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The 3D point cloud deep neural network (3D DNN) has achieved remarkable success, but its black-box nature hinders its application in many safety-critical domains. The saliency map technique is a key method to look inside the black-box and determine where a 3D DNN focuses when recognizing a point cloud. Existing point-wise point cloud saliency methods are proposed to illustrate the point-wise saliency for a given 3D DNN. However, the above critical points are alternative and unreliable. The findings are grounded on our experimental results which show that a point becomes critical because it is responsible for representing one specific local structure. However, one local structure does not have to be represented by some specific points, conversely. As a result, discussing the saliency of the local structure (named patch-wise saliency) represented by critical points is more meaningful than discussing the saliency of some specific points. Based on the above motivations, this paper designs a black-box algorithm to generate patch-wise saliency map for point clouds. Our basic idea is to design the Mask Building-Dropping process, which adaptively matches the size of important/unimportant patches by clustering points with close saliency. Experimental results on several typical 3D DNNs show that our patch-wise saliency algorithm can provide better visual guidance, and can detect where a 3D DNN is focusing more efficiently than a point-wise saliency map. Finally, we apply our patch-wise saliency map to adversarial attacks and backdoor defenses. The results show that the improvement is significant. [ABSTRACT FROM AUTHOR]

During the operation of HVAC (Heating, Ventilation, and Air-Conditioning) systems, precise energy consumption prediction plays an important role in achieving energy savings and optimizing system performance. However, the HVAC system is a complex and dynamic system characterized by a large number of variables that exhibit significant changes over time. Therefore, it is inadequate to rely on a fixed offline model to adapt to the dynamic changes in the system that consume tremendous computation time. To solve this problem, a deep neural network (DNN) model based on Just-in-Time learning with hyperparameter R (RJITL) is proposed in this paper to predict HVAC energy consumption. Firstly, relevant samples are selected using Euclidean distance weighted by Spearman coefficients. Subsequently, local models are constructed using deep neural networks supplemented with optimization techniques to enable real-time rolling energy consumption prediction. Then, the ensemble JITL model mitigates the influence of local features, and improves prediction accuracy. Finally, the local models can be adaptively updated to reduce the training time of the overall model by defining the update rule (hyperparameter R) for the JITL model. Experimental results on energy consumption prediction for the HVAC system show that the proposed DNN-RJITL method achieves an average improvement of 5.17% in accuracy and 41.72% in speed compared to traditional methods. [ABSTRACT FROM AUTHOR]

Adversarial examples are often used to test and evaluate the security and robustness of image classification models. Though adversarial attacks under white-box setting can achieve a high attack success rate, due to overfitting, the success rate of black-box attacks is relatively low. To this end, this paper proposes diversified input strategies to improve the transferability of adversarial examples. In this method, various transformation methods are applied to randomly transform the original image multiple times, thereby generating a batch of transformed images. Then, in the process of back-propagation, the loss function gradient of the transformed images is calculated, and a weighted average of the obtained gradient values is performed to generate adversarial perturbation, which is iteratively added to the original image to generate adversarial examples. Meanwhile, by increasing the variety of data augmentation transformation types and the number of input images, the proposed method effectively alleviates overfitting and improves the transferability of adversarial examples. Extensive experiments on the ImageNet dataset indicate that the proposed method can perform black-box attacks better than benchmark methods, with an average of 97.2% success rate attacking multiple models simultaneously. [ABSTRACT FROM AUTHOR]

In today's world, a Network Intrusion Detection System (NIDS) plays a vital role in order to secure the Wireless Sensor Network (WSN). However, the traditional NIDS model faced critical constraints with network traffic data due to growth in the complexity of modern attacks. These constraints have a direct impact on the overall performance of the WSN. In this paper, a new robust network intrusion classification framework based on the enhanced Visual Geometry Group (VGG-19) pre-trained model has been proposed to prolong the performance of WSN. Primarily, the pre-trained weights from the ImageNet dataset are utilized to train the parameters of the VGG-19. Afterward, a Hybrid Deep Neural Network based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) will be employed to extract the influential features from network traffic data to enlarge the intrusion detection accuracy. The proposed VGG-19 + Hybrid CNN-LSTM model exploits both binary classification and multi-classification to classify attacks as either normal or attacked. A network intrusion benchmark dataset is used to assess the performance of the suggested system. The results reveal that the proposed VGG-19 + Hybrid CNN-LSTM learning system surpasses other pre-trained models with a superior accuracy of 98.86% during the multi-classification test. [ABSTRACT FROM AUTHOR]

