Reviewerly/exHarmony huggingface dataset:
B-UAVM: A Blockchain-Supported Secure Multi-UAV Task Management Scheme

The advent of unmanned aerial vehicle (UAV) swarm technology brings possibilities to help humans complete tasks in no man’s land, such as deserts, and rainforests. However, UAV network faces many cyber threats, where attackers can impersonate legitimate entities or tamper with UAV task data. For identity security, most of the existing methods use centralized authentication schemes, which have a single point of failure problem. For data security, the existing methods only secure the task data in the ground system, ignoring the data security in the air network. Therefore, the existing methods are not suitable for ubiquitous UAV scenarios. Blockchain secures data security while eliminating the single point of failure problem, and has been widely used in distributed scenarios. In this paper, to secure entity identity and task data, we propose a Blockchain-supported Secure Multi UAV Task Management Scheme (B-UAVM). Specifically, a three-layer blockchain structure is constructed to secure multi-tasks, and achieve ubiquitous control of UAV formations. Besides, six types of blocks and three types of transactions are designed to achieve safe processing and storage of task data and entity information. Furthermore, an improved Practical Byzantine Fault Tolerance (IPBFT) consensus mechanism and a UAV-formation-action-considered ground station consensus mechanism (UFAGS) are introduced in the Server Network and Ground Control Network, respectively, to accelerate the consensus. The experimental results show that the number of transactions generated per second (TPS) of B-UAVM is about 0.5× and 3.7× of the existing method when the block size or the number of blockchain nodes increases, respectively.
Prospects and Challenges with Legal Informatics and Legal Metrology Framework in the Context of Industry 6.0

Industry 6.0 is viewed as a futuristic industrial evolution. It aims to set the synergy between man and robot by providing the wealth affluence away from the trade, and customers solutions that would provide progress to the world in every field across all planetary boundaries. Industry 6.0 comprises numerous promising technology such as quantum computing, artificial intelligence, machine learning, cloud computing, and quantum artificial computing. These technologies have the massive capabilities to help the legal industry, metrology industry and many other organizations attain their goals by delivering excellent and best-suited insights. In the future, these technologies will assist the lawmaker and jurisprudence in improving the old law justice system into the advanced one. In this article, we first introduce a glimpse of industry 6.0 from the outlook of legal information, digital transformation in metrology, and strength, limitation, opportunities, and threat analysis in brief. Then we discuss key technologies of industry 6.0, framework of legal informative, a futuristic model for a legal informatics system based on quantum-based AI, machine learning, and digital legal metrology and its challenges in adoption in regard's Industry 6.0. In subsequent sections, we discussed requirements, challenges, and progress in developed countries, preparedness of academia and industry in India. Then, we brief challenges for legal metrology in the future, opportunities for legal metrology in future legal challenges faced by lawyers and legal metrology, limitations of the study, research implication, and conclusion. Finally, the finding shows that industry 6.0 technologies have enormous capabilities to bring a revolutionary change in the legal industry but are present in the new born growth stage.
CAREPATH methodology for development of computer interpretable, integrated clinical guidelines

The process of developing a computer interpretable, integrated clinical guideline requires multiple considerations and decisions. As part of the CAREPATH project, a holistic approach to comorbidity has been adopted using an integrated clinical guideline for the management of multimorbid patients with mild cognitive impairment or mild dementia. The project’s clinical and technical teams would later interpret and implement the integrated clinical guideline into the CAREPATH holistic computer interpretable guideline. Three phases should be completed to accomplish the patient-centered computer interpretable guideline modelling, which include the conceptual modelling, interpretable modelling and localization phases, respectively. This paper describes the methodological viewpoints of this process and the relevant considerations.
Non-Gaussian Reconciliation for Continuous-Variable Quantum Key Distribution

Non-Gaussian modulation can improve the performance of continuous-variable quantum key distribution (CV QKD). For Gaussian-modulated coherent-state CV QKD, photon subtraction can realize non-Gaussian modulation, which can be equivalently implemented by non-Gaussian postselection. However, non-Gaussian reconciliation has not been deeply researched, which is one of the key technologies in CV QKD. In this paper, we propose a non-Gaussian reconciliation method to obtain identical keys from non-Gaussian data. Multidimensional reconciliation and multiedge-type low-density parity-check codes (MET LDPC) are used in a non-Gaussian reconciliation scheme, where the layered belief propagation decoding algorithm of MET LDPC codes is used to reduce the decoding complexity. Furthermore, we compare the error-correction performance of Gaussian data and non-Gaussian data. The results show that the error-correction performance of non-Gaussian data is better than Gaussian data, where the frame error rate can be reduced by 50 % for code rate 0.1 at SNR of 0.1554 and the average iteration number can be reduced by 25 %.
Changes in the attitudes of mathematics and science teachers toward the integration and use of computerized technological tools as a result of the COVID-19 pandemic

