Reviewerly/exHarmony huggingface dataset:
Automated weak signal detection and prediction using keyword network clustering and graph convolutional network

Weak signals are rarely identified in the initial stage of growth and appear significant over time, unlike strong signals clearly observed in past trends. Weak signals are important cues that need to be analyzed to rapidly and accurately predict changes in the uncertain future. Researchers have developed various methods for identifying cues that can be significantly used for prediction. However, in many cases, they heavily depend on the opinions of experts or are applicable only to weak signals in specific fields. This study proposes a weak signal detection method that extracts weak signals by selecting significant keywords from literature database and grouping relevant keywords. Furthermore, this study presents a weak signal prediction method for predicting the growth of specific weak signals by investigating and learning the growth of the extracted weak signals over 10 years. To verify the proposed method, we extracted weak signals for 10 years (2001–2010) from SCOPUS publication data from 1996 to 2009 and applied machine learning using a graph convolutional network (GCN) model with the growth data of the extracted weak signals. The results showed that the proposed methods can effectively detect and predict weak signals.
Department Wide Validation in Digital Pathology—Experience from an Academic Teaching Hospital Using the UK Royal College of Pathologists’ Guidance

we describe our experience of validating departmental pathologists for digital pathology reporting, based on the UK Royal College of Pathologists (RCPath) "Best Practice Recommendations for Implementing Digital Pathology (DP)," at a large academic teaching hospital that scans 100% of its surgical workload. We focus on Stage 2 of validation (prospective experience) prior to full validation sign-off.twenty histopathologists completed Stage 1 of the validation process and subsequently completed Stage 2 validation, prospectively reporting a total of 3777 cases covering eight specialities. All cases were initially viewed on digital whole slide images (WSI) with relevant parameters checked on glass slides, and discordances were reconciled before the case was signed out. Pathologists kept an electronic log of the cases, the preferred reporting modality used, and their experiences. At the end of each validation, a summary was compiled and reviewed with a mentor. This was submitted to the DP Steering Group who assessed the scope of cases and experience before sign-off for full validation. A total of 1.3% (49/3777) of the cases had a discordance between WSI and glass slides. A total of 61% (30/49) of the discordances were categorised as a minor error in a supplementary parameter without clinical impact. The most common reasons for diagnostic discordances across specialities included identification and grading of dysplasia, assessment of tumour invasion, identification of small prognostic or diagnostic objects, interpretation of immunohistochemistry/special stains, and mitotic count assessment. Pathologists showed similar mean diagnostic confidences (on Likert scale from 0 to 7) with a mean of 6.8 on digital and 6.9 on glass slide reporting.we describe one of the first real-world experiences of a department-wide effort to implement, validate, and roll out digital pathology reporting by applying the RCPath Recommendations for Implementing DP. We have shown a very low rate of discordance between WSI and glass slides.
Can Imaging Spectroscopy Divulge the Process Mechanism of Mineralization? Inferences from the Talc Mineralization, Jahazpur, India

Talc deposits of Jahazpur, Rajasthan, hosted by dolomite, are one of the largest high-quality talc deposits in India. In the present study, we use AVIRIS-NG datasets to study the link between the spatial pattern of talc mineralization, associated alteration minerals, and iron-oxide enrichment. It is noted that the majority of talc-bearing areas are characterized by the presence of clay minerals, such as an intimate mixture of kaolinite and muscovite, illite, dickite (indicative of phyllic and argillic alteration), and also enhanced iron enrichment. The talc-bearing zones are located adjacent to quartz-rich lithologies, and they are aligned along the Jahazpur thrust. Based on mineralogical and geological evidence, hydrothermal alteration of dolomites by silica and iron-rich fluid is proposed as major factorcontrolling talc mineralization. This study has implications for the identification of prospective zones of talc mineralization using imaging spectroscopy.
Improved YOLOv5 for Aerial Images Based on Attention Mechanism

Object detection based on unmanned aerial vehicle(UAV) platforms is essential for both engineering and research. Complex scale problems in UAV application scenarios require strong regression localization capabilities from target detection algorithms. Nonetheless, due to the constraints of UAV platform, it is difficult to increase accuracy by deepening the network. Therefore, this paper presents an improved YOLOv5 with an attention mechanism, consisting a Convolution-Swin Transformer Block(CSTB) utilizing Swin Transformer as well as a Convolution-block Attention Module(CBAM) to improve network positioning accuracy. In addition, this paper incorporates Bidirectional Feature Pyramid Network(BiFPN) [1], Spatial Pyramid Pooling-Fast(SPPF) and some network components to increase the average precision while maintaining the limited size of the model. Experiments on Visdrone2019 dataset show that the proposed approach can raise the mean Average Precision(mAP) by 5.4% compared to YOLOv5, with only 18% increase in model size.
An Accelerator for Semi-Supervised Classification with Granulation Selection

