Reviewerly/exHarmony huggingface dataset:
Legal Implication of Blockchain Technology in Public Health

Blockchain dominion runs parallel to the abstractness in trust that serves the best feature in the modern world. Technology remains well connected to data migration that is in a way the core necessity of improvisation. Realizing the significance of data privacy has the best footfall in the present generation, which is sustainably handy with blockchain technologies. Similar implications need to be initiated for the sensitive personal data contained in public health overall. Patients have easy access to e-medicines, tele-medicines, AI-based consulting, and the list of development catering devices goes on every day. Patients can get comfortable services with an easy click if blockchain comes for the health-tech industry, which may tailor an effective personalized system for someone’s health. The healthcare industry caters to important transactions, which entails protective smart contracts requiring updated and faster financials. Each healthcare area can engage with technology, providing more immediate solutions for a vast population. Public health would improvise in the developing countries, and keeping track via the blockchain would be worthwhile. Looking forward, the implementation of blockchain technology needs to comply with robust privacy and data protection mechanism. Along with the biomedical outlook needs in-hand solid support via blockchain technologies, which would resolve mismanagement of government hospitals. With that reliability and credibility as important factor to establish a better governance which seems impossible without innovative technologies like blockchain.
Inconsistency of the theory of geometric phases in adiabatic evolution

We unveil that even an infinitesimal deviation of the initial state from the eigenstates of the initial Hamiltonian can yield a significant change in the geometric phase accompanying adiabatic evolution without degeneracies of the energy levels. This leads to the counterintuitive result that the values of two geometric phases computed from the general definition of the Berry phase [Samuel and Bhandari, Phys. Rev. Lett. 60, 2339 (1988)] can dramatically differ from each other in the adiabatic limit even if their evolution paths are sufficiently close in a pointwise manner. The result of this work indicates that the widely accepted theory of geometric phases is not very consistent in the context of adiabatic evolution involving superposed eigenstates.
The Theoretical Roots of CCO

In this chapter, we present the various theories that have influenced or even defined the three schools of CCO thinking for the past 30 years. Regarding the four-flows model, proposed by Robert McPhee and Pamela Zaug, we describe the key role Anthony Giddens’s Structuration Theory has played since its inception. Regarding the roots of the system of self-referential communication systems proposed by Niklas Luhmann, we highlight the role of Edmund Husserl’s phenomenology, Maturana and Varela’s theory of self-referential systems and George Spencer-Brown’s observation theory. Finally, the theoretical roots of the Montreal school, initiated by James R. Taylor’s text/conversation model, are introduced through the presentation of some key authors’ works, namely pragmatists such as John Dewey and Charles Sanders Peirce, but also John Langshaw Austin, Harold Garfinkel, Algirdas Julien Greimas, and Bruno Latour. Beyond their differences, we also insist on what unifies the theoretical foundations of these three respective schools of thought.
Working Memory

Working memory (WM) refers to the mental capacity to temporarily and simultaneously store and process a limited amount of information to complete some ongoing cognitive tasks. Inspired by established research on WM and language in cognitive psychology and psycholinguistics, recent decades have witnessed an increasing body of empirical studies in second language acquisition (SLA) demonstrating that individual differences in WM capacity and embedded functions are related to specific L2 domains and sub-skills of learning and processing, though a finer-grained portrayal of the WM–SLA nexus has yet to be established. This chapter sets out to critically review the major strands of theoretical and empirical SLA research investigating WM effects, and to synthesize the empirical evidence and emerging patterns while analyzing prevalent methods of data elicitation. It is argued that previous WM–SLA studies have relied on a structural view of WM, and future research may also need to incorporate insights from the alternative functional or emergent views of WM, conceptualizing it as a complex, dynamic, and adaptive cognitive system modulating and shaping aspects of L2 processing and development optimally.
A van Benthem Theorem for Atomic and Molecular Logics

After recalling the definitions of atomic and molecular logics, we show how notions of bisimulation can be automatically defined from the truth conditions of the connectives of any of these logics.Then, we prove a generalization of van Benthem modal characterization theorem for molecular logics.Our molecular connectives should be uniform and contain all conjunctions and disjunctions.We use modal logic, the Lambek calculus and modal intuitionistic logic as case study and compare in particular our work with Olkhovikov's work.
Structural Equation Modeling and Factor Analysis

This chapter introduces the overall procedure of conducting statistical analyses using the structural equation modeling (SEM) framework. SEM allows researchers to study relationships among variables that are either latent or observed, continuous or categorical. We discuss several technical issues of typical SEMs that are likely to be encountered by researchers in the field of second language acquisition and individual differences. Issues include SEM analysis with categorical data, the choice of estimation method, and methods for correction for measurement errors. We also provide a summary of SEM applications in the field of second language acquisition and individual differences.
Learning unfamiliar words and perceiving non-native vowels in a second language: Insights from eye tracking

