Reviewerly/exHarmony huggingface dataset:
Experimental discrimination of two-qubit orthogonal states via local operations and classical communication

Quantum nonlocality, a form of nonclassicality, serves as a crucial resource in quantum information processing. Nonlocality in quantum systems is exemplified by the perfect discrimination of orthogonal quantum states through single joint measurements, which is unattainable using local operations and classical communication (LOCC). Bandyopadhyay [S. Bandyopadhyay, Phys. Rev. Lett. 106, 210402 (2011)] established that, with LOCC alone, at most $N\ensuremath{-}1$ copies of $N$ orthogonal quantum states are required for perfect discrimination. Here, we present the experimental verification of orthogonal quantum state discrimination employing LOCC. Our results demonstrate that up to three copies suffice for the perfect distinction of four orthogonal two-qubit states via LOCC. The verified method of perfectly distinguishing orthogonal quantum state sets offers potential applications in quantum information processes, including quantum secret sharing and quantum teleportation.
Peripheral Computations: The Basis for Data Processing in Internet of Things

Internet of Things (ІоТ) is a global mainstream trend of today, which determines the importance of studies dealing with problems related with data processing by use of ІоТ. &#x0D; A definition to the notion “Internet of Things” is given, with stressing that peripheral computing is a new trend for connected devices of IoT. The main applications of IoT are described. Peripheral computing is compared with cloud and fog computing. The need for peripheral computing in the conditions of “smart” life is demonstrated. Various forms of the launched peripheral computing infrastructure are discussed. The need for peripheral computing in the software based on IoT technology is justified. It is shown that IoT pushes up the demand for peripheral computing. Real examples of successful applications of peripheral computing and IoT across industries are shown. &#x0D; It is stressed that the strongest impediment for the massive dissemination of IoT is that its infrastructure involves infinite numbers of options for connection, which may be quite often fully incompatible with each other. It is argued that peripheral computing is needed to remove drawbacks in software design and services on the basis of cloud technologies with consideration to performance and normative requirements. The forms of peripheral computing infrastructure are shown and described: special local devices, localized or regional data processing centers. The purpose of IoT technology is mentioned, which is the collection of large scopes of data from great many censors and smart devices, to be employed for business performance enhancement. The main reasons for the necessity of peripheral computing in the software based on IoT technology are outlined: the need to increase the throughput, the possibility to minimize the delay in data transmission and processing, and normative requirements. The main advantages of digital technology introduction are highlighted: better comfort of clients, higher exploitation efficiency, creation of new income flows.
Exploring the path of high-quality development of teacher education and teaching based on collaborative filtering algorithm

Abstract In this paper, firstly, a collaborative filtering algorithm based on users is used to mine the past behaviors of the target users. Items are glanced and composed to gain insight into the user’s preferred items and to mine preference information, followed by searching for users with similar preferences to the user in the system and calculating the similarity between users and users using cosine similarity and pearson similarity, and predicting the target user’s rating of an element based on the rating value of an item given by a nearby user. Then the personalized teaching system is designed by the actual situation of the online teacher teaching courses on the platform and the course demands of the platform students, and the path of high-quality improvement of teacher training and teaching is studied, mainly focusing on three aspects: clear value orientation, good personalized system teaching planning, and implementation of multiple evaluations. When analyzing the student-teacher ratio of teacher education institutions, the student-teacher ratio of Central China Normal University has the largest value, 24.79%, while the values of East China Normal University and Nanjing Normal University are relatively small, 15.97% and 15.39%, respectively. Either based on theory or based on practice, any institution of higher education with an excessive student-teacher ratio. This study helps teachers grow professionally and provides a good foundation for eventual individualized student development.
Application of Bat Algorithm for Data Anonymization

The rapid proliferation of digital data has raised significant concerns regarding privacy and data security, necessitating the development of effective data anonymization techniques. This research presents a novel application of the Bat Algorithm (BA) for data anonymization, a nature-inspired optimization algorithm that mimics the echolocation behavior of Bats. The proposed approach leverages the BA's unique search capabilities to achieve a delicate balance between data utility and privacy preservation, a critical aspect in today's data-driven world. By treating data attributes as potential solutions and employing the BA's search process, the algorithm iteratively identifies and modifies sensitive attributes while minimizing information loss. This research contributes to the developing field of research on data anonymization by introducing a nature-inspired optimization technique that offers a promising alternative to traditional anonymization methods. Experimental results on various real-world datasets demonstrate the effectiveness of the proposed approach in achieving robust privacy protection while maintaining data quality, outperforming existing anonymization methods in terms of utility and computational efficiency. Furthermore, the proposed BA-based data anonymization approach exhibits versatility, scalability, and adaptability, making it suitable for diverse application domains, from healthcare and finance to social media and beyond. In summary, this study highlights the potential of the Bat Algorithm as a valuable tool in the field of data anonymization, offering a promising avenue for addressing the privacy challenges associated with the ever-expanding digital data landscape.
Turaco: Complexity-Guided Data Sampling for Training Neural Surrogates of Programs