Semi-supervised classification is one of the core methods to deal with incomplete tag information without manual intervention, which has been widely used in various real problems for its excellent performance. However, the existing algorithms need to store all the unlabeled instances and repeatedly use them in the process of iteration. Thus, the large population size may result in slow execution speed and large memory requirements. Many efforts have been devoted to solving this problem, but mainly focused on supervised classification. Now, we propose an approach to decrease the size of the unlabeled instance set for semi-supervised classification algorithms. In this algorithm, we first divide the unlabeled instance set into several subsets with the information granulation mechanism, then sort the divided subsets according to the contribution to the classifier. Following this order, the subsets that take great classification performance are saved. The proposed algorithm is compared with the state-of-the-art algorithms on 12 real datasets, and experiment results show it could get a similar prediction ability but have the lowest instance storage ratio.
Cultural Niche Construction as a Framework for Reorienting Human−Environment Relations

In this paper, we argue that the theory of cultural niche construction provides a cogent and fruitful framework for studying and managing human-environment relationships, including our conceptualizations of them. We first review the development of the ideas of niche construction from evolutionary to social contexts. We then discuss how various human cognitive and affective goals are achieved through our engagement and interaction with the environment, as cognitive and affective niche construction. We extend this analysis to the built environment, as urban niche construction, and provide two examples of urban design for which niche construction provides useful theoretical and practical insights. We also discuss how different urban policy initiatives are related through the lens of cultural niche construction.
Identifying the phonological backbone in the mental lexicon

Previous studies used techniques from network science to identify individual nodes and a set of nodes that were "important" in a network of phonological word-forms from English. In the present study we used a network simplification process-known as the backbone-that removed redundant edges to extract a subnetwork of "important" words from the network of phonological word-forms. The backbone procedure removed 68.5% of the edges in the original network to extract a backbone with a giant component containing 6,211 words. We compared psycholinguistic and network measures of the words in the backbone to the words that did not survive the backbone extraction procedure. Words in the backbone occurred more frequently in the language, were shorter in length, were similar to more phonological neighbors, and were closer to other words than words that did not survive the backbone extraction procedure. Words in the backbone of the phonological network might form a "kernel lexicon"-a small but essential set of words that allows one to communicate in a wide-range of situations-and may provide guidance to clinicians and researchers on which words to focus on to facilitate typical development, or to accelerate rehabilitation efforts. The backbone extraction method may also prove useful in other applications of network science to the speech, language, hearing and cognitive sciences.
Visualization of health information within immersive virtual reality environments

The Advanced Visualization Branch of the National Institute of Nursing Research uses computer technologies to study information visualization in support of self-care management. Advanced technologies, such as immersive virtual reality (IVR), afford researchers the opportunity to study health information visualization where user-initiated information search in visually dense settings precedes acquisition, interpretation, and use. While IVR has broad applicability in healthcare, we chose to target lay people managing chronic disease because of the growing unmet need to translate clinical recommendations into everyday behaviors. To explore how lay people seek, acquire, and interpret health information in everyday settings, we developed an IVR grocery store. In this environment, a person can locate food products, read and compare nutrition labels, and use information to make food selections. The goal of this perspective is to introduce the opportunities afforded by IVR to both present and study health information visualization and to highlight critical design considerations.
Facies-Guided Seismic Image Super-Resolution

Seismic field data usually suffer from low-resolution (LR) image quality. In recent years, high resolution (HR) seismic data acquisitions are either done in high-density seismic surveys, or by capturing high- and low-frequency bands in broadband seismic surveys. However, both methods increase the financial, computational, and memory overhead required for oil and gas exploration. Moreover, to improve the resolution of already acquired seismic field data in a certain area, another survey would be carried out in the same area with more advanced equipment. This is a time-consuming high-cost approach. In our previous work, we showcased the application of convolutional neural networks (CNN) for image super-resolution (SR) to increase the resolution of seismic images. While our application of SR for seismic images is optimized for better human perception, automatic seismic interpretation tasks may not benefit as much, such as facies classification considered in this work. We address this by proposing a novel loss function that incorporates facies information when training the SR model. The proposed loss function is applied to multiple SR models and results show that this facies-guided seismic image SR approach produces improved facies classification results while mitigating major sacrifices on the objective and subjective image quality of the reconstructed SR image.
Attention-Based Multihead Deep Learning Framework for Online Activity Monitoring With Smartwatch Sensors

