Reviewerly/exHarmony huggingface dataset:
Research Communities and the Collective Investment in Data Infrastructure

Abstract The field of comparative law and politics requires further investment in data infrastructure, including databases of judicial opinions, codes of various types, many features of the litigation process, which can promote coordination in scholarship at scale. The resource costs associated with producing these data are likely to be prohibitive for single researchers and even many small groups of scholars. Producing what the field needs is likely to require considerable collaboration among a relatively large, methodologically diverse set of scholars. In addition to providing vital infrastructure, this kind of collaboration can also help promote a healthy research community by encouraging learning about and coordination on good scientific standards. The approach will address communication problems that inhibit parallel work on shared research problems. Doing this well is no simple task, though. Organizing collaborative teams depends on ensuring that the incentives of team members are consistent with group goals, a challenge complicated by standard team production concerns, but especially problematic in light of disciplinary incentives that discourage infrastructural investment. Drawing on the experiences of the Varieties of Democracy Project, this chapter discusses how this kind of collaboration might manifest in the field. It highlights a decentralized team approach that balances individual incentives for academic freedom against group needs for consistency.
A quantum blind signature scheme based on dense coding for non-entangled states

Abstract Some schemes where quantum blind signatures require the use of difficult-to-prepare multiparticle entangled states. Considering the communication overhead, quantum operation complexity, verification efficiency and other relevant factors in practical situations, this article proposes a non entangled quantum blind signature scheme based on dense encoding.The information owner utilizes dense encoding and hash functions to blind the information while reducing the use of quantum resources.After receiving particles,the signer encrypts the message using a one-way function and performs a Hadamard gate operation on the selected single photon to generate the signature, and the verifier performs a Hadamard gate inverse operation on the signature and combines it with the encoding rules to restore the message and complete the verification. Compared with some typical quantum blind signature protocols, this protocol has strong blindness in privacy protection, higher flexibility in scalability and application. The signer can adjust the signature operation according to the actual situation, which greatly simplifies the complexity of the signature.Simultaneously utilizing the secondary distribution and rearrangement of non entangled quantum states, a non entangled quantum state representation of three bits of classical information was achieved,reducing the use of a large amount of quantum resources and lowering implementation costs. This improves both signature verification efficiency and communication efficiency, while at the same time, this scheme meets the requirements of unforgeability, non repudiation, and prevention of information leakage.
Hybrid deep learning models for time series forecasting of solar power

Abstract Forecasting solar power production accurately is critical for effectively planning and managing renewable energy systems. This paper introduces and investigates novel hybrid deep learning models for solar power forecasting using time series data. The research analyzes the efficacy of various models for capturing the complex patterns present in solar power data. In this study, all of the possible combinations of convolutional neural network (CNN), long short-term memory (LSTM), and transformer (TF) models are experimented. These hybrid models also compared with the single CNN, LSTM and TF models with respect to different kinds of optimizers. Three different evaluation metrics are also employed for performance analysis. Results show that the CNN–LSTM–TF hybrid model outperforms the other models, with a mean absolute error (MAE) of 0.551% when using the Nadam optimizer. However, the TF–LSTM model has relatively low performance, with an MAE of 16.17%, highlighting the difficulties in making reliable predictions of solar power. This result provides valuable insights for optimizing and planning renewable energy systems, highlighting the significance of selecting appropriate models and optimizers for accurate solar power forecasting. This is the first time such a comprehensive work presented that also involves transformer networks in hybrid models for solar power forecasting.
Absolute-separability witnesses for symmetric multiqubit states

The persistent separability of certain quantum states, known as symmetric absolutely separable (SAS), under symmetry-preserving global unitary transformations is of key significance in the context of quantum resources for bosonic systems.In this work, we develop criteria for detecting SAS states of any number of qubits.Our approach is based on the Glauber-Sudarshan P representation for finite-dimensional quantum systems.We introduce three families of SAS witnesses, one linear and two nonlinear in the eigenvalues of the state, formulated respectively as an algebraic inequality or a quadratic optimization problem.These witnesses are capable of identifying more SAS states than previously known counterparts.We also explore the geometric properties of the subsets of SAS states detected by our witnesses, shedding light on their distinctions.
İnsansız Hava Araçları (İHA) İçin Küresel Navigasyon Uydu Sistemi (GNSS) Bağımsız Navigasyon

