Bachelor's theses are a central component of undergraduate science education, but they also pose challenges for large-scale supervision under limited instructional resources. To address this issue, we piloted a model allowing students to write their theses in pairs, an approach common in universities of applied sciences but rare in research-intensive universities. This discussion paper reports on the design, implementation and evaluation of a pilot conducted in autumn 2024 at the University of Helsinki. The findings suggest that co-authored theses may reduce supervisory workload without compromising academic quality. Student feedback highlighted increased engagement, peer support and support for self-regulation, although challenges such as unequal division of labour remained. Supervisors reported reduced cognitive load and supported the model's future use. Based on the pilot, co-authored theses are now an official option in the programme. We conclude by discussing pedagogical implications, assessment practices, and recommendations for supporting collaborative thesis-writing processes.

A fundamental problem in visual data exploration concerns whether observed patterns are true or merely random noise. This problem is especially pertinent in visual analytics, where the user is presented with a barrage of patterns, without any guarantees of their statistical validity. Recently this problem has been formulated in terms of statistical testing and the multiple comparisons problem. In this paper, we identify two levels of multiple comparisons problems in visualization: the within-view and the between-view problem. We develop a statistical testing procedure for interactive data exploration that controls the family-wise error rate on both levels. The procedure enables the user to determine the compatibility of their assumptions about the data with visually observed patterns. We present use-cases where we visualize and evaluate patterns in real-world data.

Abstract Real-world datasets are often characterised by outliers; data items that do not follow the same structure as the rest of the data. These outliers might negatively influence modelling of the data. In data analysis it is, therefore, important to consider methods that are robust to outliers. In this paper we develop a robust regression method that finds the largest subset of data items that can be approximated using a sparse linear model to a given precision. We show that this can yield the best possible robustness to outliers. However, this problem is NP-hard and to solve it we present an efficient approximation algorithm, termed SLISE. Our method extends existing state-of-the-art robust regression methods, especially in terms of speed on high-dimensional datasets. We demonstrate our method by applying it to both synthetic and real-world regression problems.

Abstract Regression analysis is a standard supervised machine learning method used to model an outcome variable in terms of a set of predictor variables. In most real-world applications the true value of the outcome variable we want to predict is unknown outside the training data, i.e., the ground truth is unknown. Phenomena such as overfitting and concept drift make it difficult to directly observe when the estimate from a model potentially is wrong. In this paper we present an efficient framework for estimating the generalization error of regression functions, applicable to any family of regression functions when the ground truth is unknown. We present a theoretical derivation of the framework and empirically evaluate its strengths and limitations. We find that it performs robustly and is useful for detecting concept drift in datasets in several real-world domains.

Efficient explorative data analysis systems must take into account both what a user knows and wants to know. This paper proposes a principled framework for interactive visual exploration of relations in data, through views most informative given the user's current knowledge and objectives. The user can input pre-existing knowledge of relations in the data and also formulate specific exploration interests, which are then taken into account in the exploration. The idea is to steer the exploration process towards the interests of the user, instead of showing uninteresting or already known relations. The user's knowledge is modelled by a distribution over data sets parametrised by subsets of rows and columns of data, called tile constraints. We provide a computationally efficient implementation of this concept based on constrained randomisation. Furthermore, we describe a novel dimensionality reduction method for finding the views most informative to the user, which at the limit of no background knowledge and with generic objectives reduces to PCA. We show that the method is suitable for interactive use and is robust to noise, outperforms standard projection pursuit visualisation methods, and gives understandable and useful results in analysis of real-world data. We provide an open-source implementation of the framework.

Motivated by Gromov’s subgroup conjecture (GSC), a fundamental open conjecture in the area of geometric group theory, we tackle the problem of the existence of partic- ular types of subgroups—arising from so-called periodic apartments—for a specific set of hyperbolic groups with respect to which GSC is currently open. This problem is equiv- alent to determining whether specific types of graphs with a non-trivial combination of properties exist. The existence of periodic apartments allows for ruling the groups out as some of the remaining potential counterexamples to GSC. Our approach combines both automated reasoning techniques—in particular, Boolean satisfiability (SAT) solving—with problem-specific orderly generation. Compared to earlier attempts to tackle the problem through computational means, our approach scales noticeably better, and allows for both confirming results from a previous computational treatment for smaller parameter values as well as ruling out further groups out as potential counterexamples to GSC.

Visual exploration of high-dimensional real-valued datasets is a fundamental task in exploratory data analysis (EDA). Existing methods use predefined criteria to choose the representation of data. There is a lack of methods that (i) elicit from the user what she has learned from the data and (ii) show patterns that she does not know yet. We construct a theoretical model where identified patterns can be input as knowledge to the system. The knowledge syntax here is intuitive, such as "this set of points forms a cluster", and requires no knowledge of maths. This background knowledge is used to find a Maximum Entropy distribution of the data, after which the system provides the user data projections in which the data and the Maximum Entropy distribution differ the most, hence showing the user aspects of the data that are maximally informative given the user's current knowledge. We provide an open source EDA system with tailored interactive visualizations to demonstrate these concepts. We study the performance of the system and present use cases on both synthetic and real data. We find that the model and the prototype system allow the user to learn information efficiently from various data sources and the system works sufficiently fast in practice. We conclude that the information theoretic approach to exploratory data analysis where patterns observed by a user are formalized as constraints provides a principled, intuitive, and efficient basis for constructing an EDA system.

