Reviewerly/exHarmony huggingface dataset:
The Expressive Power of Revised Datalog on Problems with Closure Properties

In this paper, we study the expressive power of revised Datalog on the problems that are closed under substructures. We show that revised Datalog cannot define all the problems that are in PTIME and closed under substructures. As a corollary, LFP cannot define all the extension-closed problems that are in PTIME.
An Inferential Theory of Causal Reasoning

We present a general formalism of causal reasoning that encompasses both Pearl’s approach to causality and a number of key systems of nonmonotonic reasoning in artificial intelligence.
A Formal Analysis of Hollis’ Paradox

In Hollis’ paradox, A and B each chose a positive integer and whisper their number to C. C then informs them, jointly, that they have chosen different numbers and, moreover, that neither of them are able to work out who has the greatest number. A then reasons as follows: B cannot have 1, otherwise he would know that my number is greater, and by the same reasoning B knows that I don’t have 1. But then B also cannot have 2, otherwise he would know that my number is greater (since he knows I don’t have 1). This line of reasoning can be repeated indefinitely, effectively forming an inductive proof, ruling out any number – an apparent paradox. In this paper we formalise Hollis’ paradox using public announcement logic, and argue that the root cause of the paradox is the wrongful assumption that A and B assume that C’s announcement necessarily is successful. This resolves the paradox without assuming that C can be untruthful, or that A and B are not perfect reasoners, like other solutions do. There are similarities to the surprise examination paradox. In addition to a semantic analysis in the tradition of epistemic logic, we provide a syntactic one, deriving conclusions from a set of premises describing the initial situation – more in the spirit of the literature on Hollis’ paradox. The latter allows us to pinpoint which assumptions are actually necessary for the conclusions resolving the paradox.
On the Finite Model Property of Non-normal Modal Logics

In this paper, we consider the non-normal modal logics over monotonic modal logic $$\textsf{M}$$ with extensions of any combination of (N), (P), (T), and (4) i.e. $$\{\textsf{M},\textsf{MN},\ldots ,\textsf{MNPT4}\}$$ . We study the algebras corresponding to these logics and give some examples of them. We further introduce the Gentzen-style sequent calculi with soundness and completeness proved. Finally, we prove the FMP of these logics and thus decidability based on our systems by algebraic proof-theoretic methods.
Non-labelled Sequent Calculi of Public Announcement Expansions of $$\textbf{K45}$$ and $$\textbf{S5}$$

This paper proposes non-labelled sequent calculi, $$\textsf{G}(\textbf{K45PAL})$$ and $$\textsf{G}(\textbf{S5PAL})$$ , for the public announcement expansions of modal logics K45 and S5. We transform each of the recursion axioms of PAL into left and right rules for the sequent calculi. For $$\textsf{G}(\textbf{K45PAL})$$ , the cut elimination theorem is shown using the complexity measure introduced by van Ditmarsch et al. (2007). This measure was originally employed to establish semantic completeness via recursion axioms. While the cut elimination theorem fails in $$\textsf{G}(\textbf{S5PAL})$$ , we adopt Takano’s strategy (1992) to establish that the cut formula in $$\textsf{G}(\textbf{S5PAL})$$ can be restricted to the set of suitably extended subformulas (i.e., closure) of the conclusion of the cut rule.
Content Rotation in the Presence of Satiation Effect

Service providers often employ rotational schedules for offerings, such as weekly TV series updates or periodic special events in video games. These rotations not only reduce the operating costs associated with constant offerings, but also moderate consumer satiation caused by frequent consumption of the same product. Despite the prevalence of simple designs in practice, there is a lack of guidance on designing effective rotational schedules. In this paper, we explore the design of rotational schedules for service providers to maximize long-run average profits. We develop an analytical model based on utility theory, which connects consumer consumption with satiation and product correlation. Our investigation encompasses various scenarios, including single product offerings, regular and rotational product pairs, and multiple product assortments. For a single product, we characterize the optimal frequency. For a regular and rotational product pair, we observe an interesting ripple effect in consumer dynamics. In this case, we analyze the optimal frequency and choice of the rotational product, which depend on reciprocal influences between the two products. In scenarios with multiple products, we demonstrate that as the number of products increases, the assortment depth converges to a constant, and the cycle length should grow linearly. Furthermore, we find that product permutations have a marginal impact on total consumption. To address general situations, we introduce a numerical method based on mixed-integer programming and conduct numerical studies using both synthetic and real data. Our findings and insights offer a first step towards understanding the design of content rotation.
A Temporal Logic for Successive Events

