Reviewerly/exHarmony huggingface dataset:
A new evidential similarity measurement based on Tanimoto measure and its application in multi-sensor data fusion

Dempster–Shafer evidence theory is a powerful fusion approach for reasoning under uncertain circumstances. However, unreasonable results will generate when utilizing Dempster’s combination rule to fuse highly conflicting evidence. How to accurately measure the conflict degree between two pieces of evidence is still a problem to be solved. To date, the method of conflict measurement has been proposed by many authors. Unfortunately, the effectiveness of these approaches is limited. In this paper, a new method of evidential similarity measurement is devised in the light of Tanimoto measurement to describe the inconsistency between bodies of evidence. Then, a novel evidential conflict measurement approach is presented in accordance with the evidential similarity measurement to quantify the conflict degree between bodies of evidence. It is analyzed and proved that the novel approach of evidential conflict measurement satisfies the property of conflict measures. Some numerical examples are given to demonstrate the efficacy and superiority of the proposed approach. Moreover, the validity of the proposed approach is evaluated by the application of target recognition.
A systematic review of enabling blockchain in healthcare system: Analysis, current status, challenges and future direction

Blockchain is a digital ledger of duplicated transactions and distributed across the complete network of computer systems. It is a system of recording information that makes it difficult or impossible to alter, hack the system. Data sharing are highly secure and truthful due to cryptographic principles. Blockchain technology has used widely used in different areas, primarily because of the usage of cryptocurrencies. The technology also supports supply chain, business, Healthcare, IoT, privacy, and data management. This review focuses on an analysis of blockchain research art within the sector of healthcare is included. Traditional health records system used to share health record data amongst healthcare contributors, criticized for centralizing ability, failures and storm in the interchange of data using a distributed ledger. Health records have problems in system requirements such as security, privacy, ethics, performance and other regulatory confinement. Access control, data integrity consistency, and provenance are the issues to be improved in the blockchain. The most fabric platforms/frameworks used in the blockchain is Ethereum and Hyperledger.
How Far Have We Progressed in Identifying Self-admitted Technical Debts? A Comprehensive Empirical Study

Background. Self-admitted technical debt (SATD) is a special kind of technical debt that is intentionally introduced and remarked by code comments. Those technical debts reduce the quality of software and increase the cost of subsequent software maintenance. Therefore, it is necessary to find out and resolve these debts in time. Recently, many automatic approaches have been proposed to identify SATD. Problem. Popular IDEs support a number of predefined task annotation tags for indicating SATD in comments, which have been used in many projects. However, such clear prior knowledge is neglected by existing SATD identification approaches when identifying SATD. Objective. We aim to investigate how far we have really progressed in the field of SATD identification by comparing existing approaches with a simple approach that leverages the predefined task tags to identify SATD. Method. We first propose a simple heuristic approach that fuzzily Matches task Annotation Tags ( MAT ) in comments to identify SATD. In nature, MAT is an unsupervised approach, which does not need any data to train a prediction model and has a good understandability. Then, we examine the real progress in SATD identification by comparing MAT against existing approaches. Result. The experimental results reveal that: (1) MAT has a similar or even superior performance for SATD identification compared with existing approaches, regardless of whether non-effort-aware or effort-aware evaluation indicators are considered; (2) the SATDs (or non-SATDs) correctly identified by existing approaches are highly overlapped with those identified by MAT ; and (3) supervised approaches misclassify many SATDs marked with task tags as non-SATDs, which can be easily corrected by their combinations with MAT . Conclusion. It appears that the problem of SATD identification has been (unintentionally) complicated by our community, i.e., the real progress in SATD comments identification is not being achieved as it might have been envisaged. We hence suggest that, when many task tags are used in the comments of a target project, future SATD identification studies should use MAT as an easy-to-implement baseline to demonstrate the usefulness of any newly proposed approach.
PlaneTR: Structure-Guided Transformers for 3D Plane Recovery

This paper presents a neural network built upon Transformers, namely PlaneTR, to simultaneously detect and reconstruct planes from a single image. Different from previous methods, PlaneTR jointly leverages the context information and the geometric structures in a sequence-to-sequence way to holistically detect plane instances in one forward pass. Specifically, we represent the geometric structures as line segments and conduct the network with three main components: (i) context and line segments encoders, (ii) a structure-guided plane decoder, (iii) a pixel-wise plane embedding decoder. Given an image and its detected line segments, PlaneTR generates the context and line segment sequences via two specially designed encoders and then feeds them into a Transformers-based decoder to directly predict a sequence of plane instances by simultaneously considering the context and global structure cues. Finally, the pixel-wise embeddings are computed to assign each pixel to one predicted plane instance which is nearest to it in embedding space. Comprehensive experiments demonstrate that PlaneTR achieves state-of-the-art performance on the ScanNet and NYUv2 datasets.
Distributed diffusion unscented Kalman filtering based on covariance intersection with intermittent measurements

