Reviewerly/exHarmony huggingface dataset:
Formal Entity Graphs as Complex Networks: Assessing Centrality Metrics of the Archive of Formal Proofs

Formalization libraries for interactive theorem provers are rapidly growing in size, but only little is understood structurally about those developments. We aim to address the arising challenges by utilizing dependency graphs of the underlying formal entities. For the Isabelle Archive of Formal Proofs, the individual entry graphs consist of 1.8 million nodes and 2.8 million edges in total, and exhibit certain complex network characteristics: Node in-degrees weakly follow scale-free distributions with an average exponent of $$\alpha =1.81$$ , and the high clustering coefficient (avg. 0.33) together with the short average path length (3.64) indicate small-world effects. We did not find network centrality metrics to be good indicators of theory quality (measured by lint frequency): The Spearman correlation of our six different centrality metrics was weaker than in similar experiments from software systems, and with a coefficient of $$s=0.38$$ , the source lines of code metric exhibited a stronger correlation than all centrality metrics considered. In contrast, network centrality metrics worked well in predicting the most important concepts within AFP entries: Of the definitions deemed most important by entry authors, 51.7% could be identified at a precision of 27.7% (optimal $$F_1$$ -score), using in-degree centrality. At the cost of a few percentage points of precision, a second maximum of 68.8% recall can be achieved.
Wetzel: Formalisation of an Undecidable Problem Linked to the Continuum Hypothesis

In 1964, Paul Erdős published a paper [5] settling a question about function spaces that he had seen in a problem book. Erdős proved that the answer was yes if and only if the continuum hypothesis was false: an innocent-looking question turned out to be undecidable in the axioms of ZFC. The formalisation of these proofs in Isabelle/HOL demonstrate the combined use of complex analysis and set theory, and in particular how the Isabelle/HOL library for ZFC [17] integrates set theory with higher-order logic.
Loyalty Score Generation for Customers Using Sentimental Analysis of Reviews in e-commerce

Presently, the existing systems use the star ratings of products as the quality factor, but ignore the tangible feedback. It is problematic since the star ratings may differ from the intensity of written feedback given by the user. This motivated authors to come up with the system that will use both ratings and reviews together. In this paper, the author has proposed a model in which sentimental score will be generated for the reviews given by the user by using sentimental analysis and shall derive the correlation between the ratings and sentimental score generated by analyzing the customer reviews. By doing the same for all the reviews given by the user, the model will assign a loyalty score automatically to the customer. This loyalty score can be used to build customer profile, and also the sentimental score generated can be used to boost the products when the user applies a sort by ratings filter as the user will get to see not only the products that are having good ratings, but also the products that got better reviews along with it.
Diversifying agent's behaviors in interactive decision models

Modeling other agents' behaviors plays an important role in decision models for interactions among multiple agents. To optimize its own decisions, a subject agent needs to model what other agents act simultaneously in an uncertain environment. However, modeling insufficiency occurs when the agents are competitive and the subject agent cannot get full knowledge about other agents. Even when the agents are collaborative, they may not share their true behaviors due to their privacy concerns. Most of the recent research still assumes that the agents have common knowledge about their environments and a subject agent has the true behavior of other agents in its mind. Consequently, the resulting techniques are not applicable in many practical problem domains. In this article, we investigate into diversifying behaviors of other agents in the subject agent's decision model before their interactions. The challenges lie in generating and measuring new behaviors of other agents. Starting with prior knowledge about other agents' behaviors, we use a linear reduction technique to extract representative behavioral features from the known behaviors. We subsequently generate their new behaviors by expanding the features and propose two diversity measurements to select top- K $K$ behaviors. We demonstrate the performance of the new techniques in two well-studied problem domains. The top- K $K$ behavior selection embarks the study of unknown behaviors in multiagent decision making and inspires investigation of diversifying agents' behaviors in competitive agent interactions. This study will contribute to intelligent systems dealing with unknown unknowns in an open artificial intelligence world.
Mixture of Gaussian Processes Based on Bayesian Optimization

This paper gives a detailed introduction of implementing mixture of Gaussian process (MGP) model and develops its application for Bayesian optimization (BayesOpt). The paper also develops techniques for MGP in finding its mixture components and introduced an alternative gating network based on the Dirichlet distributions. BayesOpt using the resultant MGP model significantly outperforms the one based on Gaussian process regression in terms of optimization efficiency in the test on tuning the hyperparameters in common machine learning algorithms. This indicates the success of the methods, implying a promising future of wider application for MGP model and the BayesOpt based on it.
Bayesian Pseudo Labels: Expectation Maximization for Robust and Efficient Semi-supervised Segmentation

