Reviewerly/exHarmony huggingface dataset:
A study on the path of governance in health insurance fraud considering moral hazard

Combating health insurance fraud is of utmost importance to physicians, patients, and health insurers. To delve into the mechanisms of health insurance fraud between doctors and patients, this study employed evolutionary game theory to construct a model that comprehensively considers moral hazard, fraud cost, reward, punishment, bribes from patients, and other factors. Through theoretical analysis and numerical simulation of the model, the study discovered that the evolution of governance behavior in health insurance fraud is closely linked to its initial construction of the payment matrix and the initial selection of parameters for the payment matrix. Additionally, increasing penalties for fraudulent behavior, increasing the cost of fraud for both doctors and patients, and reducing moral hazard for both can effectively drive the final strategy of the system toward a non-fraudulent state. The study aims to provide valuable insights and recommendations to doctors, patients, and medical insurance institutions in establishing a sound governance environment for managing fraud behavior in health insurance.
Professional Competencies in TVET: Framework, Indicators and Assessment Instrument

This chapter starts with a discussion of the challenges in and need for developing TVET teachers’ digital teaching competencies. Informed by existing research and practices in developing professional competencies for teachers, in particular for TVET teachers, we then propose a framework that identifies core competencies required of in-service TVET teachers and practitioners in the digital age in Sect. 3.2. In Sect. 3.3, this framework is further expanded by the proposal of indicators for each of the proposed core competencies, which form the basis for an instrument developed for assessing these competencies. Section 3.4 suggests ways of how the framework, indicators, and assessment instrument can be used to guide and support TVET teachers’ professional development.
Towards quantum‐secure software defined networks

Abstract The evolution of quantum computers is considered a serious threat to public‐key cryptosystems (e.g. RSA, ECDSA, ECDH, etc.). This is indeed a big concern for security of the Internet and other data communication and storage systems. The reason is that public‐key schemes are the basis in the generation of shared symmetric keys that are used to perform data encryption/decryption in communication and data transfer protocols. One possible approach to address this issue is to use Quantum Key Distribution (QKD) (instead of public‐key schemes) for the ultra‐secure generation of symmetric keys. QKD is a physical layer technology that allows two parties (equipped with optical communication interfaces) to generate secure random keys over a quantum channel that is immune to eavesdropping threats. The keys are then used by symmetric encryption schemes (e.g. AES) to encrypt data over classical channels. This allows us to have data encryption/decryption without needing a public‐key scheme. However, due to its inherent characteristics, the implementation of QKD has mostly been considered in particular contexts only (e.g. backhaul networks, point‐to‐point connections, optical networks, etc.). This indeed limits the utility of QKD technology to only some particular applications while it has the potential to be used in a wide range of used cases. Motivated by this (increasing the usability of QKD technology), in this study, the authors propose a model that enables SDN‐based networks to utilise QKD technology and provide QKD security service (i.e., random key generation service) to network applications and security protocols in a practical and efficient way. In the proposed approach, secret keys are generated based on the distribution of quantum entanglement between QKD nodes deployed in the network. The significant characteristic of our proposed model is that it does not rely on quantum repeaters to operate. This also improves the efficiency of the employed QKD mechanisms in terms of the key generation rate.
Exemplars of Good Practice

The Ministry of Education of China launched the First National Competition for Promoting Competencies of TVET Teachers in 2010, with an intention to enhance the teaching and learning performance among all vocational schools and colleges across the country and to disseminate best teaching practices both in the classroom and in online mode.
Faster R-CNN and YOLOv3: a general analysis between popular object detection networks

Abstract Computer vision is developing really fast in recent years, and object detection is now one of the hottest topics. faster region-based convolutional neural network (faster R-CNN) and You Only Look Once (YOLOv3) are two popular modules to solve object detection problems, but they are implemented in different ways and thus have different performance in practice. This article is going to introduce these two modern modules and make a few experiments to compare the performance of each module with various datasets. This analysis will focus on speed, accuracy and the performances in different situations. Two specific studies will be mentioned as two typical examples of object detection application: face mask detection and greenhouse detection from satellite images. At last, the article will draw out a conclusion, make some suggestions for the choice of faster R-CNN and YOLOv3 and make a prospect for the future. It turns out that generally faster R-CNN fits tasks that require high accuracy better and YOLOv3 can realize real-time detection tasks.
DFFA-Net: A Differential Convolutional Neural Network for Underwater Optical Image Dehazing

