As the number of web applications and API endpoints exposed to the Internet continues to grow, so does the number of exploitable vulnerabilities. Manually identifying such vulnerabilities is tedious. Meanwhile, static security scanners tend to produce many false positives. While machine learning-based approaches are promising, they typically perform well only in scenarios where training and test data are closely related. A key challenge for ML-based vulnerability detection is providing suitable and concise code context, as excessively long contexts negatively affect the code comprehension capabilities of machine learning models, particularly smaller ones. This work introduces Trace Gadgets, a novel code representation that minimizes code context by removing non-related code. Trace Gadgets precisely capture the statements that cover the path to the vulnerability. As input for ML models, Trace Gadgets provide a minimal but complete context, thereby improving the detection performance. Moreover, we collect a large-scale dataset generated from real-world applications with manually curated labels to further improve the performance of ML-based vulnerability detectors. Our results show that state-of-the-art machine learning models perform best when using Trace Gadgets compared to previous code representations, surpassing the detection capabilities of industry-standard static scanners such as GitHub's CodeQL by at least 4% on a fully unseen dataset. By applying our framework to real-world applications, we identify and report previously unknown vulnerabilities in widely deployed software.

The latest developments in AI focus on agentic systems where artificial and human agents cooperate to realize global goals. An example is collaborative learning, which aims to train a global model based on data from individual agents. A major challenge in designing such systems is to guarantee safety and alignment with human values, particularly a fair distribution of rewards upon achieving the global goal. Cooperative game theory offers useful abstractions of cooperating agents via value functions, which assign value to each coalition, and via reward functions. With these, the idea of fair allocation can be formalized by specifying fairness axioms and designing concrete mechanisms. Classical cooperative game theory, exemplified by the Shapley value, does not fully capture scenarios like collaborative learning, as it assumes non-replicable resources, whereas data and models can be replicated. Infinite replicability requires a generalized notion of fairness, formalized through new axioms and mechanisms. These must address imbalances in reciprocal benefits among participants, which can lead to strategic exploitation and unfair allocations. The main contribution of this paper is a mechanism and a proof that it fulfills the property of mutual fairness, formalized by the Balanced Reciprocity Axiom. It ensures that, for every pair of players, each benefits equally from the participation of the other.

Optical Character Recognition (OCR) systems are frequently used to digitise text, but often produce noisy results, especially with historical, poor-quality or multilingual data.Despite advances in OCR technology, post-processing remains a significant bottleneck.We propose TOOL (Treating OCR Output as a Language), a new approach that understands OCR correction as a machine translation task.By treating noisy OCR text as a language in its own right, TOOL employs sequenceto-sequence models like Marian to translate it into clean, standardised text.This method is scalable, model-independent and language-flexible.We demonstrate this approach by translating "OCR German" to Standard German from around 1871 to the present day, improving accuracy at the token level by using matched training pairs of OCR output and base text.

Probabilistic relational models such as parametric factor graphs enable efficient (lifted) inference by exploiting the indistinguishability of objects. In lifted inference, a representative of indistinguishable objects is used for computations. To obtain a relational (i.e., lifted) representation, the Advanced Colour Passing (ACP) algorithm is the state of the art. The ACP algorithm, however, requires underlying distributions, encoded as potential-based factorisations, to exactly match to identify and exploit indistinguishabilities. Hence, ACP is unsuitable for practical applications where potentials learned from data inevitably deviate even if associated objects are indistinguishable. To mitigate this problem, we introduce the ε-Advanced Colour Passing (ε-ACP) algorithm, which allows for a deviation of potentials depending on a hyperparameter ε. ε-ACP efficiently uncovers and exploits indistinguishabilities that are not exact. We prove that the approximation error induced by ε-ACP is strictly bounded and our experiments show that the approximation error is close to zero in practice.

Lifted probabilistic inference exploits symmetries in a probabilistic model to allow for tractable probabilistic inference with respect to domain sizes. To apply lifted inference, a lifted representation has to be obtained, and to do so, the so-called colour passing algorithm is the state of the art. The colour passing algorithm, however, is bound to a specific inference algorithm and we found that it ignores commutativity of factors while constructing a lifted representation. We contribute a modified version of the colour passing algorithm that uses logical variables to construct a lifted representation independent of a specific inference algorithm while at the same time exploiting commutativity of factors during an offline-step. Our proposed algorithm efficiently detects more symmetries than the state of the art and thereby drastically increases compression, yielding significantly faster online query times for probabilistic inference when the resulting model is applied.

