Reviewerly/exHarmony huggingface dataset:
On the Formalization of the Heat Conduction Problem in HOL

Partial Differential Equations (PDEs) are widely used for modeling the physical phenomena and analyzing the dynamical behavior of many engineering and physical systems. The heat equation is one of the most well-known PDEs that captures the temperature distribution and diffusion of heat within a body. Due to the wider utility of these equations in various safety-critical applications, such as thermal protection systems, a formal analysis of the heat transfer is of utmost importance. In this paper, we propose to use higher-order-logic (HOL) theorem proving for formally analyzing the heat conduction problem in rectangular coordinates. In particular, we formally model the heat transfer as a one-dimensional heat equation for a rectangular slab using the multivariable calculus theories of the HOL Light theorem prover. This requires the formalization of the heat operator and formal verification of its various properties, such as linearity and scaling. Moreover, we use the separation of variables method for formally verifying the solution of the PDEs, which allows modeling the heat transfer in the slab under various initial and boundary conditions using HOL Light.
Unified Decomposition-Aggregation (UDA) Rules: Dynamic, Schematic, Novel Axioms

We introduce Unified Decomposition-Aggregation (UDA) Rules. They are a family of axiom schemata that are instantiated at run-time to add new axioms to a logical theory. These new axioms are implications, whose preconditions will be constructed from an analysis of the goal to be proved and the theory in which it is to be proved. We illustrate their application to query answering using the FRANK system.
Training to improve the management capacity of educational activities for children sexual violation preventing in secondary school for management officers

To meet the requirements of innovation, improve the quality of comprehensive education, and integrate internationally in the current context, the management of educational activities to prevent and combat sexual abuse of students in educational institutions is extremely necessary and urgent. The basic factor to achieve effective management of educational activities to prevent sexual abuse of students in educational institutions is the management capacity of educational administrators in general, including the secondary school management team. Based on the improvement of the quality of educational activities on prevention and combating sexual abuse for students, the article proposes the process of organizing training to improve the management capacity for secondary school administrators.
A hybrid XSS attack (H XSS ) based on fusion approach: Challenges, threats and implications in cybersecurity

Cross-site scripting (XSS) attacks have been extensively studied in the literature, although mitigating such attacks remain a challenge for cyber defenders. In this paper, we survey the existing literature on XSS attacks, focusing on the range of attacks and potential mitigation strategies. Specifically, we review the various XSS attacks from the lens of an attacker. We use a workflow diagram to define the topological relationship among XSS attacks, and to highlight key system weaknesses (e.g., chokepoints). We also present a Hybrid XSS attack (HYXSSA), designed to facilitate the identification of existing and future potential attack vectors in different modalities presented as frameworks (fi). For quantification and visualization of these frameworks, the software application as a rotate view tool is developed. Moreover, we demonstrate how these derived frameworks can be implemented, and provide a guideline to defend against XSS attacks. The implementation results for the given two attack vector shows the feasibility of mapping of attack vectors to actual mathematical vectors. Finally, we present potential challenges and opportunities associated with XSS attacks.
Gradient symbolic representations in Harmonic Grammar

Abstract This paper presents an overview of Harmonic Grammar with gradient symbolic representations (a.k.a. Gradient Harmonic Grammar), a weighted‐constraint model of grammatical computation in which language structures are mentally represented with numerically continuous levels of activity, or degree of presence. In this system, the penalty of each constraint violation is proportional to the activity of the structure that incurs it. The adoption of gradient activity permits unique advances in generative approaches to key types of idiosyncratic patterns in language. I review the main proposals that have been made in the framework, and outstanding issues such as the existence of gradient activity in output structures.
Swin Faster R-CNN for Senescence Detection of Mesenchymal Stem Cells in Bright-Field Images

iPSC-derived mesenchymal stem cells (iMSCs) can differentiate into a wide range of mesodermal cell types for tissue repair, and play an important role in cell therapy in regenerative medicine. The therapeutic effect of mesenchymal stem cells (MSCs) is largely affected by their differentiation ability and cell activity, and the therapeutic effect of senescent cells is often worse than that of young cells. Biologically senescent detection of iMSCs can only be performed by vital staining, which is time-consuming, labor-intensive, and financial-intensive. To solve this problem, we apply a deep learning method to the senescence identification research of iMSCs to achieve the purpose of rapid and accurate detection without vital staining. We propose a Swin Transformer-based Faster R-CNN network for senescence identification of MSCs in bright-field images. In particular, we exploit the self-attention mechanism of the Swin Transformer to extract features of dense cell regions in the picture, enabling the network to learn the strong discriminative properties of different classes of cells. In addition, we perform multi-scale fusion on the features extracted by the backbone network to address the problem of inconsistent cell sizes in the data. Experiments on self-collected datasets show that our method achieves good performance on each generation of datasets and achieves the highest detection precision (mAP) of 0.816 on our collected mixed datasets.
Integrating Virtual Reality and interactive game for learning structures in architecture: the case of ancient Chinese dougong cognition

