Power Aware Routing in Mobile Ad Hoc Networks

A fast image encryption algorithm based on chaotic map

Emotions appear to be a key determinant of behavior in cooperative relationships. Emotions affect behavior both directly, by motivating action, and indirectly, as actors anticipate others’ emotional responses. The influence of emotions is understandable once it is recognized that (a) the ability to benefit from cooperative relationships has been a key determinant of biological fitness throughout our species’ history, and (b) panhuman emotions are adaptations crafted by natural selection. Different emotions affect cooperative behavior in different ways: some emotions lead actors to forego the temptation to defect, some lead them to reciprocate harm suffered or benefits provided, and some lead them to repair damaged relationships. An important class of emotions influences cooperative behavior in part by motivating conformity to norms and/or punishment of norm violators. This chapter discusses thirteen emotions that seem to have the greatest impact on cooperation. In addition to reviewing empirical evidence of the role of emotions in cooperation, the chapter presents a variety of explanatory hypotheses and provides a number of discrete testable predictions.

Genetic and cultural evolution of cooperation

Four relations are defined the use of which allow a reliability network to be expressed as a mathematical statement. Series and active and standby redundancy connections are allowed. The possibility of requiring more than one of the active branches to be operative is included. At the very least the result allows for typing in place of drawing a network description.

http://ieeexplore.ieee.org/iel5/24/5216426/05216446.pdf

Network Calculus

A hyper-chaos-based image encryption algorithm using pixel-level permutation and bit-level permutation

Accurate searching in current structured P2P network is popular, but range query is very difficult and complex. In this paper, based on the Pastry, we propose a resources allocating mechanism for searching the objects among certain ranges in P2P network, and give the searching algorithm in detail. This mechanism does not need change the original P2P infrastructure, the algorithm is simple and easy to implement, and the simulation results show that the efficiency of searching is very good.

A Range Query Technology for Structured P2P Network

The recent success of Artificial Intelligence (AI) has enabled autonomous driving with better perception capabilities. However, on-ramp merging remains one of the main challenging scenarios for reliable autonomous driving. Within the limited onboard sensing range, a merging vehicle can hardly observe and predict the main road conditions properly, restricting appropriate merging maneuvers. In this poster, we outline ongoing research ideas for reliable and autonomous on-ramp merging assisted by vehicular communications. By jointly leveraging the basic safety messages (BSM) from neighboring vehicles and the surveillance images, a merging vehicle can perform reliable driving via robust multimodal reinforcement learning. Some experimental results are provided to evaluate our idea under the Simulation of Urban MObility (SUMO) platform.

Poster: Reliable On-Ramp Merging via Multimodal Reinforcement Learning

Most object detection methods have achieved impressive performance on several public benchmarks, instead, facing underwater detection tasks, it is challenging to detect marine targets because of the inherent illumination inhomogeneity in underwater images. Moreover, the imbalanced foreground-background proposals further aggravate the situation of capturing marine organisms. To address the problems, we analyze the deficiency of existing feature pyramid structures and propose a multi-depth and multi-breadth pyramid architecture named Refined Pyramid Grid (RPG). A Harmonizing Focal Loss (HFL) is then proposed to generalize the discrete labels in focal loss to the continuous version to improve the optimization. Experimental results on the real-world datasets have demonstrated the efficiency and reliability of the proposed framework regarding underwater object detection tasks. 

Towards Scale Adaptive Underwater Detection Through Refined Pyramid Grid

Graph Neural Networks (GNNs) as an emerging technique have shown excellent performance in a variety of fields, such as social networks and recommendation systems. However, GNNs may have to overcome privacy concerns as large amounts of information about their training datasets may be compromised. In this paper, we develop a privacy-preserving GNN to enforce privacy preservation, which utilizes a private Functional Mechanism (FM) to train the learning model. This mechanism perturbs the polynomial approximation of the objective function to enforce Differential Privacy (DP) in the GNN model. We show that our method can maximize the accuracy of the results with comparable prediction power to the unperturbed results while satisfying the privacy guarantees. 

Efficient Privacy Preserving Graph Neural Network for Node Classification

Due to the unstable and complex social network environment, the sole user–item interaction data become insufficient for generating precise recommendations. However, too much emphasis on user–item interactions prevents the discovery of internal connections among them, such as trustworthy user relations. In this work, we have integrated the collaborative and the sequential relations into an end-to-end graph neural network (GNN) simultaneously and proposed a novel framework, namely multirelational collaborative filtering (MRCF), to explore the evolutional social relations. MRCF mainly consists of two components: relational GNN (RGNN) and simple dot-product attention (SDPA), where RGNN is used to capture not only the collaborative but also the sequential relationship from reliable user–item historical interactions through the graph representation, while SDPA can further concentrate on the dominated interaction sequences between users and items. Moreover, a negative sampling method based on user interest is proposed to help train our model. Extensive experiments on three real-world datasets show that the proposed model performs competitively with other state-of-the-art methods in CF. 

Xiaoheng Deng

Papers

A Range Query Technology for Structured P2P Network

Poster: Reliable On-Ramp Merging via Multimodal Reinforcement Learning

Towards Scale Adaptive Underwater Detection Through Refined Pyramid Grid

Efficient Privacy Preserving Graph Neural Network for Node Classification

Multirelational Collaborative Filtering for Global Graph Neural Networks to Mine Evolutional Social Relations