Fog-enabled Edge Learning for Cognitive Content-Centric Networking in 5G

By caching content at network edges close to the users, the content-centric networking (CCN) has been considered to enforce efficient content retrieval and distribution in the fifth generation (5G) networks. Due to the volume, velocity, and variety of data generated by various 5G users, an urgent and strategic issue is how to elevate the cognitive ability of the CCN to realize context-awareness, timely response, and traffic offloading for 5G applications. In this article, we envision that the fundamental work of designing a cognitive CCN (C-CCN) for the upcoming 5G is exploiting the fog computing to associatively learn and control the states of edge devices (such as phones, vehicles, and base stations) and in-network resources (computing, networking, and caching). Moreover, we propose a fog-enabled edge learning (FEL) framework for C-CCN in 5G, which can aggregate the idle computing resources of the neighbouring edge devices into virtual fogs to afford the heavy delay-sensitive learning tasks. By leveraging artificial intelligence (AI) to jointly processing sensed environmental data, dealing with the massive content statistics, and enforcing the mobility control at network edges, the FEL makes it possible for mobile users to cognitively share their data over the C-CCN in 5G. To validate the feasibility of proposed framework, we design two FEL-advanced cognitive services for C-CCN in 5G: 1) personalized network acceleration, 2) enhanced mobility management. Simultaneously, we present the simulations to show the FEL's efficiency on serving for the mobile users' delay-sensitive content retrieval and distribution in 5G.Comment: Submitted to IEEE Communications Magzine, under review, Feb. 09, 201

2-[(E)-2-(Nitro­methyl­idene)imidazolidin-1-yl]ethanol

In the title compound, C6H11N3O3, the imidazolidine NH group is involved in a three-center N—H⋯O hydrogen bond, with intra­molecular and inter­molecular branches, to the nitro group O atoms. The centrosymmetric dimers that are formed are further connected by O—H⋯O hydrogen bonds between the hy­droxy and nitro groups into a two-dimensional polymeric structure extending parallel to (101)

1-[(1,3-Dithio­lan-2-yl)meth­yl]-6-methyl-8-nitro-1,2,3,5,6,7-hexa­hydro­imidazo[1,2-c]pyrimidine

In the title compound, C11H18N4O2S2, the dithiol­ane ring displays an envelope conformation, the tetra­hydro­pyrimidine ring has a conformation that is between half-chair and screw-boat, and the imidazole ring is essentially planar (r.m.s. deviation = 0.0017 Å). No significant directional inter­molecular inter­actions are present in the structure

Strange nonchaotic attractors and multistability in a two-degree-of-freedom quasiperiodically forced vibro-impact system

Acknowledgments This work is supported by the National Natural Science Foundation of China (NNSFC) (Nos. 11672249, 12072291, and 11732014).Peer reviewedPostprin

Few-shot Multi-domain Knowledge Rearming for Context-aware Defence against Advanced Persistent Threats

Advanced persistent threats (APTs) have novel features such as multi-stage penetration, highly-tailored intention, and evasive tactics. APTs defense requires fusing multi-dimensional Cyber threat intelligence data to identify attack intentions and conducts efficient knowledge discovery strategies by data-driven machine learning to recognize entity relationships. However, data-driven machine learning lacks generalization ability on fresh or unknown samples, reducing the accuracy and practicality of the defense model. Besides, the private deployment of these APT defense models on heterogeneous environments and various network devices requires significant investment in context awareness (such as known attack entities, continuous network states, and current security strategies). In this paper, we propose a few-shot multi-domain knowledge rearming (FMKR) scheme for context-aware defense against APTs. By completing multiple small tasks that are generated from different network domains with meta-learning, the FMKR firstly trains a model with good discrimination and generalization ability for fresh and unknown APT attacks. In each FMKR task, both threat intelligence and local entities are fused into the support/query sets in meta-learning to identify possible attack stages. Secondly, to rearm current security strategies, an finetuning-based deployment mechanism is proposed to transfer learned knowledge into the student model, while minimizing the defense cost. Compared to multiple model replacement strategies, the FMKR provides a faster response to attack behaviors while consuming less scheduling cost. Based on the feedback from multiple real users of the Industrial Internet of Things (IIoT) over 2 months, we demonstrate that the proposed scheme can improve the defense satisfaction rate.Comment: It has been accepted by IEEE SmartNet

(E)-1-(2,2-Dimeth­oxy­eth­yl)-2-(nitro­methyl­idene)imidazolidine

In the title compound, C8H15N3O4, the 2-(nitro­methyl­ene)imidazolidine fragment is close to being planar (r.m.s. deviation = 0.027 Å), which may be correlated with delocalization of the electrons and the effect of the strongly electron-withdrawing NO2 group. An intra­molecular N—H⋯O link generates an S(6) ring. The same H atom also forms a weak inter­molecular N—H⋯O hydrogen bond, which results in C(7) chains propagating in [010]

Multistability in a quasiperiodically forced piecewise smooth dynamical system

This work is supported by the National Natural Science Foundation of China (11672249, 11732014 and 11572263).Peer reviewedPostprin

Quantifying strange property of attractors in quasiperiodically forced systems

This work is supported by the National Natural Science Foundation of China (Nos. 11832009, 12002300, 12072291 and 12362002), and the Natural Science Foundation of Hebei Province, China (Grant No. A2021203013).Peer reviewedPostprin

Graph Anomaly Detection at Group Level: A Topology Pattern Enhanced Unsupervised Approach

Graph anomaly detection (GAD) has achieved success and has been widely applied in various domains, such as fraud detection, cybersecurity, finance security, and biochemistry. However, existing graph anomaly detection algorithms focus on distinguishing individual entities (nodes or graphs) and overlook the possibility of anomalous groups within the graph. To address this limitation, this paper introduces a novel unsupervised framework for a new task called Group-level Graph Anomaly Detection (Gr-GAD). The proposed framework first employs a variant of Graph AutoEncoder (GAE) to locate anchor nodes that belong to potential anomaly groups by capturing long-range inconsistencies. Subsequently, group sampling is employed to sample candidate groups, which are then fed into the proposed Topology Pattern-based Graph Contrastive Learning (TPGCL) method. TPGCL utilizes the topology patterns of groups as clues to generate embeddings for each candidate group and thus distinct anomaly groups. The experimental results on both real-world and synthetic datasets demonstrate that the proposed framework shows superior performance in identifying and localizing anomaly groups, highlighting it as a promising solution for Gr-GAD. Datasets and codes of the proposed framework are at the github repository https://anonymous.4open.science/r/Topology-Pattern-Enhanced-Unsupervised-Group-level-Graph-Anomaly-Detection