Reviewerly/exHarmony huggingface dataset:
Smooth ROC curve estimation via Bernstein polynomials

The receiver operating characteristic (ROC) curve is commonly used to evaluate the accuracy of a diagnostic test for classifying observations into two groups. We propose two novel tuning parameters for estimating the ROC curve via Bernstein polynomial smoothing of the empirical ROC curve. The new estimator is very easy to implement with the naturally selected tuning parameter, as illustrated by analyzing both real and simulated data sets. Empirical performance is investigated through extensive simulation studies with a variety of scenarios where the two groups are both from a single family of distributions (symmetric or right skewed) or one from a symmetric and the other from a right skewed distribution. The new estimator is uniformly more efficient than the empirical ROC estimator, and very competitive to eleven other existing smooth ROC estimators in terms of mean integrated square errors.
Joint blockchain and federated learning-based offloading in harsh edge computing environments

With the popularity of edge computing, numerous Internet of Things (IoT) applications have been developed and applied to various fields. However, for the harsh environment with network fluctuations and potential attacks, traditional task offloading decision-making schemes cannot meet the requirements of real-time and security. For this reason, we propose a novel task offloading decision framework to cope with the special requirements of the environment. This framework uses a task offloading decision model based on deep reinforcement learning algorithms, and is located on the user side to reduce the impact of network fluctuations. To improve the efficiency and security of the model in harsh edge computing environments, we adopt federated learning and introduce the blockchain into the process of parameter upload and decentralization of federated learning. In addition, we design a new blockchain consensus algorithm to reduce the waste of computing resources and improve the embedding and propagation speeds of the blockchain. Furthermore, we demonstrate the effect of task offloading of this model by performing offloading decisions on a simulation platform.
Effectiveness of Annotation-Based Static Type Inference

Benefits of static type systems are well-known: they offer guarantees that no type error will occur during runtime and, inherently, inferred types serve as documentation on how functions are called. On the other hand, many type systems have to limit expressiveness of the language because, in general, it is undecidable whether a given program is correct regarding types. Another concern that was not addressed so far is that, for logic programming languages such as Prolog, it is impossible to distinguish between intended and unintended failure and, worse, intended and unintended success without additional annotations.In this paper, we elaborate on and discuss the aforementioned issues. As an alternative, we present a static type analysis which is based on plspec. Instead of ensuring full type-safety, we aim to statically identify type errors on a best-effort basis without limiting the expressiveness of Prolog programs. Finally, we evaluate our approach on real-world code featured in the SWI community packages and a large project implementing a model checker.
Application of Blockchain as a Solution to the Real-World Issues in Health Care System

Blockchain technology now a day is becoming a more secure and effective way for information sharing in various areas such as the financial sector, SCM in different industries, and in the field of IoT as well. Along with these fields currently, blockchain technology is playing a vital role in the field of Health Care System. This technology brings the attention of the researcher toward the health care system as it aims to solve various issues such as interoperability and information security during the information sharing and data management. The interoperability and security enable the HCS application to share the information flawlessly among the patients and vendors. Lack of such characteristics finds out the difficulty for the patient in accessing its own health status. Hence, implementing blockchain technology will demolish such a disadvantage for making the HCS more effective and efficient. Because of this kind of potential benefit, it can be applied to various aspects such as patient data handling, SCM of medical equipment and pharmaceutical things, billing and telemedicine systems, etc. In this book chapter, we have tried to focus on the various issues present in the HCS along with the detailed study of potential benefits that have been achieved while integrating the blockchain with the health care system. Also, in this chapter represents the systematic review of various use case scenarios for blockchain in healthcare practice.
A Novel Bayesian Outlier Score Based on the Negative Binomial Distribution for Detecting Aberrantly Expressed Genes in RNA-Seq Gene Expression Count Data

The Negative Binomial distribution (NBD) is used for modeling many types of count data, including gene expression counts obtained by RNA sequencing technologies (RNA-Seq). Finding outliers in this type of data has been shown in recent research to help in identifying rare genetic disorders in humans. Existing Bayesian approaches to detecting outliers in data following the NBD are either computationally inefficient or too general and hence do not leverage the NBD's specificities in an optimal way. We present a novel Bayesian outlier score for data following the NBD, relying on recent advances in the inference of its dispersion parameter through a special method of Gibbs sampling. The novel Bayesian model on which our score is based - OutPyRX (Outlier detection in Python for RNA-Seq, eXtended version) - improves the model of its predecessor OutPyR by introducing novel parameters that are derived from OutPyR's. These novel parameters allow more than 6 times faster convergence of the novel outlier score compared to OutPyR's while having a negligible computational impact on the Gibbs sampling procedure. We show that, in terms of area under precision-recall curve (AUC) values, the novel score outcompetes existing scores on 21 out of 24 datasets that we derived from 4 real datasets by injecting artificial outliers. However, OutPyRX does not perform confounder control which is required for some datasets containing biological outliers. The model is general and can be applied to other similar count data. The code for our model is available at https://github.com/esalkovic/outpyrx.
Challenges and Opportunities in the Design of Digital Distributed Affinity Diagramming Tools

