Applications of sketches in network traffic measurement: A survey

Accurate and timely network traffic measurement is essential for network status monitoring, network fault analysis, network intrusion detection, and network security management. With the rapid development of the network, massive network traffic brings severe challenges to network traffic measurement. However, existing measurement methods suffer from many limitations for effectively recording and accurately analyzing big-volume traffic. Recently, sketches, a family of probabilistic data structures that employ hashing technology for summarizing traffic data, have been widely used to solve these problems. However, current literature still lacks a thorough review on sketch-based traffic measurement methods to offer a comprehensive insight on how to apply sketches for fulfilling various traffic measurement tasks. In this paper, we provide a detailed and comprehensive review on the applications of sketches in network traffic measurement. To this end, we classify the network traffic measurement tasks into four categories based on the target of traffic measurement, namely cardinality estimation, flow size estimation, change anomaly detection, and persistent spreader identification. First, we briefly introduce these four types of traffic measurement tasks and discuss the advantages of applying sketches. Then, we propose a series of requirements with regard to the applications of sketches in network traffic measurement. After that, we perform a fine-grained classification for each sketch-based measurement category according to the technologies applied on sketches. During the review, we evaluate the performance, advantages and disadvantages of current sketch-based traffic measurement methods based on the proposed requirements. Through the thorough review, we gain a number of valuable implications that can guide us to choose and design proper traffic measurement methods based on sketches. We also review a number of general sketches that are highly expected in modern network systems to simultaneously perform multiple traffic measurement tasks and discuss their performance based on the proposed requirements. Finally, through our serious review, we summarize a number of open issues and identify several promising research directions.     

Revisiting the Parallel Strategy for DOACROSS Loops

Journal of Computer Science and Technology - DOACROSS loops are significant parts in many important scientific and engineering applications, which are generally exploited pipeline/wave-front...     

基于高斯混合隐马尔科夫模型与人工神经网络的紧急换道行为预测方法

为了有效降低因驾驶员紧急换道行为而诱发的交通事故，提高道路交通事故链阻断效率，提出一种基于高斯混合隐马尔科夫模型（GMM-HMM）和人工神经网络（ANN）的紧急换道行为预测方法。首先利用GMM-HMM对车辆行驶状态以及驾驶行为连续观察序列进行换道意图辨识，采用ANN预测下一时段的驾驶行为，再预测换道过程中的横向加速度变化率，从而判断紧急换道的危险程度。驾驶员在环仿真实验及实车实验结果表明，该方法预测避险成功率达92.83%，实验避险成功率达90.32%。该方法能有效地对紧急换道行为进行提前警告与干预。     

基于LSTM-RF模型的城市道路交通流预测

交通流预测作为信号协调和出行时间预测等任务的基础,成为了交通领域的研究点。对于交通流预测问题,研究人员提出了多种方法,但这些方法大多只使用交通流数据的时域信息进行交通流预测,忽略了空间相关性对于预测目标路段流的影响,导致预测精度不理想。基于组合模型的思想提出了一种称为LSTM-RF的交通流预测模型。在交通流预测过程中,首先使用LSTM模型提取预测目标路段的时序特征,再将其预测值与采集的相邻上下游路段信息同时作为随机森林模型的输入特征,进行交通流时空相关性分析,获得最终的预测结果。并通过贵阳市车牌识别系统采集的城区132条路段的交通流数据进行实验验证。结果表明:该方法在预测精度上优于单一模型,并且预测误差相比单一模型有明显减少。     