Lifting probabilistic graphical models and developing lifted inference algorithms aim to use higher level groups of random variables instead of individual instances. In the past, many inference algorithms for discrete probabilistic graphical models have been lifted. Lifting continuous probabilistic graphical models has played a minor role. Since many real-world applications involve continuous random variables, this article turns its focus to lifting approaches for Gaussian Bayesian networks. Specifically, we present algorithms for constructing a lifted joint distribution for scenarios of sequences of overlapping and non-overlapping logical variables. We present operations that work in a fully lifted way including addition, multiplication, and inversion. We present how the operations can be used for lifted query answering algorithms and extend the existing query answering algorithm by a new way of evidence handling. The new way of evidence handling groups evidence that has the same effect on its neighboring variables in cases of partial overlap between the logical-variable sequences. In the theoretical complexity analysis and the experimental evaluation, we show under which conditions the existing lifted approach and the new lifted approach including evidence grouping lead to the most time savings compared to the grounded approach.

The importance of federated information retrieval (FIR) is growing in humanities research.Unlike traditional centralized information retrieval methods, where searches are conducted within a logically centralised collection of documents, FIR treats each information system as an independent source with its own unique characteristics.Searching these systems together as a centralised source results in lower precision in humanities research, even when the research data itself is structured and stored according to standardised guidelines such as EpiDoc, and requires the need to be able to trace the origin of records to avoid incorrect historical conclusions.Matching of queries against all data sets in each source is proving less effective.A global search index that enables traceable matching of key values deemed relevant would provide a more robust solution here.In this article, we propose a solution that introduces a novel EpiDoc data matching procedure, facilitating traceable FIR across distinct epigraphic sources.

The lifted junction tree algorithm (LJT) is an inference algorithm that allows for tractable inference regarding domain sizes. To answer multiple queries efficiently, it decomposes a first-order input model into a first-order junction tree. During inference, degrees of belief are propagated through the tree. This propagation significantly contributes to the runtime complexity not just of LJT but of any tree-based inference algorithm. We present a lifted propagation scheme based on the so-called Hugin scheme whose runtime complexity is independent of the degree of the tree. Thereby, lifted Hugin can achieve asymptotic speed improvements over the existing lifted Shafer-Shenoy propagation. An empirical evaluation confirms these results.

A decentralised partially observable Markov decision problem (DecPOMDP) formalises collaborative multi-agent decision making. A solution to a DecPOMDP is a joint policy for the agents, fulfilling an optimality criterion such as maximum expected utility. A crux is that the problem is intractable regarding the number of agents. Inspired by lifted inference, this paper examines symmetries within the agent set for a potential tractability. Specifically, this paper contributes (i) specifications of counting and isomorphic symmetries, (ii) a compact encoding of symmetric DecPOMDPs as partitioned DecPOMDPs, and (iii) a formal analysis of complexity and tractability. This works allows tractability in terms of agent numbers and a new query type for isomorphic DecPOMDPs.

Gaussian Bayesian networks are widely used for modeling the behavior of continuous random variables. Lifting exploits symmetries when dealing with large numbers of isomorphic random variables. It provides a more compact representation for more efficient query answering by encoding the symmetries using logical variables. This paper improves on an existing lifted representation of the joint distribution represented by a Gaussian Bayesian network (lifted joint), allowing overlaps between the logical variables. Handling overlaps without grounding a model is critical for modelling real-world scenarios. Specifically, this paper contributes (i) a lifted joint that allows overlaps in logical variables and (ii) a lifted query answering algorithm using the lifted joint. Complexity analyses and experimental results show that - despite overlaps - constructing a lifted joint and answering queries on the lifted joint outperform their grounded counterparts significantly.

Evidence often grounds temporal probabilistic relational models over time, which makes reasoning infeasible. To counteract groundings over time and to keep reasoning polynomial by restoring a lifted representation, we present temporal approximate merging (TAMe), which incorporates (i) clustering for grouping submodels as well as (ii) statistical significance checks to test the fitness of the clustering outcome. In exchange for faster runtimes, TAMe introduces a bounded error that becomes negligible over time. Empirical results show that TAMe significantly improves the runtime performance of inference, while keeping errors small.

A standard approach for inference in probabilistic formalisms with first-order constructs is lifted variable elimination (LVE) for single queries. To handle multiple queries efficiently, the lifted junction tree algorithm (LJT) employs a first-order cluster representation of a model and LVE as a subroutine. Both algorithms answer conjunctive queries of propositional random variables, shattering the model on the query, which causes unnecessary groundings for conjunctive queries of interchangeable variables. This paper presents parameterised queries as a means to avoid groundings, applying the lifting idea to queries. Parameterised queries enable LVE and LJT to compute answers faster, while compactly representing queries and answers.