Affinity diagramming is an oft-used sense-making technique in design research and practice to analyze qualitative data, utilizing a large number of sticky notes on walls. Over the past two decades, several digital tools have been tried and tested to augment or even replace the physical affinity diagramming process. Even so, the analog process usually prevails. We developed an online collaboration tool specifically tailored toward affinity diagramming to explore the challenges and opportunities of such a system in the particular case where the distributed teams do not have access to co-located settings. Here, we present a user experience study of five groups (dyads) of students based on a one-hour diagramming task under remote observation, followed by semi-structured interviews. Our study contributes three distinct insights to inform future work, namely that digital affinity diagrams 1) reduce the awareness of co-participants’ actions, 2) provide fewer cues about ownership and use than physical diagrams, and 3) save time, improve manipulation, and overview. We end with a discussion of the challenges and opportunities for the design of digital tools for distributed teams involved in sense-making tasks.
A Neural Joint Model with BERT for Burmese Syllable Segmentation, Word Segmentation, and POS Tagging

The smallest semantic unit of the Burmese language is called the syllable. In the present study, it is intended to propose the first neural joint learning model for Burmese syllable segmentation, word segmentation, and part-of-speech ( POS ) tagging with the BERT. The proposed model alleviates the error propagation problem of the syllable segmentation. More specifically, it extends the neural joint model for Vietnamese word segmentation, POS tagging, and dependency parsing [28] with the pre-training method of the Burmese character, syllable, and word embedding with BiLSTM-CRF-based neural layers. In order to evaluate the performance of the proposed model, experiments are carried out on Burmese benchmark datasets, and we fine-tune the model of multilingual BERT. Obtained results show that the proposed joint model can result in an excellent performance.
Leveraging digital tools for a better virtual teaching-learning process in a private university of Lima

In times of the COVID-19 global pandemic, modern digital tools have served to improve the virtual teaching-learning process at Peruvian universities. The objective of the article is to analyze the impact of the use of digital tools in the teaching-learning process. A survey carried out at the beginning of April 2020 among 51 students and 19 teachers from the computer science program at the University of Science and Humanities (Lima) have revealed that some of them did not have the enough skills to use these tools. Thus, before starting the virtual courses of the first semester of 2020 they were trained in the use of the platforms such as Zoom, Google Meets and Moodle. After this training students and teacher showed a much better disposition towards the use of digital tools, which could be observe during the sessions of their courses. The evaluation of these new methodologies and additional feedback by part of the students and teachers will serve to improve further the virtual teaching-learning process, which due to the ongoing pandemic is nowadays a crucial component in higher education.it was obtained as a result that teachers have improved in the use and mastery of digital tools.
Lifelong Learning Based Disease Diagnosis on Clinical Notes

Current deep learning based disease diagnosis systems usually fall short in catastrophic forgetting, i.e., directly fine-tuning the disease diagnosis model on new tasks usually leads to abrupt decay of performance on previous tasks. What is worse, the trained diagnosis system would be fixed once deployed but collecting training data that covers enough diseases is infeasible, which inspires us to develop a lifelong learning diagnosis system. In this work, we propose to adopt attention to combine medical entities and context, embedding episodic memory and consolidation to retain knowledge, such that the learned model is capable of adapting to sequential disease-diagnosis tasks. Moreover, we establish a new benchmark, named Jarvis-40, which contains clinical notes collected from various hospitals. Experiments show that the proposed method can achieve state-of-the-art performance on the proposed benchmark. Code is available at https://github.com/yifyang/LifelongLearningDiseaseDiagnosis.
The Case Against Registered Reports

Registered reports have been proposed as a way to move from eye‐catching and surprising results and toward methodologically sound practices and interesting research questions. However, none of the top‐twenty artificial intelligence journals support registered reports, and no traces of registered reports can be found in the field of artificial intelligence. Is this because they do not provide value for the type of research that is conducted in the field of artificial intelligence?
To whom the specimen goes: a look at how touch preparations and core needle biopsies are handled in different practices and the effect on fellowship education

