arXiv subjects

Markus Wagner

Publications and source records attributed to Markus Wagner.

At least 55 records · Page 3Linked to original sources

arXiv2022

Perspectives of Visualization Onboarding and Guidance in VA

A typical problem in Visual Analytics is that users are highly trained experts in their application domains, but have mostly no experience in using VA systems. Thus, users often have difficulties interpreting and working with visual representations. To overcome these problems, user assistance can be incorporated into VA systems to guide experts through the analysis while closing their knowledge gaps. Different types of user assistance can be applied to extend the power of VA, enhance the user's experience, and broaden the audience for VA. Although different approaches to visualization onboarding and guidance in VA already exist, there is a lack of research on how to design and integrate them in effective and efficient ways. Therefore, we aim at putting together the pieces of the mosaic to form a coherent whole. Based on the Knowledge-Assisted Visual Analytics model, we contribute a conceptual model of user assistance for VA by integrating the process of visualization onboarding and guidance as the two main approaches in this direction. As a result, we clarify and discuss the commonalities and differences between visualization onboarding and guidance, and discuss how they benefit from the integration of knowledge extraction and exploration. Finally, we discuss our descriptive model by applying it to VA tools integrating visualization onboarding and guidance, and showing how they should be utilized in different phases of the analysis in order to be effective and accepted by the user.

cs.HC↗

arXiv2022

On the Utility of Marrying GIN and PMD for Improving Stack Overflow Code Snippets

Software developers are increasingly dependent on question and answer portals and blogs for coding solutions. While such interfaces provide useful information, there are concerns that code hosted here is often incorrect, insecure or incomplete. Previous work indeed detected a range of faults in code provided on Stack Overflow through the use of static analysis. Static analysis may go a far way towards quickly establishing the health of software code available online. In addition, mechanisms that enable rapid automated program improvement may then enhance such code. Accordingly, we present this proof of concept. We use the PMD static analysis tool to detect performance faults for a sample of Stack Overflow Java code snippets, before performing mutations on these snippets using GIN. We then re-analyse the performance faults in these snippets after the GIN mutations. GIN's RandomSampler was used to perform 17,986 unique line and statement patches on 3,034 snippets where PMD violations were removed from 770 patched versions. Our outcomes indicate that static analysis techniques may be combined with automated program improvement methods to enhance publicly available code with very little resource requirements. We discuss our planned research agenda in this regard.

cs.SE↗

arXiv2021

Run-of-Mine Stockyard Recovery Scheduling and Optimisation for Multiple Reclaimers

Stockpiles are essential in the mining value chain, assisting in maximising value and production. Quality control of taken minerals from the stockpiles is a major concern for stockpile managers where failure to meet some requirements can lead to losing money. This problem was recently investigated using a single reclaimer, and basic assumptions. This study extends the approach to consider multiple reclaimers in preparing for short and long-term deliveries. The engagement of multiple reclaimers complicates the problem in terms of their interaction in preparing a delivery simultaneously and safety distancing of reclaimers. We also consider more realistic settings, such as handling different minerals with different types of reclaimers. We propose methods that construct a solution step by step to meet precedence constraints for all reclaimers in the stockyard. We study various instances of the problem using greedy algorithms, Ant Colony Optimisation (ACO), and propose an integrated local search method determining an efficient schedule. We fine-tune and compare the algorithms and show that the ACO combined with local search can yield efficient solutions.

cs.NE↗

arXiv2021

Novelty-Driven Binary Particle Swarm Optimisation for Truss Optimisation Problems

Topology optimisation of trusses can be formulated as a combinatorial and multi-modal problem in which locating distinct optimal designs allows practitioners to choose the best design based on their preferences. Bilevel optimisation has been successfully applied to truss optimisation to consider topology and sizing in upper and lower levels, respectively. We introduce exact enumeration to rigorously analyse the topology search space and remove randomness for small problems. We also propose novelty-driven binary particle swarm optimisation for bigger problems to discover new designs at the upper level by maximising novelty. For the lower level, we employ a reliable evolutionary optimiser to tackle the layout configuration aspect of the problem. We consider truss optimisation problem instances where designers need to select the size of bars from a discrete set with respect to practice code constraints. Our experimental investigations show that our approach outperforms the current state-of-the-art methods and it obtains multiple high-quality solutions.

