Reviewerly/exHarmony huggingface dataset:
Deontic Ambiguities in Legal Reasoning

What happens if the way in which we handle a genuine deontic conflict ---i.e., a deontic ambiguity--- matters regarding the application of other norms that are not directly affected by that conflict? We argue that the law requires sometimes propagating the ambiguity to other norms and sometimes confining it to some norms only. We explore this issue and model different reasoning patterns. The problem is addressed in a new variant of Defeasible Deontic Logic. The contribution of this paper is threefold: (a) we extend the treatment of ambiguity blocking and propagation to Defeasible Deontic Logic; (b) we discuss reasoning patterns in the law, especially in criminal law, where we need to deal with both ambiguity blocking and ambiguity propagation in the same legal system and logic; (c) we devise an annotated variant of Defeasible Deontic Logic where we distinguish literals that must be obtained through an ambiguity-blocking mechanism from those that are derived using an ambiguity-propagating mechanism.
A CNN-based approach for joint segmentation and quantification of nuclei and NORs in AgNOR-stained images

Oral cancer is the sixth most common kind of human cancer. Brush cytology for counting Argyrophilic Nucleolar Organizer Regions (AgNORs) can help early mouth cancer detection, lowering patient mortality. However, the manual counting of AgNORs still in use today is time-consuming, labor-intensive, and error-prone. The goal of our work is to address these shortcomings by proposing a convolutional neural network (CNN) based method to automatically segment individual nuclei and AgNORs in microscope slide images and count the number of AgNORs within each nucleus.We systematically defined, trained and tested 102 CNNs in the search for a high-performing solution. This included the evaluation of 51 network architectures combining 17 encoders with 3 decoders and 2 loss functions. These CNNs were trained and evaluated on a new AgNOR-stained image dataset of epithelial cells from oral mucosa containing 1,171 images from 48 patients, with ground truth annotated by specialists. The annotations were greatly facilitated by a semi-automatic procedure developed in our project. Overlapping nuclei, which tend to hide AgNORs, thus affecting their true count, were discarded using an automatic solution also developed in our project. Besides the evaluation on the test dataset, the robustness of the best performing model was evaluated against the results produced by a group of human experts on a second dataset.The best performing CNN model on the test dataset consisted of a DenseNet-169 + LinkNet with Focal Loss (DenseNet-169 as encoder and LinkNet as decoder). It obtained a Dice score of 0.90 and intersection over union (IoU) of 0.84. The counting of nuclei and AgNORs achieved precision and recall of 0.94 and 0.90 for nuclei, and 0.82 and 0.74 for AgNORs, respectively. Our solution achieved a performance similar to human experts on a set of 291 images from 6 new patients, obtaining Intraclass Correlation Coefficient (ICC) of 0.91 for nuclei and 0.81 for AgNORs with 95% confidence intervals of [0.89, 0.93] and [0.77, 0.84], respectively, and p-values < 0.001, confirming its statistical significance. Our AgNOR-stained image dataset is the most diverse publicly available AgNOR-stained image dataset in terms of number of patients and the first for oral cells.CNN-based joint segmentation and quantification of nuclei and NORs in AgNOR-stained images achieves expert-like performance levels, while being orders of magnitude faster than the later. Our solution demonstrated this by showing strong agreement with the results produced by a group of specialists, highlighting its potential to accelerate diagnostic workflows. Our trained model, code, and dataset are available and can stimulate new research in early oral cancer detection.
Channels of Corruption in Africa: An Analytical Review and Assessment of Trends in Economic and Financial Crimes

The economic and financial crimes of embezzlement and theft, bribes and kickbacks, money laundering and illicit financial flows, and through state capture, are all channels of corruption that are prevalent in Africa with significant negative effects on the continent’s sustainable development outcomes. This chapter identifies and analytically reviews and assesses the trends of these principal economic and financial crimes as channels of corruption that impact the development process and economic progress in Africa. It therefore provides an analysis of the nature and extent of the trends in economic and financial crimes, as channels of corruption in Africa, and the resultant negative impact on sustainable development in the region.
Model- and data-agnostic justifications with A Fortiori Case-Based Argumentation

AF-CBA is an example-based approach to XAI that draws on the case-based argumentation tradition in AI & Law. It means to explain binary classifications made by an opaque machine-learning model by presenting an argument graph to the user, which represents an argument game about the classification of a case on the basis of precedents derived from labelled data used in the training phase of the classifier. We improve the robustness of this method by modifying it to better handle inconsistent labelling and evaluate an alternative setup that does not require access to the labelled data by using earlier predictions instead.
Flipit Game Deception Strategy Selection Method Based on Deep Reinforcement Learning

