bluuebunny/crossref_metadata_2025 huggingface dataset:
Finding Key Bloggers, One Post At A Time
Authors: Weerkamp Wouter  , Balog Krisztian  , de Rijke Maarten
Published: February 2025

User generated content in general, and blogs in particular, form an interesting and relatively little explored domain for mining knowledge. We address the task of blog distillation: to find blogs that are principally devoted to a given topic, as opposed to blogs that merely happen to discuss the topic in passing. Working in the setting of statistical language modeling, we model the task by aggregating a blogger's blog posts to collect evidence of relevance to the topic and persistence of interest in the topic. This approach achieves state-of-the-art performance. On top of this baseline, we extend our model by incorporating a number of blog-specific features, concerning document structure, social structure, and temporal structure. These blog-specific features yield further improvements.
Orthographic similarity search for dictionary lookup of Japanese words
Authors: Yencken Lars  , Baldwin Timothy
Published: February 2025

Finding an unknown Japanese word in a dictionary is a difficult and slow task when one or more of the word's characters is unknown. For advanced learners, unknown characters evoke the form and meaning of visually similar characters they are familiar with. We propose a range of character distance metrics to allow learners to leverage known characters to search for words containing unknown but visually similar characters. This new form of dictionary search is implemented as an extension to the FOKS dictionary system.
Structure Learning of Markov Logic Networks through Iterated Local Search
Authors: Biba Marenglen  , Ferilli Stefano  , Esposito Floriana
Published: February 2025

Many real-world applications of AI require both probability and first-order logic to deal with uncertainty and structural complexity. Logical AI has focused mainly on handling complexity, and statistical AI on handling uncertainty. Markov Logic Networks (MLNs) are a powerful representation that combine Markov Networks (MNs) and first-order logic by attaching weights to first-order formulas and viewing these as templates for features of MNs. State-of-the-art structure learning algorithms of MLNs maximize the likelihood of a relational database by performing a greedy search in the space of candidates. This can lead to suboptimal results because of the incapability of these approaches to escape local optima. Moreover, due to the combinatorially explosive space of potential candidates these methods are computationally prohibitive. We propose a novel algorithm for learning MLNs structure, based on the Iterated Local Search (ILS) metaheuristic that explores the space of structures through a biased sampling of the set of local optima. The algorithm focuses the search not on the full space of solutions but on a smaller subspace defined by the solutions that are locally optimal for the optimization engine. We show through experiments in two real-world domains that the proposed approach improves accuracy and learning time over the existing state-of-the-art algorithms.
A hybrid approach to multi-agent decision-making
Authors: Trigo Paulo  , Coelho Helder
Published: February 2025

In the aftermath of a large-scale disaster, agents' decisions derive from self-interested (e.g. survival), common-good (e.g. victims' rescue) and teamwork (e.g. fire extinction) motivations. However, current decision-theoretic models are either purely individual or purely collective and find it difficult to deal with motivational attitudes; on the other hand, mental-state based models find it difficult to deal with uncertainty. We propose a hybrid, CvI-JI, approach that combines: i) collective ‘versus’ individual (CvI) decisions, founded on the Markov decision process (MDP) quantitative evaluation of joint-actions, and ii) joint-intentions (JI) formulation of teamwork, founded on the belief-desire-intention (BDI) architecture of general mental-state based reasoning. The CvI-JI evaluation explores the performance's improvement during the process of learning a coordination policy in a partially observable stochastic domain.
Of Mechanism Design Multiagent Planning
Authors: van der Krogt Roman  , de Weerdt Mathijs  , Zhang Yingqian
Published: February 2025

Multiagent planning methods are concerned with planning by and for a group of agents. If the agents are self-interested, they may be tempted to lie in order to obtain an outcome that is more rewarding for them. We therefore study the multiagent planning problem from a mechanism design perspective, showing how to incentivise agents to be truthful. We prove that the well-known truthful VCG mechanism is not always truthful in the context of optimal planning, and present a modification to fix this. Finally, we present some (domain-dependent) poly-time planning algorithms using this fix that maintain truthfulness in spite of their non-optimality.
Near Admissible Algorithms for Multiobjective Search
Authors: Perny Patrice  , Spanjaard Olivier
Published: February 2025

In this paper, we propose near admissible multiobjective search algorithms to approximate, with performance guarantee, the set of Pareto optimal solution paths in a state space graph. Approximation of Pareto optimality relies on the use of an epsilon-dominance relation between vectors, significantly narrowing the set of non-dominated solutions. We establish correctness of the proposed algorithms, and discuss computational complexity issues. We present numerical experimentations, showing that approximation significantly improves resolution times in multiobjective search problems.
Extending the Knowledge Compilation Map: Closure Principles
Authors: Fargier Hélène  , Marquis Pierre
Published: February 2025

