PROTECTION: Provably Robust Intrusion Detection system for IoT through recursive Delegation

The security of Internet of Things (IoT) ecosystems is crucial for maintaining user trust and facilitating widespread adoption. Machine Learning (ML) based Intrusion Detection and Prevention Systems (IDS/IPS) are frequently used to protect IoT networks, yet they are susceptible to adversarial attacks (AAs) and lack formal verifiability of their robustness. It has been demonstrated that meticulously designed AAs can alter the classification of ML-based IDSs, rendering them ineffective and posing risks to lives and physical infrastructure in safety-critical systems. This paper addresses these issues by introducing PROTECTION: a Provably RObust Intrusion DeTECTion system for IoT through recursive delegatION, which combines formal methods with ensemble machine learning. To enhance the robustness of ensemble ML models, we utilise Satisfiability-Modulo-Theory (SMT) to formally verify the classifier’s robustness, ensuring that output probabilities remain outside a thick decision boundary even when small perturbations are applied to the inputs. If a classifier fails to meet this criterion on any training sample, we reassign the training task to other classifiers that are iteratively trained until all samples are trained in accordance with the required property. The efficacy of the final ensemble model is thoroughly tested against various input perturbations and AAs using SMT based formal verification

UC-PUAL: A universally consistent classifier of positive-unlabelled data

Positive-unlabelled (PU) learning is a challenging task in pattern recognition, as there are only labelled-positive instances and unlabelled instances available for the training of a classifier. The task becomes even harder when the PU data show an underlying trifurcate pattern that positive instances roughly distribute on both sides of ground-truth negative instances. To address this issue, we propose a universally consistent PU classifier with asymmetric loss (UC-PUAL) on positive instances. We also propose two three-block algorithms for non-convex optimisation to enable UC-PUAL to obtain linear and kernel-induced non-linear decision boundaries, respectively. Theoretical and experimental results verify the superiority of UC-PUAL. The code for UC-PUAL is available at https://github.com/tkks22123/UC-PUAL

Study on Failure Mechanism and Dynamic Response of RC Shear Wall in Tall Buildings under Impact Load

Currently, there are few studies on the impact resistance of reinforced concrete (RC) shear walls in tall buildings. To this end, the dynamic response and failure mode of RC shear walls under impact load were investigated experimentally and numerically. Six specimens were tested using a specialized pendulum impact rig. The parametric study was conducted to reveal the effects of wall height, impact position, reinforcement ratio, drop height, and energy consumption. Based on the experimental results, an analytical model is established to predict the maximum displacement under impact load. Furthermore, more parameters were quantified by the verified numerical model using LS-DYNA. The obtained results show that the drop height and reinforcement ratio have a significant effect on the peak impact force. When the impact energy is constant, the energy absorption performance of the specimen is negatively correlated to the overall wall stiffness. The parametric results of LS-DYNA show that an increment of the axial compression ratio and wall width will significantly reduce the maximum displacement at the center of the wall. When the impact energy is low, increasing the impact velocity has a more significant effect on the displacement difference than the impact mass

Inverse Machine Learning for the Design of Perforated Beams: Parent Section and Material Prediction

This study introduces a novel machine learning-based inverse design methodology for predicting the parent cross-sectional and material properties of perforated steel beams with elliptically-based openings. Unlike conventional forward design or optimization-based methods, the proposed ap-proach frames structural design as an inverse problem. It enables the direct mapping of opening ge-ometry and resistance parameters to essential properties such as section depth, flange width, web thickness, flange thickness, and yield strength. Five advanced supervised machine learning mod-els— Histogram-Based Gradient Boosting, Random Forest, Extreme Gradient Boosting, k-Nearest Neighbours, and Support Vector Regression—were trained on a dataset generated through forward structural analysis. This methodology develops non-iterative surrogate models that enhance the gen-eralizability and accessibility of the structural design process. The models demonstrated excellent predictive performance, with coefficient of determination (R²) values exceeding 0.99 for most out-puts. Shapley Additive Explanations (SHAP) analysis identified web-post buckling resistance and section height as the most influential input features, with other variables contributing depending on the output. The proposed inverse learning framework was also benchmarked against an analytical design model to assess accuracy and consistency. To support practical implementation, a user-friendly web tool was developed, allowing engineers and researchers to access instant prediction. Overall, this research offers an efficient and explainable data-driven solution for structural design, demonstrating the practical value of artificial intelligence in engineering applications

Using the Rate of Global and Pointwise Microperimetry Change to Predict Structural Conversion in Intermediate Age-Related Macular Degeneration

