106 research outputs found
What impacts the helpfulness of online multidimensional reviews? A perspective from cross-attribute rating and ranking Inconsistency
This paper proposes investigations of the effects of information inconsistency, particularly ranking inconsistency, on the review helpfulness in a multidimensional rating system, based on information diagnosticity and attribution theory. The insight findings of this paper are: (a) The product cross-attribute dispersion has a significant negative impact on review helpfulness, while the overall attribute ranking consistency and the ranking consistency of the product’s best prominent attribute positively impact review helpfulness. (b) The product cross-attribute dispersion negatively impacts the review helpfulness for non-luxury products but it positively impacts that for luxury products, while the cross-attribute rating difference of a single review positively impacts it helpfulness only if the product is non-luxury. (c) The overall attribute ranking consistency significantly impacts the review helpfulness only for luxury products, whereas the ranking consistency of the product\u27s best and worst prominent attributes impact the review helpfulness only for non-luxury products
A Survey of Geometric Optimization for Deep Learning: From Euclidean Space to Riemannian Manifold
Although Deep Learning (DL) has achieved success in complex Artificial
Intelligence (AI) tasks, it suffers from various notorious problems (e.g.,
feature redundancy, and vanishing or exploding gradients), since updating
parameters in Euclidean space cannot fully exploit the geometric structure of
the solution space. As a promising alternative solution, Riemannian-based DL
uses geometric optimization to update parameters on Riemannian manifolds and
can leverage the underlying geometric information. Accordingly, this article
presents a comprehensive survey of applying geometric optimization in DL. At
first, this article introduces the basic procedure of the geometric
optimization, including various geometric optimizers and some concepts of
Riemannian manifold. Subsequently, this article investigates the application of
geometric optimization in different DL networks in various AI tasks, e.g.,
convolution neural network, recurrent neural network, transfer learning, and
optimal transport. Additionally, typical public toolboxes that implement
optimization on manifold are also discussed. Finally, this article makes a
performance comparison between different deep geometric optimization methods
under image recognition scenarios.Comment: 41 page
WaveAttack: Asymmetric Frequency Obfuscation-based Backdoor Attacks Against Deep Neural Networks
Due to the popularity of Artificial Intelligence (AI) technology, numerous
backdoor attacks are designed by adversaries to mislead deep neural network
predictions by manipulating training samples and training processes. Although
backdoor attacks are effective in various real scenarios, they still suffer
from the problems of both low fidelity of poisoned samples and non-negligible
transfer in latent space, which make them easily detectable by existing
backdoor detection algorithms. To overcome the weakness, this paper proposes a
novel frequency-based backdoor attack method named WaveAttack, which obtains
image high-frequency features through Discrete Wavelet Transform (DWT) to
generate backdoor triggers. Furthermore, we introduce an asymmetric frequency
obfuscation method, which can add an adaptive residual in the training and
inference stage to improve the impact of triggers and further enhance the
effectiveness of WaveAttack. Comprehensive experimental results show that
WaveAttack not only achieves higher stealthiness and effectiveness, but also
outperforms state-of-the-art (SOTA) backdoor attack methods in the fidelity of
images by up to 28.27\% improvement in PSNR, 1.61\% improvement in SSIM, and
70.59\% reduction in IS
DSAM-GN:Graph Network based on Dynamic Similarity Adjacency Matrices for Vehicle Re-identification
In recent years, vehicle re-identification (Re-ID) has gained increasing
importance in various applications such as assisted driving systems, traffic
flow management, and vehicle tracking, due to the growth of intelligent
transportation systems. However, the presence of extraneous background
information and occlusions can interfere with the learning of discriminative
features, leading to significant variations in the same vehicle image across
different scenarios. This paper proposes a method, named graph network based on
dynamic similarity adjacency matrices (DSAM-GN), which incorporates a novel
approach for constructing adjacency matrices to capture spatial relationships
of local features and reduce background noise. Specifically, the proposed
method divides the extracted vehicle features into different patches as nodes
within the graph network. A spatial attention-based similarity adjacency matrix
generation (SASAMG) module is employed to compute similarity matrices of nodes,
and a dynamic erasure operation is applied to disconnect nodes with low
similarity, resulting in similarity adjacency matrices. Finally, the nodes and
similarity adjacency matrices are fed into graph networks to extract more
discriminative features for vehicle Re-ID. Experimental results on public
datasets VeRi-776 and VehicleID demonstrate the effectiveness of the proposed
method compared with recent works.Comment: This paper has been accepted by the 20th Pacific Rim International
Conference on Artificial Intelligence in 202
GitFL: Adaptive Asynchronous Federated Learning using Version Control
As a promising distributed machine learning paradigm that enables
collaborative training without compromising data privacy, Federated Learning
(FL) has been increasingly used in AIoT (Artificial Intelligence of Things)
design. However, due to the lack of efficient management of straggling devices,
existing FL methods greatly suffer from the problems of low inference accuracy
and long training time. Things become even worse when taking various uncertain
factors (e.g., network delays, performance variances caused by process
variation) existing in AIoT scenarios into account. To address this issue, this
paper proposes a novel asynchronous FL framework named GitFL, whose
implementation is inspired by the famous version control system Git. Unlike
traditional FL, the cloud server of GitFL maintains a master model (i.e., the
global model) together with a set of branch models indicating the trained local
models committed by selected devices, where the master model is updated based
