为营造学院良好的学术环境氛围，本周将举办学术交流报告会，供师生和学生之间相互交流讨论，具体安排如下。

日期：2020年12月3日（周四）

时间：15：00（下午三点）

地点：理科楼B311

汇报人：18级李卓宙

论文题目：

《Intelligent Detection for Key Performance Indicators in Industrial-Based Cyber-Physical Systems》

论文简介：

Intelligent anomaly detection for Key Performance Indicators (KPIs) is important for keeping services reliable in industrial-based Cyber-Physical Systems (CPS). However, it is common in practice for various KPI sampling strategies to be utilized. We experimentally verify that anomaly detection is highly sensitive to irregular sampling, and accordingly go on to investigate low-cost anomaly detection for large-scale irregular KPIs. Irregular KPIs can be classified into four types: Equal Interval and Unequal Quantity (EIUQ) KPIs, Unequal Interval (UI) KPIs, Unequal Interval with Equal Duration (UIED) KPIs, and segmented irregular KPIs. We propose an anomaly detection framework based on these irregular types. Moreover, to handle the various lengths and phase shifts among EIUQ KPIs, we propose a Normalized version of Unequal Cross-Correlation (NUCC), which slides the KPIs to enable finding the most similar position. To avoid high computational costs, we analyze the low-rank feature of KPIs data and propose a matrix factorization-based alignment algorithm for UIED KPIs; this algorithm treats UIED KPIs as an incomplete matrix and recovers the KPIs to align them before performing anomaly detection. Extensive simulations using three public datasets and two real-world datasets demonstrate that our algorithm can achieve a larger F1-score than Minkowski distance and less time than dynamic time warping distance.

录用期刊：IEEE Transactions on Industrial Informatics（中科院一区）

汇报人：18级彭景盛

论文题目：

《Distributed Probabilistic Offloading in Edge Computing for 6G-enabled Massive Internet of Things》

论文简介：

Mobile edge computing (MEC) is expected to provide reliable and low-latency computation offloading for massive Internet of Things (IoT) with the next generation networks, such as the sixth-generation (6G) network. However, the successful implementation of 6G depends on network densification, which brings new offloading challenges for edge computing, one of which is how to make offloading decisions facing densified servers considering both channel interference and queuing, which is an NP-hard problem. This paper proposes a Distributed-Two-Stage Offloading (DTSO) strategy to give trade-off solutions. In the first stage, by introducing the queuing theory and considering channel interference, a combinatorial optimization problem is formulated to calculate the offloading probability of each station. In the second stage, the original problem is converted to a non-linear optimization problem, which is solved by a designed Sequential Quadratic Programming (SQP) algorithm. To make an adjustable trade-off between the latency and energy requirement among heterogeneous applications, an elasticity parameter is specially designed in DTSO. Simulation results show that, compared to the latest works, DTSO can effectively reduce latency and energy consumption and achieve a balance between them based on application preferences.

录用期刊：IEEE Internet of Things Journal（中科院一区）

汇报人：19级李宇涛

论文题目：

《A Novel Image Classification Approach via Dense-MobileNet Models》

论文简介：

As a lightweight deep neural network, MobileNet has fewer parameters and higher classification accuracy. In order to further reduce the number of network parameters and improve the classification accuracy, dense blocks that are proposed in DenseNets are introduced into MobileNet. In Dense-MobileNet models, convolution layers with the same size of input feature maps in MobileNet models are taken as dense blocks, and dense connections are carried out within the dense blocks. 1e new network structure can make full use of the output feature maps generated by the previous convolution layers in dense blocks, so as to generate a large number of feature maps with fewer convolution cores and repeatedly use the features. By setting a small growth rate, the network further reduces the parameters and the computation cost. Two Dense-MobileNet models, Dense1-MobileNet and Dense2-MobileNet, are designed. Experiments show that Dense2-MobileNet can achieve higher recognition accuracy than MobileNet, while only with fewer parameters and computation cost.

录取期刊：Mobile Information Systems | Hindawi