16 research outputs found
Defense Against Model Extraction Attacks on Recommender Systems
The robustness of recommender systems has become a prominent topic within the
research community. Numerous adversarial attacks have been proposed, but most
of them rely on extensive prior knowledge, such as all the white-box attacks or
most of the black-box attacks which assume that certain external knowledge is
available. Among these attacks, the model extraction attack stands out as a
promising and practical method, involving training a surrogate model by
repeatedly querying the target model. However, there is a significant gap in
the existing literature when it comes to defending against model extraction
attacks on recommender systems. In this paper, we introduce Gradient-based
Ranking Optimization (GRO), which is the first defense strategy designed to
counter such attacks. We formalize the defense as an optimization problem,
aiming to minimize the loss of the protected target model while maximizing the
loss of the attacker's surrogate model. Since top-k ranking lists are
non-differentiable, we transform them into swap matrices which are instead
differentiable. These swap matrices serve as input to a student model that
emulates the surrogate model's behavior. By back-propagating the loss of the
student model, we obtain gradients for the swap matrices. These gradients are
used to compute a swap loss, which maximizes the loss of the student model. We
conducted experiments on three benchmark datasets to evaluate the performance
of GRO, and the results demonstrate its superior effectiveness in defending
against model extraction attacks
Rigorous assessment and integration of the sequence and structure based features to predict hot spots
Background
Systematic mutagenesis studies have shown that only a few interface residues termed hot spots contribute significantly to the binding free energy of protein-protein interactions. Therefore, hot spots prediction becomes increasingly important for well understanding the essence of proteins interactions and helping narrow down the search space for drug design. Currently many computational methods have been developed by proposing different features. However comparative assessment of these features and furthermore effective and accurate methods are still in pressing need. Results
In this study, we first comprehensively collect the features to discriminate hot spots and non-hot spots and analyze their distributions. We find that hot spots have lower relASA and larger relative change in ASA, suggesting hot spots tend to be protected from bulk solvent. In addition, hot spots have more contacts including hydrogen bonds, salt bridges, and atomic contacts, which favor complexes formation. Interestingly, we find that conservation score and sequence entropy are not significantly different between hot spots and non-hot spots in Ab+ dataset (all complexes). While in Ab- dataset (antigen-antibody complexes are excluded), there are significant differences in two features between hot pots and non-hot spots. Secondly, we explore the predictive ability for each feature and the combinations of features by support vector machines (SVMs). The results indicate that sequence-based feature outperforms other combinations of features with reasonable accuracy, with a precision of 0.69, a recall of 0.68, an F1 score of 0.68, and an AUC of 0.68 on independent test set. Compared with other machine learning methods and two energy-based approaches, our approach achieves the best performance. Moreover, we demonstrate the applicability of our method to predict hot spots of two protein complexes. Conclusion
Experimental results show that support vector machine classifiers are quite effective in predicting hot spots based on sequence features. Hot spots cannot be fully predicted through simple analysis based on physicochemical characteristics, but there is reason to believe that integration of features and machine learning methods can remarkably improve the predictive performance for hot spots
Rigorous assessment and integration of the sequence and structure based features to predict hot spots
<p>Abstract</p> <p>Background</p> <p>Systematic mutagenesis studies have shown that only a few interface residues termed hot spots contribute significantly to the binding free energy of protein-protein interactions. Therefore, hot spots prediction becomes increasingly important for well understanding the essence of proteins interactions and helping narrow down the search space for drug design. Currently many computational methods have been developed by proposing different features. However comparative assessment of these features and furthermore effective and accurate methods are still in pressing need.</p> <p>Results</p> <p>In this study, we first comprehensively collect the features to discriminate hot spots and non-hot spots and analyze their distributions. We find that hot spots have lower relASA and larger relative change in ASA, suggesting hot spots tend to be protected from bulk solvent. In addition, hot spots have more contacts including hydrogen bonds, salt bridges, and atomic contacts, which favor complexes formation. Interestingly, we find that conservation score and sequence entropy are not significantly different between hot spots and non-hot spots in Ab+ dataset (all complexes). While in Ab- dataset (antigen-antibody complexes are excluded), there are significant differences in two features between hot pots and non-hot spots. Secondly, we explore the predictive ability for each feature and the combinations of features by support vector machines (SVMs). The results indicate that sequence-based feature outperforms other combinations of features with reasonable accuracy, with a precision of 0.69, a recall of 0.68, an F1 score of 0.68, and an AUC of 0.68 on independent test set. Compared with other machine learning methods and two energy-based approaches, our approach achieves the best performance. Moreover, we demonstrate the applicability of our method to predict hot spots of two protein complexes.</p> <p>Conclusion</p> <p>Experimental results show that support vector machine classifiers are quite effective in predicting hot spots based on sequence features. Hot spots cannot be fully predicted through simple analysis based on physicochemical characteristics, but there is reason to believe that integration of features and machine learning methods can remarkably improve the predictive performance for hot spots.</p