The existing cyber deception decision-making model based on game theory primarily focuses on the selection of spatial strategies, which ignores the optimal defense timing and can affect the execution of a defense strategy. Consequently, this paper presents a method for selecting deception strategies based on a multi-stage Flipit game. Firstly, based on the analysis of cyber deception attack and defense, we propose a concept of moving deception attack surface and analyze the characteristics of deception attack and defense interaction behaviors based on the Flipit game model. The Flipit game model is then utilized to create a single-stage deception spatial-temporal decision-making model. Additionally, we introduce the discount factor and transition probability based on a single-stage game model and construct a multi-stage cyber deception model. We provide the utility function of the multi-stage game model, and design a Proximal Policy Optimization algorithm based on deep reinforcement learning to compute the defender’s optimal spatial-temporal strategies. Finally, we utilize an application example to validate the effectiveness of the model and the advantages of the proposed algorithm in generating the multi-stage cyber deception strategy.
New Types of Topologies and Neutrosophic Topologies

In this paper, we recall the six new types of topologies that we introduced in the last years (2019-2022), such as: Refined Neutrosophic Topology, Refined Neutrosophic Crisp Topology, NeutroTopology, AntiTopology, SuperHyperTopology, and Neutrosophic SuperHyperTopology.&#x0D; &#x0D;
Effect of constraints and vertical wall interaction on workspace of a quadcopter manipulator system

The workspace analysis of a robotic arm coupled to an unmanned aerial vehicle executing close-to-target operations is significant. The workspace of a 3 degree-of-freedom manipulator mounted to the bottom of a quadcopter and having an extended workspace is analyzed in this research, along with a motion planning algorithm. The quadcopter manipulator system comprises a robotic arm attached to the quadcopter’s center of gravity at its bottom. The manipulator has an extended workspace as its end-effector can reach three-dimensional locations above and below the drone’s airframe. The arm’s workspace is determined by system kinematics. Certain factors like downwash from the drone, the robotic arm’s singularity, servo motor stall torques, and mechanical structure limit the arm’s workspace during real-time tasks. A detailed description of these factors and their impact on the arm’s reachable workspace is also provided. Based on these limitations, the motion planning algorithm verifies the viability of a specific arm configuration and, therefore, the feasibility of the task. A concept called the near-wall effect and strategies to limit its influence on aerial robots are presented to comprehend the effect of a wall on the system in tasks involving targets on a compound wall. The proposed research outcomes are evaluated using MATLAB and ROS/Gazebo simulations.
Research on Self-cognition of College Students in the Background of AI Language Model Use

This paper discusses the status and influencing factors of college students' self-cognition under the background of the use of artificial intelligence language model. A cross-sectional study design was used to survey college students from different specialties and ages, and questionnaire analysis was used for data analysis. The research results show that the use of AI language model by college students is generally with some uncertainty and dependence; the overall level of self-cognition is high, but there are some differences; the correlation between AI language model and self-cognition, and the influence factors include gender, major and age. This study is helpful to promote the self-cognition and development of college students, improve the application efficiency and popularity of AI language models, and its relationship can be further explored in the future.
Keyword-based Augmentation Method to Enhance Abstractive Summarization for Legal Documents

Since state-of-the-art machine learning models like Transformers can not handle long text well, the quality of the summarization of legal documents is still not desirable. In order to improve the ability of machine learning models to understand the context of a long document, we introduce the keywords into the models to guide the summarization to locate and capture key information from long documents such as legal cases. Different from other works leveraging keywords to enhance the model, we further investigate how keyword quality impacts summarization. To improve the performance of the summarization, we also explore different methods for effectively encoding exceptionally lengthy documents and models for keyword extraction. The experiment results demonstrated that the keywords-based augmentation method is effective for summarization and higher-quality keywords can enhance the summarization models.
Automated tabulation of clinical trial results: A joint entity and relation extraction approach with transformer-based language representations

