Executable QR codes, also known as eQR codes or just sQRy, are a special kind of QR codes that embed programs conceived to run on mobile devices like smartphones. Since the program is directly encoded in binary form within the QR code, it can be executed even when the reading device is not provided with Internet access. The applications of this technology are manifold, and range from smart user guides to advisory systems. The first programming language made available for eQR is QRtree, which enables the implementation of decision trees aimed, for example, at guiding the user in operating/maintaining a complex machinery or for reaching a specific location. In this work, an additional language is proposed, we term QRind, which was specifically devised for Industry. It permits to integrate distinct computational blocks into the QR code, e.g., machine learning models to enable predictive maintenance and algorithms to ease machinery usage. QRind permits the Industry 4.0/5.0 paradigms to be implemented, in part, also in those cases where Internet is unavailable.

In this letter, an error is identified in Proposition 3 presented in Björnson et al. (2020) which includes an erroneous expression to compute the optimal number of reflecting elements that minimizes the total power consumption and maximizes the energy efficiency in intelligent reflecting surface (IRS)-supported transmission. The correct version of this expression and a few comments are provided in this letter, and simulation results are also presented to support this claim.

This article analyzes cyberattacks against critical infrastructures, focusing on railway, aeronautical, and industrial SCADA (Supervisory Control and Data Acquisition) systems. The study explores the inherent vulnerabilities of these systems, including insecure protocols, exposure of HMI interfaces, attacks on communication networks, and exploitation of zero-days. The study details attack techniques, tools used, and real-world examples, such as attacks on railway signaling, exploitation of onboard systems in aircraft, and industrial sabotage conducted by the Stuxnet malware. The analysis of the Cyber Kill Chain demonstrates how an attacker can compromise these infrastructures, from reconnaissance to attack execution. Finally, mitigation measures are discussed, such as network segmentation, implementation of encryption in industrial communications, continuous monitoring, and regular security audits, highlighting the growing need to protect strategic infrastructures against sophisticated cyber threats.

Event-Related Potentials are crucial for analyzing brain activity in EEG recordings. However, latency variability distorts averaged signals, necessitating advanced alignment strategies. Traditional cross-correlation methods use constant displacements but lack subsample accuracy and fail to capture non-linear temporal shifts. This study evaluates different pre-processing strategies for ERP single-trial matrices before alignment, including a novel iterative and robust ERP reference selection method and optimized filtering strategies such as non-linear, anisotropic diffusion. Variational 1D alignment, which allows non-flat displacements with subsample accuracy, is applied to synthetic and real EEG datasets, including P300 oddball experiments (visual and auditory). Variational alignment with optimized filtering and reference selection preserves signal morphology and enhances signal gain to match the ground truth in synthetic data. The structure of non-linear displacements contains peak-to-peak variability, allowing signal reconstruction without loss of critical temporal features. Applying this method to real EEG data reduces amplitude differences caused by jitter while maintaining key ERP components. This method significantly improves ERP alignment, potentially enhancing BCI applications, resolution, and pattern recognition, particularly in later-stage ERPs. Additionally, explicit 1D displacement estimation enables time-resolved jitter variance analysis. This approach may reduce auditory brainstem response measurement times and extend to EEG-like data, including OPM and MEG evoked potentials, aiding in ERP-based diagnostics.

Probabilistic Neural Contextualization introduces a groundbreaking approach that bridges probabilistic reasoning with neural architectures to redefine contextual knowledge retrieval in computational models. By integrating dynamic contextualization mechanisms, it achieves enhanced adaptability and precision in managing uncertainty across diverse linguistic domains. The novel architecture incorporates advanced probabilistic inference layers, ensuring that outputs are consistently aligned with the complex demands of varied contextual scenarios. Comprehensive evaluations demonstrated marked improvements in key performance metrics, including perplexity and F1-score, validating the model's robustness and scalability. Experimental results further revealed its computational efficiency, maintaining performance consistency even under significant data variability and noise. Through seamless integration with open-source frameworks, the proposed methodology demonstrates its practical viability for large-scale applications, addressing critical challenges in knowledge-driven processing tasks. The study highlights a transformative advancement in contextual modeling, paving the way for more reliable, efficient, and intelligent computational systems capable of operating in complex and dynamic environments.

In an era where personalized viewing xperiences are key, this new-generation OTT platform introduces a groundbreaking feature: the Personalized "Watch Party" Mode with AI Hosts. This feature allows users to engage in a shared viewing experience tailored to a group's diverse content preferences and moods. The AI Host gathers input from all participants, processing their mood and genre preferences to recommend a movie that suits the group. Additionally, the AI Host enriches the experience by setting the atmosphere and plot before the movie, providing interactive commentaries, and guiding viewers throughout the watch party. Fully voice-enabled, the AI Host offers an immersive, realtime experience akin to a human host, redefining group movie nights with intelligent and personalized interaction.