Purpose Studies evaluating functional change in age-related macular degeneration (AMD) using microperimetry often measure the difference in global mean sensitivity at interval time points versus baseline. We evaluate the rate of global and pointwise microperimetry change in intermediate AMD (iAMD) in the multicenter MACUSTAR (Registration NCT03349801) study and assess their prognostic value in structural conversion to late-stage AMD. Design Prospective study. Subjects Four hundred forty-seven subjects with iAMD (Beckman classification) from 20 European sites. Methods Subjects that underwent mesopic microperimetry on ≥3 follow-up visits were included. Two methods of assessing functional progression were evaluated: (1) global mean sensitivity regression and (2) pointwise sensitivity regression at fastest progressing N number of locations (N from 1 to 10). Rates of microperimetry progression were then evaluated in an initial series of visits prior to structural conversion to late-stage AMD. Main Outcome Measures Area under the receiving operating characteristic (AUC) curves and Cox proportional hazard models were used to assess risk of structural conversion based on rate of functional progression. Results The mean age of subjects was 72 (standard deviation 7) years. The median number of visits and duration of follow-up was 6 visits and 3 years, respectively. Structural conversion to late-stage AMD was observed in 80 (17.9%) eyes. In the visits prior to conversion, there was a greater rate of global mean sensitivity loss in eyes that eventually developed late-stage AMD compared with those that did not (–1.05 vs. –0.30 decibels/year, P < 0.001). The AUC for classifying structural conversion versus no conversion was 0.72 for global sensitivity progression and 0.75–0.76 for between 1 and 10 fastest progressing N pointwise locations. The rate of global (hazard ratio 1.7, confidence interval [CI] 1.4–2.0) and pointwise (hazard ratio 1.2, CI 1.2–1.3) microperimetry progression in the initial series of visits was significantly associated with structural conversion (P < 0.0001). Conclusions In the analysis of longitudinal microperimetry data from the MACUSTAR study, the rate of global and pointwise sensitivity change was significantly greater and strongly prognostic of eyes that developed structural conversion. Our findings support use of these trend-based pointwise analysis methods in assessing functional progression in iAMD. Financial Disclosure(s) Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article

Pre-entry experience and the heterogeneity in startup performance: Evidence from the nascent artificial intelligence industry

We examine the performance differences among startups in nascent industries, taking account of the distinct knowledge contexts from which they arise. Specifically, we investigate the effect of pre-entry experience on the performance of startups originating within the same industry (i.e. inside–industry spinouts) and those from related knowledge contexts along the value chain (i.e. outside–industry spinouts). Analyzing a novel dataset that includes all U.S. artificial intelligence industry startup entrants during the period 1980 to 2014, we find that inside–industry spinouts and outside–industry spinouts have comparable survival and successful exit rates, outperforming startups with no pre-entry experience related to AI. Exploring the heterogeneity among outside–industry spinouts, we also find that the higher survival rate of this category of entrants is driven by startups founded by individuals who previously worked for firms operating in upstream supplier industries. We discuss the implications of our findings for research on strategy and industry evolution

Learning critical thinking skills with online bite-sized videos: a qualitative account of students’ perceptions

Learning to think critically is a key educational goal for higher education that presents a significant challenge for many students. Surprisingly, few studies have reported students’ views and perceptions towards instructional methods in critical thinking. The current study explored university students’ experiences and perceptions towards an online intervention designed to improve critical thinking skills. The intervention employed video-based learning to introduce four common informal logical fallacies to students across two micro-learning episodes administered online. We conducted semi-structured interviews with 30 university students to gain insight into four key areas: i) the perceived usefulness of the intervention for critical thinking development, ii) the presentation of learning materials, iii) the factors impacting their engagement, iv) and the potential of this approach to support mainstream provisions. We identified four main themes using thematic analysis: 1) building understanding and awareness of critical thinking, 2) effective video design and presentation, 3) valuing technology-enhanced learning approach, and 4) divergent experiences derived from the practice phase. These themes encapsulate students’ experiences of learning critical thinking as a highly sophisticated skill within an online learning environment and their preferences towards an effective video design. We discuss the implications of these findings for future pedagogical research and training of critical thinking in higher education

Mechanism and control method of anti-collapse resistance of novel bolted endplate joints

