There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

What is Twitter

Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction toward cognition and human-level intelligence. In this survey, we provide a comprehensive review of the knowledge graph covering overall research topics about: 1) knowledge graph representation learning; 2) knowledge acquisition and completion; 3) temporal knowledge graph; and 4) knowledge-aware applications and summarize recent breakthroughs and perspective directions to facilitate future research. We propose a full-view categorization and new taxonomies on these topics. Knowledge graph embedding is organized from four aspects of representation space, scoring function, encoding models, and auxiliary information. For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference, and logical rule reasoning are reviewed. We further explore several emerging topics, including metarelational learning, commonsense reasoning, and temporal knowledge graphs. To facilitate future research on knowledge graphs, we also provide a curated collection of data sets and open-source libraries on different tasks. In the end, we have a thorough outlook on several promising research directions.

A Survey on Knowledge Graphs: Representation, Acquisition and Applications

https://uwe-repository.worktribe.com/preview/871355/IoT5m.pdf

5G Internet of Things: A survey

The past decade has witnessed the rapid evolution in blockchain technologies, which has attracted tremendous interests from both the research communities and industries. The blockchain network was originated from the Internet financial sector as a decentralized, immutable ledger system for transactional data ordering. Nowadays, it is envisioned as a powerful backbone/framework for decentralized data processing and data-driven self-organization in flat, open-access networks. In particular, the plausible characteristics of decentralization, immutability, and self-organization are primarily owing to the unique decentralized consensus mechanisms introduced by blockchain networks. This survey is motivated by the lack of a comprehensive literature review on the development of decentralized consensus mechanisms in blockchain networks. In this paper, we provide a systematic vision of the organization of blockchain networks. By emphasizing the unique characteristics of decentralized consensus in blockchain networks, our in-depth review of the state-of-the-art consensus protocols is focused on both the perspective of distributed consensus system design and the perspective of incentive mechanism design. From a game-theoretic point of view, we also provide a thorough review of the strategy adopted for self-organization by the individual nodes in the blockchain backbone networks. Consequently, we provide a comprehensive survey of the emerging applications of blockchain networks in a broad area of telecommunication. We highlight our special interest in how the consensus mechanisms impact these applications. Finally, we discuss several open issues in the protocol design for blockchain consensus and the related potential research directions.

/pdf/a-survey-on-consensus-mechanisms-and-mining-strategy-13qvwin6yd.pdf

A Survey on Consensus Mechanisms and Mining Strategy Management in Blockchain Networks

In this paper, we generally formulate the dynamics prediction problem of various network systems (e.g., the prediction of mobility, traffic and topology) as the temporal link prediction task. Different from conventional techniques of temporal link prediction that ignore the potential non-linear characteristics and the informative link weights in the dynamic network, we introduce a novel non-linear model GCN-GAN to tackle the challenging temporal link prediction task of weighted dynamic networks. The proposed model leverages the benefits of the graph convolutional network (GCN), long short-term memory (LSTM) as well as the generative adversarial network (GAN). Thus, the dynamics, topology structure and evolutionary patterns of weighted dynamic networks can be fully exploited to improve the temporal link prediction performance. Concretely, we first utilize GCN to explore the local topological characteristics of each single snapshot and then employ LSTM to characterize the evolving features of the dynamic networks. Moreover, GAN is used to enhance the ability of the model to generate the next weighted network snapshot, which can effectively tackle the sparsity and the wide-value-range problem of edge weights in real-life dynamic networks. To verify the model’s effectiveness, we conduct extensive experiments on four datasets of different network systems and application scenarios. The experimental results demonstrate that our model achieves impressive results compared to the state-of-the-art competitors.

GCN-GAN: A Non-linear Temporal Link Prediction Model for Weighted Dynamic Networks

Powered by a number of smart devices distributed throughout the whole network, the Internet of Things (IoT) is supposed to provide services computing for massive data from devices. Fog computing, an extension of cloud-based IoT-oriented solutions, has emerged with requirements for distribution and decentralization. In this respect, the conjunction with Blockchain provides a natural solution for decentralization, as well as potentially helps fog computing overcome some deficiencies such as security and privacy then consequently expand the application scope of IoT. However, one of the key challenges of blockchain's integration with fog computing is scalability. To end this, this work proposes Groupchain, a novel scalable public blockchain of a two-chain structure suitable for fog computing of IoT services computing. Groupchain employs the leader group to collectively commit blocks for higher transaction efficiency and introduces bonus and deposit into the incentive mechanism to supervise behaviors of members in the leader group. Our security analysis shows that Groupchain retains the security of Bitcoin-like blockchain and enhances defense against attacks such as double-spend and selfish mining. We implement a prototype of Groupchain and conducted experiments. The experimental results demonstrate that Groupchain achieves optimization on transaction throughput and confirmation latency which are argued in Bitcoin.