Latent fingerprint recognition plays an essential role for law enforcement agencies to detect criminals and security purposes. One of the key stages utilized in the latent fingerprint recognition model is to automatically learn consistent minutiae from fingerprint images. However, the existing state-of-the-art recognition approaches are not adequate since live-scan fingerprint images and enhancements are necessary for each step of the recognition process. Hence, an automated recognition system along with appropriate minutiae learning algorithm is required for matching the latent fingerprint exactly. In this paper, an efficient recognition system using dictionary learning and Local Context-Perception deep neural network (LCPnet) has been proposed to enhance the accuracy of latent fingerprint recognition. Primarily, the Total Variation decomposition model is utilized to remove the smooth background noise and dictionary learning contributes to the extraction of multiple patches. Afterward, the LCPnet is trained for 12 patch types to develop a salient minutiae descriptor where every descriptor is trained using LCPnet with a particular patch size at a location surrounding the minutiae. The proposed detection system has been tested through two latent public datasets. Here, three different types of templates (LCPnet minutiae, LCPnet texture, and LCPnet minutiae+texture) are analyzed for evaluating the proposed fingerprint detection system. The performance results manifest that the proposed system acquires a superior recognition accuracy of 99.44% and 99.58% under two different datasets. [ABSTRACT FROM AUTHOR]

Facial expression recognition is a current research hotspot and can be applied to computer vision fields such as human-computer interaction and affective computing. The lack of diversity and category recognition information in the neural network input may affect the performance of the network, resulting in insufficient extraction of facial expression features. In order to address the above problems, a lightweight deep convolution neural network with convolution block attention module is proposed in this paper. The implementation of the lightweight DNN relies on the use of deep separable convolution and residual blocks. The combination of the convolution block attention module and the improved classification function can optimize the lightweight model. We use accuracy and confusion matrix to evaluate different models, ultimately achieving 71.5% and 99.5% accuracy on the Fer2013 and CK+ datasets respectively. The experimental results show that our model has good feature representation capabilities. [ABSTRACT FROM AUTHOR]

Malware detection have long become a challenge in research. The existing methods rely on malware signature which are proved not to be effective nowadays. The recent researches focus on using probabilistic model such as machine learning to detect the existence of malware. They, however, do not achieve such a good performance. Particularly, machine learning techniques still have an issue of high feature engineering overhead. In this paper, we propose a deep learning method to detect malware based on their malicious behavior. Through experimentation, we show that our method can achieve a very high accuracy rate of 98.75 in F1 measure, compared to state of the art methods. [ABSTRACT FROM AUTHOR]

In this digitized world, the demand of users emphasizes the quality and accuracy. Practically, all variants of signals are analog in nature along with contaminated with noise. In this paper, speech signal is considered. Basically speech signal varies from person to person and time to time. It requires enhancement of the signal for different applications like engineering, medicine and social purposes. Reduction of noise as well as redundant data from the signal can be produced with enhanced versions. As the speech is of nonstationary in nature, in the initial phase, it is processed and normalized. To analyze the speech signal, spectral domain is most suitable and has been utilized. For this purpose, Discrete Cosine Transform (DCT-II) is used. As it has the advantage over other transforms and the calculation is simpler, DCT-II coefficients are further used for Deep Neural Network (DNN) model to reduce the noise and enhance the signal. So that the signal of any environment and of any amount can be enhanced using this model. 100 sentences have been collected form both males and females of 5 each. The sentences have been uttered by the corresponding males and females, 10 sentences each. Though DCT-II and DNN have been applied by many researchers for signal features and image classification, the same have been utilized here for speech enhancement, which is the novelty of this work. The results found better than the other methods applied earlier and it can be best utilized for any real time application. In the result section, the visual inspection is exhibited along with the comparison values. The measuring parameters show its efficacy. [ABSTRACT FROM AUTHOR]