In this paper, a novel end-plate joint was proposed, which was designed to implement the three-stage control concept against progressive collapse. It delays the collapse process by presetting friction force, special-shaped bolt hole and adding composite cushion at the bottom of bolt hole. The anti-collapse mechanism and control strategy of the structure under the influence of multiple factors were studied by means of beam-column subassembly test and numerical and theoretical analysis. The results show that the initial stiffness of the load-displacement curve of the new bolted joint subassembly is small, but the yield plateau is long and the failure load is large. The curve has obvious three-stage characteristics, and the catenary mechanism is fully developed. That is, the collapse process of the structure was divided into three stages: “small impact”, “medium impact” and “large impact”. On this basis, the corresponding three-stage anti-collapse control strategy was proposed and verified by finite element model. During the “small impact” stage, mainly based on the reasonable initial stiffness requirements, the friction coefficient is changed through the coating of the steel plate, meanwhile the bolt preload is adjusted to determine the preset friction force of the joint, so as to meet the “small impact immovable” requirement. During the “midium impact” stage, the preset extrusion force is mainly determined by changing the ratio of the “jammed diameter” to the bolt diameter based on the reasonable slip distance and the reasonable extrusion force, so as to meet the requirement that the “midium impact stuck”. In the stage of “final impact”, the preset structure's ability to resist progressive collapse is determined by changing the performance of the composite cushion at the bottom of the bolt hole, according to the requirements of reasonable progressive collapse resistance of the structure, so as to realize the requirements of “final impact non-collapse” or delay the process of progressive collapse of the structure. The results of substructure model test and finite element analysis show that the proposed three-stage anti-continuous collapse control strategy was easy to implement, and the mechanism and capacity of anti-continuous collapse were improved obviously, which has a good research and application prospect

FedCLAM: Client Adaptive Momentum with Foreground Intensity Matching for Federated Medical Image Segmentation

Federated learning is a decentralized training approach that keeps data under stakeholder control while achieving superior performance over isolated training. While inter-institutional feature discrepancies pose a challenge in all federated settings, medical imaging is particularly affected due to diverse imaging devices and population variances, which can diminish the global model's effectiveness. Existing aggregation methods generally fail to adapt across varied circumstances. To address this, we propose FedCLAM, which integrates \textit{client-adaptive momentum} terms derived from each client's loss reduction during local training, as well as a \textit{personalized dampening factor} to curb overfitting. We further introduce a novel \textit{intensity alignment} loss that matches predicted and ground-truth foreground distributions to handle heterogeneous image intensity profiles across institutions and devices. Extensive evaluations on two datasets show that FedCLAM surpasses eight cutting-edge methods in medical segmentation tasks, underscoring its efficacy

Gendering the safety net: Social protection policy and the limits to Decent Work in Cambodia’s garment sector

The adoption of the Social Protection Floors Recommendation (SPFR) by the International Labour Conference in 2012 is widely recognised as an “historic” (Deacon 2013) and “radical” (Cichon 2013) reorientation of social protection, promising a new “universal and comprehensive” approach. Despite the SPFR’s bold ambitions, however, the implementation of social protection floors at global- and national-level has proven uneven. In practice, the social protection floors initiative has generally been “subordinate” (Seekings 2019) to the Decent Work agenda. Particularly in many lower-income settings in the global South, for instance, vertical expansion of benefits to waged workers through social insurance has taken precedence over the SPFR’s more radical promise to horizontally expand the frontiers of social assistance. In Cambodia, for example, entrenched norms of fiscal and social conservativism have focused policy attention on expanding benefits provided to the 700,000 workers in the country’s largest formal industry – the garment sector – rather than expanding the scope of social protection to include the yet more numerous informal or agricultural sector workforce. In this paper, we examine the consequences of this lopsided social protection strategy for its apparent beneficiaries: women working within the garment industry. We argue that the focus on extending support for formal workers, at the exclusion of informal workers is, in fact, detrimental to both groups. To illustrate these arguments, we draw on original data from the GCRF-funded ReFashion project, a longitudinal study tracing the impacts of the Covid-19 pandemic on a cohort of 200 garment workers in Cambodia over 24 months. We use this rich and grounded data to develop an emic perspective on social protection programming that shows how, in the absence of a robust social protection floor, gendered norms in Cambodia compel women to fill the gaps in social protection programming by the state. Women workers in the garment sector effectively fund a social safety net for family members through remittance transfers. However, garment sector salaries alone are insufficient for this task, leading to a “debtfare” (Soederberg 2014) model, in which workers finance these costs through increasing resort to personal debt. The result is a crisis of over-indebtedness among workers in the garment industry that undermines the achievement of Decent Work in the sector. We suggest that Covid-19 offers a moment for reflection, like that which followed the Global Financial Crisis of 2008 and inspired the SPRF itself, to learn from the vulnerabilities exposed by the pandemic and recentre a radical vision of social protection that delivers for all