Purpose Ancient Chinese architecture is famous for its wooden frame structure and unique position in world architecture history. As numerous types of components and complex combinations exist, the overall structural system and how they are interlocked have always been crucial but challenging parts of the study. Students find it hard to understand and remember the concepts using traditional 2D paper media, making such knowledge unattractive to pass on to the new generation. To overcome the challenges, this research aims to examine the effect of combining Virtual Reality (VR) with digital interaction games in learning structural characteristics (dougong) of ancient Chinese architecture. Design/methodology/approach This research develops an interactive cognitive system in the VR environment based on constructivist theory to improve the learning approach of ancient Chinese architecture. Applying an experimental procedure, the feedback of an experimental group using the VR cognition system and a control group using traditional learning 2D media are collected to examine the differences in learning effectiveness and user experience. Findings This study develops an interactive cognitive system to aid in learning the structural system of ancient Chinese architecture. The results indicate that integrating VR and interactive learning games can increase students' positive attitudes and learning effectiveness towards ancient Chinese architecture. Originality/value This study integrates VR technology and interactive games to improve the learning approach. It examines the effect of applying the concept of human–computer interaction in learning ancient Chinese buildings. The concept of designing the interactive cognitive system is expected to guide students gradually to be the main body of learning and stimulate their learning enthusiasm and motivation.
Museological education in the context of the digital transformation of the educational process: problems and features

The article analyzes the problems of museological education in the process of digital transformation. It is shown that in order to solve museological education, it is necessary to establish cooperation between the department where museum disciplines are taught and a specific museum (no matter what profile, a museum with collections), organize on the basis of the museum and together with the museum production and prediploma practices, just separate museum projects of various types and scale in all areas of museum activity, whether it is expositional, cultural and educational, research or fund activities. The article determines that museum activity in the modern world, like many other areas, is also subject to digitalization. The common problems facing the spheres of education and museum business are revealed.
Leveraging Implicit Gaze-Based User Feedback for Interactive Machine Learning

Interactive Machine Learning (IML) systems incorporate humans into the learning process to enable iterative and continuous model improvements. The interactive process can be designed to leverage the expertise of domain experts with no background in machine learning, for instance, through repeated user feedback requests. However, excessive requests can be perceived as annoying and cumbersome and could reduce user trust. Hence, it is mandatory to establish an efficient dialog between a user and a machine learning system. We aim to detect when a domain expert disagrees with the output of a machine learning system by observing its eye movements and facial expressions. In this paper, we describe our approach for modelling user disagreement and discuss how such a model could be used for triggering user feedback requests in the context of interactive machine learning.
Building and using capacity framework for primary school’s section chief

By defining the concepts of competency and capacity framework and the need to build a capacity framework, the article suggested the capacity framework for primary school’s section-chief including five competencies: professional capacity, section management capacity, educational environment building capacity, school-home-community relationship development capacity, and foreign language and technology using capacity. The article also shows how to use the capacity framework in developing section-chiefs at primary school.
Assessing the Performance Gain on Retail Article Categorization at the Expense of Explainability and Resource Efficiency

Current state-of-the-art methods for text classification rely on large deep neural networks. For use cases such as product cataloging, their required computational resources and lack of explainability may be problematic: not every online shop can afford a vast IT infrastructure to guarantee low latency in their web applications and some of them require sensitive categories not to be confused. This motivates alternative methods that can perform close to the mentioned methods while being explainable and less resource-demanding. In this work, we evaluate an explainable framework consisting of a representation learning model for article descriptions and a similarity-based classifier. We contrast its results with those obtained by DistilBERT, a solid low-resource baseline for deep learning-based models, on two different retail article categorization datasets; and we finally discuss the suitability of the different presented models when they need to be deployed considering not only their classification performance but also their implied resource costs and explainability aspects.
HanKA: Enriched Knowledge Used by an Adaptive Cooking Assistant

