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2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Rosenberger, Johannes Ibrahim, Abdalla Bash, Boulat A. Deppe, Christian Ferrara, Roberto Pereg, Uzi Technical University of Munich Tum School of Computation Information and Technology Germany University of Arizona Electrical and Computer Engineering Department United States Technion - Israel Institute of Technology Faculty of Electrical and Computer Engineering Helen Diller Quantum Center Israel

ISBN: (纸本)9781665475549

An upper bound to the identification capacity of discrete memoryless wiretap channels is derived under the requirement of semantic effective secrecy, combining semantic secrecy and stealth constraints. A previously established lower bound is improved by applying it to a prefix channel, formed by concatenating an auxiliary channel and the actual channel. The bounds are tight if the legitimate channel is more capable than the eavesdropper's channel. An illustrative example is provided for a wiretap channel that is composed of a point-to-point channel, and a parallel, reversely degraded wiretap channel. A comparison with results for message transmission and for identification with only secrecy constraint is provided. © 2023 IEEE.

关键词： Semantics

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How Can CoPs in Scaled Agile Settings Look Like? Toward a Taxonomy for CoPs in Large-Scale Agile Software Development 58

How Can CoPs in Scaled Agile Settings Look Like? Toward a Ta...

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58th Hawaii International Conference on System Sciences, HICSS 2025

作者： Tobisch, Franziska Schmidt, Johannes Matthes, Florian Technical University of Munich School of Computation Information and Technology Department of Computer Science Garching Germany

ISBN: (纸本)9780998133188

Fast-changing business environments and customer needs require organizations to react quickly and adapt to changes. While agile software development methods can support organizations in achieving this goal, applying these methods in a large context, like multi-team projects, entails high complexity. Several scaling agile frameworks recommend Communities of Practice (CoP), groups of people with a shared interest that exchange with each other on a regular basis, to support the application of agile at scale, as they can foster, e.g., inter-team collaboration and alignment. Still, no clear overview of aspects that must be considered when establishing and cultivating CoPs in scaled agile settings exists. Therefore, we propose a taxonomy for CoPs in this context, building on literature and findings of an interview study. The taxonomy can support practitioners and researchers, offering a structured overview of aspects relevant to establishing and cultivating CoPs in large-scale agile settings and future research directions. © 2025 IEEE computer Society. All rights reserved.

关键词： Software design

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On the Impact of Noise in Differentially Private Text Rewriting

arXiv

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arXiv 2025年

作者： Meisenbacher, Stephen Chevli, Maulik Matthes, Florian Technical University of Munich School of Computation Information and Technology Department of Computer Science Garching Germany

The field of text privatization often leverages the notion of Differential Privacy (DP) to provide formal guarantees in the rewriting or obfuscation of sensitive textual data. A common and nearly ubiquitous form of DP application necessitates the addition of calibrated noise to vector representations of text, either at the data- or model-level, which is governed by the privacy parameter Ε. However, noise addition almost undoubtedly leads to considerable utility loss, thereby highlighting one major drawback of DP in NLP. In this work, we introduce a new sentence infilling privatization technique, and we use this method to explore the effect of noise in DP text rewriting. We empirically demonstrate that non-DP privatization techniques excel in utility preservation and can find an acceptable empirical privacy-utility trade-off, yet cannot outperform DP methods in empirical privacy protections. Our results highlight the significant impact of noise in current DP rewriting mechanisms, leading to a discussion of the merits and challenges of DP in NLP, as well as the opportunities that non-DP methods present. © 2025, CC BY.

关键词： Differential privacy

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Enhancing Answer Attribution for Faithful Text Generation with Large Language Models

arXiv

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arXiv 2024年

作者： Vladika, Juraj Mülln, Luca Matthes, Florian Technical University of Munich School of Computation Information and Technology Department of Computer Science Germany

The increasing popularity of Large Language Models (LLMs) in recent years has changed the way users interact with and pose questions to AI-based conversational systems. An essential aspect for increasing the trustworthiness of generated LLM answers is the ability to trace the individual claims from responses back to relevant sources that support them, the process known as answer attribution. While recent work has started exploring the task of answer attribution in LLMs, some challenges still remain. In this work, we first perform a case study analyzing the effectiveness of existing answer attribution methods, with a focus on subtasks of answer segmentation and evidence retrieval. Based on the observed shortcomings, we propose new methods for producing more independent and contextualized claims for better retrieval and attribution. The new methods are evaluated and shown to improve the performance of answer attribution components. We end with a discussion and outline of future directions for the task. © 2024, CC BY-NC-ND.

