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Malware represents a significant security concern in today's digital landscape, as it can destroy or disable operating systems, steal sensitive user information, and occupy valuable disk space.
However, current malware detection methods, such as static-based and dynamic-based approaches, struggle to identify newly developed (``zero-day") malware and are limited by customized virtual machine (VM) environments.
To overcome these limitations, we propose a novel malware detection approach that leverages deep learning, mathematical techniques, and network science.

While Transformer-based pre-trained language models and their variants exhibit strong semantic representation capabilities, the question of comprehending the information gain derived from the additional components of PLMs remains an open question in this field. Motivated by recent efforts that prove Multilayer-Perceptrons (MLPs) modules achieving robust structural capture capabilities, even outperforming Graph Neural Networks (GNNs), this paper aims to quantify whether simple MLPs can further enhance the already potent ability of PLMs to capture linguistic information.

Previous Multimodal Information based Speech Processing (MISP) challenges mainly focused on audio-visual speech recognition (AVSR) with commendable success. However, the most advanced back-end recognition systems often hit performance limits due to the complex acoustic environments. This has prompted a shift in focus towards the Audio-Visual Target Speaker Extraction (AVTSE) task for the MISP 2023 challenge in ICASSP 2024 Signal Processing Grand Challenges.

In the multimodal (text, audio, and visual) sentiment analysis, the current methods generally consider the bi-modal sentiment interaction, resulting in inadequate mining and fusion of relations between modalities. In this paper, we propose the concept of multimodal 3D (3-Dimensional) stereoscopic attention for the first time, which constructs the tri-modal stereoscopic attention with temporal sequences simultaneously to adequately structure the sentiment interaction.

The paper proposes a novel problem in multi-class Multiple-Instance Learning (MIL) called Learning from the Majority Label (LML). In LML, the majority class of instances in a bag is assigned as the bag's label. LML aims to classify instances using bag-level majority classes. This problem is valuable in various applications. Existing MIL methods are unsuitable for LML due to aggregating confidences, which may lead to inconsistency between the bag-level label and the label obtained by counting the number of instances for each class.

Graph attention neural network (GAT) stands as a fundamental model within graph neural networks, extensively employed across various applications. It assigns different weights to different nodes for feature aggregation by comparing the similarity of features between nodes. However, as the amount and density of graph data increases, GAT's computational demands rise steeply. In response, we present FastGAT, a simpler and more efficient graph attention neural network with global-aware adaptive computational node attention.

The utility of machine learning has rapidly expanded in the last two decades and presented an ethical challenge. Papernot et. al. developed a technique, known as Private Aggregation of Teacher Ensembles (PATE) to enable federated learning in which multiple \emph{distributed teachers} are trained on disjoint data sets. This study is the first to apply PATE to an ensemble of quantum neural networks (QNN) to pave a new way of ensuring privacy in quantum machine learning (QML).

This is a poster on a proposed watermarking method. In the research, we first chose position vector domain instead of traditional spatial or frequency domain. In addition, we successfully implemented watermarking on semantic communication system. Third, we modeled watermarking channel so that researchers could systematically research watermarking process.

For more information, please check out the publication at IEEE Xplore:

Semi-supervised change detection (SSCD) utilizes partially labeled data and a large amount of unlabeled data to detect changes. However, the transformer-based SSCD network does not perform as well as the convolution-based SSCD network due to the lack of labeled data. To overcome this limitation, we introduce a new decoder called Cross Branch Feature Fusion (CBFF), which combines the strengths of both local convolutional branch and global transformer branch. The convolutional branch is easy to learn and can produce high-quality features with a small amount of labeled data.

Extracting motion pattern implied in the history trajectory is important for the pedestrian trajectory prediction task. The motion pattern determines how a pedestrian moves, including but not limited to reaction of interaction, tendency of speed and direction change. Although the motion pattern is a comprehensive concept and can’t be described concretely, it is clear that it contains both long-term and short-term factors. Inspired by this, we introduce SpectrumNet which enables more effective encoding of historical motion patterns for trajectory prediction.