Since the launch of ChatGPT, Generative AI (GenAI) has transformed virtually every domain, addressing complex problems in areas previously considered insurmountable. At the same time, the adoption of cloud computing for storing confidential data has been rapidly increasing. This swift shift to the cloud has highlighted the critical need for robust data protection measures. In this paper, we merge these two emerging fields to examine how GenAI influences cloud data security. While research on traditional AI-based solutions for cyber threats has been ongoing, the recent surge in GenAI adoption calls for a focused, systematic study to address specific research questions related to GenAI. We formulate 9 research questions and analyze data from 4 major digital repositories (IEEE, Springer, ACM, Wiley) to provide an extensive overview of how GenAI contributes to mitigating key cloud security threats such as Malware, Denial-of-Service (DoS), and Phishing, among others. We also discuss the potential risks posed by GenAI in cloud security and highlight the operational and ethical challenges that impede its widespread adoption. This systematic study aims to provide valuable insights for fellow researchers and IT professionals, guiding them in effectively leveraging GenAI to enhance cloud data security while being mindful of its limitations and challenges.

This paper considers cellular mmWave networks with full-frequency reuse where base stations (BSs) use multiuser analog beamforming to serve users (UEs) in transmission blocks of time, divided into slots. We propose a beam scheduling scheme that guarantees UEs' target rates, using the smallest possible number of slots at each BS in each transmission block. We formulate the beam scheduling task as a stochastic mixed-integer nonconvex time allocation problem, allocating time to UE-BS links, and provide a novel iterative algorithm that solves the problem. In each iteration there are three stages. Stage one optimizes the time fraction allocation for UE-BS links given an estimate of UE rates in each transmission block. Stage two allocates these time fractions to the RF chains at each BS using a sand-filling policy. Stage three schedules beams, and updates the estimate of UE rates in each block. Numerical results show that our proposed scheme significantly outperforms round-robin (RR) baseline schemes. We provide feasible UE scheduling in close to 100% of cases, compared to 44% for the RR scheme, and our median BS utilization is 68% lower than RR.

In the face of current modular robots that rely on manual coding to generate deformation instructions and are limited by binary instructions, this research proposes a Cognitive Modular Control (CMC) architecture inspired by ACT-R theory. Build simulation tool ReoForm to conduct experiments through simulation-based experimental design. The data results show that each module of the architecture demonstrates feasibility and universality in terms of time-to-completion, energy consumption, manipulability index and robustness indicators.

The k-Nearest Neighbors (kNN) algorithm is a widely-utilized nonparametric and lazy learning technique commonly applied in classification tasks. Its simplicity and effectiveness make it an attractive option for various applications, including remote sensing. This project focuses on using kNN to classify the central pixel in a 3x3 neighborhood within a multi-spectral Landsat satellite image. Each pixel in the dataset is represented by four spectral bands, and the goal is to predict the class of the central pixel based on its neighbors' spectral values. This study provides an overview of the kNN algorithm, detailing its core concepts, operational principles, and applications specific to satellite imagery classification. We explore the influence of the parameter k, the choice of distance metric, and the impact of different weighting schemes on the algorithm's performance. By comparing kNN's effectiveness with traditional statistical methods, this project aims to demonstrate the potential of kNN in handling diverse remote sensing data types and enhancing pixel classification accuracy.

Low Earth Orbit (LEO) satellite networks serve as a cornerstone infrastructure for providing ubiquitous connectivity in areas where terrestrial infrastructure is unavailable. With the emergence of Direct-to-Cell (DTC) satellites, these networks can provide direct access to mobile phones and IoT devices without relying on terrestrial base stations, leading to a surge in massive connectivity demands for the serving satellite. To address this issue, group communication is an effective paradigm that enables simultaneous content delivery to multiple users and thus optimizes bandwidth reuse. Although extensive research has been conducted to improve group communication performance, securing this communication without compromising its inherent spectrum efficiency remains a critical challenge. To address this, we introduce StarCast, a secure group encryption scheme for LEO satellite networks. Our solution leverages ciphertext-policy attribute-based encryption (CP-ABE) to implement fine-grained access control by embedding access policies directly within the ciphertext. Unlike standard secure communication approaches that require dedicated per-user channels and significantly deplete limited satellite spectrum resources, StarCast maintains efficient spectrum reuse within user groups while ensuring that only authorized users can access transmitted data. Additionally, it significantly reduces the costly key management overhead associated with conventional encryption schemes.