This paper proposes DFFA-Net, a novel differential convolutional neural network designed for underwater optical image dehazing. DFFA-Net is obtained by deeply analyzing the factors that affect the quality of underwater images and combining the underwater light propagation characteristics. DFFA-Net introduces a channel differential module that captures the mutual information between the green and blue channels with respect to the red channel. Additionally, a loss function sensitive to RGB color channels is introduced. Experimental results demonstrate that DFFA-Net achieves state-of-the-art performance in terms of quantitative metrics for single-image dehazing within convolutional neural network-based dehazing models. On the widely-used underwater Underwater Image Enhancement Benchmark (UIEB) image dehazing dataset, DFFA-Net achieves a peak signal-to-noise ratio (PSNR) of 24.2631 and a structural similarity index (SSIM) score of 0.9153. Further, we have deployed DFFA-Net on a self-developed Remotely Operated Vehicle (ROV). In a swimming pool environment, DFFA-Net can process hazy images in real time, providing better visual feedback to the operator. The source code has been open sourced.
High performance computing with inter-virtual-machine communication

ABSTRACTMost applications use Transport Layer Security (TLS) as a default to encrypt network traffic to increase privacy and security. The adversary, however, also used LS encryption to conceal attacks or command-and-control communications. Encryption is essential for deciphering and assessing risks associated with communications. This issue should be resolved in a way that provides effective and covert decryption without compromising overall security. As a result of server consolidation, virtualization has emerged as a key technique for increasing data center energy efficiency. Consolidation of servers relies on the location of virtual machines. The placement of virtual machines has been approached from many different perspectives in recent years. Virtual machine placement approaches, however, only take into account the energy consumption of the physical machines within a data center, not the energy consumption of the communication network. Through the application domain, CVM offers a model-driven, user-centered approach to design, synthesis, and communication solutions by utilizing a fundamentally new method for handling these issues. This proposal discusses the concept, architecture, model languages, design prototypes, and implementation of CVM, arguing that it is a significantly more effective approach to technical communication solutions.KEYWORDS: Transport layer securityvirtual machineLS encryptioncommunication Disclosure statementNo potential conflict of interest was reported by the author(s).
Multi-Object Detection for Inland Ship Situation Awareness Based on Few-Shot Learning

With the rapid development of artificial intelligence technology and unmanned surface vehicle (USV) technology, object detection and tracking have wide applications in marine monitoring and intelligent ships. However, object detection and tracking tasks on small sample datasets often face challenges due to insufficient sample data. In this paper, we propose a ship detection and tracking model with high accuracy based on a few training samples with supervised information based on the few-shot learning framework. The transfer learning strategy is designed, innovatively using an open dataset of vehicles on highways to improve object detection accuracy for inland ships. The Shuffle Attention mechanism and smaller anchor boxes are introduced in the object detection network to improve the detection accuracy of different targets in different scenes. Compared with existing methods, the proposed method is characterized by fast training speed and high accuracy with small datasets, achieving 84.9% (mAP@0.5) with only 585 training images.
Latent Variable Model Selection

Latent variable models are important knowledge representations for machine learning. This chapter introduces two information-theoretic criteria for model selection in latent variable models; the latent stochastic complexity (LSC) and decomposed normalized maximum likelihood code-length (DNML). It is shown how DNML can be applied to model selection for typical latent variable model classes such as naïve Bayes models, latent Dirichlet allocation models, stochastic block models, and Gaussian mixture models. The excellent performance of DNML is theoretically and empirically validated. We also show practical applications of DNML including graph summarizations, approximation of inter-event time distributions, probabilistic principal component analysis, dimensionality selection of hyperbolic graph embedding, and neural networks.
L’imperfetto resiano fra tempo, aspetto e modo

The present paper gives a description of the forms and functions of the imperfect in Resian, a Slovene-based micro-language spoken in northern Italy, which has been in a situation of language contact with Romance varieties for centuries. The comparison between the imperfects found in the oldest documents (eighteenth and nineteenth century) and those used in modern Resian shows a significant shift of its functions. The imperfect, in fact, has lost its original aspect-temporal values, now expressing mainly counterfactuality and the future in the past. Our analysis includes a discussion on the possible role of foreign influence in these developments.
Simulation of using of markov decision processes in restaurant robots

Abstract In order to make restaurant services more intelligent and complete basic services, the robot’s forward path needs to be planned. The whole restaurant could be regarded as a probabilistic model and Markov decision process (MDP) can help solve this problem. This paper proposes has a more appropriate approach - Stoical shortest path (SSP), a form of MDP. SSP can update the value of the paths iteratively and compare them to find the path with the lowest cost and could instruct the shortest way for the robot.
Multi-Point path planning for robots based on deep reinforcement learning