In this paper, a distributed diffusion unscented Kalman filtering algorithm based on covariance intersection strategy (DDUKF-CI) is proposed for target tracking with intermittent measurements. By virtue of the pseudo measurement matrix, the standard unscented Kalman filtering (UKF) with intermittent observations is transformed to the information form for the diffusion algorithm to fuse intermediate information from neighbors and improve the estimation performance. Considering unknown correlations in sensor networks, covariance intersection (CI) strategy is combined with the diffusion algorithm. Moreover, it is proved that the proposed DDUKF-CI is consistent and the estimation error is exponentially bounded in mean square using the stochastic stability theory. Finally, the performances of the proposed algorithm and the weighted average consensus unscented Kalman filtering (CUKF) are compared in a target tracking problem with a sensor network.
PU21: A novel perceptually uniform encoding for adapting existing quality metrics for HDR

Standard image quality metrics, such as PSNR or SSIM, cannot be directly computed on linear high dynamic range colour values because such values non-linearly related to our perception of visible differences. In this work, we develop a new encoding function (PU21) to convert absolute high dynamic range (HDR) linear colour values into approximately perceptually uniform (PU) values, which can be used with standard quality metrics. The proposed PU21 function is based on a recent contrast sensitivity model, fitted to the measurements up to 10000cd/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . Unlike the conventional simplified approach of deriving PU functions based on peak sensitivities, we model realistic coding artefacts to find visibility thresholds for our derivation. Furthermore, the new PU accounts for the effect of glare on image quality. The proposed PU21 improves the accuracy of quality predictions for standard metrics of PSNR, VSI, FSIM, SSIM, and MS-SSIM in their correlation with subjective scores on HDR images included in UPIQ, one of the largest HDR image quality datasets.
Toward a Competency-based Approach to Co-designing Technologies with People with Intellectual Disability

Ability-based design is a useful framework that centralizes the abilities (all that users can do) of people with disabilities in approaching the design of assistive technologies. However, although this framework aspires to support designing with people with all kinds of disabilities, it is mainly effective in supporting those whose abilities can be clearly defined and measured, in particular, physical and sensory attributes of ability. As a result, the ability-based design framework only provides limited guidance to design with users with intellectual disability, whose cognitive, physical, sensory, and practical abilities vary along a spectrum. In this article, we reflect on a long-term co-design study where we leveraged what we termed “competencies,” i.e., the representative practical skills people develop from their participation in life activities, in particular, mainstream technologies, such as social media and the Internet. Our reflection is based on our experience in designing SkillsTube, a web application we co-designed with young adults with intellectual disability to support them to learn life skills through videos. The app's design, which explored and leveraged their social media participation competencies, supported the fundamental participation of all participants and their peers. Their familiarity with the app's social media-inspired design features fostered confidence in their participation, usability, and engagement. Drawing on the findings and design process of the app, we discuss a Competency-based approach to designing with people with disabilities that extends upon ability-based design, by grounding it in user competencies.
CEPchain: A graphical model-driven solution for integrating complex event processing and blockchain

Blockchain provides an immutable distributed ledger for storing transactions. One of the challenges of blockchain is the particular processing of dynamic queries due to accumulating costs. Complex Event Processing (CEP) provides efficient and effective support for this in a way, however, that is difficult to integrate with blockchain. This paper addresses the research challenges of integrating blockchain with CEP. More specifically, we envision an effective development environment in which (i) event-driven smart contracts are modeled in a graphical way, which are, in turn, (ii) automatically transformed into complementary code that is deployed in both a CEP engine and a blockchain network, and then (iii) executed on off-chain CEP applications which, connected to different data sources and sinks, automatically invoke smart contracts when event pattern conditions are met. We follow a classic systems engineering approach for defining the concepts of our system, called CEPchain, which addresses the described requirements. CEPchain was evaluated using a real-world case study for vaccine delivery, which requires an unbroken cold chain. The results demonstrate that our approach can be applied without requiring experts on event processing and smart contract languages. Our contribution simplifies the design of integrated CEP and blockchain functionality by hiding implementation details and supporting efficient deployment.
Quick Annotator: an open‐source digital pathology based rapid image annotation tool