The expeditious propagation of Internet of Things (IoT) technologies implanted in different smart devices such as smartphones and smartwatches have a ubiquitous consequence on the modern population.These devices are employed to collect data and to aid in tracking and analyzing the users' daily activities using various human activity monitoring and recognition (HAR) techniques.However, most current HAMR approaches rely on exploratory case-based shallow feature learning architectures, which endeavor to recognize activities correctly in real-world situations.To address this issue, we offer a unique strategy for HAMR that leverages the attention mechanism with multi-head convolutional neural networks (CNNs) and Long-Short-Term-Memory (LSTM).The accuracy of activity detection is improved in the presented method by integrating attention into multi-head CNNs followed by LSTM for better feature extraction and selection.Verification investigations are carried out using data from the University of California (UCI) repository, which is publicly available.The results show that our proposed framework is more accurate than current frameworks using both the 10-fold and leave-one-subject-out cross-validation.Finally, the proposed method can recognize human activity in real-time, regardless of the type of smart device.
Privacy preserving vaccinating‐ and testing‐pass for the European Union

Abstract Physicians and scientists hope that health‐data will provide new insights into improving medical care and optimizing healthcare costs. However, data protection laws in Europe often place limits on the use of patient data. During the COVID‐19 pandemic, both digital immunity records and data on infections were needed for pandemic management. Using this example, the research project will develop a system concept for vaccination, testing, and recovery proof called privacy preserving pass for vaccination and testing (P3VT), that will make collected data available to research and policy pandemic management in real time and anonymized. P3VT consistently considers the goals of privacy‐by‐design, data minimization and transparency of the EU‐GDPR. Expert interviews validated the system from medical, technical, and privacy perspectives. P3VT offers several advantages compared to the EU digital COVID certificate such as pseudonymized proofing of vaccination, testing, and recovery for a reduction of misuse of sensitive personal data and transparency about the nature, timing, and purpose of the proof. Above that anonymized medical data are provided to improve pandemic management, and research. P3VT is thus an exemplary solution for the comprehensive provision of health‐data for research purposes combined with high‐level data protection. Further areas of application are conceivable.
The exchange theory of web3 governance

Abstract Blockchains have enabled innovation in distributed economic institutions, such as money (e.g., cryptocurrencies) and markets (e.g., decentralised exchanges), but also innovations in distributed governance, such as decentralised autonomous organisations. These innovations have generated academic interest in studying web3 governance, but as yet there is no general theory of web3 governance. In this paper, we draw on the contrast between a ‘romantic view’ of governance (characterised by consensus through community voting) and the ‘exchange view’ of governance from public choice theory (characterised by an entrepreneurial process of bargaining and exchange of voters under uncertainty). Our analysis is the first to argue that the latter ‘exchange view’ of governance is best to understand the dynamics of governance innovation in web3, providing the foundations for a new general theory of governance in this frontier field. We apply the ‘exchange view’ of governance to three case studies (Curve, Lido and Metagov), exploring how these projects enable pseudonymous, composable and permissionless governance processes to reveal value. Our approach helps illuminate how this emergent polycentric governance process can generate robustness in decentralised systems.
A physical topology for optimizing partition tolerance in consortium blockchains to reach CAP guarantee bound

Abstract Decentralized cryptocurrency systems, known as blockchains, have shown promise as an infrastructure for mutually distrustful parties to securely agree on transactions. Nevertheless, blockchain systems are constrained by the CAP Trilemma. Due to performance degradation, it is impossible to address this issue by improving simply the consensus layer or the network layer. To alleviate the CAP constraint in consortium blockchains, we propose a topological construction method to optimize the physical layer based on multi‐dimensional hypercubes with excellent partition tolerance in probability. The basic topology has the advantage of solving the mismatch problem between the overlay network and the underlying network. It is further extended to hierarchical recursive topologies with more intermediate links or short links to balance the reliability requirement with the cost of building the physical network. We prove that the proposed hypercube topology has better partition tolerance than the regular rooted tree and ring lattice topologies, and effectively fits the upper‐layer protocols at the consensus and network layers. As a result, combined with suitable transmission and consensus protocols that satisfy strong consistency and availability, the proposed topology‐constructed blockchain can reach the CAP guarantee bound.
IoT-Gov: A structured framework for internet of things governance