Assessing and understanding the impact of scattered and widespread events onto a mission is a pertinacious problem. Current approaches attempting to solve mission impact assessment employ score-based algorithms leading to spurious results. We identify a fourfold problem with score-based algorithms: (1) score-based algorithms enforce deep training of experts to employed frameworks for specification (non-context-free), (2) require reference results for interpreting obtained results (non-bias-free), (3) require assessments outside of an experts' expertise (non-local), and (4) require validation of end-results against ground truth. This paper provides a formal, mathematical model for bias- and context-free mission impact assessment. Based on a probabilistic model we reduce mission impact assessment to a well-understood mathematical problem based on definitions from local expertise and allow for a validation at data level. This is useful for areas and applications where qualitative assessments are required, such as assessments in critical infrastructures or military contexts.

Finding adequate responses to ongoing attacks on ICT systems is a pertinacious problem and requires assessments from different perpendicular viewpoints. However, current research focuses on reducing the impact of an attack irregardless of side effects caused by responses. In order to achieve a comprehensive yet accurate response to possible and ongoing attacks on a managed ICT system, we propose an approach that evaluates a response from two perpendicular perspectives: (1) A response financial impact assessment, considering the financial benefits of restoring and protecting potentially threatened operational capabilities while considering implementation and maintenance costs of responses. (2) A response operational impact assessment, which assesses potential impacts that efficient mitigation actions may inadvertently cause on the organization in an operational perspective, e.g., negative side effects of deploying mitigations. It is the key benefit of the presented approach to combine all obtained evaluations with a multi-dimensional optimization procedure such that a response plan is selected which reduces a state of risk below an admissible level while minimizing potential negative side effects of deliberately taken actions.

Modeling causal dependencies often demands cycles at a coarse-grained temporal scale. If Bayesian networks are to be used for modeling uncertainties, cycles are eliminated with dynamic Bayesian networks, spreading indirect dependencies over time and enforcing an infinitesimal resolution of time. Without a ``causal design,'' i.e., without anticipating indirect influences appropriately in time, we argue that such networks return spurious results. By identifying activator random variables, we propose activator dynamic Bayesian networks (ADBNs) which are able to rapidly adapt to contexts under a causal use of time, anticipating indirect influences on a solid mathematical basis using familiar Bayesian network semantics. ADBNs are well-defined dynamic probabilistic graphical models allowing one to model cyclic dependencies from local and causal perspectives while preserving a classical, familiar calculus and classically known algorithms, without introducing any overhead in modeling or inference.

In the database community Polystores is an emerging and promising approach for data federation that aims at designing a unified querying layer over multiple data models. In the Semantic Web community a similar in spirit approach of Ontology-Based Data Access (OBDA) has been recently proposed, attracted a lot of attention, and proved its success in several industrial scenarios. In this paper we discuss a semantic approach to building polystores using the OBDA paradigm. We also present our system Optique that is utilized in an industrial application of performing turbine diagnostics in Siemens.

In data-communication networks, network reliability is of great concern to both network operators and customers. Therefore, network operators want to determine what services could be affected by software vulnerabilities being exploited that are present within their data-communication network. To determine what services could be affected by a software vulnerability being exploited, it is fundamentally important to know the ongoing tasks in a network. A particular task may depend on multiple network services, spanning many network devices. Unfortunately, dependency details are often not documented and are difficult to discover by relying on human expert knowledge. In monitored networks huge amounts of data are available and by applying data mining techniques, we are able to extract information of ongoing network activities. From a data mining perspective, we are interested to test the potential of applying data mining techniques to real-life applications.

Real-time processing of data coming from multiple heterogeneous data streams and static databases is a typical task in many industrial scenarios such as diagnostics of large machines. A complex diagnostic task may require a collection of up to hundreds of queries over such data. Although many of these queries retrieve data of the same kind, such as temperature measurements, they access structurally different data sources. In this work we show how Semantic Technologies implemented in our system optique can simplify such complex diagnostics by providing an abstraction layer---ontology---that integrates heterogeneous data. In a nutshell, optique allows complex diagnostic tasks to be expressed with just a few high-level semantic queries. The system can then automatically enrich these queries, translate them into a collection with a large number of low-level data queries, and finally optimise and efficiently execute the collection in a heavily distributed environment. We will demo the benefits of optique on a real world scenario from Siemens.

