On Using Embeddings for Ownership Verification of Graph Neural Networks

Graph neural networks (GNNs) have emerged as a state-of-the-art approach to model and draw inferences from large scale graph-structured data in various application settings such as social networking. The primary goal of a GNN is to learn an embedding for each graph node in a dataset that encodes both the node features and the local graph structure around the node. Prior work has shown that GNNs are prone to model extraction attacks. Model extraction attacks and defenses have been explored extensively in other non-graph settings. While detecting or preventing model extraction appears to be difficult, deterring them via effective ownership verification techniques offers a potential defense. In non-graph settings, fingerprinting models, or the data used to build them, have shown to be a promising approach toward ownership verification. We hypothesize that the embeddings generated by a GNN are useful for fingerprints. Based on this hypothesis, we present GrOVe, a state-of-the-art GNN model fingerprinting scheme that, given a target model and a suspect model, can reliably determine if the suspect model was trained independently of the target model or if it is a surrogate of the target model obtained via model extraction. We show that GrOVe can distinguish between surrogate and independent models even when the independent model uses the same training dataset and architecture as the original target model. Using six benchmark datasets and three model architectures, we show that GrOVe consistently achieves low false-positive and false-negative rates. We demonstrate that GrOVe is robust against known fingerprint evasion techniques while remaining computationally efficient

GrOVe: Ownership Verification of Graph Neural Networks using Embeddings

Graph neural networks (GNNs) have emerged as a state-of-the-art approach to model and draw inferences from large scale graph-structured data in various application settings such as social networking. The primary goal of a GNN is to learn an embedding for each graph node in a dataset that encodes both the node features and the local graph structure around the node. Embeddings generated by a GNN for a graph node are unique to that GNN. Prior work has shown that GNNs are prone to model extraction attacks. Model extraction attacks and defenses have been explored extensively in other non-graph settings. While detecting or preventing model extraction appears to be difficult, deterring them via effective ownership verification techniques offer a potential defense. In non-graph settings, fingerprinting models, or the data used to build them, have shown to be a promising approach toward ownership verification. We present GrOVe, a state-of-the-art GNN model fingerprinting scheme that, given a target model and a suspect model, can reliably determine if the suspect model was trained independently of the target model or if it is a surrogate of the target model obtained via model extraction. We show that GrOVe can distinguish between surrogate and independent models even when the independent model uses the same training dataset and architecture as the original target model. Using six benchmark datasets and three model architectures, we show that consistently achieves low false-positive and false-negative rates. We demonstrate that is robust against known fingerprint evasion techniques while remaining computationally efficient.Comment: 11 pages, 5 figure

Luminal Diameters of Major Coronary Arteries

Abstract Background: To measure the luminal diameters o

Assessment of multi-components and sectoral vulnerability to urban floods in Peshawar – Pakistan

Over the last two decades, urban floods and their impacts have been on the rise worldwide, owing to both climatic changes and human activities. The present study examines different at-risk elements, such as residential, commercial, and critical facilities, to evaluate their multi-components of vulnerability to urban floods in Peshawar, Pakistan. Based on the impacts of urban floods, the weightage of each component of the vulnerability for the selected elements at risk is defined. This study presents and uses the modified Fisher's ideal quantity index to combine the different vulnerability components into a single value. Additionally, the Patnaik and Narayan vulnerability index is employed to generalize sector-wise vulnerabilities across the study area. The results show that the old physical infrastructure of commercial and manufacturing units in the Kohati Gate area is highly vulnerable to urban floods, while the residential units are the least susceptible due to their distanced location from the drainage system. In Hayatabad, encroachments along the torrent's sides, affecting housing and educational institutions, contributed to increased vulnerability to urban floods, despite their relatively lower physical vulnerability. The study provides a new platform for understanding the multi-components of vulnerability to urban floods and tackling the challenges posed by urban floods effectively

Genome-Wide Identification and Expression Analysis of SnRK2 Gene Family in Dormant Vegetative Buds of Liriodendron chinense in Response to Abscisic Acid, Chilling, and Photoperiod

Protein kinases play an essential role in plants’ responses to environmental stress signals. SnRK2 (sucrose non-fermenting 1-related protein kinase 2) is a plant-specific protein kinase that plays a crucial role in abscisic acid and abiotic stress responses in some model plant species. In apple, corn, rice, pepper, grapevine, Arabidopsis thaliana, potato, and tomato, a genome-wide study of the SnRK2 protein family was performed earlier. The genome-wide comprehensive investigation was first revealed to categorize the SnRK2 genes in the Liriodendron chinense (L. chinense). The five SnRK2 genes found in the L. chinense genome were highlighted in this study. The structural gene variants, 3D structure, chromosomal distributions, motif analysis, phylogeny, subcellular localization, cis-regulatory elements, expression profiles in dormant buds, and photoperiod and chilling responses were all investigated in this research. The five SnRK2 genes from L. chinense were grouped into groups (I–IV) based on phylogeny analysis, with three being closely related to other species. Five hormones-, six stress-, two growths and biological process-, and two metabolic-related responsive elements were discovered by studying the cis-elements in the promoters. According to the expression analyses, all five genes were up- and down-regulated in response to abscisic acid (ABA), photoperiod, chilling, and chilling, as well as photoperiod treatments. Our findings gave insight into the SnRK2 family genes in L. chinense and opened up new study options