One of the challenges in second-language learning is learning unfamiliar word forms, especially when this involves novel phoneme contrasts. The present study examines how real-time processing of newly-learned words and phonemes in a second language is impacted by the structure of learning (discrimination training) and whether asking participants to complete the same task after a 16-21 h delay favours subsequent word recognition. Specifically, using a visual world eye tracking paradigm, we assessed how English listeners processed newly-learned words containing non-native French front-rounded [y] compared to native-sounding vowels, both immediately after training and the following day. Some learners were forced to discriminate between vowels that are perceptually similar for English listeners, [y]-[u], while others were not. We found significantly better word-level processing on a variety of indices after an overnight delay. We also found that training [y] words paired with [u] words (vs. [y]-Control pairs) led to a greater decrease in reaction times during the word recognition task over the two testing sessions. Discrimination training using perceptually similar sounds had facilitative effects on second language word learning with novel phonemic information, and real-time processing measures such as eyetracking provided valuable insights into how individuals learn words and phonemes in a second language.
An anomaly detection algorithm of rolling bearing combining random matrix eigenvalues and principal eigenvectors

An anomaly detection algorithm combining eigenvalues and eigenvectors in random matrix is proposed, which can solve the problem that present algorithms based on random matrix theory overly rely on eigenvalues and ignore useful information contained in eigenvectors. Firtsly, the time window method is used to select the original data of rolling bearing, and the sampling feature matrix is constructed by extracting bearing feature. Secondly, the eigenvalues and principal eigenvector of sampling feature matrix are investigated and combined to construct comprehensive feature index and its corresponding threshold. Finally, an anomaly detection based on comprehensive feature index is proposed to detect the early anomaly of rolling bearing. The application research is carried out by using bearing datasets of intelligent maintenance center and Xi’an Jiaotong University; the result shows that compared with the single eigenvalue index and kurtosis index, the algorithm based on the comprehensive feature index can detect the abnormal condition of bearing earlier. And the accuracy and effectiveness of the anomaly detection algorithm are proved through spectrum analysis, which provides guidance and basis for fault warning and equipment maintenance.
Crime Fiction in South Asia

Detective or crime fiction has had a long and varied history in South Asia, at times inflected by local concerns, at other times transporting readers into a world of international intrigue. This chapter traces its development from its colonial origins under the aegis of a local boom in print media, ‘intrusive’ colonial law and policing and the influx of European narratives combined with precolonial tropes. The chapter focuses on iconic ‘homegrown’ detectives like Byomkesh Bakshi in the 1930s, the quintessential Bengali gentleman detective engaged in the high pursuit of truth, the debonair detectives Faridi and Imraan created by Indo-Pakistani writer Ibne Safi in the 1950s–1970s, and the heroes of ‘hard-boiled’ Hindi crime fiction in the 1970s–1990s. Crime fiction has also allowed women writers to imagine bold female detectives who challenge and debate gender norms, from Kamala Satthianadhan’s trailblazing Detective Janaki (1934) to Sujatha Massey’s Perveen Mistry. Crime fiction in South Asia has been intensely translational, not just from English but also across South Asian languages. It has pioneered its own distribution channels and spawned adaptations across media platforms.
Utility optimization-based multi-stakeholder personalized recommendation system

Purpose In a multi-stakeholder recommender system (MSRS), stakeholders are the multiple entities (consumer, producer, system, etc.) benefited by the generated recommendations. Traditionally, the exclusive focus on only a single stakeholders' (for example, only consumer or end-user) preferences obscured the welfare of the others. Two major challenges are encountered while incorporating the multiple stakeholders' perspectives in MSRS: designing a dedicated utility function for each stakeholder and optimizing their utility without hurting others. This paper proposes multiple utility functions for different stakeholders and optimizes these functions for generating balanced, personalized recommendations for each stakeholder. Design/methodology/approach The proposed methodology considers four valid stakeholders user, producer, cast and recommender system from the multi-stakeholder recommender setting and builds dedicated utility functions. The utility function for users incorporates enhanced side-information-based similarity computation for utility count. Similarly, to improve the utility gain, the authors design new utility functions for producer, star-cast and system to incorporate long-tail and diverse items in the recommendation list. Next, to balance the utility gain and generate the trade-off recommendation solution, the authors perform the evolutionary optimization of the conflicting utility functions using NSGA-II. Experimental evaluation and comparison are conducted over three benchmark data sets. Findings The authors observed 19.70% of average enhancement in utility gain with improved mean precision, diversity and novelty. Exposure, hit, reach and target reach metrics are substantially improved. Originality/value A new approach considers four stakeholders simultaneously with their respective utility functions and establishes the trade-off recommendation solution between conflicting utilities of the stakeholders.
Blockchain and distributed ledger technology