We extend the knowledge compilation map introduced by Darwiche and Marquis with new propositional fragments obtained by applying closure principles to several fragments studied so far. We investigate two closure principles: disjunction and implicit forgetting (i.e., existential quantification). Each introduced fragment is evaluated w.r.t. several criteria, including the complexity of basic queries and transformations, and its spatial efficiency is also analyzed.
A Decomposition Technique for Max-CSP
Authors: Bennaceur Hachémi  , Lecoutre Christophe  , Roussel Olivier
Published: February 2025

The objective of the Maximal Constraint Satisfaction Problem (Max-CSP) is to find an instantiation which minimizes the number of constraint violations in a constraint network. In this paper, inspired from the concept of inferred disjunctive constraints introduced by Freuder and Hubbe, we show that it is possible to exploit the arc-inconsistency counts, associated with each value of a network, in order to avoid exploring useless portions of the search space. The principle is to reason from the distance between the two best values in the domain of a variable, according to such counts. From this reasoning, we can build a decomposition technique which can be used throughout search in order to decompose the current problem into easier sub-problems. Interestingly, this approach does not depend on the structure of the constraint graph, as it is usually proposed. Alternatively, we can dynamically post hard constraints that can be used locally to prune the search space. The practical interest of our approach is illustrated, using this alternative, with an experimentation based on a classical branch and bound algorithm, namely PFC-MRDAC.
Learning Functional Object-Categories from a Relational Spatio-Temporal Representation
Authors: Sridhar Muralikrishna  , Cohn Anthony G.  , Hogg David C.
Published: February 2025

We propose a framework that learns functional object-categories from spatio-temporal data sets such as those abstracted from video. The data is represented as one activity graph that encodes qualitative spatio-temporal patterns of interaction between objects. Event classes are induced by statistical generalization, the instances of which encode similar patterns of spatio-temporal relationships between objects. Equivalence classes of objects are discovered on the basis of their similar role in multiple event instantiations. Objects are represented in a multidimensional space that captures their role in all the events. Unsupervised learning in this space results in functional object-categories. Experiments in the domain of food preparation suggest that our techniques represent a significant step in unsupervised learning of functional object categories from spatio-temporal patterns of object interaction.
Salience-driven Contextual Priming of Speech Recognition for Human-Robot Interaction
Authors: Lison Pierre  , Kruijff Geert-Jan
Published: February 2025

The paper presents an implemented model for priming speech recognition, using contextual information about salient entities. The underlying hypothesis is that, in human-robot interaction, speech recognition performance can be improved by exploiting knowledge about the immediate physical situation and the dialogue history. To this end, visual salience (objects perceived in the physical scene) and linguistic salience (objects, events already mentioned in the dialogue) are integrated into a single cross-modal salience model. The model is dynamically updated as the environment changes. It is used to establish expectations about which words are most likely to be heard in the given context. The update is realised by continuously adapting the word-class probabilities specified in a statistical language model. The paper discusses the motivations behind the approach, and presents the implementation as part of a cognitive architecture for mobile robots. Evaluation results on a test suite show a statistically significant improvement of salience-driven priming speech recognition (WER) over a commercial baseline system.
QuestSemantics - Intelligent Search and Retrieval of Business Knowledge
Authors: Blacoe Ian  , Palmisano Ignazio  , Tamma Valentina  , Iannone Luigi
Published: February 2025

Keyword-based search engines, though hugely popular, show limitations when trying to answer very specific queries. The processing of search results is performed by users, rather than by software. Ontologies provide a means to create formal, machineprocessable descriptions of the knowledge in a certain domain [14], and, by using elements of these descriptions to annotate suitable information sources, they can be analysed and manipulated in an intelligent manner. The QuestSemantics platform provides automated ontology-based metadata creation and resource annotation, with subsequent ontology-based querying of the annotated resources. The platform has been deployed in two commercial scenarios, providing useful feedback on both the feasibility and effectiveness of applying Semantic Web technologies to specific business problems.
Belief revision with reinforcement learning for interactive object recognition
Authors: Leopold Thomas  , Kern-Isberner Gabriele  , Peters Gabriele
Published: February 2025

From a conceptual point of view, belief revision and learning are quite similar. Both methods change the belief state of an intelligent agent by processing incoming information. However, for learning, the focus in on the exploitation of data to extract and assimilate useful knowledge, whereas belief revision is more concerned with the adaption of prior beliefs to new information for the purpose of reasoning. In this paper, we propose a hybrid learning method called SPHINX that combines low-level, non-cognitive reinforcement learning with high-level epistemic belief revision, similar to human learning. The former represents knowledge in a sub-symbolic, numerical way, while the latter is based on symbolic, non-monotonic logics and allows reasoning. Beyond the theoretical appeal of linking methods of very different disciplines of artificial intelligence, we will illustrate the usefulness of our approach by employing SPHINX in the area of computer vision for object recognition tasks. The SPHINX agent interacts with its environment by rotating objects depending on past experiences and newly acquired generic knowledge to choose those views which are most advantageous for recognition.
Deriving explanations from causal information
Authors: Besnard Ph.  , Cordier M.-O.  , Moinard Y.
Published: February 2025