This paper concerns pseudo labelling in segmentation. Our contribution is fourfold. Firstly, we present a new formulation of pseudo-labelling as an Expectation-Maximization (EM) algorithm for clear statistical interpretation. Secondly, we propose a semi-supervised medical image segmentation method purely based on the original pseudo labelling, namely SegPL. We demonstrate SegPL is a competitive approach against state-of-the-art consistency regularisation based methods on semi-supervised segmentation on a 2D multi-class MRI brain tumour segmentation task and a 3D binary CT lung vessel segmentation task. The simplicity of SegPL allows less computational cost comparing to prior methods. Thirdly, we demonstrate that the effectiveness of SegPL may originate from its robustness against out-of-distribution noises and adversarial attacks. Lastly, under the EM framework, we introduce a probabilistic generalisation of SegPL via variational inference, which learns a dynamic threshold for pseudo labelling during the training. We show that SegPL with variational inference can perform uncertainty estimation on par with the gold-standard method Deep Ensemble .
D’ARTAGNAN: Counterfactual Video Generation

Causally-enabled machine learning frameworks could help clinicians to identify the best course of treatments by answering counterfactual questions. We explore this path for the case of echocardiograms by looking into the variation of the Left Ventricle Ejection Fraction, the most essential clinical metric gained from these examinations. We combine deep neural networks, twin causal networks and generative adversarial methods for the first time to build D'ARTAGNAN (Deep ARtificial Twin-Architecture GeNerAtive Networks), a novel causal generative model. We demonstrate the soundness of our approach on a synthetic dataset before applying it to cardiac ultrasound videos to answer the question: "What would this echocardiogram look like if the patient had a different ejection fraction?". To do so, we generate new ultrasound videos, retaining the video style and anatomy of the original patient, while modifying the Ejection Fraction conditioned on a given input. We achieve an SSIM score of 0.79 and an R2 score of 0.51 on the counterfactual videos. Code and models are available at: https://github.com/HReynaud/dartagnan .
Holochain: a novel technology without scalability bottlenecks of blockchain for secure data exchange in health professions education

Abstract The efficacy of health professions education (HPE) is more and more reliant on leveraging new technologies as they emerge. The nascent “Web 3.0” is shifting us away from traditional centralized architectures for capture and storage of information, and aims to decentralize and distribute it. Blockchain has received a large amount of attention as a promising “Web 3.0” technology and has been applied in the HPE setting. In this paper we argue that blockchain has two core issues which have prevented its widespread adoption: scalability and environmental impact. The architecture of blockchain means that all participating nodes must store and validate all of the data. This creates a large amount of overhead, which does not scale well. Similarly, there is growing awareness that the energy requirements of validating blockchain transactions run counter to global efforts to mitigate climate change. Holochain, however, is a novel Web 3.0 technology that aims to overcome both of these issues. Its architecture is fundamentally different to blockchain, by virtue of being “agent-centric” rather than “data-centric”. That is, each piece of data does not need to be globally validated—and yet Holochain can still guarantee data validity and integrity. We identify that specific challenges in the HPE setting—tracking educational activities, accountable mechanisms for observing entrustable professional activities, and validation of third-party certification and credentialing—can all be achieved elegantly in Holochain, without experiencing any of the disadvantages evidenced in blockchain.
Quantum generative adversarial networks based on Rényi divergences

Due to the development of quantum algorithms, kinds of quantum generative adversarial networks (QGANs) are proposed to generate suitable samples for different application scenarios. However, the training of QGANs is often not smooth as expected, and the results are unsatisfactory since of the barren plateau. To solve the problems, we propose a QGANs framework based on Rényi divergence (RyQGAN), which avoids the barren plateau phenomenon in the training of QGANs. The quantum circuits to estimate the gradients for the updating parameters are designed, which shows how the gradients can be evaluated by a quantum processor. Finally, based on RyQGAN, we generate 2-qubit, 3-qubit, and 4-qubit Gibbs states. The simulation results show that the barren plateau is eliminated in the RyQGAN, and the average fidelity of RyQGAN with 50 epochs is higher than 99.8%, while that of quantum Wasserstein GANs is no more than 50%. Thus, the RyQGAN can be trained effectively and has further applications in quantum machine learning.
A Contrastive Plan Explanation Framework for Hybrid System Models