Core needle biopsies (CNBs) have proven to be an excellent source of tissue for diagnosis and ancillary testing in the era of personalized medicine, commonly yielding sufficient material for testing via a relatively minimally invasive technique. Thus, there has been an increase in touch preparations (TPs) evaluated with rapid onsite evaluation (ROSE) of these small biopsies either in isolation or with concurrent fine needle aspiration (FNA). This in turn has forced cytopathology practices to make decisions with regard to processing and workflow of CNBs, which affects cytopathology fellowship education substantially.The present review is based on a review of recent literature and an evaluation of the authors' personal experiences.Deciding whether CNBs with associated TPs should be assigned to the cytology service, the subspecialty or general surgical pathology service, or a split between cytopathology and surgical pathology, is complicated. The workflow is variable at different institutions depending on multiple factors. Each of these routes has benefits and disadvantages that can affect patient care and laboratory workflow, in addition to having downstream effects on the quality and type of education our pathology trainees receive. Herein, the advantages and disadvantages of the different approaches for CNB triage are discussed, with an emphasis on the impact upon cytopathology fellowship education.
GazeHelp: Exploring Practical Gaze-assisted Interactions for Graphic Design Tools

This system development project introduces the Adobe Photoshop plugin GazeHelp, exploring the practical application of multimodal gaze-assisted interaction in assisting current graphic design activities. It implements three core features, including QuickTool: a gaze-triggered popup that allows the user to select their next tool with gaze; X-Ray: creating a small non-destructive window at the gaze point, cutting through an artboard’s layers to expose an element on a selected underlying layer; and Privacy Shield: dimming and blocking the current art board from view when looking away from the display. Each harness the speed, gaze-contingent observational nature and presence-implying strengths of gaze respectively, and are customisable to the user’s preferences. The accompanying GazeHelpServer, complete with intuitive GUI, can also be flexibly used by other programs and plugins for further development.
Better Hybrid Systems for Disease Detections and Early Predictions

To the Editor—We have read with interest the manuscript by Nguyen et al describing an overview of hybrid simulations, advantages, and their limitations in healthcare settings [1]. The authors also discuss the data-related challenges in hybrid simulations. We appreciated the manuscript but thought it was directed at a high level, so we thought we could share our experience using hybrid methods in hospital systems. The demand for hybrid methodology was evident for the type of questions and data that we were handling, so we decided to use hybrid modeling to develop the early identification of patients with invasive candidiasis/candidemia (IC/C) using hospital-based data [2]. Once the hybrid system was built, the data were proposed to process through the models using a security-enabled structure (blockchain). We delineate the technicalities here. The uniqueness that we introduced in maintaining the hybrid machinery was that we initially developed ranked...
Fourier Transform of Percoll Gradients Boosts CNN Classification of Hereditary Hemolytic Anemias

Hereditary hemolytic anemias are genetic disorders that affect the shape and density of red blood cells. Genetic tests currently used to diagnose such anemias are expensive and unavailable in the majority of clinical labs. Here, we propose a method for identifying hereditary hemolytic anemias based on a standard biochemistry method, called Percoll gradient, obtained by centrifuging a patient's blood. Our hybrid approach consists on using spatial data-driven features, extracted with a convolutional neural network and spectral handcrafted features obtained from fast Fourier transform. We compare late and early feature fusion with AlexNet and VGG16 architectures. AlexNet with late fusion of spectral features performs better compared to other approaches. We achieved an average F1-score of 88% on different classes suggesting the possibility of diagnosing of hereditary hemolytic anemias from Percoll gradients. Finally, we utilize Grad-CAM to explore the spatial features used for classification.
Weak Estimator-Based Stochastic Searching on the Line in Dynamic Dual Environments

Stochastic point location deals with the problem of finding a target point on a real line through a learning mechanism (LM) with the stochastic environment (SE) offering directional information. The SE can be further categorized into an informative or deceptive one, according to whether p is above 0.5 or not, where p is the probability of providing a correct suggestion of a direction to LM. Several attempts have been made for LM to work in both types of environments, but none of them considers a dynamically changing environment where p varies with time. A dynamic dual environment involves fierce changes that frequently cause its environment to switch from an informative one to a deceptive one, or vice versa. This article presents a novel weak estimator-based adaptive step search solution, to enable LM to track the target in a dynamic dual environment, with the help of a weak estimator. The experimental results show that the proposed solution is feasible and efficient.
MDA-Net: Multi-Dimensional Attention-Based Neural Network for 3D Image Segmentation

Segmenting an entire 3D image often has high computational complexity and requires large memory consumption; by contrast, performing volumetric segmentation in a slice-by-slice manner is efficient but does not fully leverage the 3D data. To address this challenge, we propose a multi-dimensional attention network (MDA-Net) to efficiently integrate slice-wise, spatial, and channel-wise attention into a U-Net based network, which results in high segmentation accuracy with a low computational cost. We evaluate our model on the MICCAI iSeg and IBSR datasets, and the experimental results demonstrate consistent improvements over existing methods.
WEB-SOBA: Word Embeddings-Based Semi-automatic Ontology Building for Aspect-Based Sentiment Classification