We define an inference system to capture explanations based on causal statements, using an ontology in the form of an IS-A hierarchy. We introduce a simple logical language which makes it possible to express that a fact causes another fact and that a fact explains another fact. We present a set of formal inference patterns from causal statements to explanation statements. We introduce an elementary ontology which gives greater expressiveness to the system while staying close to propositional reasoning. We provide an inference system that captures the patterns discussed, in a datalog (limited predicate) framework.
Rule-based OWL Ontology Reasoning Using Dynamic ABOX Entailments
Authors: Meditskos Georgios  , Bassiliades Nick
Published: February 2025

In the rule-based OWL reasoning paradigm, ontologies are mapped into an internal rule engine representation format and rules are applied, such as TBOX and ABOX OWL entailment rules, in order to deduce new knowledge. In this paper we briefly introduce the notion of dynamically generating ABOX entailment rules in order to enhance the ABOX reasoning performance of a rule engine. The proposed methodology is still based on entailments rules for reasoning, using generic TBOX entailments for handling OWL semantics about concepts and roles, and dynamic ABox entailments for handling ontology instances.
Discovering Temporal Knowledge from a Crisscross of Timed Observations
Authors: Benayadi Nabil  , Le Goc Marc
Published: February 2025

This paper is concerned with the discovering of temporal knowledge from a sequence of timed observations provided by a system monitoring of dynamic process. The discovering process is based on the Stochastic Approach framework where a series of timed observations is represented with a Markov chain. From this representation, a set of timed sequential binary relations between discrete event classes is discovered with an abductive reasoning and represented as abstract chronicle models. To reduce the search space as close as possible to the potential relations between the process variables, we propose to characterize a set of series of timed observations with a unique measure of the homogeneity of the crisscross of class occurrences and to use this measure to prune abstract chronicle models.
Modular Equivalence in General
Authors: Janhunen Tomi
Published: February 2025

The notion of modular equivalence was recently introduced in the context of a module architecture proposed for logic programs under answer set semantics [12, 6, 13]. In this paper, the module architecture is abstracted for arbitrary knowledge bases, KB-functions for short, giving rise to a universal notion of modular equivalence. A further objective of this paper is to study modular equivalence in the contexts of SAT-functions, i.e., propositional theories with a module interface, and their logic programming counterpart, known as LP-functions [6]. As regards SAT-functions, we establish the full compositionality of classical semantics. This proves modular equivalence a proper congruence relation for SAT-functions. Moreover, we address the interoperability of SAT-functions and LP-functions in terms of strongly faithful transformations in both directions. These considerations justify the proposed design of KB-functions in general and pave the way for hybrid KB-functions.
WikiTaxonomy: A Large Scale Knowledge Resource
Authors: Ponzetto Simone Paolo  , Strube Michael
Published: February 2025

We present a taxonomy automatically generated from the system of categories in Wikipedia. Categories in the resource are identified as either classes or instances and included in a large subsumption, i.e. isa, hierarchy. The taxonomy is made available in RDFS format to the research community, e.g. for direct use within AI applications or to bootstrap the process of manual ontology creation.
Experience and Trust — A Systems-Theoretic Approach
Authors: Foo Norman  , Renz Jochen
Published: February 2025

An influential model of agent trust and experience is that of Jonker and Treur [Jonker and Treur 99]. In that model an agent uses its experience of the interactions of another agent to assess that agent's trustworthiness. We showed that key properties of that model are subsumed by classical mathematical systems theory. Using the latter theory we also clarify the issue of when two experience sequences may be regarded as equivalent. An intuitive feature of the Jonker and Treur model is that experience sequence orderings are respected by functions that map such sequences to trust orderings. We raise a question about another intuitive property — that of continuity of these functions, viz. that they map experience sequences that resemble each other to trust values that also resemble each other. Using fundamental results in the relationship between partial orders and topologies we also showed that these two intutive properties are essentially equivalent.
Trust-Aided Acquisition Of Unverifiable Information
Authors: Staab Eugen  , Fusenig Volker  , Engel Thomas
Published: February 2025