Abstract Motion Planning is a key technology for mobile robots, which decomposes a Motion task that cannot be completed by a single action into multiple discrete actions that can be performed. This paper aims to design a robot motion planning algorithm based on reinforcement learning and make a robot carry out continuous multi-objective point motion planning. Motion planning network is a planning algorithm based on a neural network, and DQN is a classical algorithm in the field of reinforcement learning. Based on the two kinds of algorithm for motion planning, the Deep Q - learning algorithm chooses the robot’s next target, and then through the motion planning of the network between the current coordinates to the next target path planning. This paper analyzes the performance of the multi-point motion planning algorithm, and the results show that the algorithm is able to a higher success rate of successful completion of the task planning, but the reward strategy derived from the experiment still has the possibility of optimization.
RRT path planning algorithm with enhanced sampling

Abstract Due to the low efficiency and low path quality of the RRT algorithm, this paper introduces a sampling-enhanced RRT(SE-RRT) algorithm with four improvements to the RRT algorithm. In the path search phase, the gravitational coefficient is added to improve the efficiency of finding the feasible path without losing randomness. In the path optimization phase, turning points are selected from nodes of the original path by global rewiring. Then the sampling space is established around the turning points for local re-optimization. Finally, a near-optimal path is obtained and can be iterated to the optimal path according to the demand. In the code implementation phase, efficient path collision detection is added to prevent local paths from crossing obstacles.
Fine-grained image recognition method for digital media based on feature enhancement strategy

Abstract The emergence of digital media has changed the way people live and learn. In the context of the new era, digital media is gradually integrating into people’s life and learning. Digital media contains massive images, and fine-grained image recognition for digital media has become an important topic. The challenge of fine-grained image recognition is that the difference between different categories is small, and the difference between the same categories is sometimes large. This work designs a fine-grained image recognition based on feature enhancement (FIRFE). This extracts as much information as possible from fine-grained images under weak supervision to improve the recognition accuracy. When the existing methods extract image features, the feature extraction other than the most significant local feature is not enough. This deals with local features alone and ignores the relationship between features. First, this paper designs a feature enhancement and suppression module to process image features. Secondly, this paper designs pyramid residual convolution. This uses different scale convolution kernels to capture different levels of features in the scene. Thirdly, this paper uses the softpool method to rationally allocate the information weight in the pooling process. Fourth, this paper uses feature focus module to mine more features. This focuses on obtaining similar information in multiple local features as discriminant features to further improve the recognition. Fifthly, this paper carried out systematic experiments on the designed method. The proposed method achieves 94.3%/95.7% accuracy, 92.9%/94.1% recall, and 91.4%/92.2% F1 score on different datasets. This verified the superiority of this method for fine-grained image recognition of digital media.
Beyond Bitcoin: Evaluating Energy Consumption and Environmental Impact across Cryptocurrency Projects

Since their inception with Bitcoin in the late 2000s, cryptocurrencies have grown exponentially, reshaping traditional financial paradigms. This transformative journey, while innovative, brings forth pressing concerns about their energy consumption and carbon footprint. While many studies tend to zoom in on Bitcoin, this paper broadens the perspective by evaluating energy consumption across various cryptocurrencies. We analyze nine cryptocurrency projects, chosen for their market value, technology, and data availability. These span a spectrum from pioneering to emerging digital coins, offering a holistic view of the crypto realm. To contextualize, we juxtapose the energy usage of these digital currencies with traditional payment means like Visa and Mastercard. Our analysis shows vast differences in energy use among cryptocurrencies, largely tied to their consensus algorithms. Notably, while Bitcoin stands out as highly energy-intensive, several newer digital currencies have energy footprints mirroring those of conventional payment methods. Additionally, CO2 emissions estimation presents challenges due to variances in miner locations and regional energy sources, with potential higher emissions if concentrated in carbon-intensive regions like China. Nonetheless, the silver lining emerges as many cryptocurrencies, especially those beyond Bitcoin, register considerably lower CO2 emissions. By moving the lens beyond Bitcoin, this paper paints a more nuanced picture of the environmental ramifications of the crypto world.
Analysis of the Problem-Based Learning Model’s Application for the Sustainable Development of Sports Education