For aspect-based sentiment analysis (ABSA), hybrid models combining ontology reasoning and machine learning approaches have achieved state-of-the-art results. In this paper, we introduce WEB-SOBA: a methodology to build a domain sentiment ontology in a semi-automatic manner from a domain-specific corpus using word embeddings. We evaluate the performance of a resulting ontology with a state-of-the-art hybrid ABSA framework, HAABSA, on the SemEval-2016 restaurant dataset. The performance is compared to a manually constructed ontology, and two other recent semi-automatically built ontologies. We show that WEB-SOBA is able to produce an ontology that achieves higher accuracy whilst requiring less than half of user time, compared to the previous approaches.
Feature Extraction of Sequence of Keystrokes in Fixed Text Using the Multivariate Hawkes Process

In this paper, we propose a new method of extracting the features of keystrokes. The Hawkes process based on exponential excitation kernel was used to model the sequence of keystrokes in fixed text, and the intensity function vector and adjacency matrix of the model obtained through training were regarded as the characteristics of the keystrokes. A visual analysis was carried out on the CMU keystroke raw data and the feature data extracted using the proposed method. We used one-class classifier to compare the classification effect of CMU keystroke raw data and the feature data extracted by the Hawkes process model and POHMM model. The experimental results show that the feature data extracted using the proposed method contains rich information to distinguish users. In addition, the feature data extracted using the proposed method has a slightly better classification performance than the original CMU keystroke data for some users who are not easy to distinguish.
Beyond Binary Search: Parallel In-Place Construction of Implicit Search Tree Layouts

We present parallel algorithms to efficiently permute a sorted array into the level-order binary search tree (BST), level-order B-tree (B-tree), and van Emde Boas (vEB) layouts <i>in-place</i>. We analytically determine the complexity of our algorithms and empirically measure their performance. When considering the total time to permute the data in-place and to perform a series of search queries, the vEB layout provides the best performance on the CPU. Given an input of <inline-formula><tex-math notation="LaTeX">$N$</tex-math><alternatives><mml:math><mml:mi>N</mml:mi></mml:math><inline-graphic xlink:href="berney-ieq1-3075392.gif"/></alternatives></inline-formula>=537 million 64-bit integers, the benefits of query performance (compared to binary search) outweigh the cost of in-place permutation when performing as few as 0.37% of <inline-formula><tex-math notation="LaTeX">$N$</tex-math><alternatives><mml:math><mml:mi>N</mml:mi></mml:math><inline-graphic xlink:href="berney-ieq2-3075392.gif"/></alternatives></inline-formula> queries. On the GPU, results depend on the particular architecture, with the B-tree and vEB layouts performing the best. The number of queries necessary to reach the break-even point with binary search ranges from 1.3% to 8.9% of <inline-formula><tex-math notation="LaTeX">$N$</tex-math><alternatives><mml:math><mml:mi>N</mml:mi></mml:math><inline-graphic xlink:href="berney-ieq3-3075392.gif"/></alternatives></inline-formula>=1,074 million 32-bit integers.
Approach for Ensuring Fragmentation and Integrity of Data in SEDuLOUS

With the rapid adoption of cloud services, more and more data are being uploaded on the cloud platform. These data are under threat from various threat actors constantly researching to steal, corrupt, or get control over the data. The threat actors are not only limited to malicious attackers, but these also include curious service providers, social activists, business entities, and nations. They pose a serious risk to cloud services. There are various approaches to protect data at various levels. Division and replication is one such approach where data are divided into chunks and spread over the cloud to reduce the risk of data leakage and simultaneously increase the accessibility. Under division and replication approach, SEDuLOUS provides a heuristic algorithm for data placement in a distributed cloud environment. In this research work, we have provided a comprehensive analysis on cloud data storage services and associated security issues, analysis of SEDuLOUS algorithm, and methodology to improve the SEDuLOUS by specifying minimum fragments to ensure fragmentation of all files and hashing of each chunk to identify the compromised storage nodes.
Reliable Part Guided Multiple Level Attention Learning for Person Re-Identification

Person Re-ID is challenged by background clutter, body misalignment and part missing. In this paper, we propose a reliable part-based multiple levels attention deep network to learn multiple scales salience representation. In particular, person alignment and key point detection are sequentially carried out to locate three relative stable body components, then fused attention (FA) mode is designed to capture the fine-grained salient features from effective spatial of valuable channels of each part, regional attention mode is succeeded to weight the importance of different parts for highlighting the representative parts while suppressing the valueless ones. A late fusion-based multiple-task loss is finally adopted to further optimize the valuable feature representation. Experimental results demonstrate that the proposed method achieves state-of-the-art performances on three challenging benchmarks: Market-1501, DukeMTMC-reID and CUHK03.