126 research outputs found
A Robust Method for Speech Emotion Recognition Based on Infinite Student’s t
Speech emotion classification method, proposed in this paper, is based on Student’s t-mixture model with infinite component number (iSMM) and can directly conduct effective recognition for various kinds of speech emotion samples. Compared with the traditional GMM (Gaussian mixture model), speech emotion model based on Student’s t-mixture can effectively handle speech sample outliers that exist in the emotion feature space. Moreover, t-mixture model could keep robust to atypical emotion test data. In allusion to the high data complexity caused by high-dimensional space and the problem of insufficient training samples, a global latent space is joined to emotion model. Such an approach makes the number of components divided infinite and forms an iSMM emotion model, which can automatically determine the best number of components with lower complexity to complete various kinds of emotion characteristics data classification. Conducted over one spontaneous (FAU Aibo Emotion Corpus) and two acting (DES and EMO-DB) universal speech emotion databases which have high-dimensional feature samples and diversiform data distributions, the iSMM maintains better recognition performance than the comparisons. Thus, the effectiveness and generalization to the high-dimensional data and the outliers are verified. Hereby, the iSMM emotion model is verified as a robust method with the validity and generalization to outliers and high-dimensional emotion characters
Adversarial Camouflage for Node Injection Attack on Graphs
Node injection attacks against Graph Neural Networks (GNNs) have received
emerging attention as a practical attack scenario, where the attacker injects
malicious nodes instead of modifying node features or edges to degrade the
performance of GNNs. Despite the initial success of node injection attacks, we
find that the injected nodes by existing methods are easy to be distinguished
from the original normal nodes by defense methods and limiting their attack
performance in practice. To solve the above issues, we devote to camouflage
node injection attack, i.e., camouflaging injected malicious nodes
(structure/attributes) as the normal ones that appear legitimate/imperceptible
to defense methods. The non-Euclidean nature of graph data and the lack of
human prior brings great challenges to the formalization, implementation, and
evaluation of camouflage on graphs. In this paper, we first propose and
formulate the camouflage of injected nodes from both the fidelity and diversity
of the ego networks centered around injected nodes. Then, we design an
adversarial CAmouflage framework for Node injection Attack, namely CANA, to
improve the camouflage while ensuring the attack performance. Several novel
indicators for graph camouflage are further designed for a comprehensive
evaluation. Experimental results demonstrate that when equipping existing node
injection attack methods with our proposed CANA framework, the attack
performance against defense methods as well as node camouflage is significantly
improved
Suppression of long non-coding RNA H19 inhibits proliferation, cell migration and invasion in human cervical cancer cells
Purpose: To determine the expression profile of lncRNA H19 in different cervical cancers, and to decipher its function in the growth and metastasis of cervical cancer.Methods: The analysis LncRNA H19 expression was performed using quantitative real timepolymerase chain reaction (qRT-PCR). Cell counting kit 8 (CCK8) assay was used to assess the viability of the cells. The cells were transfected with Si-H19 using Lipofectamine 2000 and the metastasis of cells was determined by cell migration and invasion assay. Immunoblotting was used to evaluate the protein expression.Results: The lncRNA H19 expression was considerably enhanced in cervical cancer cells, and was about 2.6 to 5.3 times more in cervical cancer cells relative to non-cancer cells. Inhibition of lncRNA caused significant reduction in cervical cancer cell growth in a time-dependent manner. In addition while silencing of lncRNA inhibited the metastasis of HeLa cells. Cell migration and invasion was about 26 % in Si-H19 transfected cervical cancer cells, relative to 65 % in Si-NC cervical HeLa cells. Similarly, cell invasion was 45 % in Si-H19 cervical HeLa cells relative to the negative control (Si-NC). Inhibition of HeLa cell metastasis was also concomitant with decline of metalloproteinases (MMP)-2 and 9expression.Conclusion: lncRNA regulates the growth and metastasis of cervical cancer cells. Thus, IncRNA may be an important therapeutic agent for cervical cancer.Keywords: Cervical cancer, lncRNA, Proliferation, Invasio
Robust Recommender System: A Survey and Future Directions
With the rapid growth of information, recommender systems have become
integral for providing personalized suggestions and overcoming information
overload. However, their practical deployment often encounters "dirty" data,
where noise or malicious information can lead to abnormal recommendations.
