We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

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Convex Analysisの二,三の進展について

Digital processing of speech signals

Vehicular networking has significant potential to enable diverse applications associated with traffic safety, traffic efficiency and infotainment. In this survey and tutorial paper we introduce the basic characteristics of vehicular networks, provide an overview of applications and associated requirements, along with challenges and their proposed solutions. In addition, we provide an overview of the current and past major ITS programs and projects in the USA, Japan and Europe. Moreover, vehicular networking architectures and protocol suites employed in such programs and projects in USA, Japan and Europe are discussed.

Vehicular Networking: A Survey and Tutorial on Requirements, Architectures, Challenges, Standards and Solutions

A comprehensive survey on vehicular Ad Hoc network

Speech emotion recognition, as an important auxiliary component of speech interaction technology, has always been a research hotspot. In this work, we propose a novel framework for speech emotion recognition based on deep neural network. The proposed framework is composed of two main modules: a local feature extractor module that utilizes deep recurrent layers to extract frame-level feature representations and a global feature integration module that learns utterance-level representations for emotion recognition. Two architectures, one multi-granularity convolutional layer and one multi-scale attentive layer are constructed for the feature integration module. Furthermore, we adopt two data augmentation approaches, noise injection and vocal tract length perturbation which both improve the performance and robustness of models and reduce the influence of individual variations. The proposed models achieve recognition accuracies of 92.08% and 90.41% on Emo-DB and CASIA dataset, respectively. In addition, ablation experiments are conducted to show the effectiveness of the proposed feature integration module and data augmentation approaches.

FI-Net: A Speech Emotion Recognition Framework with Feature Integration and Data Augmentation

Vehicular Ad Hoc Networks (VANETs) have attracted increasing interest over the past decade, but the design of an efficient routing method for VANETs is still a challenging task. Via analyzing characteristics of urban VANETs (such as the heterogeneous vehicular types and the impact of traffic lights), we believe that buses can be used as the mobile backbone of VANETs to aid data transmissions. In this paper, we propose a table-driven bus-based routing protocol to lighten the control of packet delivery and reduce the average delay. Simulation results demonstrate a significant improvement in terms of delivery ratio and average delay over traditional routing protocols.

Table-Driven Bus-Based Routing Protocol for Urban Vehicular Ad Hoc Networks

Delay Tolerant Network (DTN) is one kind of emerging networks characterized by long delay and intermittent connectivity. Traditional ad hoc routing protocols are inapplicable or perform poorly in DTNs because nodes are seldom fully connected. In recent years, many routing protocols (especially social-based routing) are proposed to improve the delivery ratio in DTNs, but most of them do not consider the load of nodes thus may lead to unbalanced energy consumption among nodes. In this paper, we propose an Energy Efficient Social-based Routing (EESR) protocol to reduce the load of nodes while maintaining the delivery ratio within an acceptable range by limiting the chances of forwarding in traditional social-based routing. Furthermore, we also propose an improved version of EESR to dynamically adjust the controlling parameter. Simulation results on real-life DTN traces demonstrate the efficiency of our proposed algorithms.

Energy Efficient Social-Based Routing for Delay Tolerant Networks

In Delay Tolerant Networks (DTNs), nodes seldom keep in contact with each other because of the disorder movements of nodes. Thus many traditional routing protocols may not be applicable in this emerging network. In recent years, many routing protocols have been proposed to improve the routing performance in DTNs, however, most of them do not fully mine the inherent regular pattern of the movements of nodes. In this paper, we propose a Markov Chain Prediction (MCP) model for routing in DTNs to mine the movements of nodes property with historical contact information and use this model to improve the delivery ratio of message transferring within the network.

A Markov Chain Prediction Model for Routing in Delay Tolerant Networks

There is a growing trend for service providers (SPs) to migrate their multi-tier applications from local clusters to public cloud networks. In the cloud environment, it is a great concern of SPs to improve the availability of their virtual networks, in addition to guarantees of their virtual resource requirements, such as VMs and bandwidth. However, meeting these requirements results in an inefficient utilization of physical resources, which goes against the operational goal of cloud providers. To address this challenge, in this paper, we propose an availability-aware virtual network embedding framework that simultaneously improves the availability of virtual networks and the resource efficiency. We first propose a new metric to quantify the availability cost of a multi-tier virtual network, and then formulate the embedding problem as a joint optimization of the aggregated bandwidth and availability costs. Due to the NP-hardness of the embedding problem, we devise a heuristic algorithm that can solve the problem in polynomial time. Extensive simulation results show that the proposed algorithm enables CP to achieve various trade-offs between resource efficiency and availability, and to gain more revenue (e.g., 16.1% under a datacenter load of 80%) than the availability-agonistic algorithm.

Fan Li

Papers

FI-Net: A Speech Emotion Recognition Framework with Feature Integration and Data Augmentation

Table-Driven Bus-Based Routing Protocol for Urban Vehicular Ad Hoc Networks

Energy Efficient Social-Based Routing for Delay Tolerant Networks

A Markov Chain Prediction Model for Routing in Delay Tolerant Networks

Availability-Aware Virtual Network Embedding for Multi-tier Applications in Cloud Networks