Programmers and researchers are increasingly developing surrogates of programs, models of a subset of the observable behavior of a given program, to solve a variety of software development challenges. Programmers train surrogates from measurements of the behavior of a program on a dataset of input examples. A key challenge of surrogate construction is determining what training data to use to train a surrogate of a given program. We present a methodology for sampling datasets to train neural-network-based surrogates of programs. We first characterize the proportion of data to sample from each region of a program's input space (corresponding to different execution paths of the program) based on the complexity of learning a surrogate of the corresponding execution path. We next provide a program analysis to determine the complexity of different paths in a program. We evaluate these results on a range of real-world programs, demonstrating that complexity-guided sampling results in empirical improvements in accuracy.
The Quality of Public-funded Oriented Physical Education Normal Students’ Cultivate in Guangdong Province

Background and Aim: Guangdong Province in February 2018 the province-wide launch of the Guangdong "new teacher training" construction implementation programs, used to improve the rural music, physical education, and art teacher structure imbalance of the status quo. This research aim was to avoid the risks of the cultivation of public-funded oriented physical education normal students in Guangdong Province in all phases of the cultivation process. Materials and Methods: This study was to study the motivation for applying public-funded oriented physical education to normal students in Guangdong Province, the indicator system for the development of rural feeling, and the factors influencing the willingness to comply. Analyze software programs were used to analyze the data collected from 462 public-funded oriented physical education normal students. Researchers use self-administered questionnaires and analyze descriptive statistics and exploratory factor analysis. Use the Delphi method and hierarchical analysis, with the first 2 rounds used to determine the indicator system and the third round used to calculate the weights of each indicator. The result: (1) By the discriminant validity analysis shown the value of the factors influencing were 0.921 (Teacher’s Love), 0.8800 (Self-awareness), 0.789 (Career Benefits), and 0.819 (Policy dividends). (2) By combining the results of the first and second rounds of expert questionnaires, the final evaluation index system for public-funded oriented physical education normal students’ sentiments towards local teaching was determined to consist of 4 primary indicators, 10 secondary indicators, and 30 tertiary indicators. Conclusion: The factors influencing the motivation for applying for public-funded oriented physical education for normal students include four factors: Policy dividends, Career Benefits, Self-awareness, and Teacher’s Love. Rural feeling can be used as a mediating variable to influence the effect value of learning engagement and willingness to comply, School Climate can be used as a moderating variable to regulate the relationship between learning engagement and Rural feeling, as well as learning engagement and willingness to comply. In particular, when the School Climate tends to be 'local', the effect of learning engagement on Rural feelings and willingness to comply is greater.
Exploring the Impact of Automation Bias and Complacency on Individual Criminal Responsibility for War Crimes

Abstract With advancing technology, complex decision-making in warfare, including targeting, is increasingly assisted by machines. Although involving humans in decision-making is often seen as a safeguard against machine errors, it does not always prevent them. Machines can make incorrect determinations or delay them when time is critical. In these cases, human operators, influenced by automation bias (excessive trust in machines’ determinations, despite the availability of contradicting or different information from other sources) or complacency (excessive trust in machines’ determinations, leading to reduced vigilance), may fail to recognize machine errors, potentially resulting in conduct amounting to a war crime. Considering the role of automation bias and complacency in the determination of the criminal responsibility of systems’ operators is crucial, especially, for understanding the accountability framework for war crimes involving autonomous weapon systems (AWS). By exploring how automation bias and complacency affect the determination of criminal responsibility for humans who operate AWS, this article offers insights for lawmakers at the national and international levels to understand complexities and effectively shape legislative responses with respect to criminal responsibility. This article also examines automation bias and complacency in psychology, their relevance in military operations employing AWS, and their potential to exonerate human operators from criminal responsibility. It concludes by advocating for legislative, organizational, and technical measures to counteract automation bias and complacency.
Hyperintensional models for non-congruential modal logics