This quantitative retrospective study examines a non-probable convenience sample of 104 in-service mathematics and science teachers with the aim to understand their changes in attitudes toward the integration and use of information and communications technology (ICT) as a result of the COVID-19 pandemic. Data included a self-report questionnaire. The results indicate an increase in teachers’ knowledge regarding the e-learning environments available at their schools. The teachers revealed that before the pandemic, they mostly used computer tools to support struggling students or carry out basic calculations. However, during, because they had to plan distance learning environments, they introduced adaptive pedagogical use of ICT tools for all their students. The findings reveal that most teachers experienced difficulties emanating from lack of preparation time, technological knowledge, and/or technical conditions, thus suggesting that policymakers must decide how to provide adequate knowledge and technical support to allow teachers to successfully integrate computerized technologies into their teaching.
Quantum correlations beyond entanglement between two moving atoms interacting with a coherent cavity

Studying the ability of atom-photon interactions, especially in two two-level atomic systems, to generate quantum information resources has recently become an important research topic in quantum information science. Therefore, this paper explores the ability of two moving atoms coupling with a coherent field through a two-photon transition to generate atomic quantum correlations by using local quantum uncertainty (LQU), local quantum Fisher information (LQFI) as well as logarithmic negativity (LN). Schrödinger equation is used to obtain the time evolution of the atom-cavity-atom interactions with an initial coherent cavity state and an initial atomic uncorrelated pure state. The generation of atomic LQU, LQFI, and LN correlations are exactly examined under the unitary interaction parameter effects, including the atom-cavity coupling strengths, the cavity field half-wave number, and the initial coherent state intensity. The atom-cavity-atom interaction parameters lead to notable changes in the amplitudes, speed, and regularity of the LQU, LQFI, and LN dynamics, which can be enhanced by increasing the initial coherent intensity. The cavity field half-wave number leads to generating atomic quantum correlations with regular oscillatory behavior. The sudden death-birth phenomenon of the logarithmic negativity depends on the atom-cavity-atom interaction and the atomic location parameter.
Ten years of 1 Hz solar irradiance observations at Cabauw, the Netherlands, with cloud observations, variability classifications, and statistics

Abstract. Surface solar irradiance varies on scales down to seconds, and detailed long-term observational datasets of this variable are rare but in high demand. Here, we present an observational dataset of global, direct, and diffuse solar irradiance sampled at 1 Hz as well as fully resolved variability until at least 0.1 Hz over a period of 10 years from the Baseline Surface Radiation Network (BSRN) station at Cabauw, the Netherlands. The dataset is complemented with irradiance variability classifications, clear-sky irradiance and aerosol reanalysis, information about the solar position, observations of clouds and sky type, and wind measurements up to 200 m above ground level. Statistics of variability derived from all time series include approximately 185 000 detected events of both cloud enhancement and cloud shadows. Additional observations from the Cabauw measurement site are freely available from the open-data platform of the Royal Netherlands Meteorological Institute. This paper describes the observational site, quality control, classification algorithm with validation, and the processing method of complementary products. Additionally, we discuss and showcase (potential) applications, including limitations due to sensor response time. These observations and derived statistics provide detailed information to aid research into how clouds and atmospheric composition influence solar irradiance variability as well as information to help validate models that are starting to resolve variability at higher fidelity. The main datasets are available at https://doi.org/10.5281/zenodo.7093164 (Knap and Mol, 2022) and https://doi.org/10.5281/zenodo.7462362 (Mol et al., 2022); the reader is referred to the “Code and data availability” section of this paper for the complete list.
Economic Globalisation and Africa’s Quest for Greener and More Inclusive Growth: The Missing Link

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Use of QR Code in Dairy Sector in Slovenia

The increasing use of smartphones has allowed quick response (QR) codes to build a stronger connection between consumers and products, enabling a bridge between offline and online products and providing quick access to product information, traceability, and food safety. This study focuses on two primary aspects: familiarity with QR codes and their usage by consumers and FMCG (fast-moving consumer goods) producers in Slovenia. Additionally, authors examined consumers’ motivations to scan QR codes for food products, particularly dairy products. Data collected through online research, interviews, and field investigations revealed that consumers rarely scan QR codes. To increase its usage, it should be included in an overall advertising campaign to provide instant benefit; consequently, it would increase consumer engagement with the product. Field research and interviews revealed that only a few packaged products are equipped with QR codes, primarily to support trade marketing activities. In addition, most consumers scan the code at home seeking information regarding its origin, which provides insights into future implementation possibilities. Furthermore, from the design point, QR codes should be positioned on the side panel of the packaging together with an invitation to motivate and explain how scanning would benefit the consumer.
DARWIN: An online deep learning approach to handle concept drifts in predictive process monitoring