In artificial intelligence planning, having an explanation of a plan given by a planner is often desirable. The ability to explain various aspects of a synthesized plan to an end user not only brings in trust on the planner but also reveals insights of the planning domain and the planning process. Contrastive questions such as “Why action A instead of action B?” can be answered with a contrastive explanation that compares properties of the original plan containing A against the contrastive plan containing B. In this article, we explore a set of contrastive questions that a user of a planning tool may raise and propose a re-model and re-plan framework to provide explanations to such questions. Earlier work has reported this framework on planning instances for discrete problem domains described in the Planning Domain Definition Language (PDDL) and its variants. In this article, we propose an extension for planning instances described by PDDL+ for hybrid systems that portray a mix of discrete-continuous dynamics. Specifically, given a mixed discrete-continuous system model in PDDL+ and a plan describing the set of desirable actions on the same to achieve a destined goal, we present a framework that can integrate contrastive questions in PDDL+ and synthesize alternate plans. We present a detailed case study on our approach and propose a comparison metric to compare the original plan with the alternate ones.
Video Game Bad Smells: What They Are and How Developers Perceive Them

Video games represent a substantial and increasing share of the software market. However, their development is particularly challenging as it requires multi-faceted knowledge, which is not consolidated in computer science education yet. This article aims at defining a catalog of bad smells related to video game development. To achieve this goal, we mined discussions on general-purpose and video game-specific forums. After querying such a forum, we adopted an open coding strategy on a statistically significant sample of 572 discussions, stratified over different forums. As a result, we obtained a catalog of 28 bad smells, organized into five categories, covering problems related to game design and logic, physics, animation, rendering, or multiplayer. Then, we assessed the perceived relevance of such bad smells by surveying 76 game development professionals. The survey respondents agreed with the identified bad smells but also provided us with further insights about the discussed smells. Upon reporting results, we discuss bad smell examples, their consequences, as well as possible mitigation/fixing strategies and trade-offs to be pursued by developers. The catalog can be used not only as a guideline for developers and educators but also can pave the way toward better automated tool support for video game developers.
Risk management in digitalized educational environments: Teachers’ information security awareness levels

With the spread of Information and Communication Technologies (ICT) tools and the Internet, Twenty first century technologies have significantly affected human life, and it has been desired to be obtained continuously. It has become challenging to protect information due to the increase in the methods by which malicious people can get information. As a result, it is crucial to determine people's awareness levels by revealing the risks and threats to information security. In this context, a study was conducted to show the awareness levels of teachers who come after the family in raising conscious individuals in society. For this purpose, a quantitative research method was adopted for the problem and sub-problems that form the basis of the research. The survey model, one of the research designs used within the framework of the quantitative research method, was used. Information Security Awareness Scale was applied to 394 teachers, and according to the results obtained, it was determined that the information security awareness level of the teachers was moderate. According to the attacks and threats sub-dimension, which includes technical issues, it has been determined that the awareness levels of the teachers are at a medium level. The study results show that female teachers' information security awareness levels are lower than male teachers. In comparison, the awareness levels of those who received information security awareness training and information technology teachers are higher.
OSIRIS: Organization Simulation in Response to Intrusion Strategies

OSIRIS, Organization Simulation In Response to Intrusion Strategies, is an agent-based simulation framework that models virtual organization composed of end user agents with complex and realistic behavior patterns. The purpose of OSIRIS is to predict and analyze the scale of cyberattack damage on the organization once targeted by cybercriminals with a consideration of organization members' properties, behavior patterns, and social relations. In this paper, we detail how we reflect real world organization environments and cyberattack scenarios to OSIRIS by illustrating our organization and cybercriminal design.
University Curriculum Setting and Teaching Governance System Based on Distributed Sensor Network

The rapid development of Internet, computer, and other information technology has promoted the continuous reform of school education. Network education uses existing educational resources to break through the limitation of time and space of traditional education. However, it needs a high-quality teaching management platform and a perfect teaching governance system whether the curriculum is of high-quality and can achieve the expected educational purpose and realize its educational value. In this paper, according to the current situation of network university teaching, a distributed network teaching management system is built on the basis of the digital campus platform. The university teaching system is constructed under the support of exploring intelligent technology. The system is divided into four parts: resource system, teaching system, management system, and application system. On the basis of the traditional teaching mode, the advanced multimedia teaching management system is introduced. Based on the distributed cognitive theory, the distributed structure of teaching video system is adopted. In the experiment, Class A as the control class was taught in the traditional mode, and Class B as the experimental class was taught in the distributed teaching mode. The results show that 57.8% of the students feel that there is obvious mutual promotion, deepening students’ visual cognitive understanding of the learning process can cultivate and improve students’ cognitive ability and innovation ability.
Dataset for human fall recognition in an uncontrolled environment