Abstract Image‐based biomarker discovery typically requires accurate segmentation of histologic structures (e.g. cell nuclei, tubules, and epithelial regions) in digital pathology whole slide images (WSIs). Unfortunately, annotating each structure of interest is laborious and often intractable even in moderately sized cohorts. Here, we present an open‐source tool, Quick Annotator (QA), designed to improve annotation efficiency of histologic structures by orders of magnitude. While the user annotates regions of interest (ROIs) via an intuitive web interface, a deep learning (DL) model is concurrently optimized using these annotations and applied to the ROI. The user iteratively reviews DL results to either (1) accept accurately annotated regions or (2) correct erroneously segmented structures to improve subsequent model suggestions, before transitioning to other ROIs. We demonstrate the effectiveness of QA over comparable manual efforts via three use cases. These include annotating (1) 337,386 nuclei in 5 pancreatic WSIs, (2) 5,692 tubules in 10 colorectal WSIs, and (3) 14,187 regions of epithelium in 10 breast WSIs. Efficiency gains in terms of annotations per second of 102×, 9×, and 39× were, respectively, witnessed while retaining f ‐scores &gt;0.95, suggesting that QA may be a valuable tool for efficiently fully annotating WSIs employed in downstream biomarker studies.
A New Approach for Active Automata Learning Based on Apartness

Abstract We present $$L^{\#}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>L</mml:mi> <mml:mo>#</mml:mo> </mml:msup> </mml:math> , a new and simple approach to active automata learning. Instead of focusing on equivalence of observations, like the $$L^{*}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>L</mml:mi> <mml:mrow> <mml:mrow /> <mml:mo>∗</mml:mo> </mml:mrow> </mml:msup> </mml:math> algorithm and its descendants, $$L^{\#}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>L</mml:mi> <mml:mo>#</mml:mo> </mml:msup> </mml:math> takes a different perspective: it tries to establish apartness , a constructive form of inequality. $$L^{\#}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>L</mml:mi> <mml:mo>#</mml:mo> </mml:msup> </mml:math> does not require auxiliary notions such as observation tables or discrimination trees, but operates directly on tree-shaped automata. $$L^{\#}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>L</mml:mi> <mml:mo>#</mml:mo> </mml:msup> </mml:math> has the same asymptotic query and symbol complexities as the best existing learning algorithms, but we show that adaptive distinguishing sequences can be naturally integrated to boost the performance of $$L^{\#}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>L</mml:mi> <mml:mo>#</mml:mo> </mml:msup> </mml:math> in practice. Experiments with a prototype implementation, written in Rust, suggest that $$L^{\#}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>L</mml:mi> <mml:mo>#</mml:mo> </mml:msup> </mml:math> is competitive with existing algorithms.
A Literature Review on Blockchain-enabled Security and Operation of Cyber-Physical Systems

Blockchain has become a key technology in a plethora of application domains owing to its decentralized public nature. The cyber-physical systems (CPS) is one of the prominent application domains that leverage blockchain for myriad oper-ations, where the Internet of Things (IoT) is utilized for data collection. Although some of the CPS problems can be solved by simply adopting blockchain for its secure and distributed nature, others require complex considerations for overcoming blockchain-imposed limitations while maintaining the core aspect of CPS. Even though a number of studies focus on either the utilization of block chains for different CPS applications or the blockchain-enabled security of CPS, there is no comprehensive survey including both perspectives together. To fill this gap, we present a comprehensive overview of contemporary advancement in using blockchain for enhancing different CPS operations as well as improving CPS security. To the best of our knowledge, this is the first paper that presents an in-depth review of research on blockchain-enabled CPS operation and security.
Federated deep reinforcement learning based secure data sharing for Internet of Things

The increasing number of Internet of Things (IoT) devices motivate the data sharing that improves the quality of IoT services. However, data providers usually suffer from the privacy leakage caused by direct data sharing. To solve this problem, in this paper, we propose a Federated Learning based Secure data Sharing mechanism for IoT, named FL2S. Specifically, to accomplish efficient and secure data sharing, a hierarchical asynchronous federated learning (FL) framework is developed based on the sensitive task decomposition. In addition, to improve data sharing quality, the deep reinforcement learning (DRL) technology is utilized to select participants of sufficient computational capabilities and high quality datasets. By integrating task decomposition and participant selection, reliable data sharing is realized by sharing local data models instead of the source data with data privacy preserved. Experiment results show that the proposed FL2S achieves high accuracy in secure data sharing for various IoT applications.
Limitations of receiver operating characteristic curve on imbalanced data: Assist device mortality risk scores

