Micro-action is an imperceptible non-verbal behaviour characterised by
low-intensity movement. It offers insights into the feelings and intentions of
individuals and is important for human-oriented applications such as emotion
recognition and psychological assessment. However, the identification,
differentiation, and understanding of micro-actions pose challenges due to the
imperceptible and inaccessible nature of these subtle human behaviors in
everyday life. In this study, we innovatively collect a new micro-action
dataset designated as Micro-action-52 (MA-52), and propose a benchmark named
micro-action network (MANet) for micro-action recognition (MAR) task. Uniquely,
MA-52 provides the whole-body perspective including gestures, upper- and
lower-limb movements, attempting to reveal comprehensive micro-action cues. In
detail, MA-52 contains 52 micro-action categories along with seven body part
labels, and encompasses a full array of realistic and natural micro-actions,
accounting for 205 participants and 22,422 video instances collated from the
psychological interviews. Based on the proposed dataset, we assess MANet and
other nine prevalent action recognition methods. MANet incorporates squeeze-and
excitation (SE) and temporal shift module (TSM) into the ResNet architecture
for modeling the spatiotemporal characteristics of micro-actions. Then a
joint-embedding loss is designed for semantic matching between video and action
labels; the loss is used to better distinguish between visually similar yet
distinct micro-action categories. The extended application in emotion
recognition has demonstrated one of the important values of our proposed
dataset and method. In the future, further exploration of human behaviour,
emotion, and psychological assessment will be conducted in depth. The dataset
and source code are released at this https URL

该研究提出了一个用于微动作识别的基准测试集 Micro-action-52（MA-52）和微动作网络（MANet），通过收集全面的微动作线索来解决微动作的识别和理解问题，并展示了该方法在情感识别方面的应用价值。

微动作识别基准：数据集、方法和应用

Benchmarking Micro-action Recognition: Dataset, Methods, and  Applications

MANET is a collection of mobile nodes that communicate through wireless
networks as they move from one point to another. MANET is an
infrastructure-less network with a changeable topology; as a result, it is very
susceptible to attacks. MANET attack prevention represents a serious
difficulty. Malicious network nodes are the source of network-based attacks. In
a MANET, attacks can take various forms, and each one alters the network's
operation in its unique way. In general, attacks can be separated into two
categories: those that target the data traffic on a network and those that
target the control traffic. This article explains the many sorts of assaults,
their impact on MANET, and the MANET-based defence measures that are currently
in place. The suggested SRA that employs blockchain technology (SRABC) protects
MANET from attacks and authenticates nodes. The secure routing algorithm (SRA)
proposed by blockchain technology safeguards control and data flow against
threats. This is achieved by generating a Hash Function for every transaction.
We will begin by discussing the security of the MANET. This article's second
section explores the role of blockchain in MANET security. In the third
section, the SRA is described in connection with blockchain. In the fourth
phase, PDR and Throughput are utilised to conduct an SRA review using
Blockchain employing PDR and Throughput. The results suggest that the proposed
technique enhances MANET security while concurrently decreasing delay. The
performance of the proposed technique is analysed and compared to the routing
protocols Q-AODV and DSR.

介绍了 MANET 网络的安全性问题、攻击分类、以及基于 blockchain 技术的安全路由算法 (SRA)，并通过 PDR 和吞吐量测试，分析了该算法的性能及与 Q-AODV 和 DSR 算法的比较。

使用区块链技术的安全路由协议以缓解 MANET 中的攻击

Secure Routing Protocol To Mitigate Attacks By Using Blockchain  Technology In Manet

Few-shot semantic segmentation aims to recognize novel classes with only very
few labelled data. This challenging task requires mining of the relevant
relationships between the query image and the support images. Previous works
have typically regarded it as a pixel-wise classification problem. Therefore,
various models have been designed to explore the correlation of pixels between
the query image and the support images. However, they focus only on pixel-wise
correspondence and ignore the overall correlation of objects. In this paper, we
introduce a mask-based classification method for addressing this problem. The
mask aggregation network (MANet), which is a simple mask classification model,
is proposed to simultaneously generate a fixed number of masks and their
probabilities of being targets. Then, the final segmentation result is obtained
by aggregating all the masks according to their locations. Experiments on both
the PASCAL-5^i and COCO-20^i datasets show that our method performs comparably
to the state-of-the-art pixel-based methods. This competitive performance
demonstrates the potential of mask classification as an alternative baseline
method in few-shot semantic segmentation. Our source code will be made
available at this https URL.

本文提出了一种基于遮罩的分类方法 MASK Aggregation Network (MANet)，可以更好的解决 few-shot semantic segmentation 中像素关系和对象关系的问题，并在 PASCAL-5^i 和 COCO-20^i 数据集上展现出可与最新像素方法相媲美的表现，证明了基于遮罩的分类方法作为 few-shot semantic segmentation 的一个替代基线方法的潜力。