This article presents a dataset (CAUCAFall) with ten subjects, which simulates five types of falls and five types of activities of daily living (ADLs). Specifically, the data include forward falls, backward falls, lateral falls left, lateral falls right, and falls arising from sitting. The participants performed the following ADLs: walking, hopping, picking up an object, sitting, and kneeling. The dataset considers individuals of different ages, weights, heights, and dominant legs. The data were acquired using an RGB camera in a home environment. This environment was intentionally realistic and included uncontrolled features, such as occlusions, lighting changes (natural, artificial, and night), participants different clothing, movement in the background, different textures on the floor and in the room, and a variety in fall angles and different distances from the camera to the fall. The dataset consists of 10 folders, one for each subject, and each folder includes ten subfolders with the performed activities. Each folder contains the video of the action and all the images of that action. CAUCAFall is the only database that contains details of the lighting lux of the scenarios, the distances from the human fall to the camera and the angles of the different falls with reference to the camera. The dataset is also the only one that contains labels for each image. Frames including human falls recorded were labeled as ``fall'', and ADL activities were marked ``nofall". This dataset is useful for developing and evaluating modern fall recognition algorithms, such as those that apply feature extraction, convolutional neural networks with YOLOv3-v4 detectors, and camera location and resolution increase the performance of algorithms such as OPENPOSE. Thus, the dataset enables knowledge of the real progress of research in this area since existing datasets are used in strictly controlled environments. The authors intend to contribute a dataset with real-world housing environments characteristics.
Data Analysis for Predictive Maintenance Using Time Series and Deep Learning Models—A Case Study in a Pulp Paper Industry

Predictive maintenance is fundamental for modern industries, in order to improve the physical assets availability, decision making and rationalize costs. That requires deployment of sensor networks, data storage and development of data treatment methods that can satisfy the quality required in the forecasting models. The present paper describes a case study where data collected in an industrial pulp paper press was pre-processed and used to predict future behavior, aiming to anticipate potential failures, optimize predictive maintenance and physical assets availability. The data were processed and analyzed, outliers identified and treated. Time series models were used to predict short-term future behavior. The results show that it is possible to predict future values up to ten days in advance with good accuracy.
An Assessment of the Missing Data Imputation Techniques for COVID-19 Data

In medical domain, the accuracy of the data supplied is critical. Missing values, on the other hand, are a typical occurrence in this sector for a variety of reasons. Most current science concentrates on establishing novel data imputation procedures, but more research on conducting a comprehensive review of existing algorithms is highly desired. Authors have evaluated the performance of four mostly adopted data imputation techniques, i.e., MICE, EM, mean, and KNN on a real-world dataset of COVID-19. KNN is an imputation approach that, according to the findings of the studies, is expected to be a good fit for dealing with missing data in the healthcare industry.
Spontaneous spin magnetism of donor conduction electrons of hybridized states of a crystal formed by a system of 3d-elements impurity atoms of low concentration (&lt;1 at.%)

The given work is devoted to the experimental proof of existing the spontaneous spin polarization of the donor electron system of 3d-transition element impurity atoms of low concentration (&lt;1 at.%) in a mercury selenide crystal. For this purpose there have been measured the dependences of the magnetization on the magnetic field strength at low temperatures (T=5 K). As a result of the analysis of the obtained dependences, there were extracted the impurity contributions, which are described by the magnetization curves typical of the ferromagnets, and by the magnetic parameters conforming to the spontaneous magnetism of the systems under study, which are unambiguously related to the donor conduction electrons of the outer d-shells of impurity atoms. By its nature, according to the developed theoretical concepts, the spontaneous spin polarization manifests itself in exchange interaction, taking place in hybridizing the electronic states of the impurity atom and the conduction band ones of the crystal. Keywords: 3d-impurities of the low concentration, gapless semiconductors, hybridized electronic states, spontaneous spin polarization, low-temperature ferromagnetism