Attention Is Not the Only Choice: Counterfactual Reasoning for Path-Based Explainable Recommendation
Compared with only pursuing recommendation accuracy, the explainability of a
recommendation model has drawn more attention in recent years. Many graph-based
recommendations resort to informative paths with the attention mechanism for
the explanation. Unfortunately, these attention weights are intentionally
designed for model accuracy but not explainability. Recently, some researchers
have started to question attention-based explainability because the attention
weights are unstable for different reproductions, and they may not always align
with human intuition. Inspired by the counterfactual reasoning from causality
learning theory, we propose a novel explainable framework targeting path-based
recommendations, wherein the explainable weights of paths are learned to
replace attention weights. Specifically, we design two counterfactual reasoning
algorithms from both path representation and path topological structure
perspectives. Moreover, unlike traditional case studies, we also propose a
package of explainability evaluation solutions with both qualitative and
quantitative methods. We conduct extensive experiments on three real-world
datasets, the results of which further demonstrate the effectiveness and
reliability of our method.Comment: accepted by TKD
Assessment of multi-source observation merged 1 km-grid precipitation product during the disastrous rainstorms in Guangdong
This paper aims to assess the latest 1 km-grid Analysis Real Time (ART_1 km) precipitation product developed by the National Meteorological Information Center of China Meteorological Administration (CMA), which can provide great support for disaster weather monitoring and warning, intelligent grid forecasting and weather services. Observed precipitation data from the independent stations (including non-uploaded regional meteorological stations and hydrometric stations) that were not integrated into the ART_1 km precipitation product as well as precipitation classification inspection are used to assess the quality of this product during twenty disastrous rainstorm cases from May to August during 2019-2022 in Guangdong. The results show that the ART_1 km precipitation product successfully reproduces the precipitation location, strength, and trends in these cases, with the best performance in the Pearl River Delta, the east of eastern Guangdong, and the north of northern Guangdong. The stronger the precipitation, the greater the correlation as well as the root mean square error (RMSE) and mean error (ME) between the ART_1 km precipitation and the observed precipitation. When the hourly precipitation is not classified, about 60% of these independent stations present a correlation efficient ā„ 0.8, more than 90% of the stations present an RMSE within the range of [1.0, 5.0) mm, and more than 60% of the stations present a ME within Ā±0.1 mm. When the hourly precipitation is < 5 mm, most of the stations have a correlation efficient < 0.5, an RMSE within the range of [1.0, 5.0) mm, and a ME within [0.0, 0.5] mm. When the hourly precipitation is ā„ 20 mm, 42%~56% of the stations have a correlation efficient ā„ 0.5, and most of the stations have an RMSE ā„ 10 mm and a ME < 0 mm, even when the hourly precipitation is ā„ 50 mm, most of the stations have a ME < -10 mm. Overall, ART_1 km precipitation is usually underestimated at the independent stations, and integrating observations from more sites into producing ART_1 km precipitation is helpful to improve the quality of the products
LightMAN: A Lightweight Microchained Fabric for Assurance- and Resilience-Oriented Urban Air Mobility Networks
Rapid advancements in the fifth generation (5G) communication technology and mobile edge computing (MEC) paradigm have led to the proliferation of unmanned aerial vehicles (UAV) in urban air mobility (UAM) networks, which provide intelligent services for diversified smart city scenarios. Meanwhile, the widely deployed Internet of drones (IoD) in smart cities has also brought up new concerns regarding performance, security, and privacy. The centralized framework adopted by conventional UAM networks is not adequate to handle high mobility and dynamicity. Moreover, it is necessary to ensure device authentication, data integrity, and privacy preservation in UAM networks. Thanks to its characteristics of decentralization, traceability, and unalterability, blockchain is recognized as a promising technology to enhance security and privacy for UAM networks. In this paper, we introduce LightMAN, a lightweight microchained fabric for data assurance and resilience-oriented UAM networks. LightMAN is tailored for small-scale permissioned UAV networks, in which a microchain acts as a lightweight distributed ledger for security guarantees. Thus, participants are enabled to authenticate drones and verify the genuineness of data that are sent to/from drones without relying on a third-party agency. In addition, a hybrid on-chain and off-chain storage strategy is adopted that not only improves performance (e.g., latency and throughput) but also ensures privacy preservation for sensitive information in UAM networks. A proof-of-concept prototype is implemented and tested on a micro-air–vehicle link (MAVLink) simulator. The experimental evaluation validates the feasibility and effectiveness of the proposed LightMAN solution
