A Multi-Class Intrusion Detection System Based on Continual Learning

With the proliferation of smart devices, network security has become crucial to protect systems and data. In order to identify and categorise different network threats, this study introduces a flow-based Network Intrusion Detection System (NIDS) based on continual learning with a CNN backbone. Using the LYCOS-IDS2017 dataset, the study explores several continuous learning techniques for identifying threats including denial-of-service and SQL injection. Unlike previous approaches, this work treats intrusion detection as a multi-class classification problem, rather than anomaly detection. The findings show how continuously learning models may identify network intrusions with high recall rates and accuracy while generating few false alarms. This study contributes to the development of an adaptive NIDS that can handle attack classification simultaneously with detection, and that can be trained online without periodic offline training. Additionally, utilising the improved version of the dataset adds value to the research on LYCOS-IDS2017 by presenting results for untested models

Energy and carbon intensity: A study on the cross-country industrial shift from China to India and SE Asia

The potential relocation of various industrial sectors from China to India and countries of the SE Asian region presents low cost opportunities for manufacturers, but also risks rising for energy demand and CO2 emissions. A cross-country shift of industrial output would present challenges for controlling emissions since India and SE Asian countries present higher industrial emissions intensity than China. We find that although there is a convergence in emissions intensity in the machinery manufacturing and paper and pulp industries, there are significant variations in all other industrial sectors. Indian emissions intensity is double that of China in the iron and steel and textile and leather industries and almost triple in the cement industry; Indonesian emissions intensity is almost double that of China in the non-metallic minerals and textile and leather industries and 50% higher in the chemical and petrochemical industry. We demonstrate that the expected higher emissions are driven by both a higher carbon fuel mix intensity in the recipient countries and higher energy intensity in their industrial activities. While industrial relocation could benefit certain countries financially, it would impose considerable threats to their energy supply security and capacity to comply with their Paris Agreement commitments

Energy-based decision engine for household human activity recognition

We propose a framework for energy-based human activity recognition in a household environment. We apply machine learning techniques to infer the state of household appliances from their energy consumption data and use rulebased scenarios that exploit these states to detect human activity. Our decision engine achieved a 99.1% accuracy for real-world data collected in the kitchens of two smart homes

Are ChatGPT and Other Similar Systems the Modern Lernaean Hydras of AI?

The rise of Generative Artificial Intelligence systems (“AI systems”) has created unprecedented social engagement. AI code generation systems provide responses (output) to questions or requests by accessing the vast library of open-source code created by developers over the past few decades. However, they do so by allegedly stealing the open-source code stored in virtual libraries, known as repositories. This Article focuses on how this happens and whether there is a solution that protects innovation and avoids years of litigation. We also touch upon the array of issues raised by the relationship between AI and copyright. Looking ahead, we propose the following: (a) immediate changes to the licenses for open-source code created by developers that will limit access and/or use of any open-source code to humans only; (b) we suggest revisions to the Massachusetts Institute of Technology (“MIT”) license so that AI systems are required to procure appropriate licenses from open-source code developers, which we believe will harmonize standards and build social consensus for the benefit of all of humanity, rather than promote profit-driven centers of innovation; (c) we call for urgent legislative action to protect the future of AI systems while also promoting innovation; and (d) we propose a shift in the burden of proof to AI systems in obfuscation cases

Preoperative tumor marking with indocyanine green prior of robotic colorectal resections

This prospective case-series study aimed to assess the usefulness of preoperative colonoscopic marking of colorectal tumors using Indocyanine Green (ICG) fluorescence in patients that underwent robotic surgical colorectal resections. Consecutive patients that were eligible for colorectal resection with intent to cure in a single hospital (Athens Medical Center), from February 2022 to June 2022, were included. ICG solution was injected into the submucosal layer at 2 opposite sites (180 degrees apart) distal to the tumor, without submucosal elevation. Identification of the tumor marking was then performed after switching to near-infrared (NIR) fluorescence mode. During the robotic procedure, qualitative evaluation of fluorescence was performed by the surgical team (primary surgeon, first assistant, second assistant, research fellow). All 10 patients underwent robotic surgical approach and operations included right-sided colectomy (n = 1), left-sided colectomy (n = 6) and low anterior resection (n = 3). Visualisation of this dye with near-infrared light was very clear with bright intensity in all patients when the marking was performed one day prior of surgery. Preoperative tumor marking with ICG was identified intraoperatively in all cases and the techinque was easily reproducible

Sustainable Business Models for Sustainable Concrete – The Triple Layered Proposition

Addressing the growing need for sustainability, novel concrete solutions become increasingly popular for mitigating the negative environmental impacts found in cement production, such as high CO2 emissions output and raw materials overuse, providing conventional concrete products alternatives. The industry is lacking a common analytical framework for business models to clearly define sustainable concrete value streams present across economic, environmental, and social layers. Our research utilises the Triple-Layer Business Model Canvas (TL-BMC) to analyse a piloted sustainable concrete product (CIRCLE), describes its multi-layered value, and effectively provides the common framework for sustainable concrete business model adaptation. We conclude that the Triple-Layered Business Model Canvas (TL-BMC) is the most appropriate framework that enables the identification and establishment of successful business models focused on sustainable concrete

Design of MW-Class Coaxial Gyrotron Cavities With Mode-Converting Corrugation Operating at the Second Cyclotron Harmonic

This article presents investigations on the design of coaxial gyrotron cavities with mode-converting corrugations, operating at the second harmonic of the electron cyclotron frequency with output power of the order of megawatts. The suppression of the competing modes interacting at the fundamental cyclotron frequency is achieved by the combination of a corrugated coaxial insert and mode-converting corrugation on the outer wall. The outer corrugation couples the key competing modes to lower order modes with reduced quality factor. The design steps, which form a generally applicable design procedure, are described in detail. As an illustrative example, the proposed procedure is used for the design of a cavity for a fusion-relevant, second-harmonic MW-class gyrotron, operating at 170 GHz with the TE 37,1837,18 mode. From the simulations, it is found that for the proposed design, this mode is excited with an output power of around/ ∼ 1.5 MW. Two additional paths for cavity optimization toward even higher output power are also presented