Abstract Real-world datasets are often characterised by outliers, points far from the majority of the points, which might negatively influence modelling of the data. In data analysis it is hence important to use methods that are robust to outliers. In this paper we develop a robust regression method for finding the largest subset in the data that can be approximated using a sparse linear model to a given precision. We show that the problem is NP-hard and hard to approximate. We present an efficient algorithm, termedslise, to find solutions to the problem. Our method extends current state-of-the-art robust regression methods, especially in terms of scalability on large datasets. Furthermore, we show that our method can be used to yield interpretable explanations for individual decisions by opaque, black box, classifiers. Our approach solves shortcomings in other recent explanation methods by not requiring sampling of new data points and by being usable without modifications across various data domains. We demonstrate our method using both synthetic and real-world regression and classification problems.

In this paper we consider the following important problem: when we explore data visually and observe patterns, how can we determine their statistical significance? Patterns observed in exploratory analysis are traditionally met with scepticism, since the hypotheses are formulated while viewing the data, rather than before doing so. In contrast to this belief, we show that it is, in fact, possible to evaluate the significance of patterns also during exploratory analysis, and that the knowledge of the analyst can be leveraged to improve statistical power by reducing the amount of simultaneous comparisons. We develop a principled framework for determining the statistical significance of visually observed patterns. Furthermore, we show how the significance of visual patterns observed during iterative data exploration can be determined. We perform an empirical investigation on real and synthetic tabular data and time series, using different test statistics and methods for generating surrogate data. We conclude that the proposed framework allows determining the significance of visual patterns during exploratory analysis.

Today’s ever-increasing amount of data places new demands on cognitive ergonomics and requires new design ideas to ensure successful human–data interaction. Our aim was to identify the cognitive factors that must be considered when designing systems to improve decision-making based on large amounts of data. We constructed a task that simulates the typical cognitive demands people encounter in data analysis situations. We demonstrate some essential cognitive limitations using a behavioural experiment with 20 participants. The studied task presented the participants with critical and noncritical attributes that contained information on two groups of people. They had to select the response option (group) with the higher level of critical attributes. The results showed that accuracy of judgement decreased as the amount of information increased, and that judgement was affected by irrelevant information. Our results thus demonstrate critical cognitive limitations when people utilise data and suggest a cognitive bias in data-based decision-making. Therefore, when designing for cognition, we should consider the human cognitive limitations that are manifested in a data analysis context. Furthermore, we need general cognitive ergonomic guidelines for design that support the utilisation of data and improve data-based decision-making.

Deriving insights from high-dimensional data is one of the core problems in data mining. The difficulty mainly stems from the large number of variable combinations to potentially consider. Hence, an obvious question is whether we can automate the search for interesting patterns. Here, we consider the setting where a user wants to learn as efficiently as possible about real-valued attributes. We introduce a method to find subgroups in the data that are maximally informative (in the Information Theoretic sense) with respect to one or more real-valued target attributes. The succinct subgroup descriptions are in terms of arbitrarily-typed description attributes. The approach is based on the Subjective Interestingness framework FORSIED to use prior knowledge when mining most informative patterns.

Confidence intervals are a popular way to visualize and analyze data distributions. Unlike p-values, they can convey information both about statistical significance as well as effect size. However, very little work exists on applying confidence intervals to multivariate data. In this paper we define confidence intervals for multivariate data that extend the one-dimensional definition in a natural way. In our definition every variable is associated with its own confidence interval as usual, but a data vector can be outside of a few of these, and still be considered to be within the confidence area. We analyze the problem and show that the resulting confidence areas retain the good qualities of their one-dimensional counterparts: they are informative and easy to interpret. Furthermore, we show that the problem of finding multivariate confidence intervals is hard, but provide efficient approximate algorithms to solve the problem.

Abstract The supertree construction problem is about combining several phylogenetic trees with possibly conflicting information into a single tree that has all the leaves of the source trees as its leaves and the relationships between the leaves are as consistent with the source trees as possible. This leads to an optimization problem that is computationally challenging and typically heuristic methods, such as matrix representation with parsimony (MRP), are used. In this paper we consider the use of answer set programming to solve the supertree construction problem in terms of two alternative encodings. The first is based on an existing encoding of trees using substructures known as quartets, while the other novel encoding captures the relationships present in trees through direct projections. We use these encodings to compute a genus-level supertree for the family of cats (Felidae). Furthermore, we compare our results to recent supertrees obtained by the MRP method.

The resolution-based grounded semantics constitutes one of the most interesting approaches for the evaluation of abstract argumentation frameworks. This particular semantics satisfies a large number of desired properties, among them all properties proposed by Baroni and Giacomin. In recent years, the analysis of argumentation semantics has been extended by further topics, among them characterizations for equivalence notions, intertranslatability issues, and expressibility in terms of signatures (all possible sets of extensions a semantics is capable to express). In this line of research, resolution-based grounded semantics has been neglected so far. We close this gap here, compare the expressibility of resolution-based grounded semantics with other prominent semantics, provide a characterization for strong equivalence and complement existing complexity results.