The ability of Unmanned Aerial Vehicles (UAV) to perform autonomous navigation depends on the accurate determination of their positions provided by the Global Navigation Satellite System (GNSS). For position determination and environmental orientation during flight, UAVs are usually equipped with electronic equipment such as GNSS, Inertial Measurement Unit (IMU), gyroscope and accelerometer. However, the GNSS signal may be lost or distorted due to poor weather, obstacles or terrain, the unfavorable position of satellites, spoofing and jamming. In such cases of GNSS signal loss or deterioration, the IMU alone becomes unable to provide reliable UAV location information. Especially in cases where there is not enough visibility and the UAV cannot be brought to the take-off point by manual operation, the loss of the GNSS signal causes great losses. In this paper, GNSS independent flight and navigation studies are included. It is seen that the use of hybrid navigation solutions has great importance in GNSS independent UAV flights.
Blockchain Traceability Adoption in Low-Carbon Supply Chains: An Evolutionary Game Analysis

Blockchain technology has brought innovation to supply chain management, particularly in managing carbon emissions in the manufacturing sector. However, there is a research gap regarding the policy tools and the role of local governments in implementing blockchain technology to achieve carbon emissions traceability. Additionally, the strategic relationships and policy implications resulting from the implementation of blockchain technology are not examined systematically. An effective method for examining the strategies used in interactions between supply chain stakeholders and governments is evolutionary game theory, or EGT. This paper employs mathematical modelling and MATLAB 2016 software simulation to examine the decision-making process of manufacturing companies when considering implementing blockchain technology traceability. Specifically, the subjects in the model include product manufacturers (PM), product suppliers (PS), and local governments (LGs). The aim is to examine the decision-making behavior of carbon traceability participants in blockchain technology. This paper analyses the most effective blockchain-based traceability strategies for low-carbon supply chain members under a variety of scenarios by modifying the parameters. The findings suggest the following: (1) Manufacturers and suppliers need to manage the cost of blockchain traceability, collaborate to create an environmentally friendly product certification system, and improve brand image. (2) Local governments should set up efficient reward and punishment systems to incentivize supply chain stakeholders to engage in the blockchain traceability system. The aforementioned discoveries furnish policymakers with guidance to encourage the implementation of blockchain-based carbon footprint traceability technology, thereby establishing a transparent carbon footprint traceability framework across the entire supply chain.
Accelerating Quantum Algorithms with Precomputation

Real-world applications of computing can be extremely time-sensitive. It would be valuable if we could accelerate such tasks by performing some of the work ahead of time. Motivated by this, we propose a cost model for quantum algorithms that allows quantum precomputation; i.e., for a polynomial amount of ``free&amp;apos;&amp;apos; computation before the input to an algorithm is fully specified, and methods for taking advantage of it. We analyze two families of unitaries that are asymptotically more efficient to implement in this cost model than in the standard one. The first example of quantum precomputation, based on density matrix exponentiation, could offer an exponential advantage under certain conditions. The second example uses a variant of gate teleportation to achieve a quadratic advantage when compared with implementing the unitaries directly. These examples hint that quantum precomputation may offer a new arena in which to seek quantum advantage.
MedLingua: A Conceptual Framework for a Multilingual Medical Conversational Agent

This study introduces a hybrid model for an advanced medical chatbot addressing crucial healthcare communication challenges. Leveraging a hybrid ML model, the chatbot aims to provide accurate and prompt responses to users' health-related queries. The proposed model will overcome limitations observed in previous medical chatbots by integrating a dual-stemming approach, P-Stemmer and NLTK-Stemmer, accommodating both semitic and non-semitic languages. The system prioritizes the analysis of cognates, identification of symptoms, doctor recommendations, and prescription generation. It integrates an automatic translation module to facilitate a smooth multilingual diagnostic experience. Following the Scrum methodology for agile development, the framework ensures adaptability to evolving research needs and stays current with recent medical discoveries. This groundbreaking idea aims to improve the effectiveness and availability of healthcare services by introducing an intelligent, multilingual chatbot. This technology enables patients to communicate with doctors from diverse linguistic backgrounds through an automated language translation model, eliminating language barriers and extending healthcare access to rural regions worldwide.•A simple but efficient hybrid conceptual model for advancement in smart medical assistance.•This conceptual model can be applied to implement a medical chatbot that can understand multiple languages.•This method can be utilized to address medical chatbot limitations and enhance accuracy in response generation.
Metaphors of Digital Transformation

Abstract This chapter explores metaphors used by organizational actors to understand digital transformation and digitalized organizations. Following a “pigeon-holing approach” (Örtenblad 2017), we identify and categorize two sets of metaphors. The first set of metaphors consists of the metaphors of digital transformation: that is, the metaphors that deal with change—the wave metaphor and the digit metaphor. These metaphors help us better understand different dimensions of digital transformation as a technological and organizational change process. The second set consists of the metaphors of digitalized organizations: that is, the metaphors that deal with transformed organizations. Based on Morgan’s (1986) metaphors, the authors identify three root metaphors, namely the machine metaphor, brain metaphor, and instrument of domination metaphor. They argue that using several metaphors as categories helps us identify differences between various phenomena associated with digital transformation and digitalized organizations, and offer an overview of organizational actors’ understandings of these phenomena.
Exploring the influence of anthropomorphic appearance on usage intention on online medical service robots (OMSRs): A neurophysiological study

