. Empirical studies examine blockchain technology's actual applications, whereas conceptual studies examine theoretical frameworks and models. Integrating blockchain technology with Artificial intelligence (AI) and eXtensible Business Reporting Language (XBRL) enhances its use in accounting systems. The study shows that blockchain technology might alter management accounting operations, improving financial data quality and dependability for decision-making. Electronic procurement, when integrated with Enterprise Resource Planning (ERP) and Accounting Information Systems (AIS), can improve transparency, efficiency, and operational automation. This report suggests additional research, adoption strategies, training methodologies, data privacy concerns, and ongoing monitoring.
Recent Development of Private Banking Sector – A Special Reference towards Green Banking Initiatives

The object of green banking initiatives is developed in recent times with the innovative ideas and growth of banking sector. The banking sector vital role of changes and growth for private sector banks in the environmental protection and sustainable development. This study concern that recent development of private sector banks in the southern districts of tamilnadu. This paper analysed that different age groups and educational status customer’s usage and awareness of green banking initiatives in the private sector banks in study area. It intends to developed initiatives for awareness of customer’s use of private sectors. It find out the green banking initiatives in encouraging the environmental protection and awareness of the customers. Finally, this study encouraging and awareness of green banking initiatives in customers and providing training for to get into use for green banking services.
How Artificial Intelligence is Revolutionizing the Banking Sector: The Applications and Challenges

Artificial Intelligence (AI) has emerged as a transformative technology with the potential to revolutionize various industries, including banking. The study provides an overview of the applications and challenges of AI in the banking sector. It highlights the significant impact of AI on improving operational efficiency, enhancing customer experience, and enabling more accurate risk assessment and fraud detection. The study also explores the growth of machine learning algorithms and overview of Application Programming Interface (API)-driven banking ecosystems. However, the adoption of AI in the banking sector also presents certain challenges, such as data privacy and security concerns, ethical considerations, and the need for effective regulation.
Evaluating Drug Effectiveness for Antihypertensives in Heart Failure Prognosis: Leveraging Composite Clinical Endpoints and Biomarkers from Electronic Health Records

Arterial hypertension is a major risk factor for heart failure and antihypertensives such as angiotensin converting enzyme (ACE) inhibitors and β-blockers are considered as its first-line treatment. Drug response prediction models designed to determine the most effective antihypertensive drug for a patient are hindered by the interpatient response variability. Although typically pharmacogenetic data have been used to investigate the association of genetic variants with the antihypertensive response, genome-wide association studies are currently expensive and the translation of genotype guided antihypertensive therapy to clinical practice is challenging. With the generation of electronic health records (EHR) data summarized over the patient's disease prognosis and interventions, it is still an underused resource for antihypertensive effectiveness studies in heart failure management. In this study, we first use the clinical events in the EHR related to the patient's hard clinical endpoints and biomarkers associated with the heart failure condition to design selection strategies that determine the antihypertensive effectiveness, then develop annotated corpora using the strategies and eventually evaluate supervised deep learning classifiers on the annotated data. We annotated the EHR sequences of approximately 9500 patients with binary labels corresponding to the drug effectiveness across two different antihypertensive classes and our trained classifier was able to obtain the best F1 performance of 0.97.
Sectioning biomedical abstracts using pointer networks

Identification of semantic subheadings in biomedical article abstracts helps individuals find relevant literature at a faster pace. Prior research presented a wide range of techniques to structure abstracts and achieved high precision and recall scores. However, almost all these models are trained and tested on data sets mentioning Randomized Controlled Trials (RCT), a specific study design with a characteristic language. A machine learning model with high representation power may overfit the patterns in a RCT data set and perform poorly in the data mentioning other study designs. In this work, we explore the limitations with the RCT data set and propose a more representative data set to perform the abstract sectioning task in the overall biomedical domain. We introduce a novel biomedical data set called GMED, which includes all study types published in the Medline repository. We will use three high-capacity deep learning models, train and test them on both the RCT and the GMED and show that training on RCT data set does not generalize well to the general biomedical domain. In the second part of our work, we propose a new model that uses pointer networks for the first time in a document-level segmentation and classification task. The proposed approach successfully integrates a pointer network into a transformer-based language model and outperforms all the previous SOTA models in both the RCT and GMED data sets.
Proteus: Towards a Manageability-focused Home-based Health Monitoring Infrastructure