Abstract In this work, we illustrate applications of a semantic framework for non-congruential modal logic based on hyperintensional models. We start by discussing some philosophical ideas behind the approach; in particular, the difference between the set of possible worlds in which a formula is true (its intension) and the semantic content of a formula (its hyperintension), which is captured in a rigorous way in hyperintensional models. Next, we rigorously specify the approach and provide a fundamental completeness theorem. Moreover, we analyse examples of non-congruential systems that can be semantically characterized within this framework in an elegant and modular way. Finally, we compare the proposed framework with some alternatives available in the literature. In the light of the results obtained, we argue that hyperintensional models constitute a basic, general and unifying semantic framework for (non-congruential) modal logic.
Cybersecurity in UK Universities: mapping (or managing) threat intelligence sharing within the higher education sector

Abstract Higher education has recently been identified as a sector of concern by the UK National Cyber Security Centre (NCSC). In 2021, the NCSC reported that universities and higher education institutions (HEI) had been exponentially targeted by cyber-criminals. Existing challenges were amplified or highlighted over the course of the global pandemic when universities struggled to continue to function through hybrid and remote teaching provision that relied heavily on their digital estate and services. Despite the value of the sector and the vulnerabilities within it, higher education has received relatively little attention from the cybersecurity research community. Over 2 years, we carried out numerous interventions and engagements with the UK higher education sector. Through interviews with cybersecurity practitioners working in the sector as well as roundtables, and questionnaires, we conducted a qualitative and quantitative analysis of threat intelligence sharing, which we use as a proxy for measuring and analysing collaboration. In a unique approach to studying collaboration in cybersecurity, we utilized social network analysis. This paper presents the study and our findings about the state of cybersecurity in UK universities. It also presents some recommendations for future steps that we argue will be necessary to equip the higher education sector to continue to support UK national interests going forward. Key findings include the positive inclination of those working in university cybersecurity to collaborate as well as the factors that impede that collaboration. These include management and insurance constraints, concerns about individual and institutional reputational damage, a lack of trusted relationships, and the lack of effective mechanisms or channels for sectoral collaboration. In terms of the network itself, we found that it is highly fragmented with a very small number of the possible connections active, none of the organizations we might expect to facilitate collaboration in the network are playing a significant role, and some universities are currently acting as key information bridges. For these reasons, any changes that might be led by sectoral bodies such as Jisc, UCISA or government bodies such as NCSC, would need to go through these information brokers.
GRASP: a goodness-of-fit test for classification learning

Abstract Performance of classifiers is often measured in terms of average accuracy on test data. Despite being a standard measure, average accuracy fails in characterising the fit of the model to the underlying conditional law of labels given the features vector (Y∣X), e.g. due to model misspecification, over fitting, and high-dimensionality. In this paper, we consider the fundamental problem of assessing the goodness-of-fit for a general binary classifier. Our framework does not make any parametric assumption on the conditional law Y∣X and treats that as a black-box oracle model which can be accessed only through queries. We formulate the goodness-of-fit assessment problem as a tolerance hypothesis testing of the form H0:E[Df(Bern(η(X))‖Bern(η^(X)))]≤τ where Df represents an f-divergence function, and η(x), η^(x), respectively, denote the true and an estimate likelihood for a feature vector x admitting a positive label. We propose a novel test, called Goodness-of-fit with Randomisation and Scoring Procedure (GRASP) for testing H0, which works in finite sample settings, no matter the features (distribution-free). We also propose model-X GRASP designed for model-X settings where the joint distribution of the features vector is known. Model-X GRASP uses this distributional information to achieve better power. We evaluate the performance of our tests through extensive numerical experiments.
Future photovoltaic solar power resources in Zambia: a CORDEX-CORE multi-model synthesis

Abstract The exploration of renewable energy such as wind and solar radiation has the potential of reducing reliance on fossil fuels, thus cutting emissions of carbon dioxide, particulate matter, and several other greenhouse gasses. However, recent findings indicate that wind speed across Zambia is very slow, it is increasing but remains unlikely to support large commercial wind farms. In this study, we explore the future impacts of climate change on solar photovoltaic resources. To do this, we examine the new high-resolution (25 km) Coordinated Regional Climate Downscaling Experiment—CORDEX-CORE simulations for the African domain, using two different emission scenarios until 2100. At an annual scale, results indicate a weak but steady decrease in PV Res of around 0.02 W/m 2 per annum under RCP2.6 and about 0.005 W/m 2 per annum under RCP8.5. Results further show that at an average of ~ 237 ± 3.3 W/m 2 and 212 ± 2.5 W/m 2 , respectively, RCP2.6 comes along with 12 ± 3% more PV Res than RCP8.5. Thus RCP2.6, a greener and climate-friendly pathway, points towards a higher renewable energy potential across Zambia compared to the business-as-usual pathway.
Improving CNNs classification with pathologist-based expertise: the renal cell carcinoma case study