Online medical service robots (OMSRs) are becoming increasingly important in the medical industry, and their design has become a highly focused issue. This study investigated the neuroeconomics underlying the formation of usage intention, specifically evaluating the impact of anthropomorphic appearance and age on users' intentions to use OMSRs. Event-related potentials were used to analyze electroencephalography signals recorded from participants. This study found that OMSRs with a low anthropomorphic appearance induced larger P200 and P300 amplitudes, resulting in increased attentional resources compared to OMSRs with a moderate or high anthropomorphic appearance. OMSRs with moderate anthropomorphic appearances captured more attention and elicited larger P200 and P300 than those with high anthropomorphic appearances. Regarding age characteristics, OMSRs with senior features attracted more attention and induced larger P200 and P300 amplitudes. In terms of usage intention, compared to the others, users demonstrate a stronger usage intention towards the low anthropomorphism of OMSRs. Additionally, compared to the senior ones, users also exhibit a stronger usage intention toward a young appearance of OMSRs. These findings provide valuable insights for robot designers and practitioners to improve the appearance of OMSRs.
Shaping photons: Quantum information processing with bosonic cQED

With its rich dynamics, the quantum harmonic oscillator is an innate platform for understanding real-world quantum systems and could even excel as the heart of a quantum computer. A particularly promising and rapidly advancing platform that harnesses quantum harmonic oscillators for information processing is the bosonic circuit quantum electrodynamics (cQED) system. In this article, we provide perspectives on the progress, challenges, and future directions in building a bosonic cQED quantum computer. We describe the main hardware building blocks and how they facilitate quantum error correction, metrology, and simulation. We conclude with our views of the key challenges that lie on the horizon, as well as scientific and cultural strategies for overcoming them and building a practical quantum computer with bosonic cQED hardware.
Changing Digital and Telecytology Practices post COVID-19 Comparing ASC survey results from 2016 to 2023

During the COVID-19 pandemic, the need for digital pathology tools became more urgent. However, there needs to be more knowledge of the use in cytology. We aimed to evaluate current digital cytology practices and attitudes and compare the results with a pre-COVID-19 American Society of Cytopathology (ASC) survey.Fourteen survey questions assessing current attitudes toward digital cytology were developed from a 2016 ASC Digital Pathology Survey. Ten new survey questions were also created to evaluate telecytology use. The survey was e-mailed to ASC members over 6 weeks in 2023.A total of 123 individuals responded (116 in 2016). Attitudes toward digital cytology were unchanged; most participants stated digital cytology is beneficial (87% 2023 versus 90% 2016). The percentage of individuals using digital cytology was unchanged (56% in 2016 and 2023). However, telecytology for rapid onsite assessment (ROSE) is now considered the best application (55% 2023 versus 31% 2016). Forty-three institutions reported using digital and telecytology tools; 40% made implementations after 2020; most did not feel that COVID-19 affected digital cytology (56%). Telecytology for ROSE is the most common application now (78%) compared with education (30%) in 2016. Limitations for implementing digital imaging in cytology included inability to focus (38%) and expense (33%).General attitudes toward digital tools by the cytology community have essentially remained the same between 2016 and now. However, telecytology for ROSE is increasingly being used, which supports a need for validation and competency guidelines.
Residual Contextual Hourglass Network for Single-Image Deraining

Abstract Rain streaks could blur and distort images, significantly impacting further image processing. Single-image deraining is a hotspot and has practical application value, while most existing methods still have problems such as residual rain streaks and inadequate recovery of detail textures. To address these issues, we propose a Residual Contextual Hourglass Network (RCHNet) for single-image deraining, which could adapt to remove rain streaks in complex environments. Firstly, a contextual distillation block is presented to obtain local and global features across different scales. Further, residual downsampling block and residual upsampling block are used to maintain the residual nature of our architecture and better restore the details of the image. Finally, a dual attention mechanism is introduced to compensate for the spatial information lost by the downsampling. Extensive experiments on five synthetic datasets and a real-world dataset demonstrate that our proposed RCHNet outperforms existing state-of-the-art deraining approaches. On average across all synthetic datasets, the PSNR score of RCHNet is as high as 33.31 dB.
Towards more reliable evaluation in pedestrian detection by rethinking “ignore regions”