We propose a mechanism for the acquisition of information from potentially unreliable sources. Our mechanism addresses the case where the acquired information cannot be verified. The idea is to intersperse questions (“challenges”) for which the correct answers are known. By evaluating the answers to these challenges, probabilistic conclusions about the correctness of the unverifiable information can be drawn. Less challenges need to be used if an information provider has shown to be trustworthy. Our approach can resist collusion and shows great promise for various application scenarios such as grid-computing or peer-to-peer networks.
Automatic Animation Generation of a Teleoperated Robot Arm
Authors: Belghith Khaled  , Auder Benjamin  , Kabanza Froduald  , Bellefeuille Philipe  , Hartman Leo
Published: February 2025

In this paper we describe the Automatic Task Demonstration Generator (ATDG), a system implemented into a software prototype for teaching the operation of a robot manipulator deployed on the International Space Station (ISS). The ATDG combines the use of path planning and camera planning to take into account the complexity of the manipulator, the limited direct view of the ISS exterior, and the unpredictability of lighting conditions in the workspace. The path-planning algorithm not only avoids obstacles in the workspace as is normal for a path-planner, but in addition takes into account the position of corridors for safe operations and the placement of cameras on the ISS. The camera planner is then invoked to find the right arrangement of cameras to follow the manipulator on its trajectory. This allows the on-the-fly production of useful and pedagogical task demonstrations to help the student carry out tasks involving the manipulation of the robot on the ISS. Even if the system has been developed for robotic manipulations, it could be used for any application involving the filming of unpredictable complex scenes.
Towards Social Dynamic Dependence Networks for Institutions
Authors: Villata Serena
Published: February 2025

Social networks are structures that aim to represent the relationships among the actors of a society. In the multiagent model, these networks depict the dependencies among the agents. The dependencies reflect the relation between the goals of the agents and the agents who have the power to achieve them. Like any social structure, also a multiagent system can be regulated by a set of norms and institutional powers that form an institution. Differently than informal social networks, the institutional social structure has an inherent dynamic character which cannot be captured by the current dependence networks. The networks have to reflect the changes of dependencies among agents created by the exercise of institutional powers. The aim of this paper is to define more dynamic social networks. From a description of an agent system it is possible to build a static dependence network. In the same way we describe how to pass from an abstract representation of what is an institution with its institutional powers to a dynamic social dependence network.
The roles of tool and human mediatorsin Web-based course of self-regulation
Authors: Latva-Karjanmaa Raija
Published: February 2025

The study focused on identifying the roles of tool and human mediator on a web-based course of developing better learning skills, i.e. self-regulative skills. Mediators act as supporters of the learning process. The assumption is that in virtual environments mediators can have particular roles, which are different from a face-to-face learning situation. Online tool mediators can support the student's learning skills, which has been proved earlier and again in this study. In addition to this the mediators can build awareness of possibilities to develop as learners, mediate a feeling of personal space, freedom and integrity. and also act as a mediators of guidance, support and control.
How efficient is it to communicate customer knowledge with the aid of audio-visual metaphors?
Authors: Alotaibi Mutlaq  , Rigas Dimitrios
Published: February 2025

The shift in customer role from a source of revenue to a source of innovation has resulted in an increased demand for a direct interaction with customers, via more advanced and real-time solutions. This may drive organisations to complement Electronic Customer Knowledge Management Systems (E-CKMS) with new interactive multimodal metaphors. There is little known about CKM empirical studies, which investigated whether multimodal metaphors as part of a CKM interface can outperform the text with graphics only metaphors. Thus, this paper describes an investigation about the role of multimodal interaction in improving E-CKMS efficiency compared to text with graphics only metaphors. The examination of this hypothesis was carried out empirically on two E-CKMS experimental platforms (text with graphics only and multimodal) designed for this study. Two independent groups of users (n=20 for each group) evaluated the two interface versions of E-CKMS. Results suggested that the multimodal CKM interface was more efficient than the text with graphics only interface.
Exchanging the Business Concepts through Ontology System
Authors: Tsumaki Toshihiko
Published: February 2025

The businesses reorganization by the merging and the dividing is progressing in order to strengthen of their competitiveness in the market. In the business reorganization based on cooperation the businesses activities, it is necessary to understand the terms and the concept each other. We propose a new approach that uses the interaction instead of the integration for supporting the cooperation between different business units. The concepts in the business unit are independently expressed by means of the ontology technique and implemented into the corresponding ontology units.
Analogy Based Cost Estimation Configuration with Rules
Authors: Bibi Stamatia  , Stamelos Ioannis
Published: February 2025