Predictive process monitoring (PPM) is a specific task under the umbrella of Process Mining that aims to predict several factors of a business process (e.g., next activity prediction) based on the knowledge learned from historical event logs. Despite recent PPM algorithms have gained predictive accuracy using deep learning, they commonly perform an offline analysis of event data assuming that logged processes remain in a steady state over time. However, this is often not the real-world case due to concept drifts. The main goal of this work is to solve the next-activity prediction problem under dynamic conditions of business data streams. To this aim, we propose DARWIN as a novel PPM method that detects concept drifts and adapts a deep neural model to concept drifts. A deep empirical analysis of different factors that may influence the performance of DARWIN in streaming scenarios is provided. Experiments with various benchmark event streams show the effectiveness of the proposed approach.
A Target Re-Identification Method Based on Shot Boundary Object Detection for Single Object Tracking

With the advantages of simple model structure and performance-speed balance, the single object tracking (SOT) model based on a Transformer has become a hot topic in the current object tracking field. However, the tracking errors caused by the target leaving the shot, namely the target out-of-view, are more likely to occur in videos than we imagine. To address this issue, we proposed a target re-identification method for SOT called TRTrack. First, we built a bipartite matching model of candidate tracklets and neighbor tracklets optimized by the Hopcroft–Karp algorithm, which is used for preliminary tracking and judging the target leaves the shot. It achieves 76.3% mAO on the tracking benchmark Generic Object Tracking-10k (GOT-10k). Then, we introduced the alpha-IoU loss function in YOLOv5-DeepSORT to detect the shot boundary objects and attained 38.62% mAP75:95 on Microsoft Common Objects in Context 2017 (MS COCO 2017). Eventually, we designed a backtracking identification module in TRTrack to re-identify the target. Experimental results confirmed the effectiveness of our method, which is superior to most of the state-of-the-art models.
A Nondegenerate <i>n</i>-Dimensional Hyperchaotic Map Model with Application in a Keyed Parallel Hash Function

The construction of multidimensional discrete hyperchaotic maps with ergodicity and larger Lyapunov exponents is desired in cryptography. Here, we have designed a general [Formula: see text]D ([Formula: see text]) discrete hyperchaotic map ([Formula: see text]D-DHCM) model that can generate any nondegenerate [Formula: see text]D chaotic map with Lyapunov exponents of desired size through setting the control matrix. To verify the effectiveness of the [Formula: see text]D-DHCM, we have provided two illustrative examples and analyzed their dynamic behavior, and the results demonstrated that their state points have ergodicity within a sufficiently large interval. Furthermore, to address the finite precision effect of the simulation platform, we analyzed the relationship between the size of Lyapunov exponent and the randomness of the corresponding state time sequence of the [Formula: see text]D-DHCM. Finally, we designed a keyed parallel hash function based on a 6D-DHCM to evaluate the practicability of the [Formula: see text]D-DHCM. Experimental results have demonstrated that [Formula: see text]D discrete chaotic maps constructed using [Formula: see text]D-DHCM have desirable Lyapunov exponents, and can be applied to practical applications.
Self-Supervised Remote Sensing Image Dehazing Network Based on Zero-Shot Learning

Traditional dehazing approaches that rely on prior knowledge exhibit limited efficacy when confronted with the intricacies of real-world hazy environments. While learning-based dehazing techniques necessitate large-scale datasets for effective model training, the acquisition of these datasets is time-consuming and laborious, and the resulting models may encounter a domain shift when processing real-world hazy images. To overcome the limitations of prior-based and learning-based dehazing methods, we propose a self-supervised remote sensing (RS) image-dehazing network based on zero-shot learning, where the self-supervised process avoids dense dataset requirements and the learning-based structures refine the artifacts in extracted image priors caused by complex real-world environments. The proposed method has three stages. The first stage involves pre-processing the input hazy image by utilizing a prior-based dehazing module; in this study, we employed the widely recognized dark channel prior (DCP) to obtain atmospheric light, a transmission map, and the preliminary dehazed image. In the second stage, we devised two convolutional neural networks, known as RefineNets, dedicated to enhancing the transmission map and the initial dehazed image. In the final stage, we generated a hazy image using the atmospheric light, the refined transmission map, and the refined dehazed image by following the haze imaging model. The meticulously crafted loss function encourages cycle-consistency between the regenerated hazy image and the input hazy image, thereby facilitating a self-supervised dehazing model. During the inference phase, the model undergoes training in a zero-shot manner to yield the haze-free image. These thorough experiments validate the substantial improvement of our method over the prior-based dehazing module and the zero-shot training efficiency. Furthermore, assessments conducted on both uniform and non-uniform RS hazy images demonstrate the superiority of our proposed dehazing technique.
Surveillance capitalism and systemic digital risk: The imperative to collect and connect and the risks of interconnectedness