Objective: This study illustrates the ambiguity of ROC in evaluating two classifiers of 90-day LVAD mortality. This paper also introduces the precision recall curve (PRC) as a supplemental metric that is more representative of LVAD classifiers performance in predicting the minority class. Background: In the LVAD domain, the receiver operating characteristic (ROC) is a commonly applied metric of performance of classifiers. However, ROC can provide a distorted view of classifiers ability to predict short-term mortality due to the overwhelmingly greater proportion of patients who survive, i.e. imbalanced data. Methods: This study compared the ROC and PRC for the outcome of two classifiers for 90-day LVAD mortality for 800 patients (test group) recorded in INTERMACS who received a continuous-flow LVAD between 2006 and 2016 (mean age of 59 years; 146 females vs. 654 males) in which mortality rate is only %8 at 90-day (imbalanced data). The two classifiers were HeartMate Risk Score (HMRS) and a Random Forest (RF). Results: The ROC indicates fairly good performance of RF and HRMS classifiers with Area Under Curves (AUC) of 0.77 vs. 0.63, respectively. This is in contrast with their PRC with AUC of 0.43 vs. 0.16 for RF and HRMS, respectively. The PRC for HRMS showed the precision rapidly dropped to only 10% with slightly increasing sensitivity. Conclusion: The ROC can portray an overly-optimistic performance of a classifier or risk score when applied to imbalanced data. The PRC provides better insight about the performance of a classifier by focusing on the minority class.
Quantum algorithms for quantum dynamics: A performance study on the spin-boson model

Quantum algorithms for quantum dynamics simulations are traditionally based on implementing a Trotter-approximation of the time-evolution operator. This approach typically relies on deep circuits and is therefore hampered by the substantial limitations of available noisy and near-term quantum hardware. On the other hand, variational quantum algorithms have become an indispensable alternative, enabling small-scale simulations on present-day hardware. However, despite the recent development of variational quantum algorithms for quantum dynamics, a detailed assessment of their efficiency and scalability is yet to be presented. To fill this gap, we applied a variational quantum algorithm based on McLachlan's principle to simulate the dynamics of a spin-boson model subject to varying levels of realistic hardware noise as well as in different physical regimes, and discuss the algorithm's accuracy and scaling behavior as a function of system size. We observe a good performance of the variational approach used in combination with a general, physically motivated wavefunction ansatz, and compare it to the conventional first-order Trotter-evolution. Finally, based on this, we make scaling predictions for the simulation of a classically intractable system. We show that, despite providing a clear reduction of quantum gate cost, the variational method in its current implementation is unlikely to lead to a quantum advantage for the solution of time-dependent problems.
All optical metropolitan quantum key distribution network with post-quantum cryptography authentication

Quantum key distribution (QKD) provides information theoretically secures key exchange requiring authentication of the classic data processing channel via pre-sharing of symmetric private keys. In previous studies, the lattice-based post-quantum digital signature algorithm Aigis-Sig, combined with public-key infrastructure (PKI) was used to achieve high-efficiency quantum security authentication of QKD, and its advantages in simplifying the MAN network structure and new user entry were demonstrated. This experiment further integrates the PQC algorithm into the commercial QKD system, the Jinan field metropolitan QKD network comprised of 14 user nodes and 5 optical switching nodes. The feasibility, effectiveness and stability of the post-quantum cryptography (PQC) algorithm and advantages of replacing trusted relays with optical switching brought by PQC authentication large-scale metropolitan area QKD network were verified. QKD with PQC authentication has potential in quantum-secure communications, specifically in metropolitan QKD networks.
Mutation-based Arithmetic Optimization Algorithm for Global Optimization

Real-life optimization problems need an algorithm which efficiently explores the search area to obtain a global optimal solution. The arithmetic optimization algorithm (AOA) is a recently developed algorithm that performs search operation using the basic mathematic functions; Addition, multiplication, division, and Subtraction. However, the AOA stuck at the local optimum solution for some functions due to inadequate balance between diversification and intensification. Therefore, in the present work, a balance is provided between exploration and exploitation search mechanism by incorporating mutation strategy into the AOA and named as mutation-based arithmetic optimization algorithm (m-AOA). The proposed algorithm's performance is tested on twenty-three benchmark functions having different characteristics. Results obtained are compared in terms of performance parameters, including average, standard deviation, median, worst and best values with other optimization algorithms. The comparison suggested that the proposed method outperforms the other algorithms.