A succession of events is a sequence of events such that after one event is finished, the next one occurs successively. In this paper, we extended linear temporal logic with a new modality to capture the case that a sequence of events successively occurs. We compared the expressivity between this extended linear temporal logic and the standard linear temporal logic.
Belief Revision with Satisfaction Measure

In this paper we propose a satisfaction measure for the theory of belief revision, which should be an alternative to the classical distance measure for defining revision operators. We present the idea of satisfaction measure as a monotonic condition, and characterize how it affects the revision operation by proving an extension of the AGM representation theorem (H. Katsuno and A. Mendelzon, 1991). A unique syntactical form, the disjunction of all prime implicants, is used to compile the belief bases. Then we develop a method to generate revision operators meet that monotonic condition. Lastly, we compare such an operator to four operators of other kinds with an example, then highlight its novelty and advantages.
Building Trust and Safety in Artificial Intelligence with Abstract Interpretation

Deep neural networks (DNNs) are currently the dominant technology in artificial intelligence (AI) and have shown impressive performance in diverse applications including autonomous driving [9], medical diagnosis [2], and text generation [10].
How Fitting is Your Abstract Domain?

Abstract interpretation offers sound and decidable approximations for undecidable queries related to program behavior. The effectiveness of an abstract domain is entirely reliant on the abstract domain itself, and the worst-case scenario is when the abstract interpreter provides a response of “don’t know", indicating that anything could happen during runtime. Conversely, a desirable outcome is when the abstract interpreter provides information that exceeds a specified level of precision, resulting in a more precise answer. The concept of completeness relates to the level of precision that is forfeited when performing computations within the abstract domain. Our focus is on the domain’s ability to express program behaviour, which we refer to as adequacy. In this paper, we present a domain refinement strategy towards adequacy and a simple sound proof system for adequacy, designed to determine whether an abstract domain is capable of providing satisfactory responses to specified program queries. Notably, this proof system is both language and domain agnostic, and can be readily incorporated to support static program analysis.
Constraining Parse Ambiguity with Grammatical Codes

This paper describes a wide coverage “toolkit” for producing syntactic parse and semantic dependency analysis for English sentence input, with a focus on the syntactic parser component. This is pursued from an overarching application aim of empowering students who are medium to advanced learners of English to experience techniques of language analysis that stem from their word analysis. The idea is to unite the strengths of both human provided word analysis and a rule-based automatic system that creates structure from disambiguated word information. The central concern is then with the word information that should be supported and the impacts of this information on subsequent depictions of derived analysis.
Events and Relative Clauses

This work is the continuation of the development of polynomial event semantics (a dialect of Neo-Davidsonian event semantics), using the FraCaS textual entailment corpus as a whetstone. This time we grapple with various, often complicated, relative clauses. Relative clauses have hardly been analyzed before in event semantics. Although simple cases are straightforward, challenges arise when a clause contains quantification, coordination or negation. We deal with such complications in the present paper, focusing on entailments.
Logic Operators and Quantifiers in Type-Theory of Algorithms

In this work, I introduce the Type-Theory of Algorithms (TTA), which is an extension of Moschovakis Type-Theory of Algorithms and its reduction calculus, by adding logic operators and quantifiers. The formal language has two kinds of terms of formulae, for designating state-independent and state-dependent propositions and predications. The logic operators include conjunction, disjunction, conditional implication, and negation. I add state-dependent quantifiers, for enhancing the standard quantifiers of predicate logic. I provide an extended reduction calculus of the Type-Theory of Acyclic Algorithms, for reductions of terms to their canonical forms. The canonical forms of the terms provide the algorithmic semantics for computing the denotations.
A Proof-Theoretic Analysis of the Meaning of a Formula in a Combination of Intuitionistic and Classical Propositional Logic

This paper provides a proof-theoretic analysis of the meaning of a formula in a combination of intuitionistic and classical propositional logic, based on the analysis proposed by Restall (2009). Restall showed that his analysis is applicable to both intuitionistic and classical propositional logic separately, but this paper shows that it is also applicable to a combination of the two logics called $$\mathbf {C+J}$$ . In addition, two points of improvement of Restall’s analysis are mentioned, and they are overcome by employing the method provided by Takano (2018). Moreover, this paper explains how the analysis of $$\mathbf {C+J}$$ , which is based on Restall’s analysis and improved by Takano’s method, is related to the bilateralism-unilateralism debate. It is shown that a unilateral approach is possible for $$\mathbf {C+J}$$ , although Restall’s original analysis is based on bilateralism.
Error Localization for Sequential Effect Systems

