Reviewerly/exHarmony huggingface dataset:
Operator Counterparts of Types of Reasoning

Abstract Logical and philosophical literature provides different classifications of reasoning. In the Polish literature on the subject, for instance, there are three popular ones accepted by representatives of the Lvov-Warsaw School: Jan Łukasiewicz, Tadeusz Czeżowski and Kazimierz Ajdukiewicz (Ajdukiewicz in Logika pragmatyczna [Pragmatic Logic]. PWN, Warsaw (1965, 2nd ed. 1974). Translated as: Pragmatic Logic. Reidel &amp; PWN, Dordrecht, 1975). The author of this paper, having modified those classifications, distinguished the following types of reasoning: (1) deductive and (2) non-deductive, and additionally two types of them in each of the two, depending on the manner of combining their premises with the conclusion through the relation of classical logical entailment. Consequently, the four types of reasoning: unilateral deductive (incl. its sub-types: deductive inference and proof), bilateral deductive (incl. complete induction), and reductive (incl. the sub-types: explanation and verification), logically nonvaluable (incl. inference by analogy, statistic inference), correspond to four operators of derivability. They are defined formally on the ground of Tarski’s axiomatic theory of deductive systems, by means of the consequence operation Cn (Tarski in Monatshefte Math Phys 37:361–404, 1930a, C R Soc Sci Lett Vars 23:22–29, 1930b). Also, certain metalogical properties of these operators are given, as well as their relations with Tarski’s consequence operations $$Cn^+$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>C</mml:mi> <mml:msup> <mml:mi>n</mml:mi> <mml:mo>+</mml:mo> </mml:msup> </mml:mrow> </mml:math> ( $$Cn^+ = Cn$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>C</mml:mi> <mml:msup> <mml:mi>n</mml:mi> <mml:mo>+</mml:mo> </mml:msup> <mml:mo>=</mml:mo> <mml:mi>C</mml:mi> <mml:mi>n</mml:mi> </mml:mrow> </mml:math> ) and dual consequences $$Cn^{-1}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>C</mml:mi> <mml:msup> <mml:mi>n</mml:mi> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> (Słupecki in Zeszyty Naukowe Uniwersytetu Wrocławskiego Seria B Nr 3:33–40, 1959, Słupecki et al. in Stud Log 29:76–123, 1971, Wybraniec-Skardowska, in: Wybraniec-Skardowska, Bryll (eds) Z badań nad teorią zdań odrzuconych [Studies in the Theory of Rejected Propositions], Series B, Studia i Monografie, Zeszyty Naukowe Wyższej Szkoły Pedagogicznej w Opolu, Opole, 1969), and $$Cn^-$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>C</mml:mi> <mml:msup> <mml:mi>n</mml:mi> <mml:mo>-</mml:mo> </mml:msup> </mml:mrow> </mml:math> (Wójcicki in Bull Sect Log 2(2):54–57, 1973)).
Simulation of breast lesions based upon fractal Perlin noise

Steadily increasing use of computational/virtual phantoms in medical physics has motivated expanding development of new simulation methods and data representations for modelling human anatomy. This has emphasized the need for increased realism, user control, and availability. In breast cancer research, virtual phantoms have gained an important role in evaluating and optimizing imaging systems. For this paper, we have developed an algorithm to model breast abnormalities based on fractal Perlin noise. We demonstrate and characterize the extension of this approach to simulate breast lesions of various sizes, shapes, and complexity.Recently, we developed an algorithm for simulating the 3D arrangement of breast anatomy based on Perlin noise. In this paper, we have expanded the method to also model soft tissue breast lesions. We simulated lesions within the size range of clinically representative breast lesions (masses, 5-20 mm in size). Simulated lesions were blended into simulated breast tissue backgrounds and visualized as virtual digital mammography images. The lesions were evaluated by observers following the BI-RADS assessment criteria.Observers categorized the lesions as round, oval or irregular, with circumscribed, microlobulated, indistinct or obscured margins. The majority of the simulated lesions were considered by the observers to have a realism score of moderate to well. The simulation method provides almost real-time lesion generation (average time and standard deviation: 1.4 ± 1.0 s).We presented a novel algorithm for computer simulation of breast lesions using Perlin noise. The algorithm enables efficient simulation of lesions, with different sizes and appearances.
Declarative Reasoning on Explanations Using Constraint Logic Programming

Explaining opaque Machine Learning (ML) models is an increasingly relevant problem. Current explanation in AI (XAI) methods suffer several shortcomings, among others an insufficient incorporation of background knowledge, and a lack of abstraction and interactivity with the user. We propose reasonx, an explanation method based on Constraint Logic Programming (CLP). reasonx can provide declarative, interactive explanations for decision trees, which can be the ML models under analysis or global/local surrogate models of any black-box model. Users can express background or common sense knowledge using linear constraints and MILP optimization over features of factual and contrastive instances, and interact with the answer constraints at different levels of abstraction through constraint projection. We present here the architecture of reasonx, which consists of a Python layer, closer to the user, and a CLP layer. reasonx’s core execution engine is a Prolog meta-program with declarative semantics in terms of logic theories.
Stable Normative Explanations: From Argumentation to Deontic Logic

