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.

We consider a multihop sensor network in which sensors sample physical processes of interest and transmit the sample values in packets to a single destination. The destination remotely estimates the physical processes at the sensors from the received packets. The performance of remote estimation is measured in terms of the Age of Incorrect Information (AoII), which is a recently proposed goal-oriented communication metric. Our problem is to design multihop scheduling policies that minimize the average AoII (AAoII) across sensors under interference constraints. In order to characterize the minimum, we first consider centralized scheduling policies with knowledge of instantaneous AoII of the sensors. We formulate the multihop scheduling problem as a restless multiarmed bandit problem (RMAB) under the restriction that only a single packet from a sensor is allowed in the network at a time and under a complete interference model. We show that the optimal policy for a single-sensor problem obtained from the RMAB has a threshold structure and analytically characterize its AAoII. We also show that the RMAB is Whittle indexable and derive the indices in closed form. The Whittle indices are used to obtain an index policy for benchmarking multihop scheduling policies. A heuristic policy based on the index policy, which uses a belief of AAoII is then proposed. We compare the AAoII of the index policy, heuristic as well as Age-Difference policy from prior work using simulations. We also explore extensions to 𝐾-hop interference models, lossy link scenarios, and packet preemption cases. The proposed policies are shown to have better AAoII performance compared with Age-Difference policies.

Recent advancements in semantic change detection (SCD) often divide the task into two subtasks: semantic segmentation (SS) and binary change detection (BCD), typically employing three decoding heads optimized jointly based on their respective losses. However, this approach faces challenges in aligning semantic feature spaces across temporal domains, complicating the construction of change features. In this paper, we propose the novel PRO-HRSCD framework, which utilizes two decoding heads: one dedicated to BCD and another for the SS subtask. The SS subtasks for bi-temporal images are unified within a prototype-based SS (PRO-SS) decoding head, which aligns semantic features across temporal domains using online prototype learning rather than relying solely on joint optimization. The prototype-based contrastive loss targets the semantic features themselves, effectively addressing large intraclass variance and bridging the feature gap between temporal domains. Additionally, a temporal correlation exploration module (TCEM) is integrated into the BCD head, leveraging the highest resolution features of bi-temporal images encoded by HRNet to enhance temporal dependency modeling, leading to mutual reinforcement with PRO-SS. Experimental results on a benchmark dataset demonstrate that our method surpasses state-of-the-art SCD approaches. Our source code is available at https://github.com/sdust-mmlab/PRO-HRSCD.

Doppler Weather Radars (DWRs) play a crucial role in atmospheric monitoring by providing high-resolution precipitation and wind data. However, electromagnetic interference (EMI) can compromise signal quality, reducing accuracy. Traditional band-pass filters used in DWRs often have a wide frequency range, allowing unwanted signals to pass through and degrading performance. The increasing demand for compact, high-performance filters in telecommunications and radar systems necessitates innovative design approaches.

This paper introduces a novel framework that leverages ontology engineering to enhance big data science, addressing challenges such as data heterogeneity, integration, and scalability. The framework employs semantic technologies to unify diverse datasets, enabling advanced knowledge representation, reasoning, and predictive modelling. By integrating ontological methods with big data analytics, the framework demonstrates improved data integration and analysis across multiple domains. Case studies in big data management and healthcare systems illustrate the practical applications and benefits of the framework, including enhanced data quality, semantic alignment, and process efficiency. This work advances the field of big data science by providing a scalable and interoperable solution for managing complex datasets, paving the way for future research and applications in data-driven domains.

A novel, compact MIMO antenna design is introduced, measuring only 23x38 mm², suitable for ultrawideband (UWB) applications. The antenna features two square-shaped elements and a T-shaped stub with a vertical slot to minimize mutual coupling. Additional strips between the monopole elements create a band notch in the 5.15-5.85 GHz WLAN frequency range. The antenna’s performance is evaluated based on impedance matching, mutual coupling, VSWR, and correlation coefficient (ECC). Simulation results demonstrate an impedance bandwidth of 2.7-13.4 GHz (S11 ≤ -10 dB) and an envelope correlation coefficient of less than 0.002 across the frequency band. The isolation between the two antenna elements exceeds -20 dB throughout the band, covering WCDMA, WLAN, WiMax, and UWB frequency ranges.