Comparative growth response related to hair mineral analysis in dromedary camel calves

Background: The dromedary camel plays significant role in supporting livelihood of pastoral and agro-pastoral systems as well as a source of income generation and national economy in arid regions. Aim: Current study was executed to check the comparative growth response relative to hair mineral status in Marecha camel calves reared under intensive (IMS) and extensive (EMS) management system at Thal desert Punjab, Pakistan. Methods: Twelve male and female Camelus dromedarius calves of almost same weight and age were divided into two groups of 6 (3 male and 3 female). The calves of first group were maintained at Camel Breeding and Research Station Rakh Mahni in semi-open housing system while the second group in available housing under field conditions. The first group calves were fed concentrate at 1 kg/head/day along with gram straw (Cicer arientinum) ad libitum while in second group calves were allowed grazing/browsing for 10 hours daily along with household refusals including kitchen wastes. Watering was done twice a day. Impressum digital weighing scale was used for fortnightly weighing. Data collected on different parameters was subjected to statistical analysis with 2×2 factorial arrangements of treatments under completely randomized design. Results: After 120 days’ trial period the mean body weight and average daily gain (ADG) of male and female calves was significantly increased (P<0.05) in IMS as 80.8±2.7, 77.8±2.7 kg and 0.67±0.02, 0.65±0.02 kg/d than EMS as 64.5±2.6, 52.9±2.6 kg and 0.54±0.02, 0.44±0.02 kg/d of male and female calves. Intake of crop residues (P<0.05) was found to be 6.9±0.45 and 6.4±0.45 kg/d in male and female calves, respectively in IMS and 3.5±0.23 for male and female calves both in EMS. The conversion index g/kg ADI was 97.1, 101.5 and 154.3, 125.7 for male and female calves, respectively in IMS and EMS. Regarding hair mineral status Ca, Mg, Cu, Zn, Fe and Mn concentrations were found to be significantly different (P<0.05) among calf groups in IMS and EMS. Conclusion: This study indicates that wool analysis and management of weight gain in camel calves may be further explored to support increased meat supply in arid regions

Parametric analysis using CFD to study the impact of geometric and numerical modeling on the performance of a small scale horizontal axis wind turbine.

The reliance on Computational Fluid Dynamics (CFD) simulations has drastically increased over time to evaluate the aerodynamic performance of small-scale wind turbines. With the rapid variability in customer demand, industrial requirements, economic constraints, and time limitations associated with the design and development of small-scale wind turbines, the trade-off between computational resources and the simulation’s numerical accuracy may vary significantly. In the context of wind turbine design and analysis, high fidelity simulation under full geometric and numerical complexity is more accurate but pose significant demands from a computational standpoint. There is a need to understand and quantify performance deterioration of high fidelity simulations under reduced geometric or numerical approximation on a single small scale turbine model. In the present work, the flow past a small-scale Horizontal Axis Wind Turbine (HAWT) was simulated under various geometric and numerical configurations. The geometric complexity was varied based on stationary and rotating turbine conditions. In the stationary case, simple 2D airfoil, 2.5D blade, 3D blade sections are evaluated, while rotational effects are introduced for the configuration 3D blade, rotor only, and the full-scale wind turbine with and without the inclusion of a nacelle and tower. In terms of numerical complexity, the Single Reference Frame (SRF), Multiple Reference Frames (MRF), and the Sliding Meshing Interface (SMI) is analyzed over Tip Speed Ratios (TSR) of 3, 6, 10. The quantification of aerodynamic coefficients of the blade (Cl, Cd ) and turbine (Cp, Ct ) was conducted along with the discussion on wake patterns in comparison with experimental data

Investigating the impact of pandemic job stress and transformational leadership on innovative work behavior: The mediating and moderating role of knowledge sharing

Only a few studies have been conducted on job stress and transformational leadership (TL), particularly in the environment of the COVID-19 pandemic. This study aims to overcome this gap and attempts to explore the impact of pandemic job stress (PJS) and TL on employees’ innovative work behavior (IWB) through knowledge sharing (KNS), while focusing on the importance of innovations for organizational survival and growth. The data were collected from 357 faculty members of higher education institutions in Pakistan and analyzed using the partial least squares estimation, a structural equation modeling (PLS-SEM) technique. The results demonstrate that PJS positively impacts employees’ IWB, negating the negative relationship between job stress and IWB found in previous studies. Moreover, this study found a positive impact of TL and KNS on IWB. KNS also moderates the relationship between PJS and IWB while partially mediating the relationship between TL and IWB. Lastly, the theoretical and practical implications are discussed