We describe a new concrete approach to giving predictable error locations for sequential (flow-sensitive) effect systems. Prior implementations of sequential effect systems rely on either computing a bottom-up effect and comparing it to a declaration (e.g., method annotation) or leaning on constraint-based type inference. These approaches do not necessarily report program locations that precisely indicate where a program may “go wrong” at runtime. Instead of relying on constraint solving, we draw on the notion of a residual from literature on ordered algebraic structures. Applying these to effect quantales (a large class of sequential effect systems) yields an implementation approach which accepts exactly the same program as an original effect quantale, but for effect-incorrect programs is guaranteed to fail type-checking with predictable error locations tied to evaluation order. We have implemented this idea in a generic effect system implementation framework for Java, and report on experiences applying effect systems from the literature and novel effect systems to Java programs. We find that the reported error locations with our technique are significantly closer to the program points that lead to failed effect checks.
BREWasm: A General Static Binary Rewriting Framework for WebAssembly

Binary rewriting is a widely adopted technique in software analysis. WebAssembly (Wasm), as an emerging bytecode format, has attracted great attention from our community. Unfortunately, there is no general-purpose binary rewriting framework for Wasm, and existing effort on Wasm binary modification is error-prone and tedious. In this paper, we present BREWasm, the first general purpose static binary rewriting framework for Wasm, which has addressed inherent challenges of Wasm rewriting including high complicated binary structure, strict static syntax verification, and coupling among sections. We perform extensive evaluation on diverse Wasm applications to show the efficiency, correctness and effectiveness of BREWasm. We further show the promising direction of implementing a diverse set of binary rewriting tasks based on BREWasm in an effortless and user-friendly manner.
Reverse Template Processing Using Abstract Interpretation

Template languages transform tree-structured data into text. We study the reverse problem, transforming the template into a parser that returns all the tree-structured data that can produce a given text. Programs written in template languages are generally not injective (they have multiple preimages), not affine (some input variables can appear at several locations in the output), and erasing (they provide only a partial view of the source), which makes the problem challenging. We propose to solve this problem using concepts from abstract interpretation, like the denotational style of abstract semantics, soundness, exactness, or reduction, to reason about the precision and the recovery of all the preimages. This work shows that Abstract Interpretation is a very useful theory when reasoning about the reversal of non-injective programs.
Assessing Digital Competency Levels Among Mexican Higher Education Teachers

This work aimed to measure and describe the level of digital competencies from a pedagogical approach in UASLP teachers, validate the theoretical relationship between its dimensions, and identify behavioral patterns among the teachers. This was a quantitative study with a mixed approach with a correlational, cross-sectional, and non-experimental scope. The study sample was 270 professors from a Mexican public university. Exploratory and Confirmatory Factor Analysis, cluster analysis, and non-parametric hypothesis tests were used. The validity of the construct of the proposed instrument was confirmed. The levels of competence measured were found to be convenient, as well as their degree of integration in educational practice. Higher levels of digital skills were found in teachers with fewer years of experience, possibly related to being a digital native. The findings identify two groups of teachers: ICT-Transformative Teachers, who excel in integrating ICT into their teaching practices, and ICT-Reluctant Teachers, which significantly explain the level of competence. ICT-Transformative Teachers demonstrate resistance and difficulties. Additionally, digital teaching competencies are interrelated and partially explain teachers’ capabilities. In conclusion, teachers’ digital competencies should be addressed through a comprehensive training plan, considering the pedagogical approach and contextual elements, like generational aspects.
Verifiable Cloud-Based Data Publish-Subscribe Service With Hidden Access Policy

Cloud-based publish-subscribe (pub-sub) services provide a decoupling method for publishers and subscribers to effectively exchange targeted information and massive data on the cloud platform. Data publishers implement fine-grained access control to set subscription privileges for outsourced data through an access policy. However, in the context of semi-honest cloud platforms, the publisher's access policy may be collected, and incomplete or incorrect subscription results may be returned (e.g., to save communication costs). Existing solutions pay little attention to protecting the data publisher's access policy and cannot provide efficient verification for local results. In this paper, we propose a verifiable multi-keyword data publish-subscribe scheme with a hidden access policy (VMP/S). Specifically, VMP/S combines attribute-based keyword search and data aggregation technology to achieve secure fine-grained access control, thereby protecting the privacy of the access policy. Additionally, the scheme provides an effective method for verifying local results by using equal-length verification information to confirm the correctness of feedback subscription data. Furthermore, we introduce a novel verification method for access control to enhance subscription performance efficiency. We demonstrate that VMP/S achieves IND-CKA security and ensures the privacy of the access policy through a comprehensive security analysis. Through experimental simulations, we confirm its effectiveness.
Reinforcement Learning-Based News Recommendation System