The purpose of this study was to analyze importance and performance levels by applying the problem-based learning (PBL) education model for the sustainable development of sports education. In this study, 331 university sports majors who received PBL education at least once were administered a questionnaire from 15 July to 20 September 2022. Frequency analysis (using SPSS 25.0), exploratory factor analysis, reliability analysis, descriptive statistical analysis, and IPA were used for data processing. The results are as follows: The first quadrant of the IPA matrix comprised attributes such as determining the cause of the problem, acquiring sufficient knowledge through learning, gathering information related to the problem, getting to the heart of the matter, and building intimacy among team members. The second quadrant included planning for oneself when learning, considering the importance of self-learning, and actively answering questions. The third quadrant included enthusiasm and sincerity toward team learning. The fourth quadrant included 11 attributes: proper planning and execution, understanding others’ points of view, the joy of team learning, prioritizing problem-solving methods, creative communication, communication with team members, proactive communication, everyone on the team working hard, feeling a sense of accomplishment, a sense of challenge, and an increase in self-confidence.
Verification of Quantum Systems Using Barrier Certificates

Various techniques have been used in recent years for verifying quantum computers, that is, for determining whether a quantum computer/system satisfies a given formal specification of correctness. Barrier certificates are a recent novel concept developed for verifying properties of dynamical systems. In this article, we investigate the usage of barrier certificates as a means for verifying behaviours of quantum systems. To do this, we extend the notion of barrier certificates from real to complex variables. We then develop a computational technique based on linear programming to automatically generate polynomial barrier certificates with complex variables taking real values. Finally, we apply our technique to several simple quantum systems to demonstrate their usage.
Comparing the Expressiveness of the π-calculus and CCS

This paper shows that the π-calculus with implicit matching is no more expressive than CCS γ , a variant of CCS in which the result of a synchronisation of two actions is itself an action subject to relabelling or restriction, rather than the silent action τ. This is done by exhibiting a compositional translation from the π-calculus with implicit matching to CCS γ that is valid up to strong barbed bisimilarity. The full π-calculus can be similarly expressed in CCS γ enriched with the triggering operation of Meije . I also show that these results cannot be recreated with CCS in the rôle of CCS γ , not even up to reduction equivalence, and not even for the asynchronous π-calculus without restriction or replication. Finally, I observe that CCS cannot be encoded in the π-calculus.
Data-Driven Inference of Chemical Reaction Networks via Graph-Based Variational Autoencoders

We propose a data-driven machine learning framework that automatically infers an explicit representation of a Chemical Reaction Network (CRN) together with its dynamics. The contribution is twofold: on one hand, our technique can be used to alleviate the computational burden of simulating a complex, multi-scale stochastic system; on the other hand, it can be used to extract an interpretable model from data. Our methodology is inspired by Neural Relational Inference and implements a graph-based Variational Autoencoder with the following structure: the encoder maps the observed trajectories into a representation of the CRN structure as a bipartite graph, and the decoder infers the respective reaction rates. Finally, the first two moments of the stochastic dynamics are computed with the standard linear noise approximation algorithm. Our current implementation demonstrates the applicability of the framework to single-reaction systems. Extending the framework towards inferring more complex CRN in a fully automated and data-driven manner involves implementation challenges related to neural network architecture and hyperparameter search, and is a work in progress.
Formal Verification of Bit-Vector Invertibility Conditions in Coq

Abstract We prove the correctness of invertibility conditions for the theory of fixed-width bit-vectors—used to solve quantified bit-vector formulas in the Satisfiability Modulo Theories (SMT) solver cvc5— in the Coq proof assistant. Previous work proved many of these in a completely automatic fashion for arbitrary bit-width; however, some were only proved for bit-widths up to 65, even though they are being used to solve formulas over larger bit-widths. In this paper we describe the process of proving a representative subset of these invertibility conditions in Coq. In particular, we describe the library for bit-vectors in Coq, our extensions to it, and proofs of the invertibility conditions.
A Critical Pair Criterion for Level-Commutation of Conditional Term Rewriting Systems

Abstract The rewrite relation of a conditional term rewriting system (CTRS) can be divided into a hierarchy of rewrite relations of term rewriting systems (TRSs) by the depth of the recursive use of rewrite relation in conditions; a CTRS is said to be level-confluent if each of these TRSs are confluent, and level-confluence implies confluence. We introduce level-commutation of CTRSs that extends the notion of level-confluence, in a way similar to extending confluence to commutation, and give a critical pair criterion for level-commutation of oriented CTRSs with extra variables (3-CTRSs). Our result generalizes a criterion for commutation of TRSs of (Toyama, 1987), and properly extends a criterion for level-confluence of orthogonal oriented 3-CTRSs (Suzuki et al., 1995). We also present criteria for level-confluence and commutation of join and semi-equational 3-CTRSs that may have overlaps.