Cooking a meal is a challenging and recurring task that requires the consideration of various environmental influences and constraints as well as significant domain knowledge. One way to reduce the complexity is to follow recipes that provide an ordered set of tasks for the preparation of a dish. This concept was already transferred to cooking assistants which present the recipe to the cook while adding further assistance like device control or step-by-step visualization. Although recipes and assistants simplify the cooking process itself, other factors like the available devices or differences in cooking skills are ignored. Aside from that, current assistants are often limited to one recipe at a time, ignoring the regularly occurring requirement to prepare a meal of multiple components. Considering these challenges, we propose our adaptive cooking assistant HanKA that considers the individual user skill and environmental kitchen setup, while assisting the cook at the preparation of their freely combined and synchronized recipes. This is achieved through a modular approach consisting of the automatic detection and control of the available devices and user interfaces, the scheduling of multiple recipes based on the distributed knowledge representation, and a deviation management that considers the user experience. Hereby, we created an adaptive cooking assistant that considers various influences that occur in a cooking scenario, resulting in a better assistance for the user.
Knowledge Graph Embeddings with Ontologies: Reification for Representing Arbitrary Relations

Knowledge graph embeddings offer prospects to integrate machine learning and symbolic reasoning. Learning algorithms are designed that map constants, concepts, and relations to geometric entities in a real-valued domain $$\mathbb {R}^n$$ . By identifying logics that feature these geometric entities as their model, one is able to achieve a tight integration of logic reasoning with machine learning. However, interesting description logics are more expressive than current knowledge graph embeddings, as description logics allow concept definitions using arbitrary relations, such as non-functional relationships and partial ones. By contrast, geometric models of relations used so far in knowledge graph embeddings such as translations, rotations, or linear functions can only represent total functional relationships. In this paper we describe a new geometric model of the description logic $$\mathcal {ALC}$$ based on cones that exploits reification combined with linear functions to represent arbitrary relations. While this paper primarily describes reification in context of a particular model for $$\mathcal {ALC}$$ , the proposed reification technique is general and applicable with other ontology languages and knowledge graph embeddings.
Is Climate Change a Topic of Concern to Indians? Assessing and Predicting Sentiments Using Deep Learning Techniques

Climate change is a significant concern to all of us. It is now becoming a hot topic of discussion among people around the world with social media being a ubiquitous platform for debate. As in other countries, the Government of India has started various initiatives to minimize the causes of climate change. But the success of all such initiatives depends on people’s participation and understanding. Therefore, this study’s aims are two-fold: (a) to capture the perception of Indian people towards different topics of climate change; (b) to mine the sentiment of Indian people using ‘deep learning’ algorithms. Data from various social media platforms have been used for this research. The study showed that people in India have demonstrated concern about topics related to climate change. The study also found that the convolutional neural network (CNN) was the most effective algorithm for sentiment classification. The results can help different stakeholders, including the Government of India, prioritize various actions to mitigate climate change’s causes and effects based on citizens’ sentiment.
Self-Driving Software Benchmark for Model-Based Development

This study describes the MATLAB/Simulink benchmark of an open-source self-driving system based on Robot Operating System 2 (ROS 2). In recent years, self-driving systems have been the subject of research and development worldwide. Due to the lack of open source models for self-driving systems, model-based development, a common approach to the development of in-vehicle systems, is not yet fully used in the development of self-driving systems. The provided MATLAB/Simulink benchmarks support the design of ROS 2-based self-driving systems using MATLAB/Simulink. Improvements to the benchmark’s design issues are discussed. Furthermore, Simulink’s profiling function makes it easier to make redesign decisions. The model can run using only sensor data, and the runtime evaluation revealed that the benchmark models could reduce the runtime, although the number of cores used was different.
Internal Security Team Penetration Test Process

Chapter 13 noted that most black hat hackers and security people practice penetration (Pen) attacks at many points on a project. Here, I take this type of attack into a Pen test practice. Penetration is how the hacking world thinks, and we testers need to think like them to be most effective. This chapter deals with Pen attacks as a "special case."
Designing Metaphorical Multivariate Symbols to Optimize Dockless Bike Sharing

While dockless bike sharing is gaining popularity, oversupplied and poorly maintained bikes introduce chaos and waste (e.g., so-called zombie bikes that unused). Spatiotemporal pattern visualizations can help policy-making and infrastructure improvement (e.g., allocating parking areas). However, multivariate symbolizing (e.g., supply, flow, usage) to optimize dockless bike sharing is challenging. In this paper, we introduce metaphor theory to design multivariate symbols. First, we systemically explore the coupling of three metaphor types (orientational, ontological and structural) with symbols at three levels of iconicity. Then, we construct metaphorical symbols for optimizing dockless bike sharing following a user-centred design process. We also offer an evaluation using eye-tracking and questionnaire techniques. The results indicate that, compared with bin-packing and multiview symbols, metaphorical symbols significantly improved effectiveness and efficiency, and reduced participants' cognitive load. Our evaluation presents preliminary evidence that metaphors can offer new organizational mechanisms for map symbols to represent multivariate naturally and effectively.
Retraction: Research on the Construction and Characteristic Development of College English Teachers Based on Big Data Analysis (J. Phys.: Conf. Ser. 1648 032166)