关键词： Question answering

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Computing Orthogonal Projections Can Be Too computational Expensive 63

Computing Orthogonal Projections Can Be Too Computational Ex...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Boche, Holger Pohl, Volker Poor, H. Vincent Technical University of Munich School of Computation Information and Technology Müunchen80333 Germany München80799 Germany Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States

ISBN: (纸本)9798350316339

Orthogonal projections are widely used to determine optimal controllers, filters and approximations. This paper shows that despite their simple theoretical foundation and their regular use in practical applications, the complexity for computing orthogonal projections might be very high. The paper proves that the causal projection on L2 maps computable continuous functions onto not computable functions. Moreover, it is shown that the orthogonal projection associated with the polynomial approximation in L2 shows complexity blowup in the sense that it maps polynomial-time computable continuous functions onto polynomials that are not polynomial-time computable. Finally, it is shown that the coefficients of the Wiener prediction filter for stationary stochastic processes might not be computable in polynomial time, even for smooth and polynomial-time computable spectral densities. © 2024 IEEE.

关键词： Wiener filtering

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State Representations as Incentives for Reinforcement Learning Agents: A Sim2Real Analysis on Robotic Grasping

State Representations as Incentives for Reinforcement Learni...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Panagiotis Petropoulakis Ludwig Gräf Mohammadhossein Malmir Josip Josifovski Alois Knoll Department of Computer Engineering School of Computation Information and Technology Technical University of Munich Germany

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Choosing an appropriate representation of the environment for the underlying decision-making process of the reinforcement learning agent is not always straightforward. The state representation should be inclusive enough to allow the agent to informatively decide on its actions and disentangled enough to simplify policy training and the corresponding sim2real transfer. Given this outlook, this work examines the effect of various representations in incentivizing the agent to solve a specific robotic task: antipodal and planar object grasping. A continuum of state representations is defined, starting from hand-crafted numerical states to encoded image-based representations, with decreasing levels of induced task-specific knowledge. The effects of each representation on the ability of the agent to solve the task in simulation and the transferability of the learned policy to the real robot are examined and compared against a model-based approach with complete system knowledge. The results show that reinforcement learning agents using numerical states can perform on par with non-learning baselines. Furthermore, we find that agents using image-based representations from pre-trained environment embedding vectors perform better than end-to-end trained agents, and hypothesize that separation of representation learning from reinforcement learning can benefit sim2real transfer. Finally, we conclude that incentivizing the state representation with task-specific knowledge facilitates faster convergence for agent training and increases success rates in sim2real robot control. 2 2 Supplementary materials can be found on the project webpage: https://***/PetropoulakisPanagiotis/igae

关键词： Training Representation learning Knowledge engineering Decision making Reinforcement learning Grasping Vectors Numerical models Robots Cybernetics

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Fully Dynamic Maximum Independent Sets of Disks in Polylogarithmic Update Time 40

Fully Dynamic Maximum Independent Sets of Disks in Polylogar...

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40th International Symposium on computational Geometry, SoCG 2024

作者： Bhore, Sujoy Nöllenburg, Martin Tóth, Csaba D. Wulms, Jules Department of Computer Science and Engineering Indian Institute of Technology Bombay Mumbai India Institute of Logic and Computation Algorithms and Complexity Group TU Wien Austria Department of Mathematics California State University Northridge Los AngelesCA United States Department of Computer Science Tufts University MedfordMA United States Department of Mathematics and Computer Science TU Eindhoven Netherlands

ISBN: (纸本)9783959773164

A fundamental question is whether one can maintain a maximum independent set (MIS) in polylogarithmic update time for a dynamic collection of geometric objects in Euclidean space. For a set of intervals, it is known that no dynamic algorithm can maintain an exact MIS in sublinear update time. Therefore, the typical objective is to explore the trade-off between update time and solution size. Substantial efforts have been made in recent years to understand this question for various families of geometric objects, such as intervals, hypercubes, hyperrectangles, and fat objects. We present the first fully dynamic approximation algorithm for disks of arbitrary radii in the plane that maintains a constant-factor approximate MIS in polylogarithmic expected amortized update time. Moreover, for a fully dynamic set of n unit disks in the plane, we show that a 12-approximate MIS can be maintained with worst-case update time O(log n), and optimal output-sensitive reporting. This result generalizes to fat objects of comparable sizes in any fixed dimension d, where the approximation ratio depends on the dimension and the fatness parameter. Further, we note that, even for a dynamic set of disks of unit radius in the plane, it is impossible to maintain O(1 + Ε)-approximate MIS in truly sublinear update time, under standard complexity assumptions. Our results build on two recent technical tools: (i) The MIX algorithm by Cardinal et al. (ESA 2021) that can smoothly transition from one independent set to another;hence it suffices to maintain a family of independent sets where the largest one is an O(1)-approximate MIS. (ii) A dynamic nearest/farthest neighbor data structure for disks by Kaplan et al. (DCG 2020) and Liu (SICOMP 2022), which generalizes the dynamic convex hull data structure by Chan (JACM 2010), and quickly yields a "replacement" disk (if any) when a disk in one of our independent sets is deleted. © Sujoy Bhore, Martin Nöllenburg, Csaba D. Tóth, and Jules Wulms.