Practically all programming languages allow the programmer to split a program into several modules which brings along several advantages in software development. In this paper, we are interested in the area of answer-set programming where fully declarative and nonmonotonic languages are applied. In this context, obtaining a modular structure for programs is by no means straightforward since the output of an entire program cannot in general be composed from the output of its components. To better understand the effects of disjunctive information on modularity we restrict the scope of analysis to the case of disjunctive logic programs (DLPs) subject to stable-model semantics. We define the notion of a DLP-function, where a well-defined input/output interface is provided, and establish a novel module theorem which indicates the compositionality of stable-model semantics for DLP-functions. The module theorem extends the well-known splitting-set theorem and enables the decomposition of DLP-functions given their strongly connected components based on positive dependencies induced by rules. In this setting, it is also possible to split shared disjunctive rules among components using a generalized shifting technique. The concept of modular equivalence is introduced for the mutual comparison of DLP-functions using a generalization of a translation-based verification method.

The goal of this article is to foster modular program development in answer set programming using a Gaifman-Shapiro-style module architecture. More specifically, a method for verifying the equivalence of logic program modules is devised and proved correct. The idea is to adapt a translation-based verification technique, which was originally devised for complete programs only, for program modules. In addition, optimization strategies are addressed in order to exploit the modular structure of programs in verification tasks. A number of experiments on verification strategies are also conducted using lpeq which implements the verification method for the smodels system. The preliminary experimental results reported in this article suggest that the modularization of equivalence verification leads to potential time savings especially if the modules involved share a common context.

Abstract We introduce an extended tableau calculus for answer set programming (ASP). The proof system is based on the ASP tableaux defined in the work by Gebser and Schaub (Tableau calculi for answer set programming. In Proceedings of the 22nd International Conference on Logic Programming (ICLP 2006), S. Etalle and M. Truszczynski, Eds. Lecture Notes in Computer Science, vol. 4079. Springer, 11–25) with an added extension rule. We investigate the power of Extended ASP Tableaux both theoretically and empirically. We study the relationship of Extended ASP Tableaux with the Extended Resolution proof system defined by Tseitin for sets of clauses, and separate Extended ASP Tableaux from ASP Tableaux by giving a polynomial-length proof for a family of normal logic programs {Φn} for which ASP Tableaux has exponential-length minimal proofs with respect to n. Additionally, Extended ASP Tableaux imply interesting insight into the effect of program simplification on the lengths of proofs in ASP. Closely related to Extended ASP Tableaux, we empirically investigate the effect of redundant rules on the efficiency of ASP solving.

Abstract In this paper, a Gaifman–Shapiro-style module architecture is tailored to the case of smodels programs under the stable model semantics. The composition of smodels program modules is suitably limited by module conditions which ensure the compatibility of the module system with stable models. Hence the semantics of an entire smodels program depends directly on stable models assigned to its modules. This result is formalized as a module theorem which truly strengthens V. Lifschitz and H. Turner's splitting-set theorem (June 1994, Splitting a logic program. In Logic Programming: Proceedings of the Eleventh International Conference on Logic Programming, Santa Margherita Ligure, Italy, P. V. Hentenryck, Ed. MIT Press, 23–37) for the class of smodels programs. To streamline generalizations in the future, the module theorem is first proved for normal programs and then extended to cover smodels programs using a translation from the latter class of programs to the former class. Moreover, the respective notion of module-level equivalence, namely modular equivalence, is shown to be a proper congruence relation: it is preserved under substitutions of modules that are modularly equivalent. Principles for program decomposition are also addressed. The strongly connected components of the respective dependency graph can be exploited in order to extract a module structure when there is no explicit a priori knowledge about the modules of a program. The paper includes a practical demonstration of tools that have been developed for automated (de)composition of smodels programs.

Abstract In answer set programming (ASP), a problem at hand is solved by (i) writing a logic program whose answer sets correspond to the solutions of the problem, and by (ii) computing the answer sets of the program using ananswer set solveras a search engine. Typically, a programmer creates a series of gradually improving logic programs for a particular problem when optimizing program length and execution time on a particular solver. This leads the programmer to a meta-level problem of ensuring that the programs are equivalent, i.e., they give rise to the same answer sets. To ease answer set programming at methodological level, we propose a translation-based method for verifying the equivalence of logic programs. The basic idea is to translate logic programsPandQunder consideration into a single logic program EQT(P,Q) whose answer sets (if such exist) yield counter-examples to the equivalence ofPandQ. The method is developed here in a slightly more general setting by taking thevisibilityof atoms properly into account when comparing answer sets. The translation-based approach presented in the paper has been implemented as a translator calledlpeqthat enables the verification of weak equivalence within thesmodelssystem using the same search engine as for the search of models. Our experiments withlpeqandsmodelssuggest that establishing the equivalence of logic programs in this way is in certain cases much faster than naive cross-checking of answer sets.