Remote latency side channels reveal sensitive information by only observing latency variations in the attacker’s traffic with the victim. While these attacks are easy to mount and scale, there is no practical defense. Considering the more powerful attacker-in-the-middle scenarios, available mitigations require the cooperation of all communication partners for protection, and cause prohibitive overheads.

In this paper, we propose AckwardDelay, a new unilateral, purely client-side, and lightweight defense against remote latency side channels. In detail, we piece-wise apply constant-time principles on the latency side channel and computationally show that a fully remote attacker requires more than 250 samples to reduce the initial search space to below 1% with our recommended parameters, even in a scenario favoring the attacker. Based on our proof-of-concept AckwardDelay implementation with less than 1000 lines of code on Linux, we demonstrate that the accuracy of website- and video-fingerprinting attacks is reduced to random guessing in practice. With an increase of only 5.55% on website-loading times and a reduction of only 0.51% in transfer rates, we conclude that AckwardDelay is a practical, lightweight, and effective mitigation applicable to the vast number of client systems such as smart phones, tablets, and laptops.

Während die Quantisierung von Modellgewichten zum Sparen von Speicherplatz Normalität ist, war das starke Verkleinern des KV-Cache bisher eher exotisch. Ein Beitrag von Google hat das Thema nun in den Mainstream gerückt.

At the HUMAN’25 workshop, four members of the hypertext research community reflected on the “hypertext as method” argument; specifically, the idea that hypertext should be understood as a method of inquiry rather than as merely a type of system. These community members presented position statements to address challenges stemming from this proposed method, including designing interfaces for LLMs, supporting annotation and note-taking as cognitive tools, evaluating AI as a collaborator in intellectual work, and applying hypertext analysis to historical information networks. These positions and the subsequent discussion contained several common themes, including a preference for augmentation over automation and concerns that generative AI may encourage users to disengage from critical thinking.

This paper addresses the challenge of real-time tool condition monitoring in tapping processes using machine learning techniques, with a focus on cross-material generalization and robust fault detection. The study leverages a historical dataset from 1998, comprising 2,195 tapping experiments on two steel alloys - 16 MnCr 5 and 42 CrMo 4 - monitoring torque (Mz) signals to predict binary quality outcomes (good/bad) based on defined quality criteria. To overcome limitations in prior work, the authors introduce a feature extraction method that captures both amplitude and duration characteristics across distinct phases of the torque signal,. The evaluation framework includes increasingly challenging train/test splits: random, run-wise holdout, and cross-material (training on 16 MnCr 5, testing on 42 CrMo 4), enabling assessment of real-world generalizability.

Multiple machine learning models are tested using both raw time-series data (after cleaning and normalization) and engineered features. Results show Matthews Correlation Coefficients (MCC) of 0.40 - 0.41 under cross-material testing-indicating moderate but meaningful generalization across materials with different mechanical properties. This performance level suggests that fundamental physical regularities in successful tapping produce consistent torque signatures, enabling transferable detection of anomalies without retraining. Findings support the feasibility of plug-and-play monitoring systems in agile manufacturing environments, where minimal setup and broad applicability are essential.

Download-Link: Revisiting 1998 Torque Data: A Machine Learning Analysis of Time Series Data for Tapping Experiments

Wer nicht in Server-GPUs investieren, aber trotzdem große Sprachmodelle selbst betreiben will, findet in Unified-Memory-Workstations eine bezahlbare Alternative. iX zeigt, wie sich Geräte aus dem AMD-, Nvidia- und Apple-Ökosystem schlagen.

Additive Manufacturing (AM) is increasingly adopted in the aerospace industry, as benefits like resource efficiency are complemented by distributed manufacturing possibilities that enhance supply chain resilience. However, replacing con ventional, established manufacturing methods with Laser Powder Bed Fusion in a highly regulated domain such as civil aviation comes at the price of increased requirements and thus costs for certification and quality assurance, which limit the attractiveness of AM. This paper presents a new data-based certification platform using Machine Learning (ML), a subdomain of artificial intelligence (AI), to enable faster and more cost-efficient design and manufacturing approval for additively manufactured aircraft components. The platform connects all relevant stakeholders and guides them through the certification process. As data sharing across different stakeholders and ML applications are central to the platform, a data governance concept aligned with European legislation, based on project-specific closed groups comprising direct supplier-customer relationships was developed. In addition, a compatible platform business model is described, presenting the roles of stakeholders and their respective value contributions. To this end, a deep dive into the landscape of current certification approaches and requirements was conducted and the impact of AM and the general use of AI on aircraft component certification was evaluated from technical, regulatory, legal, and economic perspectives

The integration of Lean Six Sigma (LSS) principles with Industry 4.0 technologies offers an effective way to improve operational performance in modern manufacturing environments. This case study presents the systematic application of LSS within an Industry 4.0 manufacturing laboratory, employing the DMAIC (Define, Measure, Analyze, Improve, Control) framework to identify and eliminate inefficiencies. In a context dominated by automation, cyber-physical systems, and data-driven decision making, the study addresses persistent challenges including inaccurate sensor readings, limited user knowledge of advanced systems, and suboptimal material storage configurations. Through a combination of statistical analysis, root-cause identification, and iterative process redesign, the intervention resulted in measurable improvements in process reliability, user experience, and overall workflow efficiency. The findings highlight how LSS can serve as an effective bridge between traditional continuous-improvement methodologies and digitalized manufacturing operations. Moreover, the outcomes position the integration of LSS as a foundational enabler for Industry 5.0, where human-centricity, resilience, and sustainability will increasingly shape manufacturing system design.

We investigate the effectiveness of anti-phishing support systems through a quantitative study involving 453 participants. To this end, we developed a tool that allows participants to immerse themselves in a realistic setting, tasked with classifying emails as either phishing or legitimate, while being assisted by support systems. Despite the prevalence of support systems in webmailers and email clients, our results indicate no significant difference in correctly assessing emails of varying difficulty between these systems and the control group. We found a minor negative effect of the support system that uses tooltips compared to other support systems. In the subsequent survey, we found that the support systems are appreciated and considered helpful by users, as supported by the results of the UEQ-S, even if they have no observable effect. Email context, such as the contact list, as well as hovering over the links, had stronger effects on the classification than the tested support systems. 

Key account management (KAM) aims to develop successful and mutually beneficial business relationships with a company’s most important customers. Considering the increasing fragmentation of value chains in business markets as well as new academic insights on value and value creation, the purpose of this study is to examine their effects on KAM and particularly who a company’s most important customers really are.

This conceptual paper aims to add to the customer value-oriented evolution of KAM. The exploratory insights gained from the case study of a supplier in the electronics industry are mainly used to illustrate the impact of multistage business markets on KAM.

Based on a case study of an electronics component supplier and by applying a multistage marketing (MSM) as well as a customer-perceived value perspective, the authors demonstrate conceptually and empirically that the KAM perspective needs to be extended along the business market value chain for the proper selection and management of key accounts. The findings also underscore the importance of aligning goal structures across both external and internal KAM processes – on the corporate and organizational unit level.

The paper contribution to the KAM research field in two ways: it broadens KAM’s approach along the business market value chain by applying MSM as well as it proposes a value-oriented conceptualization of intra- and inter-organizational alignment by integrating the newest insights on customer value and value creation.