LightMAN: A Lightweight Microchained Fabric for Assurance- and Resilience-Oriented Urban Air Mobility Networks
Rapid advancements in the fifth generation (5G) communication technology and mobile edge computing (MEC) paradigm have led to the proliferation of unmanned aerial vehicles (UAV) in urban air mobility (UAM) networks, which provide intelligent services for diversified smart city scenarios. Meanwhile, the widely deployed Internet of drones (IoD) in smart cities has also brought up new concerns regarding performance, security, and privacy. The centralized framework adopted by conventional UAM networks is not adequate to handle high mobility and dynamicity. Moreover, it is necessary to ensure device authentication, data integrity, and privacy preservation in UAM networks. Thanks to its characteristics of decentralization, traceability, and unalterability, blockchain is recognized as a promising technology to enhance security and privacy for UAM networks. In this paper, we introduce LightMAN, a lightweight microchained fabric for data assurance and resilience-oriented UAM networks. LightMAN is tailored for small-scale permissioned UAV networks, in which a microchain acts as a lightweight distributed ledger for security guarantees. Thus, participants are enabled to authenticate drones and verify the genuineness of data that are sent to/from drones without relying on a third-party agency. In addition, a hybrid on-chain and off-chain storage strategy is adopted that not only improves performance (e.g., latency and throughput) but also ensures privacy preservation for sensitive information in UAM networks. A proof-of-concept prototype is implemented and tested on a micro-airāvehicle link (MAVLink) simulator. The experimental evaluation validates the feasibility and effectiveness of the proposed LightMAN solution
Effects of foliar application of magnesium sulfate on photosynthetic characteristics, dry matter accumulation and its translocation, and carbohydrate metabolism in grain during wheat grain filling
Our previous studies showed that foliar applied magnesium sulfate during the booting stage can efectively increase wheat
grain weight. To explore the efects of foliar application of magnesium sulfate during wheat grain flling on photosynthetic
characteristics of fag leaf and grain flling, an experiment was conducted using winter wheat cultivars to assess the efects
of foliar application of magnesium sulphate on photosynthetic characteristics of fag leaves, carbohydrate metabolism
in grains, and dry matter translocation in diferent organs in the Zhoumai 27 and Aikang 58 cultivars at diferent growth
stages. The results indicated throughout the diferent stages of growth, the fag leaves exhibited a high net photosynthetic
rate (Pn), stomatal conductance (Gs) and transpiration (Tr), and a decrease in the concentration of intercellular CO2 (Ci).
Therefore, foliar application of magnesium sulfate during the booting stage maintained high canopy photosynthesis after
anthesis. Simultaneously, exogenous supply of magnesium sulphate enhanced the sucrose synthase (SUS) and invertase
(INV) enzyme activities in detached wheat grains, meanwhile reinforced the activities of most starch synthesis enzyme such
as ADP-glucose pyrophosphorylase and soluble starch synthase, and consequently lead to a higher content of grain starch.
Furthermore, feld experiment also confrmed foliar application of magnesium sulphate can improve superior dry matter
accumulation and translocation in grain
Exogenous application of glycine betaine alleviates cadmium toxicity in super black waxy maize by improving photosynthesis, the antioxidant system and glutathione-ascorbic acid cycle metabolites
This study was conducted to examine how glycine betaine (GB) ameliorates the toxic efects of cadmium (Cd) in super black
waxy maize. In a Cd toxicity test, biomass accumulation in maize seedlings decreased in comparison with those in the control
group (CK). The maize plants exposed to Cd stress exhibited reductions in photosynthetic parameters [net photosynthetic
rate (Pn), stomatal conductance (Gs), intracellular CO2 concentration (Ci
) and transpiration rate (E)], chlorophyll content and
maximum photosynthetic efciency (Fv/Fm). However, the exogenous application of GB alleviated the negative efects of Cd
stress and increased the biomass, photosynthetic parameters (Pn, Gs, Ci
, E), chlorophyll content and Fv/Fm of the seedlings.
In addition, Cd can decrease the activity of antioxidant enzymes and ascorbic acid (AsA)-glutathione (GSH) cycle enzymes,
such as superoxide dismutase, peroxidase, catalase, ascorbate peroxidase, dehydroascorbate reductase, monodehydroascorbate
reductase and glutathione reductase, but the activity of these enzymes increased following GB addition. Cd also increased
markedly the content of reactive oxygen species and malondialdehyde, but supplementation with GB signifcantly decreased
their content. Furthermore, the levels of AsA and GSH decreased under treatment with Cd, but exogenous GB application
increased the content of these compounds. This study shows that GB can alleviate Cd toxicity and provides a theoretical
basis for super black maize resistance to heavy metal stress