cs.NE↗

arXiv2021

Efficiently solving the thief orienteering problem with a max-min ant colony optimization approach

We tackle the Thief Orienteering Problem (ThOP), an academic multi-component problem that combines two classical combinatorial problems, namely the Knapsack Problem and the Orienteering Problem. In the ThOP, a thief has a time limit to steal items that distributed in a given set of cities. While traveling, the thief collects items by storing them in their knapsack, which in turn reduces the travel speed. The thief has as the objective to maximize the total profit of the stolen items. In this article, we present an approach that combines swarm-intelligence with a randomized packing heuristic. Our solution approach outperforms existing works on almost all the 432 benchmarking instances, with significant improvements.

cs.NE↗

arXiv2021

Rosita++: Automatic Higher-Order Leakage Elimination from Cryptographic Code

Side-channel attacks are a major threat to the security of cryptographic implementations, particularly for small devices that are under the physical control of the adversary. While several strategies for protecting against side-channel attacks exist, these often fail in practice due to unintended interactions between values deep within the CPU. To detect and protect from side-channel attacks, several automated tools have recently been proposed; one of their common limitations is that they only support first-order leakage. In this work, we present the first automated tool for detecting and eliminating higher-order leakage from cryptographic implementations. Rosita++ proposes statistical and software-based tools to allow high-performance higher-order leakage detection. It then uses the code rewrite engine of Rosita (Shelton et al. NDSS 2021) to eliminate detected leakage. For the sake of practicality we evaluate Rosita++ against second and third order leakage, but our framework is not restricted to only these orders. We evaluate Rosita++ against second-order leakage with three-share implementations of two ciphers, PRESENT and Xoodoo, and with the second-order Boolean-to-arithmetic masking, a core building block of masked implementations of many cryptographic primitives, including SHA-2, ChaCha and Blake. We show effective second-order leakage elimination at a performance cost of 36% for Xoodoo, 189% for PRESENT, and 29% for the Boolean-to-arithmetic masking. For third-order analysis, we evaluate Rosita++ against the third-order leakage using a four-share synthetic example that corresponds to typical four-share processing. Rosita++ correctly identified this leakage and applied code fixes.

cs.CR↗

arXiv2021

A weighted-sum method for solving the bi-objective traveling thief problem

Many real-world optimization problems have multiple interacting components. Each of these can be NP-hard and they can be in conflict with each other, i.e., the optimal solution for one component does not necessarily represent an optimal solution for the other components. This can be a challenge for single-objective formulations, where the respective influence that each component has on the overall solution quality can vary from instance to instance. In this paper, we study a bi-objective formulation of the traveling thief problem, which has as components the traveling salesperson problem and the knapsack problem. We present a weighted-sum method that makes use of randomized versions of existing heuristics, that outperforms participants on 6 of 9 instances of recent competitions, and that has found new best solutions to 379 single-objective problem instances.

cs.NE↗

arXiv2021

Online Deployment Algorithms for Microservice Systems with Complex Dependencies

Cloud and edge computing have been widely adopted in many application scenarios. With the increasing demand of fast iteration and complexity of business logic, it is challenging to achieve rapid development and continuous delivery in such highly distributed cloud and edge computing environment. At present, microservice-based architecture has been the dominant deployment style, and a microservice system has to evolve agilely to offer stable Quality of Service (QoS) in the situation where user requirement changes frequently. Many research have been conducted to optimally re-deploy microservices to adapt to changing requirements. Nevertheless, complex dependencies between microservices and the existence of multiple instances of one single microservice in a microservice system have not been fully considered in existing works. This paper defines SPPMS, the Service Placement Problem in Microservice Systems that feature complex dependencies and multiple instances, as a Fractional Polynomial Problem (FPP) . Considering the high computation complexity of FPP, it is then transformed into a Quadratic Sum-of-Ratios Fractional Problem (QSRFP) which is further solved by the proposed greedy-based algorithms. Experiments demonstrate that our models and algorithms outperform existing approaches in both quality and computation speed.