We present Probabilistic Doxastic Temporal (PDT) Logic, a formalism to represent and reason about probabilistic beliefs and their temporal evolution in multi-agent systems. This formalism enables the quantification of agents’ beliefs through probability intervals and incorporates an explicit notion of time. We discuss how over time agents dynamically change their beliefs in facts, temporal rules, and other agents’ beliefs with respect to any new information they receive. We introduce an appropriate formal semantics for PDT Logic and show that it is decidable. Alternative options of specifying problems in PDT Logic are possible. For these problem specifications, we develop different satisfiability checking algorithms and provide complexity results for the respective decision problems. The use of probability intervals enables a formal representation of probabilistic knowledge without enforcing (possibly incorrect) exact probability values. By incorporating an explicit notion of time, PDT Logic provides enriched possibilities to represent and reason about temporal relations.

With the growing deployment of host and network intrusion detection systems in increasingly large and complex communication networks, managing low-level events from these systems becomes critically important. A network has multiple tasks, which consist of multiple network services aiding the execution of a task. An emerging track of security research has focused on event prioritization and correlation to rank the criticality of events and reduce the number of low-level events. To prioritize and correlate events, the ongoing tasks in an enterprise network are identified, as the goal of network operators is to protect ongoing tasks when a security breach occurs. The prioritization of an event depends on the criticality of an ongoing task that is potentially threatened by the event. Additionally, in order to support network operators, we correlate all events that target the same task. A particular task may depend on multiple network services and involve multiple network devices. So, if one network service becomes unavailable, other network services will be affected over time since they Unfortunately, dependency details are often not documented and are difficult to discover by relying on human expert knowledge. In order to solve this problem, a network dependency analysis based on network traffic is conducted. We rely on pattern mining techniques to discover tasks in a monitored enterprise network. A formal description of the identified tasks is provided and events are prioritized and correlated based on this model. The pattern mining based network dependency analysis algorithm is evaluated based on a real-world network and three networks that where created with a network simulator.

The recent success of distributed and dynamic infrastructures for knowledge sharing has raised the need for semiautomatic/automatic ontology evolution strategies. Ontology evolution is generally defined as the timely adaptation of an ontology to changing requirements and the consistent propagation of changes to dependent artifacts. In this article, we present an ontology evolution approach in the context of multimedia interpretation. Ontology evolution in this context relies on the results obtained through reasoning for the interpretation of multimedia resources, through population of the ontology with new individuals or through enrichment of the ontology with new concepts and new semantic relations. The article analyses the results of interpretation, population and enrichment obtained in evaluation experiments in terms of measures such as precision and recall. The evaluation reveals encouraging results.

We propose an approach for extending a tableau-based satisfiability algorithm by an arithmetic component. The result is a hybrid concept satisfiability algorithm for the Description Logic (DL) 𝒜ℒ𝒞𝒬 which extends 𝒜ℒ𝒞 with qualified number restrictions. The hybrid approach ensures a more informed calculus which, on the one hand, adequately handles the interaction between numerical and logical restrictions of descriptions, and on the other hand, when applied is a very promising framework for average case optimizations.

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.

We present a formal framework for media interpretation that leverages low-level information extraction to a higher level of abstraction in order to support semantics-based information retrieval for the Semantic Web. The overall goal of the framework is to provide high-level content descriptions of documents for maximizing precision and recall of semantics-based information retrieval.

This article presents the description logic ALCRP(D) with concrete domains and a role-forming predicate operator as its prominent aspects. We demonstrate the feasibility of ALCRP(D) for reasoning about spatial objects and their qualitative spatial relationships and provide an appropriate concrete domain for spatial objects. The general significance of ALCRP(D) is demonstrated by adding temporal reasoning to spatial and terminological reasoning using a combined concrete domain. The theory is motivated as a basis for knowledge representation and query processing in the domain of geographic information systems. In contrast to existing work in this domain, which mainly focuses either on conceptual reasoning or on reasoning about qualitative spatial relations, we integrate reasoning about spatial information with terminological reasoning. Key words: Description logic, spatial reasoning, spatio temporal reasoning, theoretical foundations for GIS.

The paper motivates the facilities provided by Description Logics in an object-oriented programming scenario. It presents a unification approach of Description Logics and object-oriented programming that allows both views to be conveniently used for different subproblems in a modern software-engineering environment. The main thesis of this paper is that in order to use Description Logics in practical applications, a seamless integration with object-oriented system development methodologies must be realized.