Zuboff's The Age of Surveillance Capitalism provides a powerful analysis of the emergence of surveillance capitalism as a particular type of informational capitalism. Many of the important impacts of this project of creating larger and more integrated systems of ‘behavioural surplus’ are captured powerfully by Zuboff; yet as different risk and organisational scholars such as Beck, Perrow, and Vaughan have argued, integrated systems often do not function as intended. While the imperfection of these systems may raise the possibility that surveillance capitalism may not be as bad as Zuboff suggests, there is also a way in which these systems not functioning as intended can make surveillance capitalism an even more dystopian possibility. In this vein, this paper asks: what are the consequences when the tools of a surveillance capitalist society break down? This paper argues that it is by thinking through Zuboff's framework that we can identify the systemic fragility of a surveillance capitalist society. This systemic fragility emerges through how surveillance capitalism generates imperatives towards the maximal collection of data for exploitation, which in turn generates a corresponding imperative to connect all aspects of life. Both of these imperatives, of collect and connect, in turn create an immensely fragile digital system, which has vast ramifications throughout social life, such that small imperfections and gaps in the system can magnify risk throughout society.
Automated Classification for Open-Ended Questions with BERT

Abstract Manual coding of text data from open-ended questions into different categories is time consuming and expensive. Automated coding uses statistical/machine learning to train on a small subset of manually-coded text answers. Recently, pretraining a general language model on vast amounts of unrelated data and then adapting the model to the specific application has proven effective in natural language processing. Using two data sets, we empirically investigate whether BERT, the currently dominant pretrained language model, is more effective at automated coding of answers to open-ended questions than other non-pretrained statistical learning approaches. We found fine-tuning the pretrained BERT parameters is essential as otherwise BERT is not competitive. Second, we found fine-tuned BERT barely beats the non-pretrained statistical learning approaches in terms of classification accuracy when trained on 100 manually coded observations. However, BERT’s relative advantage increases rapidly when more manually coded observations (e.g., 200–400) are available for training. We conclude that for automatically coding answers to open-ended questions BERT is preferable to non-pretrained models such as support vector machines and boosting.
Towards a geometry deductive database prover

Abstract The Geometry Automated-Theorem-Provers (GATP) based on the deductive database method use a data-based search strategy to improve the efficiency of forward chaining. An implementation of such a method is expected to be able to efficiently prove a large set of geometric conjectures, producing readable proofs. The number of conjectures a given implementation can prove will depend on the set of inference rules chosen, the deductive database method is not a decision procedure. Using an approach based in an SQL database library and using an in-memory database, the implementation described in this paper tries to achieve the following goals. Efficiency in the management of the inference rules, the set of already known facts and the new facts discovered, by the use of the efficient data manipulation techniques of the SQL library. Flexibility, by transforming the inference rules in SQL data manipulation language queries, will open the possibility of meta-development of GATP based on a provided set of rules. Natural language and visual renderings, possible by the use of a synthetic forward chaining method. Implemented as an open source library, that will open its use by third-party programs, e.g. the dynamic geometry systems.
It's about Time: Analytical Time Periodization

Abstract This paper presents a novel approach to the problem of time periodization, which involves dividing the time span of a complex dynamic phenomenon into periods that enclose different relatively stable states or development trends. The challenge lies in finding such a division of the time that takes into account diverse behaviours of multiple components of the phenomenon while being simple and easy to interpret. Despite the importance of this problem, it has not received sufficient attention in the fields of visual analytics and data science. We use a real‐world example from aviation and an additional usage scenario on analysing mobility trends during the COVID‐19 pandemic to develop and test an analytical workflow that combines computational and interactive visual techniques. We highlight the differences between the two cases and show how they affect the use of different techniques. Through our investigation of possible variations in the time periodization problem, we discuss the potential of our approach to be used in various applications. Our contributions include defining and investigating an earlier neglected problem type, developing a practical and reproducible approach to solving problems of this type, and uncovering potential for formalization and development of computational methods.