The Complexity and Expressive Power of Limit Datalog

Motivated by applications in declarative data analysis, in this article, we study Datalog Z —an extension of Datalog with stratified negation and arithmetic functions over integers. This language is known to be undecidable, so we present the fragment of limit Datalog Z programs, which is powerful enough to naturally capture many important data analysis tasks. In limit Datalog Z , all intensional predicates with a numeric argument are limit predicates that keep maximal or minimal bounds on numeric values. We show that reasoning in limit Datalog Z is decidable if a linearity condition restricting the use of multiplication is satisfied. In particular, limit-linear Datalog Z is complete for Δ 2 EXP and captures Δ 2 P over ordered datasets in the sense of descriptive complexity. We also provide a comprehensive study of several fragments of limit-linear Datalog Z . We show that semi-positive limit-linear programs (i.e., programs where negation is allowed only in front of extensional atoms) capture coNP over ordered datasets; furthermore, reasoning becomes coNEXP-complete in combined and coNP-complete in data complexity, where the lower bounds hold already for negation-free programs. In order to satisfy the requirements of data-intensive applications, we also propose an additional stability requirement, which causes the complexity of reasoning to drop to EXP in combined and to P in data complexity, thus obtaining the same bounds as for usual Datalog. Finally, we compare our formalisms with the languages underpinning existing Datalog-based approaches for data analysis and show that core fragments of these languages can be encoded as limit programs; this allows us to transfer decidability and complexity upper bounds from limit programs to other formalisms. Therefore, our article provides a unified logical framework for declarative data analysis which can be used as a basis for understanding the impact on expressive power and computational complexity of the key constructs available in existing languages.

Geographical Distribution and Pattern of Pesticides in Danish Drinking Water 2002–2018: Reducing Data Complexity

Pesticides are a large and heterogenous group of chemicals with a complex geographic distribution in the environment. The purpose of this study was to explore the geographic distribution of pesticides in Danish drinking water and identify potential patterns in the grouping of pesticides. Our data included 899,169 analyses of 167 pesticides and metabolites, of which 55 were identified above the detection limit. Pesticide patterns were defined by (1) pesticide groups based on chemical structure and pesticide–metabolite relations and (2) an exploratory factor analysis identifying underlying patterns of related pesticides within waterworks. The geographic distribution was evaluated by mapping the pesticide categories for groups and factor components, namely those detected, quantified, above quality standards, and not analysed. We identified five and seven factor components for the periods 2002–2011 and 2012–2018, respectively. In total, 16 pesticide groups were identified, of which six were representative in space and time with regards to the number of waterworks and analyses, namely benzothiazinone, benzonitriles, organophosphates, phenoxy herbicides, triazines, and triazinones. Pesticide mapping identified areas where multiple pesticides were detected, indicating areas with a higher pesticide burden. The results contribute to a better understanding of the pesticide pattern in Danish drinking water and may contribute to exposure assessments for future epidemiological studies.

Answering Fuzzy Queries over Fuzzy DL-Lite Ontologies

A prominent problem in knowledge representation is how to answer queries taking into account also the implicit consequences of an ontology representing domain knowledge. While this problem has been widely studied within the realm of description logic ontologies, it has been surprisingly neglected within the context of vague or imprecise knowledge, particularly from the point of view of mathematical fuzzy logic. In this paper, we study the problem of answering conjunctive queries and threshold queries w.r.t. ontologies in fuzzy DL-Lite. Specifically, we show through a rewriting approach that threshold query answering w.r.t. consistent ontologies remains in ${AC}^{0}$ in data complexity, but that conjunctive query answering is highly dependent on the selected triangular norm, which has an impact on the underlying semantics. For the idempotent Gödel t-norm, we provide an effective method based on a reduction to the classical case.