关键词： Data structures

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Rolling Horizon Co-evolution for Snake AI Competition 13th

Rolling Horizon Co-evolution for Snake AI Competition

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13th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2024

作者： Li, Hui Zhou, Jiayi Zhang, Qingquan Research Institute of Trustworthy Autonomous System Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9783031578076

The Snake game, a classic in the gaming world, gains new dimensions with the Snake AI competition, where two players controlled by AI algorithms can now compete simultaneously in the same game session. This competition holds significance in advancing our understanding of artificial intelligence (AI) algorithms. In the 2020 and 2021 Snake AI competitions, popular algorithms, using graph-based search or heuristic strategies, demonstrate competitive performance, such as the A* algorithm, Monte Carlo Tree Search (MCTS). Contrary to these heuristic approaches, the Rolling Horizon Co-evolution Algorithm (RHCA), characterised by its core principles of rolling horizon evaluation and co-evolution, maintains two populations, one for each player, to co-evolve with each other without reliance on heuristics. RHCA has been verified its effectiveness in a two-player spaceship game. In this paper, we extend the RHCA application to the two-player Snake AI game, comparing it with other state-of-the-art methods. Additionally, we introduce various obstacles to create different complex scenarios, ensuring a comprehensive analysis. Experimental results reveal RHCA’s superior and stable performance, especially in resource-constrained and complex scenarios. Furthermore, an analysis of RHCA’s behaviours across maps with diverse obstacle scenarios highlights its ability to make intelligent decisions in competing with state-of-the-art methods. © IFIP International Federation for Information Processing 2024.

关键词： Trees (mathematics)

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Effects of Initialization Methods on the Performance of Multi-Objective Evolutionary Algorithms

Effects of Initialization Methods on the Performance of Mult...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Gong, Cheng Pang, Lie Meng Nan, Yang Ishibuchi, Hisao Zhang, Qingfu City University of Hong Kong Department of Computer Science Kowloon Tong Hong Kong Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China

ISBN: (纸本)9798350337020

Population initialization is always needed in evolutionary multi-objective optimization (EMO) algorithms. Intuitively, a well-designed initialization method can help facilitate the evolutionary process and improve the performance of EMO algorithms. However, very few studies have investigated the effects of initialization methods on the performance of EMO algorithms. Many existing EMO algorithms randomly generate an initial population to start the evolutionary process. To fill this research gap and attract more attention from EMO researchers to this important yet under-explored issue, in this paper, we examine the effects of various initialization methods that may become promising alternatives to the commonly-used random initialization method. Each initialization method is evaluated through computational experiments on test problems of various sizes with 5-1000 decision variables. Experimental results clearly demonstrate the advantage of well-designed initialization methods over the random initialization method. This study provides useful insights into EMO algorithm design and motivates further research on population initialization. © 2023 IEEE.

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Parallel exploration via negatively correlated search

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Frontiers of computer Science 2021年第5期15卷 123-135页

作者： Peng YANG Qi YANG Ke TANG Xin YAO Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China

Effective exploration is key to a successful search *** recently proposed negatively correlated search(NCS)tries to achieve this by coordinated parallel exploration,where a set of search processes are driven to be negatively correlated so that different promising areas of the search space can be visited *** successful applications of NCS,the negatively correlated search behaviors were mostly devised by intuition,while deeper(e.g.,mathematical)understanding is *** this paper,a more principled NCS,namely NCNES,is presented,showing that the parallel exploration is equivalent to a process of seeking probabilistic models that both lead to solutions of high quality and are distant from previous obtained probabilistic *** learning,for which exploration is of particular importance,are considered for empirical *** proposed NCNES is applied to directly train a deep convolution network with 1.7 million connection weights for playing Atari *** results show that the significant advantages of NCNES,especially on games with uncertain and delayed rewards,can be highly owed to the effective parallel exploration ability.

关键词： evolutionary computation reinforcement learning exploration

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