The rise of artificial intelligence and smart devices with decision-making capabilities has highlighted the critical need for governance in the field of IoT. Effective governance can ensure accountability, reliability, security, and privacy in the development and use of these technologies, as well as safeguard against unethical or malicious activities. Moreover, it can drive innovation and growth in the IoT and AI industries, while fostering public trust in these technologies. The objective of this paper is to propose a new governance framework for IoT, called IoT-Gov. To achieve this objective, the paper first defines the requirements for IoT governance and assesses the compatibility of existing IT and IoT governance frameworks with these requirements. The limitations of current frameworks prove the need for a new framework, and IoT-Gov is presented as a solution. The paper explains the layers and processes of the proposed framework and provides a high-level simulation of its deployment. Specifically, the paper illustrates how the proposed framework can be applied in an example scenario of access control in a smart hospital parking, enabled with Blockchain technology. Finally, the paper concludes with an exploration of potential challenges and future research directions for the framework.
Online continual learning for human activity recognition

Sensor-based human activity recognition (HAR), with the ability to recognise human activities from wearable or embedded sensors, has been playing an important role in many applications including personal health monitoring, smart home, and manufacturing. The real-world, long-term deployment of these HAR systems drives a critical research question: how to evolve the HAR model automatically over time to accommodate changes in an environment or activity patterns. This paper presents an online continual learning (OCL) scenario for HAR, where sensor data arrives in a streaming manner which contains unlabelled samples from already learnt activities or new activities. We propose a technique, OCL-HAR, making a real-time prediction on the streaming sensor data while at the same time discovering and learning new activities. We have empirically evaluated OCL-HAR on four third-party, publicly available HAR datasets. Our results have shown that this OCL scenario is challenging to state-of-the-art continual learning techniques that have significantly underperformed. Our technique OCL-HAR has consistently outperformed them in all experiment setups, leading up to 0.17 and 0.23 improvements in micro and macro F1 scores.
Blockchain Based Simulated Virtual Machine Placement Hybrid Approach for Decentralized Cloud and Edge Computing Environments

In the distributed cloud environment, each server (computing server (CS)) is configured with Local Resource Monitors (LRM), which runs independently and performs Virtual Machine (VM) migrations to nearby servers. Approaches like predictive VM migration considering peer servers’ CPU usage, setting up rotative decision-making capacity among the peer server are some approaches proposed by the authors for decentralized cloud and edge computing environment and edge computing during their study. Decentralized cloud and edge computing environment suffer from overutilization caused due to multiple VM placements by peer servers on the same server. This work proposes adaptive predictive VM placement using blockchain with two- threshold for the decentralized cloud and edge computing environment combined with the edge computing environment. In this work, each server in the framework considers its own and peer server’s current and future CPU utilization before it takes a decision for VM migration. Experimental results reveal that the proposed dynamic threshold based predictive approach has better results compared with randomized peer-to-peer VM placement. The use of blockchain during VM placement allows the identified server to maintain its current and future utilization below the upper threshold usage limit and also ensure tampering proof communication in the peer server during VM placement.
isQ: An Integrated Software Stack for Quantum Programming

We introduce isQ, a new software stack for quantum programming in an imperative programming language, also named isQ. The aim of isQ is to make programmers write quantum programs as conveniently as possible. In particular: <list list-type="ordered" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <list-item><label>1)</label> The isQ language and its compiler contain many useful features, including but not limited to: classical control flow, such as recursion; decomposition of self-defined unitary gates; and oracle programming and its circuit realization. </list-item> <list-item><label>2)</label> To make it flexible, an isQ program can be compiled into several different kinds of intermediate representation and assemblies, including QIR, eQASM, OpenQASM 3.0, and QCIS (specially tailored for the superconducting quantum hardware at USTC). </list-item> <list-item><label>3)</label> Besides interfacing isQ with real superconducting hardware, a QIR simulator is also developed for the demonstration and testing of isQ programs. </list-item> </list> <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">isQ software stack encompasses abundant compiler optimizations of high-level quantum programs. To realize it, a distinct MLIR dialect name isQ-IR is proposed.
Forward and backward secure updatable ElGamal encryption scheme for cloud storage

Updatable encryption can support key/ciphertext update functionality, resist key compromise attacks, and be applicable to cloud storage. However, as a type of partitioning strategy, leakage sets or firewalls technique has been adopted by the existing updatable encryption schemes to build a relaxed security model, which forbids the adversary from querying the secret key at the challenge-equal epochs and hardly captures forward and backward security. In this paper, an enhanced security model can be built by considering a corruption oracle that allows any secret key query without requiring leakage sets. Then we utilize the indistinguishability obfuscation technique of the punctured program to construct an updatable ElGamal encryption scheme with no-directional key update and uni-directional ciphertext update. Furthermore, under the framework of the enhanced security model, the proposed scheme is proven to be IND-CPA. Finally, by comparison with the existing updatable encryption schemes, the proposed scheme eliminates leakage sets and firewalls, and can achieve both forward and backward security.