Analogy-based estimation is a widely adopted method in software cost estimation that identifies analogous projects to the one under estimation and uses their data to derive an estimate, i.e. it is a Case Based Reasoning approach. The similarity measures between pairs of projects are critical for identifying the most appropriate historical data from which the estimation will be generated. Usually the similarity measures are selected empirically, using jackknife-like procedures. Typically, the measures that identify the most similar projects in most of the cases are considered the most appropriate ones and are applied in every new estimation procedure. However there are situations that the default similarity measures may not be the most appropriate ones. In this study we determine the situations in which the default parameters are not the best and we propose the similarity measures for these cases. In particular we provide rules that point out which projects are not accurately estimated with the default parameters.
Method for Building Dictionaries to Extract Reputation Information
Authors: Maruyama Hiroshi  , Suzuki Kennshi  , Nakamura Taichi
Published: February 2025

This paper proposes the method of building dictionaries for extracting reputation information. We propose a method of building a highly credible dictionary from the staggering volume of information that customers post on Web pages. The principle of our proposed method is based on assigning a certainty factor which represents the probability that extracted words really do express a feature or evaluation expression and registering only words which have a certainty factor greater than a pre-set threshold value. As a result of experiment, the method of building a highly credible dictionary has been established.
Personalized Teaching of a Programming language over the web: Stereotypes and rule-based mechanisms
Authors: Chrysafiadi Konstantina  , Virvou Maria
Published: February 2025

In this paper we describe the student model that has been developed for a web-based educational application that teaches the programming language Pascal. The tutoring system is called Web_Tutor_Pas. The application adapts its responses to each individual student dynamically. The system's student model is web-based on a three-dimensional stereotype approach. One dimension concerns the knowledge level of the student the second dimension concerns the type of programming errors (logical or syntax) and the third concerns previous knowledge of the student on other programming languages. The system takes into account information about the student from the student model and using a rule-based mechanism, provides individualized instruction, examination and advice.
Implementation Issues of a Knowledge-based Geographical Information System
Authors: Kabassi Katerina  , Charou Eleni  , Martinis Aristotelis
Published: February 2025

The research presented in this paper introduces a user context approach for the implementation of an adaptive Geographical Information System (GIS). The main focus of the paper is on presenting some implementation issues about the data used and their evaluation with respect to their suitability for the user interacting with the system. For the evaluation of the geographical information, the system uses a simple decision making model and selects the one that seems more appropriate for a user. In this way, the GIS has the ability of adapting its interaction to each user and make interaction more user friendly.
Determination of Opening Learning Scenarios in Intelligent Tutoring Systems
Authors: Kozierkiewicz Adrianna
Published: February 2025

The main purpose of intelligent tutoring systems is to guarantee an effective learning and offer the optimal learning path for each student. Therefore, determination of learning scenario is a very important task in a learning process. After a new student is registered in the system, he is classified to the appropriate group. Before he begins to learn an opening scenario is determined based on final scenarios of students who belong to the class of learners similar to the new one. The new student is offered the optimal learning path suitable for his preferences, learning styles and personal features. In this paper new knowledge structure, which involves version of lessons, is proposed. For the defined knowledge structure definitions of learning scenario, distance function and the procedure of the scenario determination are presented.
Acquisition of a New Type of Lexical-Semantic Relation from German Corpora
Authors: Lemnitzer Lothar  , Gupta Piklu  , Wunsch Holger
Published: February 2025

In this paper we will report on work in progress towards increasing the relational density of the German wordnet. It is also an experiment in corpus-based lexical acquisition. The source of the acquisition is a large corpus of German newspaper texts. The target is the German wordnet (GermaNet). We acquire a new type of lexical-semantic relation, i.e. the relation between the verbal head of a predicate and the nominal head of its argument. We investigate how the insertion of instances of this relation into the German wordnet GermaNet affects the neighbourhood of the nodes which are connected by an instance of the new relation. Special attention is given in this paper to the language-specific aspects of the acquisition process.
The Ultimate Systems Development Method Based on Finite State Machine
Authors: Koono Zenya  , Chen Hui
Published: February 2025

This design method stands upon three bases. The first is to use a finite state machine (FSM) model of around three states, where the design is very easy. The second is to use an event driven OS, which enables direct execution from the specification level to the final implementation model. The third is the hierarchical architecture of the above-mentioned FSM's, which minimizes the software size. This method features high productivity and high quality. Although it is a development for embedded systems, it may be applicable to any systems as an ultimate design method.
An Algorithm for Ordering Refactorings Based on Fine-Grained Model Transformations
Authors: Saadeh Emmad  , Kourie Derrick  , Boake Andrew
Published: February 2025

In this paper, we propose a new algorithm to find automatically the optimal ordering of a batch of refactorings in which to apply these refactorings. The algorithm detects implicit sequential dependencies, resolves conflicts between the different refactorings in the batch and minimizes the number of refactoring operations by removing the redundant ones. It is based on the semantics of a predefined set of fine-grain transformations (FGTs) to describe any refactoring, and relies on logic-based representations of the underlying UML model. The algorithm saves time and effort and uses several innovative techniques to improve the performance at the time of refactoring.
The Organisation of Time and Space in Three Gestural Media
Authors: Danylak Roman  , Edmonds Ernest
Published: February 2025