Cloud computing technologies offer significant advantages in scalability and performance, enabling rapid deployment of applications. The adoption of microservices-oriented architectures has introduced an ecosystem characterized by an increased number of applications, frameworks, abstraction layers, orchestrators, and hypervisors, all operating within distributed systems. This complexity results in the generation of vast quantities of logs from diverse sources, making the analysis of these events an inherently challenging task, particularly in the absence of automation. To address this issue, Machine Learning techniques leveraging Large Language Models (LLMs) offer a promising approach for dynamically identifying patterns within these events. In this study, we propose a novel anomaly detection framework utilizing a microservices architecture deployed on Kubernetes and Istio, enhanced by an LLM model. The model was trained on various error scenarios, with Chaos Mesh employed as an error injection tool to simulate faults of different natures, and Locust used as a load generator to create workload stress conditions. After an anomaly is detected by the LLM model, we employ a dynamic Bayesian network to provide probabilistic inferences about the incident, proving the relationships between components and assessing the degree of impact among them. Additionally, a ChatBot powered by the same LLM model allows users to interact with the AI, ask questions about the detected incident, and gain deeper insights. The experimental results demonstrated the model's effectiveness, reliably identifying all error events across various test scenarios. While it successfully avoided missing any anomalies, it did produce some false positives, which remain within acceptable limits.

Fine-tuning large-scale neural networks for specific tasks often requires significant computational power and memory, making it an expensive and resource-intensive process. To address this issue, Parameter-Efficient Fine-Tuning (PEFT) methods have been introduced, enabling adaptation with a reduced number of trainable parameters. Among these techniques, Low-Rank Adaptation (LoRA) has gained widespread recognition for its efficiency and ease of integration. This survey provides a thorough exploration of LoRA, covering its mathematical principles, implementation techniques, and real-world applications. By comparing LoRA to traditional full fine-tuning and other PEFT methods, we highlight its ability to maintain strong performance while significantly cutting down on computational overhead. Empirical evaluations across various NLP benchmarks further demonstrate its effectiveness in reducing memory consumption without sacrificing accuracy. Beyond its advantages, we also examine the challenges associated with LoRA, such as rank selection sensitivity, compatibility with different model architectures, and potential hardware optimizations. Additionally, we discuss promising future directions, including hybrid fine-tuning strategies, dynamic rank adjustment, and expansion into multi-modal learning. By enabling more efficient adaptation of complex models, LoRA represents a crucial advancement in scalable fine-tuning. This survey aims to serve as a valuable resource for researchers and practitioners looking to leverage LoRA for optimized model training across diverse applications.

The development of high data rate communication technologies have resulted in cell densification, which in turn has led to the development of centralized radio access networks (C-RANs) followed by open radio access networks (O-RANs). The O-RAN segregates the base station into three logical entitities; the central unit (CU), the distributed unit (DU) and the radio unit (RU). The CU, DU and RU require low latency, low jitter and high data rate connections for seamless operation, which is known as X-haul. A passive optical network (PON) is a potential solution for X-haul design. Unfortunately, a PON along with its original uplink protocol is not readily suitable for the purpose. The packetization procedure of PON introduces jitter to the Xhaul bit stream. Further, the delay requirements of the X-haul limit the number of sources that can be connected to the Xhaul. Advanced features like coordinated multipoint requires synchronisation among the different X-haul bit streams as well. Therefore, in this paper, we develop an optimal uplink system that allows PON to be used as an X-haul connection technology. The proposal maximizes the throughput of the PON while conforming to the delay and synchronisation requirements. Moreover, the proposal nullifies the jitter introduced by the PON scheduler. We have performed extensive simulations for verifying our results.

Resilience is a critical factor for dynamic evacuation guidance systems, which must remain functional in harsh environments. However, most evacuation studies have seldom addressed system resilience. This study proposes a distributed dynamic evacuation guidance system that sustains functionality even when some components are damaged during evacuation, thereby enhancing the system's overall reliability and redundancy by avoiding single points of failure. We evaluated the system's performance through asynchronous multi-agent simulations to assess its effectiveness in maintaining guidance amid a spreading fire that compromises its components. The experiments revealed that the proposed system with failed components performed comparably to a fully operational system when failures occurred in response to fire severity. The adverse effects of random component failure were mitigated using two strategies: spatial interpolation and persistent guidance, resulting in performance comparable to that of a failure-free system.

The Location Service ETSI standard has evolved to closing a critical gap between the standards for positioning mechanism of 5G mobile networks and the Multi-access Edge Computing (MEC) standard framework. Location Service (LS) generally allows, among other functions, to optimize services according to users' geographical location. Although LS already offers features typical of the next generation of mobile networks, such as 3D positioning of user equipment, the LS standardization framework is still evolving to meet the demands in support of next-generation mobile networks, most notably the sixth generation paradigm. Some of the features in the current standards, for instance, the concepts of zones, carry the promise of possibly transformative LS applications in future mobile networks. For instance, LS can help the building of a "6G network of (sub-)networks", with various technology solutions, towards the fully decentralized network operation. These and the related developments are driving further evolution of MEC and LS standards to support such new features. This article addresses the evolution of current Location API standards towards 6G mobile networks. We propose extensions and improvements to the current Location API data model to include data fields to characterize novel features and the envisioned novel use cases in the next-generation mobile networks. We show that the proposed modifications allow us to abstract the user's location into a tuple of physical and network locations, allowing MEC applications to perform complex data flow optimization.