Abstract It remains a challenging task to detect pedestrians in crowds and it needs more efforts to understand why the detectors fail. When we perform an error analysis based on the traditional evaluation strategy, we find that it produces many misleading false positives, which in fact cover occluded pedestrians. The reason for this is that we usually have two kinds of annotations in the dataset: regular pedestrians (detection targets) labeled by full-body boxes and ignored pedestrians (NOT detection targets) labeled by visible boxes. Ignored pedestrians are labeled as an additional category termed the “ignore region”. Nevertheless, our detectors always predict a full-body box for each pedestrian. This gap results in the following case: when a detector successfully predicts a full-body box for those ignored pedestrians, a false positive is triggered due to the low overlap between the predicted full-body box and the labeled visible box for the ignored pedestrian. This becomes even more harmful as the detector improves and becomes more capable of locating occluded pedestrians. To alleviate this issue, we devise a new pedestrian detection pipeline, which considers the additional visible box at both the detection and evaluation stages. During detection, we predict an extra visible box apart from the full-body box for every instance; during evaluation, we employ visible boxes instead of full-body boxes to match the “ignore region”. We apply the new pipeline to dozens of detection methods and validate the effectiveness of our pipeline in reducing the over-reporting of false positives and providing more reliable evaluation results.
Blockchain and Access Control Encryption-Empowered IoT Knowledge Sharing for Cloud-Edge Orchestrated Personalized Privacy-Preserving Federated Learning

Federated learning (FL) is emerging as a powerful paradigm for distributed data mining in the context of Internet of Things (IoT) big data. It addresses privacy concerns associated with data outsourcing by enabling local data training and knowledge (i.e., model) sharing. However, simplistic local knowledge sharing can inadvertently expose user privacy to advanced attacks, such as model inversion or gradient leakage. Furthermore, achieving fine-grained and personalized privacy protection for IoT users remains a challenge. In this paper, we propose a novel solution called hierarchical blockchain-empowered cloud-edge orchestrated federated learning (HBCE-FL) to address these challenges. HBCE-FL is designed to provide secure, intelligent, and distributed data analysis for IoT users. To tackle FL’s privacy issues, we develop a multi-level access control encryption and blockchain-based approach for sharing IoT knowledge within the HBCE-FL framework. Our approach classifies IoT users into different levels based on their individual privacy requirements, enabling fine-grained privacy protection. The blockchain is employed for identity authentication, key management, and message sanitization. For scenarios involving IoT users with non-IID data, we integrate federated multi-task learning into HBCE-FL to ensure fairness, robustness, and privacy. Finally, we conduct experiments using classic MNIST and CIFAR10 datasets to validate our approach. The experimental results illustrate that HBCE-FL effectively achieves personalized privacy-preserving FL without losing IoT data availability. Regardless of whether IoT data are homogeneous or heterogeneous, our approach enhances model accuracy and convergence rates by enabling secure IoT knowledge access and sharing for IoT users.
Construction and Application of an Interactive Teaching Platform for Aesthetic Education in Ecological Environments

Kruskal algorithm, as a minimum spanning tree optimization analysis algorithm, has been well used in the field of industrial production to find the optimal parameters. In this study, an interactive teaching platform based on Kruskal algorithm is built based on the ecological aesthetic education theory. Secondly, based on multiple internet of things systems, through the analysis of the problems existing in traditional classroom teaching, combined with the characteristics of ecological aesthetic education, the interactive platform can automatically generate more efficient teaching courses and related methods. Finally, this study verifies the reliability and stability of the above platform through experiments and practices. The experimental results show that the application of the theory and method of ecological aesthetic education in the interactive teaching platform based on Kruskal algorithm achieves the combination of traditional classroom teaching and ecological aesthetic education and gives full play to the main role of students in the classroom.
Postdigital Teaching of Critical Thinking in Higher Education: Non-Instrumentalised Sociality and Interactivity

Abstract In the wake of the digital revolution, the digital teaching of critical thinking takes established forms in higher education. Its technologies are productively understood in postdigital terms as a diverse, inconsistent and cobbled-together collection of platforms and software. This paper considers the limits, problems and advantages of this messy and layered amalgam of technologies. Examining them shows that the benefits of digital critical thinking teaching are frequently bound up with digitisations’ support of sociability and interactivity. This is counterbalanced by difficulties and limits encountered in digitised teaching of critical thinking, often framed as deficiencies in students, teachers, institutions or technologies. However, following Bernard Stiegler’s work and postdigital scholarship, these distinctions can be countered to understand critical thinking technologies as performed within social, technical and psychic milieus processes. Stiegler’s emphasis on temporality allows for a critical analysis of the constraints of digital forms of sociality and interactivity. His notion of otium suggests that techniques cultivating interruptions and layering in digital critical thinking technologies engage their negativity and enable temporal zones in which reflective thought can emerge.