Human bodily gestures are now clearly present as a factor in human-computer interface design, used in commercially available game interfaces such as Sony's Eye Toy. However, many challenges still exist for this as yet new process, including the development of design strategies to further enable potential. This paper describes the organisation of time and space in three media forms where gestures are prominent, namely theatre, film and interactive film, showing their interrelation and aspects of technological innovation. The plan processes of these media is the focus of discussion. The plan from the interactive gestural film game is from an original artwork To be or not to be.
An Ontology Supporting a Car Advanced Driver Assistance System
Authors: Pisanelli Domenico M.  , De Lazzari Claudio  , Bugli-Innocenti Emilio  , Zanetti Norma
Published: February 2025

This paper introduces a modular ontology supporting a car advanced driver assistance system (ADAS). Such a system is aimed at making road transport more efficient and effective, safer and more environmentally friendly.
Linear Mathematical Driver for the Future of Software Testing Process
Authors: Elsheikh Asim A.  , Alhaj Salahideen M.  , Alnaqeep Linda R.
Published: February 2025

Software engineering comprehends several disciplines Devoted to prevent and remedy malfunctions and to warrant adequate behaviour. Software testing is one of the crucial activities in the system development life cycle. The purpose of testing can be quality assurance, verification and validation, or reliability estimation. This paper proposed a Linear Mathematical Driver for the future of software testing process from the point view of Achievements, Challenges, Aspects, and Dreams. This work is for sure is going to be the first and the essential step in formalizing the roadmap. Since weighting system for challenges and aspects, will used with the Linear Mathematical Driver to give output numeric values, which is the main contribution for this work. These numeric values can be used by research community to, systemize, develop, control, and improve the future of software testing process.
Adaptive Rehabilitation of the Riachuelo Building in the Historical Centre of São Paulo
Authors: Bruna Paulo  , Gouveia Sonia
Published: February 2025

Historical Context; The Riachuelo Building; Respectful Rehabilitation; Comments on Restrictions and Flexibility; Notes
A New Classification of Collectives
Authors: Wood Zena  , Galton Antony
Published: February 2025

We are surrounded by collective phenomena, with examples existing on many levels of granularity. Despite our frequent experiences of such phenomena they seem to have been largely ignored within the field of ontology. In this paper, existing ontologies are examined to determine the extent to which they can adequately represent collective phenomena, and are found wanting in a number of important respects. Our goal is to find good ways of representing collective phenomena in a way which does justice to the often subtle relationship that exists between the view of a collective as a whole and its constitution as a plurality of individual participants. An important prerequisite for this is to determine the range of variation that exists within the broad class of collectives. A number of example collective phenomena have been studied to extract appropriate classification criteria. The results from this study are used to produce a new typology of collectives which is intended to establish a basis for the adequate treatment of collectives within an ontology. The paper concludes with a set of further research questions that have been raised during the development of the classification.
Island Reasoning for 𝒜ℒ𝒞ℋℐ Ontologies
Authors: Wandelt Sebastian  , Moeller Ralf
Published: February 2025

In the last years, the vision of the Semantic Web fostered the interest in reasoning over ever larger sets of assertional statements in ontologies. It is easily conjectured that, soon, real-world ontologies will not fit into main memory anymore. If this was the case, state-of-the-art description logic reasoning systems cannot deal with these ontologies any longer, since they rely on in-memory structures.
SKIing with DOLCE: toward an e-Science Knowledge Infrastructure
Authors: Brodaric Boyan  , Reitsma Femke  , Qiang Yi
Published: February 2025

An ontology of general science knowledge (SKIo) is developed to enhance machine representation and use of scientific theories in emerging e-Science Knowledge Infrastructures. SKIo specializes the DOLCE foundational ontology with science knowledge primitives, such as science theory, model, data, prediction, and induction. These are arranged to reflect the complex knowledge structures used in science, such as scientific ideas playing different roles within and between theories. SKIo is encoded with OWL-DL, uses the DOLCE Descriptions and Situations module, and provides defining conditions for its primitives to enable an extensible bridge between DOLCE and domain science ontologies. An application to environmental theories is demonstrated, and its utility to other natural sciences is promising.
Knowledge Discovery with Explained Case-Based Reasoning
Authors: Armengol Eva
Published: February 2025