This paper reconstructs in the context of formal argumentation the notion of stable explanation developed elsewhere in Defeasible Logic. With this done, we discuss the deontic meaning of this notion and show how to build from argumentation neighborhood structures for deontic logic where a stable explanation can be characterised.
On the Expressive Power of Assumption-Based Argumentation

The expressiveness of any given formalism lays the theoretical foundation for more specialized topics such as investigating dynamic reasoning environments. The modeling capabilities of the formalism under investigation yield immediate (im)possibility results in such contexts. In this paper we investigate the expressiveness of assumption-based argumentation (ABA), one of the major structured argumentation formalisms. In particular, we examine so-called signatures, i.e., sets of extensions that can be realized under a given semantics. We characterize the signatures of common ABA semantics for flat, finite frameworks with and without preferences. We also give several results regarding conclusion-based semantics for ABA.
Contrastive Explanations for Answer-Set Programs

Answer-Set Programming (ASP) is a popular declarative reasoning and problem solving formalism. Due to the increasing interest in explainability, several explanation approaches have been developed for ASP. However, while those formalisms are correct and interesting on their own, most are more technical and less oriented towards philosophical or social concepts of explanation. In this work, we study the notion of contrastive explanation, i.e., answering questions of the form “Why P instead of Q?”, in the context of ASP. In particular, we are interested in answering why atoms are included in an answer set, whereas others are not. Contrastive explainability has recently become popular due to its strong support from the philosophical, cognitive, and social sciences and its apparent ability to provide explanations that are concise and intuitive for humans. We formally define contrastive explanations for ASP based on counterfactual reasoning about programs. Furthermore, we demonstrate the usefulness of the concept on example applications and give some complexity results. The latter also provide a guideline as to how the explanations can be computed in practice.
Planning with Partial Observability by SAT

Geffner & Geffner (2018) have shown that finding plans by reduction to SAT is not limited to classical planning, but is competitive also for fully observable non-deterministic planning. This work extends these ideas to planning with partial observability. Specifically, we handle partial observability by requiring that during the execution of a plan, the same actions have to be taken in all indistinguishable circumstances. We demonstrate that encoding this condition directly leads to far better scalability than an explicit encoding of observations-to-actions mapping, for high numbers of observations.
Weak Argumentation Semantics and Unsafe Odd Cycles: Results and a Conjecture

Some semantics for argumentation, including the newly introduced weakly admissible semantics, allow us to ignore attacks from arguments that are perceived as problematic. A key intuition motivating such semantics is that arguments that indirectly attack themselves may be problematic in such a way that this is justified. In this paper, we formalise this intuition and provide a class of semantics that are weakly admissible, coincide with the stable semantics on a large class of argumentation frameworks that admit stable sets, and only ignore attacks from arguments on unsafe cycles of odd length. We also show that no member of our class of semantics coincide with the semantics that takes all $$\subseteq $$ -maximal weakly admissible sets as extensions. However, we show that this semantics satisfies an even stronger property, if the following conjecture is true: if an argumentation framework has no non-empty weakly admissible sets, then every argument lies on an unsafe odd cycle.
Comparing Planning Domain Models Using Answer Set Programming

Automated planning is a prominent area of Artificial Intelligence, and an important component for intelligent autonomous agents. A critical aspect of domain-independent planning is the domain model, that encodes a formal representation of domain knowledge needed to reason upon a given problem. Despite the crucial role of domain models in automated planning, there is lack of tools supporting knowledge engineering process by comparing different versions of the models, in particular, determining and highlighting differences the models have. In this paper, we build on the notion of strong equivalence of domain models and formalise a novel concept of similarity of domain models. To measure the similarity of two models, we introduce a directed graph representation of lifted domain models that allows to formulate the domain model similarity problem as a variant of the graph edit distance problem. We propose an Answer Set Programming approach to optimally solve the domain model similarity problem, that identifies the minimum number of modifications the models need to become strongly equivalent, and we demonstrate the capabilities of the approach on a range of benchmark models.
Beyond $$\mathcal {ALC}_{\textsf {reg}}$$: Exploring Non-Regular Extensions of PDL with Description Logics Features