The impact of data-complexity and team characteristics on performance in the classification model

This article investigates the impact of data-complexity and team-specific characteristics on machine learning competition scores. Data from five real-world binary classification competitions hosted on Kaggle.com were analyzed. The data-complexity characteristics were measured in four aspects including standard measures, sparsity measures, class imbalance measures, and feature-based measures. The results showed that the higher the level of the data-complexity characteristics was, the lower the predictive ability of the machine learning model was as well. Our empirical evidence revealed that the imbalance ratio of the target variable was the most important factor and exhibited a nonlinear relationship with the model’s predictive abilities. The imbalance ratio adversely affected the predictive performance when it reached a certain level. However, mixed results were found for the impact of team-specific characteristics measured by team size, team expertise, and the number of submissions on team performance. For high-performing teams, these factors had no impact on team score.

Diving Deep into the Weak Keys of Round Reduced Ascon

At ToSC 2021, Rohit et al. presented the first distinguishing and key recovery attacks on 7 rounds Ascon without violating the designer’s security claims of nonce-respecting setting and data limit of 264 blocks per key. So far, these are the best attacks on 7 rounds Ascon. However, the distinguishers require (impractical) 260 data while the data complexity of key recovery attacks exactly equals 264. Whether there are any practical distinguishers and key recovery attacks (with data less than 264) on 7 rounds Ascon is still an open problem.In this work, we give positive answers to these questions by providing a comprehensive security analysis of Ascon in the weak key setting. Our first major result is the 7-round cube distinguishers with complexities 246 and 233 which work for 282 and 263 keys, respectively. Notably, we show that such weak keys exist for any choice (out of 64) of 46 and 33 specifically chosen nonce variables. In addition, we improve the data complexities of existing distinguishers for 5, 6 and 7 rounds by a factor of 28, 216 and 227, respectively. Our second contribution is a new theoretical framework for weak keys of Ascon which is solely based on the algebraic degree. Based on our construction, we identify 2127.99, 2127.97 and 2116.34 weak keys (out of 2128) for 5, 6 and 7 rounds, respectively. Next, we present two key recovery attacks on 7 rounds with different attack complexities. The best attack can recover the secret key with 263 data, 269 bits of memory and 2115.2 time. Our attacks are far from threatening the security of full 12 rounds Ascon, but we expect that they provide new insights into Ascon’s security.

The 7-Round Subspace Trail-Based Impossible Differential Distinguisher of Midori-64

This paper analyzes the subspace trail of Midori-64 and uses the propagation law and mutual relationship of the subspaces of Midori-64 to provide a 6-round Midori-64 subspace trail-based impossible differential key recovery attack. The data complexity of the attack is 2 54.6 chosen plaintexts, and the computational complexity is 2 58.2 lookup operations. Its overall complexity is less than that of the known 6-round truncated impossible differential distinguisher. This distinguisher is also applicable to Midori-128 with a secret S -box. Additionally, utilizing the properties of subspaces, we prove that a subspace trail-based impossible differential distinguisher of Midori-64 contains at most 7 rounds. This is 1 more than the upper bound of Midori-64’s truncated impossible differential distinguisher which is 6. According to the Hamming weights of the starting and ending subspaces, we classify all 7-round Midori-64 subspace trail-based impossible differential distinguishers into two types and they need 2 59.6 and 2 51.4 chosen plaintexts, respectively.

Environmentally Driven Aggregate Façade Systems

Even though computer simulation of environmental factors and manufacturing technologies have experienced a fast development, architectural workflows that can take advantage of the possibilities created by these developments have been left behind and architectural design processes have not evolved at the same rate. This paper presents a design to fabrication workflow that explores data driven design to improve performance of facades, implementing for this purpose computational tools to handle environmental data complexity and proposes robotic fabrication technologies to facilitate façade components fabrication.

How to Approximate Ontology-Mediated Queries

We introduce and study several notions of approximation for ontology-mediated queries based on the description logics ALC and ALCI. Our approximations are of two kinds: we may (1) replace the ontology with one formulated in a tractable ontology language such as ELI or certain TGDs and (2) replace the database with one from a tractable class such as the class of databases whose treewidth is bounded by a constant. We determine the computational complexity and the relative completeness of the resulting approximations. (Almost) all of them reduce the data complexity from coNP-complete to PTime, in some cases even to fixed-parameter tractable and to linear time. While approximations of kind (1) also reduce the combined complexity, this tends to not be the case for approximations of kind (2). In some cases, the combined complexity even increases.