The goal of Knowledge Discovery is to extract knowledge from a set of data. Most common techniques used in knowledge discovery are clustering methods, whose goal is to analyze a set of objects and obtain clusters based on the similarity among these objects. A desirable characteristic of clustering results is that these should be easily understandable by domain experts. In fact, these are characteristics that exhibit the results of eager learning methods (such as ID3) and lazy learning methods when used for building lazy domain theories. In this paper we propose LazyCL, a procedure using a lazy learning method to produce explanations on clusters of unlabeled cases. The analysis of the relations among these explanations converges to a correct clustering of the data set.
Aggregation Operators and Ruled Surfaces
Authors: Recasens J.
Published: February 2025

Aggregation operators in two variables that are ruled quadric surfaces are studied.
Representing Qualitative Trajectories by Autonomous Mobile Robots
Authors: Peris J.C.  , Plana J.  , Escrig M.T.
Published: February 2025

Description of trajectories has been usually dealt in quantitative terms by means of mathematical formulas. Human beings do not use formulas to describe the trajectories of objects in movement, for instance, for shooting or catching a ball. Common sense reasoning about the physical world has been commonly solved by qualitative models. In this paper, we are going to qualitatively describe trajectories of objects in movement, at constant speed and in a 2D space, by an AiboTMrobot.
Incomplete Algorithms
Authors: Kautz Henry  , Sabharwal Ashish  , Selman Bart
Published: February 2025

Research on incomplete algorithms for satisfiability testing lead to some of the first scalable SAT solvers in the early 1990's. Unlike systematic solvers often based on an exhaustive branching and backtracking search, incomplete methods are generally based on stochastic local search. On problems from a variety of domains, such incomplete methods for SAT can significantly outperform DPLL-based methods. While the early greedy algorithms already showed promise, especially on random instances, the introduction of randomization and so-called uphill moves during the search significantly extended the reach of incomplete algorithms for SAT. This chapter discusses such algorithms, along with a few key techniques that helped boost their performance such as focusing on variables appearing in currently unsatisfied clauses, devising methods to efficiently pull the search out of local minima through clause re-weighting, and adaptive noise mechanisms. The chapter also briefly discusses a formal foundation for some of the techniques based on the discrete Lagrangian method.
Random Satisfiability
Authors: Achlioptas Dimitris
Published: February 2025

In the last twenty years a significant amount of effort has been devoted to the study of randomly generated satisfiability instances. While a number of generative models have been proposed, uniformly random k-CNF formulas are by now the dominant and most studied model. One reason for this is that such formulas enjoy a number of intriguing mathematical properties, including the following: for each k≥3, there is a critical value, rk, of the clauses-to-variables ratio, r, such that for rrkit is unsatisfiable with probability that tends to 1 as n→∞.
Exploiting Runtime Variation in Complete Solvers
Authors: Gomes Carla P.  , Sabharwal Ashish
Published: February 2025

It has become well know over time that the performance of backtrack-style complete SAT solvers can vary dramatically depending on “little” details of the heuristics used, such as the way one selects the next variable to branch on and in what order the possible values are assigned to the variable. Extreme variations can result even from simple tie breaking mechanisms necessarily employed in all SAT solvers. The discovery of this extreme runtime variation has been both a stumbling block and an opportunity. This chapter focuses on providing an understanding of this intriguing phenomenon, particularly in terms of the so-called heavy tailed nature of the runtime distributions of systematic SAT solvers. It describes a simple formal model based on expensive mistakes to explain runtime distributions seen in practice, and discusses randomization and restart strategies that can be used to effectively overcome the negative impact of heavy tailed behavior. Finally, the chapter discusses the notion of backdoor variables, which explain the unexpectedly short runs one also often sees in practice.
Symmetry and Satisfiability
Authors: Sakallah Karem A.
Published: February 2025

Symmetry is at once a familiar concept (we recognize it when we see it!) and a profoundly deep mathematical subject. At its most basic, a symmetry is some transformation of an object that leaves the object (or some aspect of the object) unchanged. For example, a square can be transformed in eight different ways that leave it looking exactly the same: the identity “do-nothing” transformation, 3 rotations, and 4 mirror images (or reflections). In the context of decision problems, the presence of symmetries in a problem's search space can frustrate the hunt for a solution by forcing a search algorithm to fruitlessly explore symmetric subspaces that do not contain solutions. Recognizing that such symmetries exist, we can direct a search algorithm to look for solutions only in non-symmetric parts of the search space. In many cases, this can lead to significant pruning of the search space and yield solutions to problems which are otherwise intractable.
Software Verification
Authors: Kroening Daniel
Published: February 2025

This chapter covers an application of propositional satisfiability to program analysis. We focus on the discovery of programming flaws in low-level programs, such as embedded software. The loops in the program are unwound together with a property to form a formula, which is then converted into CNF. The method supports low-level programming constructs such as bit-wise operators or pointer arithmetic.
An Attempt to Express the Semantics of the Adaptive Devices
Authors: da Rocha Ricardo Luis de Azevedo
Published: February 2025