Research on improving recommender systems' robustness against such dirty data
has thus gained significant attention. This survey provides a comprehensive
review of recent work on recommender systems' robustness. We first present a
taxonomy to organize current techniques for withstanding malicious attacks and
natural noise. We then explore state-of-the-art methods in each category,
including fraudster detection, adversarial training, certifiable robust
training against malicious attacks, and regularization, purification,
self-supervised learning against natural noise. Additionally, we summarize
evaluation metrics and common datasets used to assess robustness. We discuss
robustness across varying recommendation scenarios and its interplay with other
properties like accuracy, interpretability, privacy, and fairness. Finally, we
delve into open issues and future research directions in this emerging field.
Our goal is to equip readers with a holistic understanding of robust
recommender systems and spotlight pathways for future research and development
Reliability Analysis of Vision Transformers
Vision Transformers (ViTs) that leverage self-attention mechanism have shown
superior performance on many classical vision tasks compared to convolutional
neural networks (CNNs) and gain increasing popularity recently. Existing ViTs
works mainly optimize performance and accuracy, but ViTs reliability issues
induced by soft errors in large-scale VLSI designs have generally been
overlooked. In this work, we mainly study the reliability of ViTs and
investigate the vulnerability from different architecture granularities ranging
from models, layers, modules, and patches for the first time. The investigation
reveals that ViTs with the self-attention mechanism are generally more
resilient on linear computing including general matrix-matrix multiplication
(GEMM) and full connection (FC) and show a relatively even vulnerability
distribution across the patches. ViTs involve more fragile non-linear computing
such as softmax and GELU compared to typical CNNs. With the above observations,
we propose a lightweight block-wise algorithm-based fault tolerance (LB-ABFT)
approach to protect the linear computing implemented with distinct sizes of
GEMM and apply a range-based protection scheme to mitigate soft errors in
non-linear computing. According to our experiments, the proposed fault-tolerant
approaches enhance ViTs accuracy significantly with minor computing overhead in
presence of various soft errors
Exploring Winograd Convolution for Cost-effective Neural Network Fault Tolerance
Winograd is generally utilized to optimize convolution performance and
computational efficiency because of the reduced multiplication operations, but
the reliability issues brought by winograd are usually overlooked. In this
work, we observe the great potential of winograd convolution in improving
neural network (NN) fault tolerance. Based on the observation, we evaluate
winograd convolution fault tolerance comprehensively from different
granularities ranging from models, layers, and operation types for the first
time. Then, we explore the use of inherent fault tolerance of winograd
convolution for cost-effective NN protection against soft errors. Specifically,
we mainly investigate how winograd convolution can be effectively incorporated
with classical fault-tolerant design approaches including triple modular
redundancy (TMR), fault-aware retraining, and constrained activation functions.
According to our experiments, winograd convolution can reduce the
fault-tolerant design overhead by 55.77\% on average without any accuracy loss
compared to standard convolution, and further reduce the computing overhead by
17.24\% when the inherent fault tolerance of winograd convolution is
considered. When it is applied on fault-tolerant neural networks enhanced with
fault-aware retraining and constrained activation functions, the resulting
model accuracy generally shows significant improvement in presence of various
faults
Improved whale swarm algorithm for solving material emergency dispatching problem with changing road conditions
To overcome the problem of easily falling into local extreme values of the whale swarm algorithm to solve the material emergency dispatching problem with changing road conditions, an improved whale swarm algorithm is proposed. First, an improved scan and Clarke-Wright algorithm is used to obtain the optimal vehicle path at the initial time. Then, the group movement strategy is designed to generate offspring individuals with an improved quality for refining the updating ability of individuals in the population. Finally, in order to maintain population diversity, a different weights strategy is used to expand individual search spaces, which can prevent individuals from prematurely gathering in a certain area. The experimental results show that the performance of the improved whale swarm algorithm is better than that of the ant colony system and the adaptive chaotic genetic algorithm, which can minimize the cost of material distribution and effectively eliminate the adverse effects caused by the change of road conditions
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