Evidence-based medicine, the practice in which healthcare professionals refer to the best available evidence when making decisions, forms the foundation of modern healthcare. However, it relies on labour-intensive systematic reviews, where domain specialists must aggregate and extract information from thousands of publications, primarily of randomised controlled trial (RCT) results, into evidence tables. This paper investigates automating evidence table generation by decomposing the problem across two language processing tasks: named entity recognition, which identifies key entities within text, such as drug names, and relation extraction, which maps their relationships for separating them into ordered tuples. We focus on the automatic tabulation of sentences from published RCT abstracts that report the results of the study outcomes. Two deep neural net models were developed as part of a joint extraction pipeline, using the principles of transfer learning and transformer-based language representations. To train and test these models, a new gold-standard corpus was developed, comprising over 550 result sentences from six disease areas. This approach demonstrated significant advantages, with our system performing well across multiple natural language processing tasks and disease areas, as well as in generalising to disease domains unseen during training. Furthermore, we show these results were achievable through training our models on as few as 170 example sentences. The final system is a proof of concept that the generation of evidence tables can be semi-automated, representing a step towards fully automating systematic reviews.
Mental and Value Features of Ukrainian Society in the Context of “Civilizations Clash” as the Main Object of Information War in Ukraine

Peculiarities of the value-world archetypes formation of Ukrainian society and dynamics of political process in Ukraine are considered through the prism of the civilizational theory of A. Toynbee and S. Huntington. Mental and value guidelines of citizens and social groups became the main object of information wars. So, cultural and linguistic value aspect is a key in the democratic state-building of Ukraine. Field study was conducted. Comparative analysis of the value orientations of citizens of four megaregions of Ukraine was done on its basis. It includes: eastern, western, southern and north-central. Values that divide and unite Ukrainian society were identified. Matrix of value methodology of political systems research on four binary value oppositions in three corresponding axiological clusters is offered: value absolutes; universal modalities and socio-archetypal values. It allowed determining the place of Ukrainian society in the value-political space of the modern world. Such a concept also defined basic principles of state humanitarian policy to develop and consolidate in Ukrainian society those values that should bring Ukraine closer to democracy.
Analysis of critical thinking skills, cognitive learning outcomes, and student activities in learning the human excretory system using an interactive flipbook

This study aims to: (1) analyze the achievement of critical thinking skills, (2) analyze the achievement of cognitive learning outcomes, (3) determine the learning activities carried out, and (4) determine student responses to the use of interactive flipbook media. This study is Quasi Experiment research with a posttest-only control design employing a purposive sampling technique. The data in this study are (1) data on the achievement of critical thinking ability indicators, (2) data on achievement of cognitive levels (C4, C5, and C6), (3) data on student learning activities carried out, and (4) data on student responses to use of interactive flipbook media. The instruments used are (1) critical thinking tests in the form of description questions, (2) cognitive learning outcomes tests in the form of multiple-choice questions, (3) student activity observation sheets, and (4) student response questionnaires. The results obtained in learning the human excretory system using interactive flipbooks are: (1) the level of achievement of critical thinking skills on each indicator can be achieved well, (2)the level achievement of cognitive learning outcomes (C4, C5, and C6) in each category can be achieved well, (3) learning activities that appear and implement in the learning process include visual activities, oral activities, and listening activities, and (4) the use of interactive flipbook media gets a positive response from students. Most respondents agree to use interactive flipbook media to learn the human excretory system. Students also assess that interactive flipbook media are interesting, flexible, practical, meaningful, and not dull.
Robust clustering with adaptive order graph learning

Non-negative matrix factorization and spectral clustering are widely recognized techniques in data analysis due to their distinctive geometric interpretation and proven effectiveness across various applications. However, despite their popularity, these methods are not without their limitations. Specifically, they are susceptible to noise (outliers), which can significantly impact their clustering performance. Furthermore, their ability to learn spatial information heavily relies on the quality of the initial adjacency matrix. To mitigate the mentioned limitations, this paper proposes a novel clustering approach that integrates non-negative matrix factorization with spectral clustering. The proposed method utilizes a novel robust loss function to quantify the reconstruction error of samples, thereby improving the model's capacity to handle noise (outliers) and enhance clustering performance. Additionally, our method incorporates an adaptive-order graph learning strategy that enhances the model's adaptability to initial adjacency matrices. To optimize the proposed model, we have devised a highly efficient algorithm and conducted a analysis of its convergence properties. Experimental results on diverse datasets substantiate the effectiveness of our method in robust clustering and spatial learning tasks.
A Coq Implementation of the Program Algebra in Jifeng He’s New Roadmap for Linking Theories of Programming