The prognosis of renal cell carcinoma (RCC) malignant neoplasms deeply relies on an accurate determination of the histological subtype, which currently involves the light microscopy visual analysis of histological slides, considering notably tumor architecture and cytology. RCC subtyping is therefore a time-consuming and tedious process, sometimes requiring expert review, with great impact on diagnosis, prognosis and treatment of RCC neoplasms. In this study, we investigate the automatic RCC subtyping classification of 91 patients, diagnosed with clear cell RCC, papillary RCC, chromophobe RCC, or renal oncocytoma, through deep learning based methodologies. We show how the classification performance of several state-of-the-art Convolutional Neural Networks (CNNs) are perfectible among the different RCC subtypes. Thus, we introduce a new classification model leveraging a combination of supervised deep learning models (specifically CNNs) and pathologist's expertise, giving birth to a hybrid approach that we termed ExpertDeepTree (ExpertDT). Our findings prove ExpertDT's superior capability in the RCC subtyping task, with respect to traditional CNNs, and suggest that introducing some expert-based knowledge into deep learning models may be a valuable solution for complex classification cases.
Detecting the Attacks Using Blockchain-Based Decentralized Security Architecture in IoT Environment

There has been a surge in the demand for embedded devices as people prepare for the arrival of the Internet of Things (IoT), which plans for the autonomous interaction of sensors and actuators to provide various forms of smart services. Due to their limited processing power, data storage, and connectivity, Internet of Things gadgets are ripe targets for cybercriminals. Engineering scalable security solutions that are best suited to the IoT ecosystem is essential for ensuring the safe growth of IoT. In recent years, blockchain skill has arose as a central tenet of IoT-based app creation. The blockchain may be used to address concerns about data privacy, security, and reliance on a third party in IoT programmes. Individuals and communities alike stand to gain from blockchain’s incorporation into the Internet of Things. Nevertheless, a DDoS assault on a mining pool in 2017 revealed serious vulnerabilities in the blockchain-enabled Internet of Things (IoT). The data that this programme creates is also massive. As Deep Learning (DL) allows for fully autonomous large data analysis and decision-making skills, it is employed as a decision-making and analytical tool. This research presents a unique distributed (IDS) to identify DDoS assaults on a financial institution’s statement as a solution to the problems mentioned above. Long Short-Term Memory (LSTM) is trained to identify attacks against banks and other financial institutions using the Banking Dataset, and its effectiveness is then measured. The findings produced utilising banking-IoT data flow prove the technique’s efficacy.
A Permissioned Blockchain Approach for Real-Time Embedded Control Systems

In real-time embedded control (RTEC) systems, sensors collect data which is processed and sent to different control nodes. RTEC deployments have numerous applications in diverse verticals like industrial control, healthcare, and vehicular networks. In such cases, a trusted and verifiable control is required, particularly when the data is kept in a distributed manner, and is exchanged over open wireless channels. Thus, blockchain (BC) is a viable option to store the sensor data between RTEC systems, which maintains a trusted ledger of associated operations. Existing works have not focused on the integration of BC in RTEC systems. Motivated by the gap, the paper presents a systematic approach to integrating BC in RTEC ecosystems. We present a reference architecture and discuss the device registration, the hyperledger fabric set up, and the task offloading strategy between edge gateways and cloud nodes, and present the performance analysis of the architecture. The discussion of open issues and challenges also highlights the practical implications of the approach, emphasizing its importance for future deployments of real-time embedded control systems.
UAV Smart Navigation: Combining Delaunay Triangulation and the Bat Algorithm for Enhanced Efficiency

In the realm of unmanned aerial vehicles (UAVs), path planning is crucial. The objective is to create a safe, practical, and optimum flight route for UAVs to fly across unfamiliar terrain while avoiding obstacles. Because of their high computational complexity and inability to handle changing surroundings, traditional route planning algorithms confront hurdles in real-world applications. As a result, academics have created many sophisticated ways to handle UAV route planning challenges. This research provides a unique technique for intelligent UAV route planning. The program first divides the research region using the Delaunay Triangulation method. After obtaining the partitions, the coverage points are located, and the issue is framed as the Traveling Salesperson Problem (TSP). The Bat Optimization Algorithm (BOA) is used to find an optimum route for the TSP. The suggested technique seeks to find the best route for UAVs in dynamic situations. The suggested approach was verified by comparing it to other existing algorithms. The findings demonstrate that the suggested method can construct an optimum route that meets all requirements while avoiding obstacles. Moreover, the method may swiftly identify a new route as the environment changes. The study findings show that the suggested approach beats the other techniques in total computing time, saving roughly 0.03 s. Moreover, the suggested technique reduces the UAV’s route by around 0.02 cm, substantially improving existing algorithms.