A data collection infrastructure is vital for generating sufficient amounts and diversity of data necessary for developing algorithms in home-based health monitoring. However, the manageability---deployment and operation efforts---of such an infrastructure has long been overlooked. Even a small size of a dozen homes may incur enormous manual efforts on the research team, including installing, configuring and updating of sensor, edge devices; continuous monitoring for faults and errors to prevent data losses, and integrating new sensing modalities. In this paper, we present Proteus, an easily managed infrastructure designed to automate much of the work in deploying and operating such systems. Proteus includes: i) scalable, continuous deployment and update of devices with automatic bootstrapping; ii) automatic fault and error monitoring and recovery with watchdogs and LED feedback, and complementary edge and cloud storage backups; and iii) an easy-to-use data-agnostic pipeline for integrating new modalities. We demonstrate our system's robustness through different sets of experiments: 3 sensor nodes running for 24 days sending data (17.3 Mbps aggregate rate), and 16 emulated sensors (92.8 Mbps aggregate rate). All such experiments have data loss rates less than 1%. Further we reduce human efforts by 25-fold and code required for adding new data modality by 25-fold. Our results show that Proteus is a promising solution for enabling research teams to effectively manage home-based health monitoring at small to medium sizes.
Text-to-ESQ: A Two-Stage Controllable Approach for Efficient Retrieval of Vaccine Adverse Events from NoSQL Database

The Vaccine Adverse Event Reporting System (VAERS) contains detailed reports of adverse events following vaccine administration. However, efficiently and accurately searching for specific information from VAERS poses significant challenges, especially for medical experts. Natural language querying (NLQ) methods tackle the challenge by translating the input questions into executable queries, allowing for the exploration of complex databases with large amounts of information. Most existing studies focus on the relational database and solve the Text-to-SQL task. However, the capability of full-text for Text-to-SQL is greatly limited by the data structures and functionality of the SQL databases. In addition, the potential of natural language querying has not been comprehensively explored in the healthcare domain. To overcome these limitations, we investigate the potential of NoSQL databases, specifically Elasticsearch, and forge a new research direction for NLQ, which we refer to as Text-to-ESQ generation. This exploration requires us to re-design various aspects of NLQ, such as the target application and the advantages of NoSQL database. In our approach, we develop a two-stage controllable (TSC) framework consisting of a question-to-question (Q2Q) translation module and an ESQ condition extraction (ECE) module. These modules are carefully designed to efficiently retrieve information from the VEARS data stored in a NoSQL database. Additionally, we construct a dedicated question-ESQ pair dataset called VAERSESQ, to support the task in the healthcare domain. Extensive experiments were conducted on the VAERSESQ dataset to evaluate the proposed methods. The results, both quantitative and qualitative, demonstrate the accuracy and efficiency of our approach in generating queries for NoSQL databases, thus enabling efficient retrieval of VEARS data.
PINet: Privileged Information Improve the Interpretablity and generalization of structural MRI in Alzheimer's Disease

The irreversible and progressive atrophy by Alzheimer's Disease resulted in continuous decline in thinking and behavioral skills. To date, CNN classifiers were widely applied to assist the early diagnosis of AD and its associated abnormal structures. However, most existing black-box CNN classifiers relied heavily on the limited MRI scans, and used little domain knowledge from the previous clinical findings. In this study, we proposed a framework, named as PINet, to consider the previous domain knowledge as a Privileged Information (PI), and open the black-box in the prediction process. The input domain knowledge guides the neural network to learn representative features and introduced intepretability for further analysis. PINet used a Transformer-like fusion module Privileged Information Fusion (PIF) to iteratively calculate the correlation of the features between image features and PI features, and project the features into a latent space for classification. The Pyramid Feature Visualization (PFV) module served as a verification to highlight the significant features on the input images. PINet was suitable for neuro-imaging tasks and we demonstrated its application in Alzheimer's Disease using structural MRI scans from ADNI dataset. During the experiments, we employed the abnormal brain structures such as the Hippocampus as the PI, trained the model with the data from 1.5T scanners and tested from 3T scanners. The F1-score showed that PINet was more robust in transferring to a new dataset, with approximatedly 2% drop (from 0.9471 to 0.9231), while the baseline CNN methods had a 29% drop (from 0.8679 to 0.6154). The performance of PINet was relied on the selection of the domain knowledge as the PI. Our best model was trained under the guidance of 12 selected ROIs, major in the structures of Temporal Lobe and Occipital Lobe. In summary, PINet considered the domain knowledge as the PI to train the CNN model, and the selected PI introduced both interpretability and generalization ability to the black box CNN classifiers.
A Multi-Layered GRU Model for COVID-19 Patient Representation and Phenotyping from Large-Scale EHR Data

The unprecedented scale of the COVID-19 pandemic created an alarming shortage of healthcare resources. To enable a more efficient resource allocation and targeted treatment, in this manuscript, we conducted a data-driven study of COVID-19 patients to predict patient outcomes and identify patient phenotypes. Specifically, we developed a multi-layered gated recurrent units-based model, referred to as mGRU-CP, to learn patient embeddings and estimate patient survival probabilities by leveraging their electronic health record (EHR) data in the COVID-19 Research Data Commons. We empirically compared mGRU-CP against four state-of-the-art baseline methods on three sets of patient features. The experimental results demonstrate that mGRU-CP could achieve competitive or superior performance over the baseline methods in all the settings. Our analysis also shows that the learned patient embeddings in mGRU-CP could enable meaningful patient phenotyping to better understand patient mortalities. Our study is significant in understanding patients in the past COVID-19 pandemic, and provides computational tools to predict patient outcomes and inform associated healthcare resource allocation for the future pandemics proactively.