Adaptive devices were introduced formally in [7] as a generalization of the adaptive automaton [6]. The semantics were defined by an entirely traditional operational way, based on automaton transitions. This paper starts another path, to describe the semantics of adaptive devices using structural operational semantics [9, 12], λ-calculus [1, 2] and a typing system based on the algorithm W of Damas and Milner [3, 8]. The results achieved showed that adaptive devices are complex to describe, and that a sound and complete typing system should be used to fully analyze them.
Intelligent Vehicle Survey and Applications
Authors: Vermaas Luiz Lenarth Gabriel  , Honório Leonardo de Melo  , de Jesus Edison Oliveira  , Freire Muriell  , Barbosa Daniele A.
Published: February 2025

Intelligent or Autonomous Vehicles are not visionary technologies that may be present in a far away future. From high tech military unmanned systems and automatic reverse parallel parking to cruise control and Antilock Brake Systems (ABS), these technologies are becoming embedded in people's daily life. The methodologies applied in these applications vary widely across areas, ranging from new hardware development to complex software algorithms. The purpose of this work is to present a survey about the benefits and applications of autonomous ground vehicles and to propose a new learning methodology, which provides the vehicle the capacity of learning maneuvers with a human driver. Four main areas will be discussed: system's topology, instrumentation, high level control, and computational vision. Section 1 will present the most common architecture of autonomous vehicles. The instrumentation used in intelligent vehicles such as differential global positioning system (DGPS), inertial navigation system (INS), radar, ladar and infrared sensor will be described in section 2, as well as the techniques used for simultaneous registration and fusion of multiple sensors. Section 3 presents an overview of some techniques and methods used for visual control in autonomous ground vehicles. Section 4 will describe the most efficient techniques for autonomous driving and parking, collision avoidance and cooperative driving. Finally, section 5 will propose a new algorithm based on Artificial Immune Systems, where a fuzzy system for autonomous maneuvering will be learnt by a data set of actions taken by a human driver. In order to validate the proposed method, the results of its application in an automatic parallel parking maneuver will be showed.
Ontology Enabled Graphical Database Query Tool for End-Users
Authors: Barzdins Guntis  , Liepins Edgars  , Veilande Marta  , Zviedris Martins
Published: February 2025

In this paper we show how semantic web technologies are used in a real application in the domain of national medical databases where an important technological gap between the legacy relation databases and OWL ontologies is bridged by the recently standardized UML profile for OWL. After data has been exported from multiple relational databases into a single shared RDF database structured according to an integrated OWL ontology there emerges a need for a convenient end-user query tools. We describe a fully graphical access to the exported data through the graphical front-end tool (named “DEMO” tool) based on the UML profile for OWL and SPARQL query language.
Environment Testing Enabled Software – a Step Towards Execution Context Awareness
Authors: Rauhvargers Krišs  , Bicevskis Janis
Published: February 2025

The paper addresses a problem in software operation that maintenance tasks may lead to software failures. A model for automated checking of software execution environment is proposed. The provided solution is to develop a “profile” for each deployable item which contains information about software requirements regarding its execution. The profile document is added to software deliverables together with a set of tools capable of validating the adequacy of the execution environment according to the document. Regular checks of execution environment may be performed during system operation. The paper discusses first practical results of the proposed approach.
Audit Log Unification and Review with Log Management Information Bases
Authors: To Wai Man  , Tryfonas Theo  , Farthing Dave W.
Published: February 2025

Computer auditing involves the manual collection and review of independent log files that may be completely different in format and contents, yet they may be conceptually related as they could be part of the picture for an incident. The aim of this paper is to investigate the feasibility of heterogeneous log integration via the use of Log Management Information Bases, i.e. metadata descriptions of each proprietary log that would allow for log ‘unification’ in a standardised language such as XML, and their subsequent correlation into a software module that could be either an independent application or an add-on module of generalised audit software.
Analysis of Open Source Software Evolution Using Evolution Curve Method
Authors: Damaševičius Robertas
Published: February 2025

Design and evolution of modern information systems is influenced by many factors: technical, organizational, social, and psychological. This is especially true for open source software systems (OSSS), when many developers from different backgrounds interact, share their ideas and contribute towards the development and improvement of a software product. The evolution of an OSSS is a continuous process of source code development, adaptation, improvement and maintenance. Studying changes to the various characteristics of source code can help us understand the evolution of a software system. In this paper, the software evolution process is analyzed using a proposed Evolution curve (E-curve) method, which is based on information theoretic metrics of source code. The method allows identifying major evolution stages and transition points of an analyzed software system. The application of the E-curves is demonstrated for the eMule system.