cs.DC↗

arXiv2021

What makes a good Node.js package? Investigating Users, Contributors, and Runnability

The Node.js Package Manager (i.e., npm) archive repository serves as a critical part of the JavaScript community and helps support one of the largest developer ecosystems in the world. However, as a developer, selecting an appropriate npm package to use or contribute to can be difficult. To understand what features users and contributors consider important when searching for a good npm package, we conduct a survey asking Node.js developers to evaluate the importance of 30 features derived from existing work, including GitHub activity, software usability, and properties of the repository and documentation. We identify that both user and contributor perspectives share similar views on which features they use to assess package quality. We then extract the 30 features from 104,364 npm packages and analyse the correlations between them, including three software features that measure package ``runnability"; ability to install, build, and execute a unit test. We identify which features are negatively correlated with runnability-related features and find that predicting the runnability of packages is viable. Our study lays the groundwork for future work on understanding how users and contributors select appropriate npm packages.

cs.SE↗

arXiv2021

Metaheuristics "In the Large"

Following decades of sustained improvement, metaheuristics are one of the great success stories of optimization research. However, in order for research in metaheuristics to avoid fragmentation and a lack of reproducibility, there is a pressing need for stronger scientific and computational infrastructure to support the development, analysis and comparison of new approaches. We argue that, via principled choice of infrastructure support, the field can pursue a higher level of scientific enquiry. We describe our vision and report on progress, showing how the adoption of common protocols for all metaheuristics can help liberate the potential of the field, easing the exploration of the design space of metaheuristics.

cs.NE↗

arXiv2021

Generating Instances with Performance Differences for More Than Just Two Algorithms

In recent years, Evolutionary Algorithms (EAs) have frequently been adopted to evolve instances for optimization problems that pose difficulties for one algorithm while being rather easy for a competitor and vice versa. Typically, this is achieved by either minimizing or maximizing the performance difference or ratio which serves as the fitness function. Repeating this process is useful to gain insights into strengths/weaknesses of certain algorithms or to build a set of instances with strong performance differences as a foundation for automatic per-instance algorithm selection or configuration. We contribute to this branch of research by proposing fitness-functions to evolve instances that show large performance differences for more than just two algorithms simultaneously. As a proof-of-principle, we evolve instances of the multi-component Traveling Thief Problem~(TTP) for three incomplete TTP-solvers. Our results point out that our strategies are promising, but unsurprisingly their success strongly relies on the algorithms' performance complementarity.

cs.NE↗

arXiv2021

GTOPX Space Mission Benchmarks

This contribution introduces the GTOPX space mission benchmark collection, which is an extension of GTOP database published by the European Space Agency (ESA). GTOPX consists of ten individual benchmark instances representing real-world interplanetary space trajectory design problems. In regard to the original GTOP collection, GTOPX includes three new problem instances featuring mixed-integer and multi-objective properties. GTOPX enables a simplified user handling, unified benchmark function call and some minor bug corrections to the original GTOP implementation. Furthermore, GTOPX is linked from it's original C++ source code to Python and Matlab based on dynamic link libraries, assuring computationally fast and accurate reproduction of the benchmark results in all three programming languages. Space mission trajectory design problems as those represented in GTOPX are known to be highly non-linear and difficult to solve. The GTOPX collection, therefore, aims particularly at researchers wishing to put advanced (meta)heuristic and hybrid optimization algorithms to the test. The goal of this paper is to provide researchers with a manual and reference to the newly available GTOPX benchmark software.

