This paper conducted a thorough investigation into the primary difficulty of the cross-channel automatic modulation classification (AMC) task by examining data distribution and feature space of different channel conditions. We concluded that the disruption of the target channel feature space structure breakdown the mapping relationship across channels, serving as the main contributor to model performance degradation. Based on the above conclusion, in order to improve the performance of cross-channel AMC, we introduce the Auxiliary Feature Learning-Guided Network (AFLNet). This network improves the structure of the target feature space through two uniquely designed tasks and facilitates efficient cross-domain alignment via a collaborative alignment mechanism. Specifically, AFLNet integrates similarity-based and confidence-based auxiliary feature learning tasks to enhance the discriminability of the target feature space and maintain the correspondence of category structures across different channels, thereby reducing the difficulty of feature alignment. The collaborative alignment mechanism combines adversarial training-based and self-training-based feature alignment methods, leveraging their mutually reinforcing effect and complementary strengths in global alignment and class-level alignment to enhance overall alignment performance. We carried out extensive experiments across four scenarios characterized by substantial channel variations, verifying that AFLNet achieves state-of-the-art with accuracy improvement of up to 9.71%.

Due to the lack of rotation invariance in traditional convolution operations, even acting a slight rotation on the input can severely degrade the performance of Convolutional Neural Networks (CNNs). To address this, we propose a Rotation-Invariant Coordinate Convolution (RIC-C), which achieves natural invariance to arbitrary rotations around the input center without additional trainable parameters or data augmentation. We first evaluate the rotational invariance of RIC-C using the MNIST dataset and compare its performance with most previous rotation-invariant CNN models. RIC-C achieves state-of-the-art classification on the MNIST-rot test set without data augmentation and with lower computational costs. Then, the interchangeability of RIC-C with traditional convolution operations is demonstrated by seamlessly integrating it into common CNN models like VGG, ResNet, and DenseNet. We conduct remote sensing image classification on the NWPU VHR-10, MTARSI and AID datasets and patch matching experiments on the UBC benchmark dataset, showing that RIC-C significantly enhances the performance of CNN models across different applications, especially when training data is limited. Our codes can be downloaded from https://github.com/HanlinMo/Rotation-Invariant-Coordinate-Convolutional-Neural-Network.git.

The topic of achieving rotational invariance in convolutional neural networks (CNNs) has gained considerable attention recently, as this invariance is crucial for many computer vision tasks. In this letter, we propose a sorting convolution operation (SConv), which achieves invariance to arbitrary rotations without additional learnable parameters or data augmentation. It can directly replace conventional convolution operations in a classic CNN model to achieve the model's rotational invariance. Based on MNIST-rot dataset, we first analyze the impact of convolution kernel size, sampling grid and sorting method onSConv's rotational invariance, and compare our method with previous rotation-invariant CNN models. Then, we combineSConvwith VGG, ResNet and DenseNet, and conduct classification experiments on texture and remote sensing image datasets. The results show thatSConvsignificantly improves the performance of these models, especially when training data is limited. Our code can be downloaded from https://github.com/HanlinMo/Sorting-Convolution-Operation-for-Achieving-Rotational-Invariance.

In this paper, we have proved the existence of projective moment invariants of images using finite combinations of weighted moments, with relative projective differential invariants as weight functions. We have given some instances constructed in that way, and analyzed possible issues could affect the performance. Some procedures are taken to estimate partial derivatives of discrete images, and a new method is designed to normalize the number of pixels for discrete images to minimize the changes before and after the projective transformation. We have carried out experiments using popular image databases and real images to test the performance. And the results show that the invariants proposed in this paper have better stability and discriminability than other previously used moment invariants in image retrieval and classification. Users can directly extract invariant features of images for a given planar object from different viewpoints without knowing the parameters of the 2D projective transformations. Therefore, the projective moment invariant could be potentially useful for planar object recognition, image description and classification.

Abstract An individual's decision‐making behavior is heavily influenced by and adapted to external environmental factors. Given that software development is a human‐centered activity, individual decision‐making behavior may affect the software project quality. Although environmental factors affecting decision‐making behavior in software projects have been identified in prior literature, there is not yet an objective and a full taxonomy of these factors. Thus, it is not trivial to manage these complex and diverse factors. To address this deficiency, we first design a semantic similarity algorithm between words by utilizing the synonymy and hypernymy relationships in WordNet. Further, we propose a method to measure semantic similarity between phrases and apply it into k‐means clustering algorithm to group these factors. Subsequently, we obtain a taxonomy of the environmental factors affecting individual decision‐making behavior in software projects, which includes 11 broad categories, each containing 2 to 5 sub‐categories. The taxonomy presented herein is obtained by an objective method, and quite comprehensive, with appropriate references provided. The taxonomy holds significant value for researchers and practitioners; it can help them to better understand the major aspects of environmental factors, also to predict and guide the behavior of individuals during decision making towards a successful completion of software projects.

Affine moment invariant (AMI) is a kind of hand-crafted image feature, which is invariant to affine transformations. This property is precisely what the standard convolution neural network (CNN) is difficult to achieve. In this letter, we present a kind of network architecture to introduce AMI into CNN, which is called AMI-Net. We achieved this by calculating AMI on the feature maps of the hidden layers. These AMIs will be concatenated with the standard CNN's FC layer to determine the network's final output. By calculating AMI on the feature maps, we can not only extend the dimension of AMIs, but also introduce affine transformation invariant into CNN. Two network architectures and training strategies of AMI-Net are illuminated, one is two-stage, and the other is end-to-end. To prove the effectiveness of the AMI-Net, several experiments have been conducted on common image datasets, MNIST, MNIST-rot, affNIST, SVHN, and CIFAR-10. By comparing with the corresponding standard CNN, respectively, we verify the validity of AMI-net.