on both all the pushed branch models and their version information, and only
the branch models after the pull operation are dispatched to devices. By using
our proposed Reinforcement Learning (RL)-based device selection mechanism, a
pulled branch model with an older version will be more likely to be dispatched
to a faster and less frequently selected device for the next round of local
training. In this way, GitFL enables both effective control of model staleness
and adaptive load balance of versioned models among straggling devices, thus
avoiding the performance deterioration. Comprehensive experimental results on
well-known models and datasets show that, compared with state-of-the-art
asynchronous FL methods, GitFL can achieve up to 2.64X training acceleration
and 7.88% inference accuracy improvements in various uncertain scenarios
Protect Federated Learning Against Backdoor Attacks via Data-Free Trigger Generation
As a distributed machine learning paradigm, Federated Learning (FL) enables
large-scale clients to collaboratively train a model without sharing their raw
data. However, due to the lack of data auditing for untrusted clients, FL is
vulnerable to poisoning attacks, especially backdoor attacks. By using poisoned
data for local training or directly changing the model parameters, attackers
can easily inject backdoors into the model, which can trigger the model to make
misclassification of targeted patterns in images. To address these issues, we
propose a novel data-free trigger-generation-based defense approach based on
the two characteristics of backdoor attacks: i) triggers are learned faster
than normal knowledge, and ii) trigger patterns have a greater effect on image
classification than normal class patterns. Our approach generates the images
with newly learned knowledge by identifying the differences between the old and
new global models, and filters trigger images by evaluating the effect of these
generated images. By using these trigger images, our approach eliminates
poisoned models to ensure the updated global model is benign. Comprehensive
experiments demonstrate that our approach can defend against almost all the
existing types of backdoor attacks and outperform all the seven
state-of-the-art defense methods with both IID and non-IID scenarios.
Especially, our approach can successfully defend against the backdoor attack
even when 80\% of the clients are malicious
Modeling and Verifying Uncertainty-Aware Timing Behaviors using Parametric Logical Time Constraint
International audienceThe Clock Constraint Specification Language (CCSL) is a logical time based modeling language to formalize timing behaviors of real-time and embedded systems. However, it cannot capture timing behaviors that contain uncertainties, e.g., uncertainty in execution time and period. This limits the application of the language to real-world systems, as uncertainty often exists in practice due to both internal and external factors. To capture uncertainties in timing behaviors, in this paper we extend CCSL by introducing parameters into constraints. We then propose an approach to transform parametric CCSL constraints into SMT formulas for efficient verification. We apply our approach to an industrial case which is proposed as the FMTV (Formal Methods for Timing Verification) Challenge in 2015, which shows that timing behaviors with uncertainties can be effectively modeled and verified using the parametric CCSL
Enumeration and Deduction Driven Co-Synthesis of CCSL Specifications Using Reinforcement Learning
International audienceThe Clock Constraint Specification Language (CCSL) has become popular for modeling and analyzing timing behaviors of real-time embedded systems. However, it is difficult for requirement engineers to accurately figure out CCSL specifications from natural language-based requirement descriptions. This is mainly because: i) most requirement engineers lack expertise in formal modeling; and ii) few existing tools can be used to facilitate the generation of CCSL specifications. To address these issues, this paper presents a novel approach that combines the merits of both Reinforcement Learning (RL) and deductive techniques in logical reasoning for efficient co-synthesis of CCSL specifications. Specifically, our method leverages RL to enumerate all the feasible solutions to fill the holes of incomplete specifications and deductive techniques to judge the quality of each trial. Our proposed deductive mechanisms are useful for not only pruning enumeration space, but also guiding the enumeration process to reach an optimal solution quickly. Comprehensive experimental results on both well-known benchmarks and complex industrial examples demonstrate the performance and scalability of our method. Compared with the state-of-theart, our approach can drastically reduce the synthesis time by several orders of magnitude while the accuracy of synthesis can be guaranteed
Quantitative Performance Evaluation of Uncertainty-Aware Hybrid AADL Designs Using Statistical Model Checking
International audience— Architecture Analysis and Design Language (AADL) is widely used for the architecture design and analysis of safety-critical real-time systems. Based on the Hybrid annex which supports continuous behavior modeling, Hybrid AADL enables seamless interactions between embedded control systems and continuous physical environments. Although Hybrid AADL is promising in dependability prediction through analyzable architecture development, the worst-case performance analysis of Hybrid AADL designs can easily lead to an overly pessimistic estimation. So far, Hybrid AADL cannot be used to accurately quantify and reason the overall performance of complex systems which interact with external uncertain environments intensively. To address this problem, this paper proposes a statistical model checking based framework that can perform quantitative evaluation of uncertainty-aware Hybrid AADL designs against various performance queries. Our approach extends Hybrid AADL to support the modeling of environment uncertainties. Furthermore, we propose a set of transformation rules that can automatically translate AADL designs together with designers' requirements into Networks of Priced Timed Automata (NPTA) and performance queries, respectively. Comprehensive experimental results on the Movement Authority (MA) scenario of Chinese Train Control System Level 3 (CTCS-3) demonstrate the effectiveness of our approach
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