Blockchain and DLT are still in an experimental phase for many customs authorities, with around a third testing it through proofs of concept (PoCs) and pilot projects using mainly permissioned (i.e. private) blockchains. Only Argentina and Uruguay report full deployment of this technology.
Machine learning approach to the Floquet–Lindbladian problem

Similar to its classical version, quantum Markovian evolution can be either time-discrete or time-continuous. Discrete quantum Markovian evolution is usually modeled with completely positive trace-preserving maps, while time-continuous evolution is often specified with superoperators referred to as "Lindbladians." Here, we address the following question: Being given a quantum map, can we find a Lindbladian that generates an evolution identical-when monitored at discrete instances of time-to the one induced by the map? It was demonstrated that the problem of getting the answer to this question can be reduced to an NP-complete (in the dimension N of the Hilbert space, the evolution takes place in) problem. We approach this question from a different perspective by considering a variety of machine learning (ML) methods and trying to estimate their potential ability to give the correct answer. Complimentarily, we use the performance of different ML methods as a tool to validate a hypothesis that the answer to the question is encoded in spectral properties of the so-called Choi matrix, which can be constructed from the given quantum map. As a test bed, we use two single-qubit models for which the answer can be obtained using the reduction procedure. The outcome of our experiment is that, for a given map, the property of being generated by a time-independent Lindbladian is encoded both in the eigenvalues and the eigenstates of the corresponding Choi matrix.
Digital health innovation for older adults through design thinking

KEYWORDS: Digital healthdesign thinkinghealthcarecurriculumagingculturally appropriate technologyeducationgraduate
PushPIN: A Pressure-Based Behavioral Biometric Authentication System for Smartwatches

Smartwatches support diverse applications but suffer from security issues due to their limited resources; their small size poorly supports the rich, accurate input required for screen lock authentication. Additionally, traditional approaches to unlocking smart devices, such as Personal identification number, are highly susceptible to attacks such as guessing and video observation. Therefore, we propose PushPIN, a novel scheme that combines knowledge-based and behavioral biometric approaches to increase security. Input symbols are composed of the selection of one of four different targets with one of five different pressure levels, for a total of 20 possibilities. We complement this passcode by capturing behavioral biometric features from screen touches and wrist motion during input. We present two studies to assess the performance of PushPIN. The first assesses both usability and security against a random guessing attack. It shows acceptable usability—recall times of approximately 8 s and no errors—and strong security: equal error rates of 0.51%. The second study examines the resistance of PushPIN against a video observation attack, ultimately revealing that 36.67% of PushPINs could be cracked, performance that represents a substantial improvement over prior work on pressure-based authentication input. We conclude that pressure-based input can increase the security, while maintaining reasonable usability, of smartwatch lock systems.
A method to classify bone marrow cells with rejected option

Bone marrow cell morphology has always been an important tool for the diagnosis of blood diseases. Still, it requires years of experience from a suitable person. Furthermore, the outcomes of their recognition are subjective and there is no objective quantitative standard. As a result, developing a deep learning automatic classification system for bone marrow cells is extremely important. However, typical classification machine learning systems only produce classification answers, and will not refuse to generate predictions when the prediction reliability is low. It will pose a big problem in some high-risk systems such as bone marrow cell recognition. This paper proposes a bone marrow cell classification method with rejected option (CMWRO) to classify 11 bone marrow cells. CMWRO is based on convolutional neural networks, ICP and SoftMax (CNN-ICP-SoftMax), containing a classifier with rejected option. When the rejected rate (RR) of tested samples is 0.3143, it can ensure that the precision, sensitivity, accuracy of the accepted samples reach 0.9921, 0.9917 and 0.9944 respectively. And the rejected samples will be handled by other ways, such as identified by doctors. Besides, the method has a good filtering effect on cell types that the classifier is not trained, such as abnormal cells and cells with less sample distribution. It can reach more than 82% in filtering efficiency. CMWRO improves the doctors' trust in the results of accepted samples to a certain extent. They only need to carefully identify the samples that CMWRO refuses to recognize, and finally combines the two results. It can greatly improve the efficiency and accuracy of bone marrow cell recognition.
Calibration of focus-adjustable cameras for triangulation sensors by model-based camera parameter estimation