Wireless sensing has made significant progress in tasks ranging from action recognition, vital sign estimation, pose estimation, etc. After over a decade of work, wireless sensing currently stands at the tipping point transitioning from proofof-concept systems to the large-scale deployment. We envision a future service scenario where wireless sensing service providers distribute sensing models to users. During usage, users might request new sensing capabilities. For example, if someone is away from home on a business trip or vacation for an extended period, they may want a new sensing capability that can detect falls in elderly parents or grandparents and promptly alert them. In this paper, we propose CCS (continuous customized service), enabling model updates on users' local computing resources without data transmission to the service providers. To address the issue of catastrophic forgetting in model updates-where updating model parameters to implement new capabilities leads to the loss of existing capabilities-we design knowledge distillation and weight alignment modules. These modules enable the sensing model to acquire new capabilities while retaining the existing ones. We conducted extensive experiments on the large-scale XRF55 dataset across Wi-Fi, millimeter-wave radar, and RFID modalities to simulate scenarios where four users sequentially introduced new customized demands. The results affirm that CCS excels in continuous model services across all the above wireless modalities, significantly outperforming existing approaches like OneFi [1].

Low-code and no-code platforms have revolutionized software development by leaps and bounds. These tools have enabled individuals with minimal coding experience to create and deploy applications quickly and efficiently. These platforms offer ready to use plugins to accelerate development along with options such as drag-and-drop components, pre-built templates to ease accessing various libraries. The tools help software developers to create software programs at speed unlike traditional methods of manually writing every piece of code. This paper outlines the impact of lowcode/no-code platforms, including their function in speeding innovation and reducing development costs, as well as addressing the growing demands of digital transformation. At the same time, it looks at the challenges and obstacles from scalability through security to customization that organizations need to consider when implementing these platforms. Furthermore, the potential future of low-code/no-code development in forging collaboration between technical and non-technical teams in software development. Ultimately, these platforms will bridge the gap between company needs and IT solutions much faster. However, their integration into enterprise settings must be carefully managed so as to ensure sustainability as well as efficacy.

In this paper, we provide informed arguments for using columnar in-memory database technology, in particular the Hyrise database, for the high-performant implementation of the highly combinatorial data mining tool GrandReport. In service of that we provide a targeted review of columnar databases, a targeted review of columnar in-memory databases, and a comparison of nine established columnar in-memory databases. On the basis of this, we discuss advantages of using the Hyrise database as the future implementation platform for the GrandReport tool.

A document by Maria Elena Valcher. Click on the document to view its contents.

The escalating complexity and adaptability of ransomware attacks have exposed critical gaps in traditional detection models, which struggle to effectively identify evolving threat behaviors without a significant margin of error. The Adaptive Pattern Scoring Mechanism (APSM) offers a novel approach to this challenge, leveraging a dynamic scoring framework that recalibrates based on real-time behavioral anomalies, enabling enhanced precision in distinguishing ransomware activities from benign events. APSM addresses inherent limitations in signaturebased and heuristic methods by integrating an adaptive scoring algorithm, which continuously adjusts detection thresholds and assigns differential weights across key behavioral dimensions, achieving a robust balance between detection accuracy and resource efficiency. Empirical evaluation reveals APSM's high accuracy and precision rates, with lower false-positive rates than conventional approaches, indicating its significant capability to detect ransomware with minimal operational disruption. The integration of dynamic scoring not only optimizes APSM's performance in real-time environments but also enables scalability across high-load conditions, marking a substantial advancement in automated cybersecurity infrastructures. Ultimately, APSM provides a sophisticated, resource-efficient solution that aligns closely with the requirements of modern ransomware detection, showing its value as a responsive tool in proactive threat management.

This paper serves as a comprehensive guide for practitioners and scholars aiming to understand the channel coding and decoding schemes integral to the 5G NR standard, with a particular focus on LDPC and polar codes. We start by explaining the design procedures that underlie these channel codes, offering fundamental information from the perspectives of both encoding and decoding. In order to determine the present status of research in this area, we also provide a thorough literature review. Notably, we add comprehensive, standard-specific information to these foundational evaluations that is frequently difficult to extract from technical specification documents. The significance of reviewing and refining the foundations of the aforementioned codes lies in their potential to serve as candidate error-correcting codes for the future 6G standard and beyond.

This paper presents optimal reference voltage saturation (ORVS) strategies for nonlinear current control of synchronous machine drives. The ORVS are formulated as analytically solvable optimization problems which maximize the feasible current change in the presence of voltage constraints due to the limited dc-link voltage of voltage-source converters (VSCs). Two ORVS strategies are proposed: one for circular voltage constraint and one for hexagonal voltage constraint, enabling full utilization of the voltage hexagon. The effectiveness of the proposed ORVS are validated through laboratory experiments conducted with a highly nonlinear, anisotropic reluctance synchronous machine (RSM) and an interior permanent magnet synchronous machine (IPMSM). The performance of ORVS under both circular and hexagonal voltage constraints are compared with classical reference voltage saturation (CRVS). The experimental results demonstrate that the proposed ORVS outperform CRVS regarding faster current dynamics and current tracking accuracy with significantly reduced cross-coupling effects.