Medical image segmentation is crucial for early cancer diagnosis, yet traditional methods relying on manual positioning by doctors are inefficient and time-consuming. To address this, we propose FVM-UNet, a U-shaped segmentation model that integrates the Visual State Space (VSS) module, Cross-Fusion Block (CFB), and Channel-Spatial Attention Module (CSAM) bottleneck, combining the strengths of CNN and State Space Models (SSMs). Deep supervision is employed for multiscale mask training, enhancing feature extraction and segmentation accuracy. Experimental results on the Synapse, ISIC2017, and ISIC2018 datasets demonstrate competitive performance, with FVM-UNet achieving significant improvements in segmentation accuracy, particularly in dermatological applications. Our model reduces complexity while improving performance, providing valuable insights for future research. The code is accessible at https://github.com/Nellerm/FVM-UNet.git.

Moving object tracking on cost-effective hardware is a crucial need in numerous research and industrial applications. However, current deep learning-based tracking algorithms usually prioritize exceptional performance at the expense of increased computational load. Due to the unavailability of expensive GPUs for many tracking tasks, these popular trackers often fall short in providing robust tracking capabilities with affordable computational resources. This study introduces RCNNshift, a kernel-based tracker that relies on feature extraction from a random-coupled neural network. This visual cortex inspired neural model can extract image features without requiring cumbersome pre-training or deep neural connections. By utilizing an enhanced one-dimensional feature representation, RCNNshift demonstrates superior performance compared to other kernel-based object tracking methods, even those employing higher-dimensional feature spaces. Its improvement in precision and success plots of OPE, compared to the Meanshift and Camshift in the HSV and RGB color spaces, exceeds over 160% and 190% respectively. Comparative experiments have validated the robustness of RCNNshift, showcasing its superior performance over various kernel-based and particle filter trackers. Its combination of robustness and computational efficiency makes RCNNshift an ideal choice for mid to low-end object tracking tasks such as surveillance and underwater tracking. The source code is available at https://github.com/HaoranLiu507/RCNNshift.

Music Emotion Recognition (MER) involves identifying the emotional content conveyed by music. This field is becoming increasingly significant due to its broad range of applications, including music recommendation systems, mood-based playlists, and therapeutic tools. This paper presents a novel MER model designed for song-level analysis, leveraging the Transformer Encoder architecture. The model incorporates various embedding techniques to capture both local and global contexts within musical data, thereby improving the extraction of crucial features for emotion recognition. Additionally, a Self-Attention Pooling Layer is used to effectively integrate and interpret complex musical features. Experiments using the DEAM dataset reveal that this model excels in emotion identification, surpassing existing approaches and offering promising directions for future research in the field of MER.

Abstract Recent years have witnessed the popularity of using two-stream convolutional neural networks for action recognition. However, existing two-stream convolutional neural network-based action recognition approaches are incapable of distinguishing some roughly similar actions in videos such as sneezing and yawning. To solve this problem, we propose a Multi-head Attention-based Two-stream EfficientNet (MAT-EffNet) for action recognition, which can take advantage of the efficient feature extraction of EfficientNet. The proposed network consists of two streams (i.e., a spatial stream and a temporal stream), which first extract the spatial and temporal features from consecutive frames by using EfficientNet. Then, a multi-head attention mechanism is utilized on the two streams to capture the key action information from the extracted features. The final prediction is obtained via a late average fusion, which averages the softmax score of spatial and temporal streams. The proposed MAT-EffNet can focus on the key action information at different frames and compute the attention multiple times, in parallel, to distinguish similar actions. We test the proposed network on the UCF101, HMDB51 and Kinetics-400 datasets. Experimental results show that the MAT-EffNet outperforms other state-of-the-art approaches for action recognition.

Due to the inefficiency of multiple binary images encryption, a parallel binary image encryption framework based on the typical variants of spiking neural networks, spiking neural P (SNP) systems is proposed in this paper. More specifically, the two basic units in the proposed image cryptosystem, the permutation unit and the diffusion unit, are designed through SNP systems with multiple channels and polarizations (SNP-MCP systems), and SNP systems with astrocyte-like control (SNP-ALC systems), respectively. Different from the serial computing of the traditional image permutation/diffusion unit, SNP-MCP-based permutation/SNP-ALC-based diffusion unit can realize parallel computing through the parallel use of rules inside the neurons. Theoretical analysis results confirm the high efficiency of the binary image proposed cryptosystem. Security analysis experiments demonstrate the security of the proposed cryptosystem.

Landslide recognition is widely used in natural disaster risk management. Traditional landslide recognition is mainly conducted by geologists, which is accurate but inefficient. This article introduces multiple instance learning (MIL) to perform automatic landslide recognition. An end-to-end deep convolutional neural network is proposed, referred to as Multiple Instance Learning–based Landslide classification (MILL). First, MILL uses a large-scale remote sensing image classification dataset to build pre-train networks for landslide feature extraction. Second, MILL extracts instances and assign instance labels without pixel-level annotations. Third, MILL uses a new channel attention–based MIL pooling function to map instance-level labels to bag-level label. We apply MIL to detect landslides in a loess area. Experimental results demonstrate that MILL is effective in identifying landslides in remote sensing images.

As the most widely applied mobile operating system for smartphones, Android is challenged by fast growing security problems, which are caused by malicious applications. Behaviors of malicious applications have become more and more inconspicuous, which largely increase the difficulties of security detection. This paper provides a new security sandbox approach of Android based on hook mechanism, to further enrich Android malware detection technologies. This new sandbox monitors the behaviors of target application by using a process hook-based dynamic tracking method during its running period. Compared to existing techniques, (1) this approach can create a virtual space where apk can be installed, run, and uninstalled, and it is isolated from the outside and (2) a risk assessment approach based on behavior analysis is given so that users can obtain an explicit risk prognosis for an application to improve their safety. Tests on malware and normal application samples verify this new security sandbox.