Abstract The working range of triangulation-based measuring systems is often limited by the depth of field of the applied optical system. Approaches such as increasing the depth of field range by reducing the aperture are limited in their effectiveness by a lower light yield and thus require a longer exposure time. Focus adjustable optics enable the depth of field to be modified and thus effect the location and shape of the working range. However, the validity of the model-based camera calibration is limited to the corresponding focus setting, especially with optics that exhibit the effect of focus breathing, i. e. whose focal length change with the focus setting. This paper presents an approach to model the calibration parameters over the entire focus range of a lens using function fitting of the camera parameters and the influence of different configurations on this process is examined.
Volatility changes in cryptocurrencies: evidence from sparse VHAR-MGARCH model

This study examines the volatility changes of 20 cryptocurrencies from January 2018 to May 2021 using sparse VHAR-MGARCH model. Our proposed model incorporates the high-dimensionality and time-varying conditional heterogeneity of cryptocurrency markets. We examined the time-varying spillover index, dynamic correlation structure, and connectivity between cryptocurrencies. Our empirical analysis clearly shows that there was a volatility shift on 13 March 2020, due to a market crash caused by COVID-19. This naturally divides the data into three periods: pre-crisis, during the crisis, and post-crisis regimes. The pre-crisis regime exhibited long-term cyclic fluctuations in the spillover index. However, after the market crash, the spillover index remained at a very high level with almost no interconnections between cryptocurrencies. The post-crisis regime showed quite a few irregular and sharp spikes in the spillover index, together with record-breaking prices and volumes.
A Deep Learning Model and Training Technique for 240 Hours Load Forecasting in Korea Power System

It is essential to forecast 240 hours load accurately for stable power system operation in South Korea. Training technique for 240 hours load forecasting using deep learning is proposed. Suitable training technique for 240 hours load forecasting is developed using deep learning model. The 240 hours load forecasting method is proposed applying training technique which consists of two different types for weather prediction. According to usable weather factors depending on forecast ranges, training technique is designed by reflecting time intervals between forecasting time and forecasting target. The proposed method has improved prediction accuracy compared to the existing exponential weighted moving average method.
Beyond the snapshot: optimizing prognostication and prediction by moving from fixed to functional multidimensional cancer pathology

Abstract The role of pathology in patient management has evolved over time from the retrospective review of cells, tissue, and disease (‘what happened’) to a prospective outlook (‘what will happen’). Examination of a static, two‐dimensional hematoxylin and eosin (H&amp;E)‐stained tissue slide has traditionally been the pathologist's primary task, but novel ancillary techniques enabled by technological breakthroughs have supported pathologists in their increasing ability to predict disease status and behaviour. Nevertheless, the informational limits of 2D, fixed tissue are now being reached and technological innovation is urgently needed to ensure that our understanding of disease entities continues to support improved individualized treatment options. Here we review pioneering work currently underway in the field of cancer pathology that has the potential to capture information beyond the current basic snapshot. A selection of exciting new technologies is discussed that promise to facilitate integration of the functional and multidimensional (space and time) information needed to optimize the prognostic and predictive value of cancer pathology. Learning how to analyse, interpret, and apply the wealth of data acquired by these new approaches will challenge the knowledge and skills of the pathology community. © 2022 The Authors. The Journal of Pathology published by John Wiley &amp; Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
Detection with fast feature pyramids and lightweight convolutional neural network: a practical aircraft detector for optical remote images

Aircraft detection in optical remote sensing images has important practical value and attracts great attention in recent years. Due to some clustered backgrounds and various deformations like variability in viewpoints, scales, and directions, it is still a big challenge. In recent years, deep learning, especially convolutional neural networks (CNNs), has achieved remarkable success not only in natural image object detection tasks but also in remote sensing image object detection tasks. However, due to the limitations of the actual application deployment environment only limited processing units and storage space can be used, and most deep learning-based aircraft detection algorithms cannot work well. To this end, a unique aircraft detection framework in optical remote sensing images is proposed, which is based on fast feature pyramids and a specialized lightweight CNN. The algorithm can be summarized into two steps. In the first step, a highly reliable region proposal generation algorithm is designed to predict region proposals. In this phase, a prior square sliding window method and an efficient linear search algorithm are proposed. In the next step, a lightweight CNN is designed to achieve fast and accurate aircraft classification. The proposed model only requires about 237 KB of parameter storage and can infer in a poor central processing unit platform. Comprehensive experiments on three publicly optical remote sensing aircraft datasets demonstrate the superiority and effectiveness of the proposed method. It is also shown that the proposed method is light and fast in comparison with some state-of-the-art methods.