We investigate the impact of non-regular path expressions on the decidability of satisfiability checking and querying in description logics. Our primary object of interest is $$\mathcal {ALC}_{\textsf {vpl}}$$ , an extension of $$\mathcal {ALC}$$ with path expressions using visibly-pushdown languages, which was shown to be decidable by Löding et al. in 2007. The paper present a series of undecidability results. We prove undecidability of $$\mathcal {ALC}_{\textsf {vpl}}$$ with the seemingly innocent $$\textsf{Self}$$ operator. Then, we consider the simplest non-regular (visibly-pushdown) language $$ r ^\# s ^\# {:}{=}\{ r ^n s ^n \mid n \in \mathbb {N}\}$$ . We establish undecidability of the concept satisfiability problem for $${\mathcal {A}\mathcal {L}\mathcal {C}}_{\textsf {reg}}$$ extended with nominals and $$ r ^\# s ^\#$$ , as well as of the query entailment problem for $$\mathcal {ALC}$$ -TBoxes, where such non-regular atoms are present in queries.
delphic: Practical DEL Planning via Possibilities

Dynamic Epistemic Logic (DEL) provides a framework for epistemic planning that is capable of representing non-deterministic actions, partial observability, higher-order knowledge and both factual and epistemic change. The high expressivity of DEL challenges existing epistemic planners, which typically can handle only restricted fragments of the whole framework. The goal of this work is to push the envelop of practical DEL planning, ultimately aiming for epistemic planners to be able to deal with the full range of features offered by DEL. Towards this goal, we question the traditional semantics of DEL, defined in terms on Kripke models. In particular, we propose an equivalent semantics defined using, as main building block, so-called possibilities: non well-founded objects representing both factual properties of the world, and what agents consider to be possible. We call the resulting framework delphic. We argue that delphic indeed provides a more compact representation of epistemic states. To substantiate this claim, we implement both approaches in ASP and we set up an experimental evaluation to compare delphic with the traditional, Kripke-based approach. The evaluation confirms that delphic outperforms the traditional approach in space and time.
Generative Datalog and Answer Set Programming – Extended Abstract

Generative Datalog is an extension of Datalog that incorporates constructs for referencing parameterized probability distributions. This augmentation transforms the evaluation of a Generative Datalog program into a stochastic process, resulting in a declarative formalism suitable for modeling and analyzing other stochastic processes. This work provides an introduction to Generative Datalog through the lens of Answer Set Programming (ASP), demonstrating how Generative Datalog can explain the output of ASP systems that include @-terms referencing probability distributions. From a theoretical point of view, extending the semantics of Generative Datalog to stable negation proved to be challenging due to the richness of ASP relative to Datalog in terms of linguistic constructs. On a more pragmatic side, the connection between the two formalisms lays the foundation for implementing Generative Datalog atop efficient ASP systems, making it a practical solution for real-world applications.
Excursions in First-Order Logic and Probability: Infinitely Many Random Variables, Continuous Distributions, Recursive Programs and Beyond

The unification of the first-order logic and probability has been seen as a long-standing concern in philosophy, AI and mathematics. In this talk, I will briefly review our recent results on revisiting that unification. Although there are plenty of approaches in communities such as statistical relational learning, automated planning, and neuro-symbolic AI that leverage and develop languages with logical and probabilistic aspects, they almost always restrict the representation as well as the semantic framework in various ways which do not fully explain how to combine first-order logic and probability theory in a general way. In many cases, this restriction is justified because it may be necessary to focus on practicality and efficiency. However, the search for a restriction-free mathematical theory remains ongoing. In this article, we discuss our recent results regarding the development of languages that support arbitrary quantification, possibly infinitely many random variables, both discrete and continuous distributions, as well as programming languages built on top of such features to include recursion and branching control.
Data Graphs with Incomplete Information (and a Way to Complete Them)

We introduce a modal language for reasoning about data graphs with incomplete information. Such data graphs are formally represented as models in which data value functions are partial—to capture what is unknown. In this setting, we also allow for unknown data values to be learned. Our main result is a sound and strongly complete axiomatization for the logic.
Logic, Accountability and Design: Extended Abstract

This note is a contribution to the methodology of applied, computational logics in light of their potential role in securing the accountability of Artificial Intelligence (AI) systems.
Formalizing Statistical Causality via Modal Logic

We propose a formal language for describing and explaining statistical causality. Concretely, we define Statistical Causality Language (StaCL) for expressing causal effects and specifying the requirements for causal inference. StaCL incorporates modal operators for interventions to express causal properties between probability distributions in different possible worlds in a Kripke model. We formalize axioms for probability distributions, interventions, and causal predicates using StaCL formulas. These axioms are expressive enough to derive the rules of Pearl’s do-calculus. Finally, we demonstrate by examples that StaCL can be used to specify and explain the correctness of statistical causal inference.
Interpersonal communication as an acmeological problem in contemporary education