Groupchain: Towards a Scalable Public Blockchain in Fog Computing of IoT Services Computing

With the emergence of ever-advancing network threats, the guarantee of system security becomes increasingly crucial, especially in the dynamic and decentralized ad-hoc networks. One essential part of cybersecurity is intrusion detection, which identifies anomalous activities according to traffic patterns. However, the class-imbalanced data have caused a challenging problem where the number of abnormal samples is significantly lower than that of the normal ones. This class imbalance problem confines the performance of intrusion classifiers and results in low robustness to unknown anomalies. In this paper, we propose a novel Imbalanced Generative Adversarial Network (IGAN) to tackle the class imbalance problem. In the primary novelty of our model, we introduce an imbalanced data filter and convolutional layers to the typical GAN, generating new representative instances for minority classes. Further, an IGAN-based Intrusion Detection System, namely IGAN-IDS, is established to cope with class-imbalanced intrusion detection, using the instances generated by IGAN. Concretely, IGAN-IDS consists of three modules: feature extraction, IGAN, and deep neural network. First, we utilize a feed-forward neural network (FNN) to transform raw network attributes into feature vectors. Then, the IGAN generates new samples expressed in the latent space. Finally, the deep neural network, composed of convolutional layers and fully-connected layers, executes the final intrusion detection. We conduct experiments on three benchmark datasets to evaluate the performance of IGAN-IDS, comparing against 15 other methods. The experimental results demonstrate that our proposed IGAN-IDS outperforms the state-of-the-art approaches.

IGAN-IDS: An imbalanced generative adversarial network towards intrusion detection system in ad-hoc networks

Heterogeneous Internet of Things (IoT) and multi-access mobile edge computing (MA-MEC) are believed as supporting technologies for building a smart city. The advancement and flourish of IoT are facilitating the entry of human society into the Internet of Everything era, which lay the foundation of the smart city. To address the conflict between computation capability and low-cost mobile devices in IoT, the MA-MEC is available for supporting the resource-limited and computation-sensitive services and applications by computation offloading and distributed content delivery/caching. However, deploying cloud computing capability within the radio access network may face serious security threats, which stem from not only the existing technologies and networks but also the MA-MEC-based IoT itself. Therefore, in this paper, the solutions to address the security threats are investigated from physical layer perspectives, since physical layer security technologies have the advantages of achieving perfect secrecy, low-computational complexity, and resource consumption, and good adaptation for channel changes. Specifically, we investigate the secure wiretap coding, resource allocation, signal processing, and multi-node cooperation, along with physical layer key generation and authentication, to cope with the emerging security challenges. Finally, the paper is concluded with some possible future research directions.

/pdf/enhancing-information-security-via-physical-layer-approaches-380hxjjmel.pdf

Enhancing Information Security via Physical Layer Approaches in Heterogeneous IoT With Multiple Access Mobile Edge Computing in Smart City

Recent artificial intelligence research has witnessed great interest in automatically generating text descriptions of images, which are known as the image captioning task. Remarkable success has been achieved on domains where a large number of paired data in multimedia are available. Nevertheless, annotating sufficient data is labor-intensive and time-consuming, establishing significant barriers for adapting the image captioning systems to new domains. In this study, we introduc a novel Multitask Learning Algorithm for cross-Domain Image Captioning (MLADIC). MLADIC is a multitask system that simultaneously optimizes two coupled objectives via a dual learning mechanism: image captioning and text-to-image synthesis, with the hope that by leveraging the correlation of the two dual tasks, we are able to enhance the image captioning performance in the target domain. Concretely, the image captioning task is trained with an encoder–decoder model (i.e., CNN-LSTM) to generate textual descriptions of the input images. The image synthesis task employs the conditional generative adversarial network (C-GAN) to synthesize plausible images based on text descriptions. In C-GAN, a generative model $G$ synthesizes plausible images given text descriptions, and a discriminative model $D$ tries to distinguish the images in training data from the generated images by $G$ . The adversarial process can eventually guide $G$ to generate plausible and high-quality images. To bridge the gap between different domains, a two-step strategy is adopted in order to transfer knowledge from the source domains to the target domains. First, we pre-train the model to learn the alignment between the neural representations of images and that of text data with the sufficient labeled source domain data. Second, we fine-tune the learned model by leveraging the limited image–text pairs and unpaired data in the target domain. We conduct extensive experiments to evaluate the performance of MLADIC by using the MSCOCO as the source domain data, and using Flickr30k and Oxford-102 as the target domain data. The results demonstrate that MLADIC achieves substantially better performance than the strong competitors for the cross-domain image captioning task.

Kai Lei

Papers

GCN-GAN: A Non-linear Temporal Link Prediction Model for Weighted Dynamic Networks

Groupchain: Towards a Scalable Public Blockchain in Fog Computing of IoT Services Computing

IGAN-IDS: An imbalanced generative adversarial network towards intrusion detection system in ad-hoc networks

Enhancing Information Security via Physical Layer Approaches in Heterogeneous IoT With Multiple Access Mobile Edge Computing in Smart City

Multitask Learning for Cross-Domain Image Captioning