Jifeng He has proposed a roadmap for linking theories of programming and presents an algebra of programs capable of generating both denotational and operational representations from the refinement relation. In this paper, we implement this algebra of programs and its refinement relation using the interactive theorem prover Coq. Encoding the algebra into CIC (Calculus of Inductive Constructions), the main formalism in Coq, facilitates machine-aided interactive proving for the properties of programs using predefined algebraic laws. The implementation of the algebra for finite programs enables us to prove that every finite program can be reduced to the normal form and to check the refinement between two finite programs. The implementation of the algebra for infinite programs supports formalizing recursive programs with one variable and checking the refinement between one finite and one infinite program. Then, we present examples of proving the refinement relationship between two finite programs and a finite program and an infinite program.
Graphon mean-field control for cooperative multi-agent reinforcement learning

The marriage between mean-field theory and reinforcement learning has shown a great capacity to solve large-scale control problems with homogeneous agents. To break the homogeneity restriction of mean-field theory, a recent interest is to introduce graphon theory to the mean-field paradigm. In this paper, we propose a graphon mean-field control (GMFC) framework to approximate cooperative heterogeneous multi-agent reinforcement learning (MARL) with nonuniform interactions and heterogeneous reward functions and state transition functions among agents and show that the approximate order is of O(1N), with N the number of agents. By discretizing the graphon index of GMFC, we further introduce a smaller class of GMFC called block GMFC, which is shown to well approximate cooperative MARL in terms of the value function and the policy. Finally, we design a Proximal Policy Optimization based algorithm for block GMFC that converges to the optimal policy of cooperative MARL. Our empirical studies on several examples demonstrate that our GMFC approach is comparable with the state-of-art MARL algorithms while enjoying better scalability.
Research on Accounting Information Integrity Verification Method based on Blockchain

Conventional accounting information integrity verification method mainly uses SOA mode to obtain verification parameters, which is susceptible to the influence of cloud memory verification transmission bandwidth, resulting in high information integrity verification overhead, therefore, this paper designs a new accounting information integrity verification method based on blockchain. The experimental results show that the designed blockchain-based accounting information integrity verification method has a smaller verification overhead under different data blocks, which proves that the designed accounting information integrity verification method has a better verification effect, has the advantage of low energy consumption, has a certain application value, and makes certain methodological innovations for solving the distortion problem of accounting information.
UTP, Circus, and Isabelle

We dedicate this paper with great respect and friendship to He Jifeng on the occasion of his 80th birthday. Our research group owes much to him. The authors have over 150 publications on unifying theories of programming (UTP), a research topic Jifeng created with Tony Hoare. Our objective is to recount the history of Circus (a combination of Z, CSP, Dijkstra’s guarded command language, and Morgan’s refinement calculus) and the development of Isabelle/UTP. Our paper is in two parts. (1) We first discuss the activities needed to model systems: we need to formalise data models and their behaviours. We survey our work on these two aspects in the context of Circus. (2) Secondly, we describe our practical implementation of UTP in Isabelle/HOL. Mechanising UTP theories is the basis of novel verification tools. We also discuss ongoing and future work related to (1) and (2). Many colleagues have contributed to these works, and we acknowledge their support.
MOZAIK: An End-to-End Secure Data Sharing Platform

The proliferation of Internet of Things (IoT) devices has led to exponential data growth that can be harnessed for personalized services, cost savings, and environmental benefits.However, collecting and sharing this data comes with significant risks, including hacking attacks, breaches of sensitive data, and non-compliance with privacy regulations.This paper proposes a comprehensive, end-to-end secure system, MOZAIK, for privacy-preserving data collection, analysis, and sharing to address these challenges.We perform a requirements analysis from the perspectives of security, privacy, legal, and functionality, highlighting the various mechanisms employed to safeguard sensitive data throughout the entire data cycle.This includes the use of lightweight encryption, distributed computation, and anonymous communication mechanisms to reduce security and privacy risks and to protect against single points of failure.MOZAIK provides a trusted and secure platform for data sharing and processing that can enable the creation of a data market and data economy.
Justification, stability and relevance for case-based reasoning with incomplete focus cases

We define and study the notions of stability and relevance for precedent-based reasoning, focusing on Horty's result model of precedential constraint. According to this model, precedents constrain the possible outcomes for a focus case, which is a yet undecided case, where precedents and the focus case are compared on their characteristics (called dimensions). In this paper, we refer to the enforced outcome for the focus case as its justification status. In contrast to earlier work, we do not assume that all dimension values of the focus case have been established with certainty: rather, each dimension is assigned a set of possible values. We define a focus case as stable if its justification status is the same for every choice of the possible values. For focus cases that are not stable, we study the task of identifying relevance: which possible values should be excluded to make the focus case stable? We show how the tasks of identifying justification, stability and relevance can be exploited for human-in-the-loop decision support. Finally, we discuss the computational complexity of these tasks and provide efficient algorithms.