Recommender systems have seen wide adoption in different domains. The motive of such systems has evolved from providing generic recommendations in the past to providing customized and user-focused recommendations. To achieve this aim, the complexity and sophistication of the underlying techniques such systems use have evolved. Current recommender systems use advanced Artificial Intelligence techniques to provide intelligent recommendations and adapt their future workings to the user’s interest and requirements. One such technique currently being used in the literature to achieve this aim is Reinforcement Learning. However, a drawback of this technique is that it is data intensive and needs to be trained on data that represent different scenarios to ensure that the recommended output in a given scenario is accurate. In this article, we present an approach, namely Reinforcement Learning-based News Recommendation System (RL-NRS), to address this drawback in the domain of news recommendation. We explain the different stages of RL-NRS in detail and compare its performance with news articles recommended by Google for a particular search term.
Too Many Cooks: Exploring How Graphical Perception Studies Influence Visualization Recommendations in Draco

Findings from graphical perception can guide visualization recommendation algorithms in identifying effective visualization designs. However, existing algorithms use knowledge from, at best, a few studies, limiting our understanding of how complementary (or contradictory) graphical perception results influence generated recommendations. In this paper, we present a pipeline of applying a large body of graphical perception results to develop new visualization recommendation algorithms and conduct an exploratory study to investigate how results from graphical perception can alter the behavior of downstream algorithms. Specifically, we model graphical perception results from 30 papers in Draco-a framework to model visualization knowledge-to develop new recommendation algorithms. By analyzing Draco-generated algorithms, we showcase the feasibility of our method to (1) identify gaps in existing graphical perception literature informing recommendation algorithms, (2) cluster papers by their preferred design rules and constraints, and (3) investigate why certain studies can dominate Draco's recommendations, whereas others may have little influence. Given our findings, we discuss the potential for mutually reinforcing advancements in graphical perception and visualization recommendation research.
Enhancing Performance of Movie Recommendations Using LSTM With Meta Path Analysis

Movie recommendation algorithms play an important role in assisting consumers in identifying films that match their own likes. Deep Learning, particularly Long Short-Term Memory (LSTM) networks, have showed substantial promise in collecting sequential patterns to improve movie recommendations among the different techniques used for this purpose. Long Short-Term Memory-Inter Intra Metapath Aggregation (LSTM-IIMA) in movie recommendation systems is proposed in this study, with a specific focus on incorporating intra and inter metapath analysis. The intra metapath analysis investigates interactions within a single metapath, whereas the inter metapath analysis investigates links between numerous metapaths. Intra and inter metapath analyses are used in the LSTM-based movie recommendation system LSTM-IIMA to capitalise on these rich linkages. Each metapath sequence records the dependencies of a user’s interactions with films and other things. The LSTM architecture has been modified to handle these metapath sequences, processing them to record temporal dependencies and entity interactions. To optimize the parameters and minimize prediction errors, the model is trained using supervised learning techniques. To measure the quality and usefulness of the recommendations, the LSTM-IIMA evaluation incorporates metrics such as precision, recall, ablation analysis, time efficiency and Area Under the Curve (AUC). The performance of the system is compared to that of alternative recommendation techniques HAN and MAGNN. Overall, incorporating intra and inter metapath analysis into the LSTM-IIMA improves its ability to capture complex linkages and dependencies between movies, users, and other things.
CTMU-Net: An Improved U-Net for Semantic Segmentation of Remote-Sensing Images Based on the Combined Attention Mechanism

With the development of remote-sensing technology, it is important to use semantic segmentation methods to obtain detailed information in remote-sensing images.However, the objects in the images reveal significant intra-class differences and slight inter-class differences, thus affecting the acquisition of terrain information.To tackle the problems, this paper proposes an improved U-Net for the semantic segmentation of remote-sensing images.Firstly, the local importance-based pooling is introduced to alleviate the loss of feature details in the coding part.Secondly, a combined attention module with a double-branch structure is designed, which models the local relationship and the global relationship at the same time to obtain more typical features.Finally, in order to make full use of the feature information extracted from the coding part, the combined attention module and the channel attention module are added to different positions in U-Net.In order to validate the proposed method, we conduct experiments on the WHDLD dataset and compare the experimental results with other semantic segmentation methods.On the WHDLD dataset, the MPA, MIOU, and FWIOU of the proposed method reach 76%, 64.11%, and 75.64%, respectively, revealing its priority.To demonstrate the generalization of the proposed method, generalization experiments are conducted via the LandCover.aidataset and the Massachusetts-building dataset.The simulation results testify that the proposed method provides an excellent generalization ability.