cs.NE↗

arXiv2021

MATE: A Model-based Algorithm Tuning Engine

In this paper, we introduce a Model-based Algorithm Turning Engine, namely MATE, where the parameters of an algorithm are represented as expressions of the features of a target optimisation problem. In contrast to most static (feature-independent) algorithm tuning engines such as irace and SPOT, our approach aims to derive the best parameter configuration of a given algorithm for a specific problem, exploiting the relationships between the algorithm parameters and the features of the problem. We formulate the problem of finding the relationships between the parameters and the problem features as a symbolic regression problem and we use genetic programming to extract these expressions. For the evaluation, we apply our approach to configuration of the (1+1) EA and RLS algorithms for the OneMax, LeadingOnes, BinValue and Jump optimisation problems, where the theoretically optimal algorithm parameters to the problems are available as functions of the features of the problems. Our study shows that the found relationships typically comply with known theoretical results, thus demonstrating a new opportunity to consider model-based parameter tuning as an effective alternative to the static algorithm tuning engines.

cs.NE↗

arXiv2020

Benchmarking in Optimization: Best Practice and Open Issues

This survey compiles ideas and recommendations from more than a dozen researchers with different backgrounds and from different institutes around the world. Promoting best practice in benchmarking is its main goal. The article discusses eight essential topics in benchmarking: clearly stated goals, well-specified problems, suitable algorithms, adequate performance measures, thoughtful analysis, effective and efficient designs, comprehensible presentations, and guaranteed reproducibility. The final goal is to provide well-accepted guidelines (rules) that might be useful for authors and reviewers. As benchmarking in optimization is an active and evolving field of research this manuscript is meant to co-evolve over time by means of periodic updates.

cs.NE↗

arXiv2020

Rosita: Towards Automatic Elimination of Power-Analysis Leakage in Ciphers

Since their introduction over two decades ago, side-channel attacks have presented a serious security threat. While many ciphers' implementations employ masking techniques to protect against such attacks, they often leak secret information due to unintended interactions in the hardware. We present Rosita, a code rewrite engine that uses a leakage emulator which we amend to correctly emulate the micro-architecture of a target system. We use Rosita to automatically protect masked implementations of AES, ChaCha, and Xoodoo. For AES and Xoodoo, we show the absence of observable leakage at 1,000,000 traces with less than 21% penalty to the performance. For ChaCha, which has significantly more leakage, Rosita eliminates over 99% of the leakage, at a performance cost of 64%.

cs.CR↗

arXiv2020

The Dynamic Travelling Thief Problem: Benchmarks and Performance of Evolutionary Algorithms

Many real-world optimisation problems involve dynamic and stochastic components. While problems with multiple interacting components are omnipresent in inherently dynamic domains like supply-chain optimisation and logistics, most research on dynamic problems focuses on single-component problems. With this article, we define a number of scenarios based on the Travelling Thief Problem to enable research on the effect of dynamic changes to sub-components. Our investigations of 72 scenarios and seven algorithms show that -- depending on the instance, the magnitude of the change, and the algorithms in the portfolio -- it is preferable to either restart the optimisation from scratch or to continue with the previously valid solutions.

cs.NE↗

arXiv2020

Ants can orienteer a thief in their robbery

The Thief Orienteering Problem (ThOP) is a multi-component problem that combines features of two classic combinatorial optimization problems: Orienteering Problem and Knapsack Problem. The ThOP is challenging due to the given time constraint and the interaction between its components. We propose an Ant Colony Optimization algorithm together with a new packing heuristic to deal individually and interactively with problem components. Our approach outperforms existing work on more than 90% of the benchmarking instances, with an average improvement of over 300%.

cs.AI↗

arXiv2020

Genetic Improvement @ ICSE 2020

Following Prof. Mark Harman of Facebook's keynote and formal presentations (which are recorded in the proceedings) there was a wide ranging discussion at the eighth international Genetic Improvement workshop, GI-2020 @ ICSE (held as part of the 42nd ACM/IEEE International Conference on Software Engineering on Friday 3rd July 2020). Topics included industry take up, human factors, explainabiloity (explainability, justifyability, exploitability) and GI benchmarks. We also contrast various recent online approaches (e.g. SBST 2020) to holding virtual computer science conferences and workshops via the WWW on the Internet without face-2-face interaction. Finally we speculate on how the Coronavirus Covid-19 Pandemic will affect research next year and into the future.

cs.SE↗