The study aimed to develop the concept of an innovative computer system with a dynamic architecture based on monocopters that function as distributed computing nodes. The research methodology included an analysis of design principles, modelling of the activation and energy distribution process, as well as the study of cooling capabilities and adaptive control of the system structure. As a result of the study, a concept of a computer system consisting of monocopters that perform the functions of computing nodes was developed. The key components of these monocopters were identified their structure, which includes local computing modules, power supplies and communication facilities for wireless communication. Activation of the system using external microwave power transfer, which became the basis for providing power to monocopters and initiating their functions, was emphasised. The study noted that the microwave energy entering the inductive coils can be converted into an electric current, providing the initial power to the system. The idea also envisages that monocopters can determine their position relative to other elements using a wireless data bus, allowing for an ordered three-dimensional structure that adapts to external conditions and requirements. The study indicates the potential of the system for the dynamic organisation of computational tasks that can be distributed among monocopters. This improves the reliability and efficiency of the system in the event of a change in topology or failure of individual nodes. Additionally, the study noted that the system can be scaled without delays in operations, creating new opportunities for the application of this technology in various fields, including robotics, adaptive materials and technologies with reduced dependence on conventional energy storage. Thus, the study results indicate the possibility of creating an adaptive and mobile computer system that can effectively respond to changing external conditions and provide high data processing performance.
Stress is a growing issue in modern workplaces, affecting mental well-being, productivity, and health. This paper explores the use of computer mouse movement patterns as non-invasive behavioral indicators of psychological stress. The study presents a system for detecting stress levels based on mouse interaction data. An experimental task was designed to induce different stress levels. The collected data were used to train machine learning models, several of which achieved high classification accuracy. The results demonstrate the potential of mouse-based behavioral analysis as a practical approach to real-time workplace stress detection. This research contributes to the development of accessible stress monitoring tools and highlights the value of behavioral data analysis for improving occupational health.
The task of authorship attribution is a classification problem where a given text is analyzed by a machine learning algorithm with the goal of verifying its author. This task has great application in the world of forensic analysis and intellectual property problems. In this work, we propose to use Support Vector Machines paired with Term Frequency - Inverse Term Frequency to solve authorship attribution in cases where the author is part of a predetermined set of authors, called a closed set. This method could also be applied to large language models in order to find out if those have a "style" of writing. In the end, we show how we compared our results with related works and how we managed to improve the results on a well known benchmark dataset by 6.4 %.
This paper is concerned with the existence, generation and counting of ternary rotation symmetric bent functions. The concept of rank of functions is introduced and it is shown that rotation symmetric bent ternary functions do exist and may be given a compact value vector representation with entries ordered by rank. A basic set of 3-place ternary rotation symmetric functions is extracted from a database of 3-place ternary bent functions. With the help of spectral invariant operations and some new operations a first set of 624 ternary rotation symmetric bent functions with n = 3 was obtained.
Sentential Decision Diagrams (SDDs) are a generalization of Binary Decision Diagrams (BDDs) that allow decisions based on "sentences" of variables, rather than only individual variables. SDDs are ordered based on a binary tree of variables (a vtree) which can represent their more complex structure. Like BDDs, the order of an SDD significantly impacts its size. We present two novel SDD minimization methods based on Rudell's sifting algorithm and a symmetric sifting algorithm, both of which are BDD minimization methods. To evaluate the performance of our proposed techniques, we compile the combinational benchmarks from the LGSynth'91 benchmark set and dynamically minimize each circuit with our proposed techniques and two variations of the current state-of-the-art method. Our methods show an average improvement ratio of 20.2 % in SDD size compared with the current state-of-the-art technique. For larger circuits (>= 50 inputs), the improvement ratio increases to 39.2 %.
The paper presents a discussion of Haar spectra for binary bent functions. The presentation is restricted to bent functions of n = 4 variables, since in this case the total of 896 bent functions allows an exhaustive search and discussion of their features. Possibilities for a straightforward extension to functions of a larger number of variables are illustrated by examples for n = 6. The definition and multiresolution feature of Haar coefficients and their organization into packets provides a deeper insight into bent functions, and in particular the relationships between values in their function vectors. In the spectral domain, these relationships are expressed as the appearance of Walsh spectra for smaller number of variables in the Haar spectra of bent functions for a given n. Reconstruction of function values from Haar coefficients shows that for bent functions, function values appear in pairs of values in certain orders. Destroying these pairs results in non-bent functions.
Assessing the quality of random bitstreams used to support cryptographic and other applications is crucially important as any detectable deviations from true randomness can introduce exploitable vulnerabilities resulting in a loss of security. Existing randomness tests, such as those recommended by the U.S. National Institute of Standards and Technology (NIST), examine statistical characteristics of bitstreams such as bit distributions, periodicity, cumulative sums and other properties. However, these methods do not explicitly test for generalized causality within a bitstream - instances where earlier sequences influence the likelihood of later sequences that are undetectable through correlation-based analyses. To address this gap, we propose a new approach, the Granger-inspired Test for Randomness (GTR), that applies principles of Granger causality to detect causal relationships within a single bitstream. To validate our approach, we conduct experiments using a 10-million-bit sample acquired from the NIST Randomness Beacon as a baseline case. We compare GTR to other methods that assess random bitstream quality. Our findings suggest that GTR outperforms many randomness tests, identifying subtle structural dependencies in bitstreams that are not detected with many current tests.
We present a statistical methodology for investigating oscillations in the underlying number of causes contributing to a phenomenon. We utilize the Walsh transform of a ternary signal derived from the unbiased MLEs of the shape parameters in an ordinal indexed series of Pareto distributions. To this end, we outline the details of our methodology, discuss a simulation study, and apply our method to real world data (NHTSA recall data). Our results indicate that the proposed methodology is able to reliably detect oscillations in the shape parameter.