This article has been retracted by IOP Publishing following an allegation that raises concerns this article may have been created, manipulated, and/or sold by a commercial entity. In addition, IOP Publishing has seen no evidence that reliable peer review was conducted on this article, despite the clear standards expected of and communicated to conference organisers. The authors of the article have been given opportunity to present evidence that they were the original and genuine creators of the work, however at the time of publication of this notice, IOP Publishing has not received any response. IOP Publishing has analysed the article and agrees there are enough indicators to cause serious doubts over the legitimacy of the work and agree this article should be retracted. The authors are encouraged to contact IOP Publishing Limited if they have any comments on this retraction. Retraction published: 16 September 2022
Retraction: Discussion on the Application of Computer Technology in Media in the New Media Era (J. Phys.: Conf. Ser. 1648 032007)

This article has been retracted by IOP Publishing following an allegation that raises concerns this article may have been created, manipulated, and/or sold by a commercial entity. In addition, IOP Publishing has seen no evidence that reliable peer review was conducted on this article, despite the clear standards expected of and communicated to conference organisers. The authors of the article have been given opportunity to present evidence that they were the original and genuine creators of the work, however at the time of publication of this notice, IOP Publishing has not received any response. IOP Publishing has analysed the article and agrees there are enough indicators to cause serious doubts over the legitimacy of the work and agree this article should be retracted. The authors are encouraged to contact IOP Publishing Limited if they have any comments on this retraction. Retraction published: 16 September 2022
Retraction: Teaching Reform of English Reading Ability Based on Big Data (J. Phys.: Conf. Ser. 1744 032012)

This article has been retracted by IOP Publishing following an allegation that raises concerns this article may have been created, manipulated, and/or sold by a commercial entity. In addition, IOP Publishing has seen no evidence that reliable peer review was conducted on this article, despite the clear standards expected of and communicated to conference organisers. The authors of the article have been given opportunity to present evidence that they were the original and genuine creators of the work, however at the time of publication of this notice, IOP Publishing has not received any response. IOP Publishing has analysed the article and agrees there are enough indicators to cause serious doubts over the legitimacy of the work and agree this article should be retracted. The authors are encouraged to contact IOP Publishing Limited if they have any comments on this retraction. Retraction published: 16 September 2022
Retraction: Analyze the Development of Public English Courses education in Vocational schools Based on AI and Big Data (J. Phys.: Conf. Ser. 1744 042210)

This article has been retracted by IOP Publishing following an allegation that raises concerns this article may have been created, manipulated, and/or sold by a commercial entity. In addition, IOP Publishing has seen no evidence that reliable peer review was conducted on this article, despite the clear standards expected of and communicated to conference organisers. The authors of the article have been given opportunity to present evidence that they were the original and genuine creators of the work, however at the time of publication of this notice, IOP Publishing has not received any response. IOP Publishing has analysed the article and agrees there are enough indicators to cause serious doubts over the legitimacy of the work and agree this article should be retracted. The authors are encouraged to contact IOP Publishing Limited if they have any comments on this retraction. Retraction published: 16 September 2022
Heuristics

This chapter introduces heuristics and mathematical optimization. It introduces the framework to formulate an optimization process. Next, it introduces linear programming, mixed-integer programming, and constraint programming techniques. To run the optimization code, the chapter introduces several types of solvers and compares the solvers. The chapter salutes and sincerely thanks George Dantzig for creating trillions of dollars of value and saving countless years of life across the globe through the power of optimization. This chapter showcases two industrial use cases: manufacturing general assembly line balancing and surgical suture management. Finally, the chapter concludes with model and simulation process and choosing solver technology.
Data Engineering Techniques for Machine Learning and Heuristics

This chapter introduces food security data sources and data engineering attributes for handling agricultural datasets. The chapter starts with heuristics data and frequencies and introduces food security long tail. Next, the chapter introduces data enrichment techniques to fix any data-related issues. Finally, the chapter concludes with food security risk model that was due to potential food-fuel policy change.