The aim of this study is to investigate and analyse interpersonal communication as a prominent issue in modern educational practice. The authors employed various general scientific research methods including analysis and synthesis, the comparative method, as well as the inductive and deductive approaches. Additionally, a theoretical analysis of relevant research by other scholars was carried out in the final stage of the study. Throughout the research, the authors identified different types of communication and barriers to effective communication. Furthermore, three aspects of a teacher's speech culture were identified. This study explores the educational perspectives within the field of educational science and the emerging scientific concept of educational acmeology in global educational research. The focus on educational acmeology addresses the objective of enhancing teachers' psychological and educational skills, professional competence and the interplay between education and psychology. The findings contribute to the development of professional and scientific relationships among teachers as a result of successful acmeological practices. The practical significance of this research lies in its potential application to examine interpersonal communication as an acmeological problem in contemporary educational settings.
On Heuer’s Procedure for Verifying Strong Equivalence

In answer set programming, two groups of rules are considered strongly equivalent if replacing one group by the other within any program does not affect the set of stable models. Jan Heuer has designed and implemented a system that verifies strong equivalence of programs in the ASP language mini-gringo. The design is based on the syntactic transformation $$\tau ^*$$ that converts mini-gringo programs into first-order formulas. Heuer’s assertion about $$\tau ^*$$ that was supposed to justify this procedure turned out to be incorrect, and in this paper we propose an alternative justification for his algorithm. We show also that if $$\tau ^*$$ is replaced by the simpler and more natural translation $$\nu $$ then the algorithm will still produce correct results.
Deterministic Weighted Automata Under Partial Observability

Weighted automata is a basic tool for specification in quantitative verification, which allows to express quantitative features of analysed systems such as resource consumption. Quantitative specification can be assisted by automata learning as there are classic results on Angluin-style learning of weighted automata. The existing work assumes perfect information about the values returned by the target weighted automaton. In assisted synthesis of a quantitative specification, knowledge of the exact values is a strong assumption and may be infeasible. In our work, we address this issue by introducing a new framework of partially-observable deterministic weighted automata, in which weighted automata return intervals containing the computed values of words instead of the exact values. We study the basic properties of this framework with the particular focus on the challenges of active learning partially-observable deterministic weighted automata.
<scp>DeepMUI</scp>: A novel method to identify malicious users on online social network platforms

Summary The use of online social network (OSN) platforms has become an essential component of contemporary society, facilitating global connectivity, and information sharing among individuals. The proliferation of malicious users has emerged as a noteworthy obstacle, exerting a detrimental effect on the authenticity of the data disseminated through these channels. A malicious profile is created with the intention of disseminating false information, manipulating perspectives, and executing harmful actions, including phishing schemes, identity theft, and the propagation of malware. Consequently, the identification of malicious users has emerged as an essential undertaking for both OSN platforms and researchers. The objective of this study is to investigate the issue of identifying malicious users on OSN platforms. The DeepMUI model has been introduced as a new approach to identifying malicious users on OSN platforms, utilizing user profile metadata‐derived characteristics. The DeepMUI architecture is composed of long short‐term memory and convolutional neural network models. Additionally, it integrates alterations to the pooling layer to improve its overall efficacy. The experiments have demonstrated that DeepMUI exhibits promising results in the task of identifying malicious users, with greater accuracy and minimal loss compared to existing methods.
Personalised Emotion Detection from Text Using Machine Learning

This research discusses an emotion recognition system, which is an important component of many effective computing technologies for natural language processing, based on open-source platforms with automatic speech recognition (ASR) and text analysis, and which is used for user-based customised sentiment analysis. PocketSphinx as ASR and Word2vec model, K-means clustering, and TfidfVectoriser for text automatic analysis are used to design the proposed framework. Further, the dataset that is used for testing and training the model is from International Survey on Emotion Antecedents and Reactions (ISEAR). This research yields a user-dependent system that will function as a tailored assistant for identifying emotional responses and discovering innovative applications. The suggested model greatly outperforms the prior models, with an efficiency of 81% and an f-measure of 89%.
Solving a Seismic Mystery With the Audio From a Diver's Camera: A Case of Shallow Water <i>T</i>‐Waves in the Persian Gulf

Abstract Sparse offshore coverage of seismic networks has hindered detailed studies of submarine earthquakes and their associated seismic hazard. We present results of our analysis of a diver's recording of acoustic signals from an M L = 5.6 earthquake in the Persian Gulf. We model the signals as a set of several shallow water T phases the frequency and group velocity of which are determined by bathymetry. We show that the audio track from this recording can provide reliable estimates of earthquake location and seismic moment. We also show that the reported shaking in the southern Persian Gulf, &gt;170 km from the source of this small earthquake could result from T waves traveling through the entire width of the basin. Our results point to rudimentary and affordable underwater microphones similar to those used in the divers' cameras as tools to build efficient, low‐cost networks for the study of offshore events and early warning.