The author’s dissertation, entitled “Scalable SAT Solving and its Application”, advances the efficient resolution of instances of the propositional satisfiability (SAT) problem, one of the prototypical “hard problems” of computer science with many scientific and industrial real-world applications. A particular focus is put on exploiting massively parallel computational environments, such as high-performance computing (HPC) systems or cloud computing. The dissertation has resulted in world-leading solutions for scalable automated reasoning and in a number of awards from the SAT community, and has most recently been acknowledged with a GI Dissertation Award. The article at hand summarizes the topic, approaches, and central results of the dissertation, estimates the work’s long-term impact and its role for future research, and closes with some personal notes.
With great power comes great responsibility. As Artificial Intelligence in general and Natural Language Processing, in particular, continue to shape the world we live in, there is an increased need for Computer Scientists in academia and industry to acknowledge the pivotal role our discipline plays in shaping the future of our society. Accepting this role should come with the responsibility to make positive contributions, whether through research, teaching, application, or by contributing to the public discourse surrounding AI technology and its regulation. This text outlines how Natural Language Processing can contribute to shaping a better future in which society at large can benefit from technological advances.
As artificial intelligence (AI) increasingly permeates high-stakes domains such as healthcare, transportation, and law enforcement, ensuring its trustworthiness has become a critical challenge. This article proposes an integrative Explainable AI (XAI) framework to address the challenges of interpretability, explainability, interactivity, and robustness. By combining XAI methods, incorporating human-AI interaction and using suitable evaluation techniques, the implementation of this framework serves as a holistic XAI approach. The article discusses the framework’s contribution to trustworthy AI and gives an outlook on open challenges related to interdisciplinary collaboration, AI generalization and AI evaluation.
Machine learning (ML) is increasingly deployed in critical domains such as healthcare, finance, and autonomous driving, where the use of sensitive data raises significant privacy challenges. My research places individuals and their data at the center of ML privacy, building systems that protect individuals’ privacy without sacrificing performance. I focus on (1) exploring the threat space in ML privacy to inspire targeted protection, (2) analyzing the root cause of privacy leakage from ML models, and (3) developing individualized privacy guarantees that protect data according to individuals’ unique needs while improving privacy-utility trade-offs. My vision is to advance privacy-preserving ML to address the evolving challenges of increasingly complex ML models and systems. As models grow in scale, integrate diverse data modalities, and become embedded in critical societal applications, protecting individual privacy becomes both more urgent but also more challenging. My goal is to create methods that ensure privacy across a broad spectrum of ML applications, while also addressing the interplay between privacy and other trustworthy ML aspects, and aligning technical privacy measures with legal and societal expectations to meet individual rights.
It is the article’s overall aim to elucidate the contingency and volatility inherent to academic biographies. In order to derive these, but also underpin them, the article begins by outlining the argument that Mareike Lisker makes in her Master’s thesis, which explores the nexus between the disciplines of computer science and philosophy. There, she posits that the demand for more digital literacy places an onerous responsibility on individual users when it comes to the control of their own data. She argues that the individual users are structurally ill-equipped to meet that responsibility in face of all-encompassing tracking infrastructures. In accordance with the aim of the article, the thesis’ topic will be situated within Lisker’s scientific career path. This path will then be traced up until her current PhD project, which focusses on content moderation on decentralized platforms.
When we look at the global knowledge graph landscape, we quickly find that there are billions of interconnected facts that have the potential to answer all kinds of questions. However, a persistent challenge lies in finding corresponding questions that align with these facts. The availability of these questions along with matching SPARQL queries is an important prerequisite for fine-tuning Large Language Models for domain-specific query generation, which is why we propose Queryfy , a novel framework that leverages Large Language Models to automate the task of deriving questions and queries from knowledge graphs, empowering users to harness their full potential.
Digital and Computational Archaeology (DCA) is a rapidly evolving field in Germany, and this paper aims to provide a comprehensive introduction to the term and its usage in the German context. The second part of the paper examines the state of higher education in DCA in Germany, analyzing the available courses and academic positions. The paper is supported by a statistical overview of German DCA courses, compiled using data from the IANUS research data center. We then provide a critical analysis and contextualization of the teaching hotspots and developments over the last 10 years. Finally, we present recent developments to provide an outlook on the future of the DCA landscape in Germany.ACM CCS Applied computing -> Arts and humanities.
The search for patterns in archaeological contexts is crucial to understanding ancient civilizations, including the organization of their societies and economies. This study focuses on the interdisciplinary application of mathematical logical data analysis to Egyptology, particularly examining food production in the Workmen's Village of Amarna. The introduction provides historical context about Amarna, founded by Pharaoh Akhenaten, and outlines the settlement structure and excavations of the Workmen's Village. The research utilizes archaeobotanical and archaeological data, along with digital humanities tools, to identify activity areas related to food production. The findings reveal spatial patterns in the distribution of tools such as mortars, pestles, and ovens, providing insights into the self-sufficiency and daily life of the villagers. Logical data analysis helps in finding rules and patterns in a dataset which are not visible otherwise, predicting the distribution of unexcavated areas and thus in this case enhancing our understanding of ancient Egyptian food production practices in state-planned settlements.ACM CCS Applied computing -> Art and humanities.
Digital technologies allow to connect more closely collections of machines in museums and universities to machines in historic monuments. The article discusses why digital collections should be established (1), how the digital models should be produced (2) and how the virtual models could be used by historians of technology as well as trainers of machine builders (3).