Profiling drunk driving recidivists in Denmark

Drunk drivers are a menace to themselves and to other road users, as drunk driving significantly increases the risk of involvement in road accidents and the probability of severe or fatal injuries. Although injuries and fatalities related to road accidents have decreased in recent decades, the prevalence of drunk driving among drivers killed in road accidents has remained stable, at around 25% or more during the past 10 years. Understanding drunk driving, and in particular, recidivism, is essential for designing effective countermeasures, and accordingly, the present study aims at identifying the differences between non-drunk drivers, drunk driving non-recidivists and drunk driving recidivists with respect to their demographic and socio-economic characteristics, road accident involvement and other traffic and non-traffic-related law violations. This study is based on register-data from Statistics Denmark and includes information from 2008 to 2012 for the entire population, aged 18 or older, of Denmark. The results from univariate and multivariate statistical analyses reveal a five year prevalence of 17% for drunk driving recidivism, and a significant relation between recidivism and the drunk drivers’ gender, age, income, education, receipt of an early retirement pension, household type, and residential area. Moreover, recidivists are found to have a higher involvement in alcohol-related road accidents, as well as other traffic and, in particular, non-traffic-related offences. These findings indicate that drunk driving recidivism is more likely to occur among persons who are in situations of socio-economic disadvantage and marginalisation. Thus, to increase their effectiveness, preventive measures aiming to reduce drunk driving should also address issues related to the general life situations of the drunk driving recidivists that contribute to an increased risk of drunk driving recidivism.     

Integer programming methods for large-scale practical classroom assignment problems

In this paper we present an integer programming method for solving the Classroom Assignment Problem in University Course Timetabling. We introduce a novel formulation of the problem which generalises existing models and maintains tractability even for large instances. The model is validated through computational results based on our experiences at the University of Auckland, and on instances from the 2007 International Timetabling Competition. We also expand upon existing results into the computational difficulty of room assignment problems.     

Traffic video‐based intelligent traffic control system for smart cities using modified ant colony optimizer

Road traffic congestion is a serious problem in today's world and it happens because of urbanization and population growth. The traffic reduces the transport efficiency in the city, increases the waiting time and travel time, and also increases the usage of fuel and air pollution. To overcome these issues this papers propose an intelligent traffic control system using the Internet of Vehicles (IoV). The vehicles or nodes present in the IoV can communicate between themselves. This technique helps in determining the traffic intensity and the best route to reach the destination. The area of study used in this paper is Vellore city in Tamilnadu, India. The city map is separated into many segments of equal size and Ant Colony Algorithm (AOC) is applied to the separated maps to find the optimal route to reach the destination. Further, Support Vector Machine (SVM) is used to calculate the traffic density and to model the heavy traffic. The proposed algorithm performs better in finding the optimal route when compared to that of the existing path selection algorithms. From the results, it is evident that the proposed IoV‐based route selection method provides better performance.     

交通事故的自动判案研究

该文针对法律领域民事案件中的“交通事故”类案件进行研究,期望在该“交通事故”数据集上实现自动判案。从“中国裁判文书网”采集14 000条数据文本,并对数据进行人工标注。基于对数据集的分析,分别对数据进行粗粒度和细粒度分类,粗粒度为4类,细粒度为8类。该文使用了三种模型: 基于SVM的模型、基于BI-GRU的模型和基于Attention+BI-GRU的模型。实验结果表明: 在该数据集上,对数据进行粗粒度分类时,基于Attention+BI-GRU的模型F1值为80.26%,基于SVM的模型为77.24%,基于BI-GRU的模型为72.65%。在细粒度分类时,基于BI-GRU的模型F1值为48.59%,基于SVM的模型为38.29%,基于Attention+BI-GRU的模型为40.87%。     

Survey on Passivity Based Control of Induction Machine

Induction machines (IM) constitute a theoretically interesting and practically important class of nonlinear systems. They are frequently used as wind generators for their power/cost ratio. They are described by a fifth‐order nonlinear differential equation with two inputs and only three state variables available for measurement. The control task is further complicated by the fact that IM are subject to unknown (load) disturbances and the parameters can be of great uncertainty. One is then faced with the challenging problem of controlling a highly nonlinear system, with unknown time‐varying parameters, where the regulated output, besides being unmeasurable, is perturbed by an unknown additive signal. Passivity‐based control (PBC) is a well‐established structure‐preserving design methodology which has shown to be very powerful to design robust controllers for physical systems described by Euler‐Lagrange equations of motion. PBCs provide a natural procedure to "shape" the potential energy yielding controllers with a clear physical interpretation in terms of interconnection of the system with its environment and are robust vis á vis to unmodeled dissipative effects. One recent approach of PBC is the Interconnection and Damping Assignment Passivity‐Based Control (IDA‐PBC) which is a very useful technique to control nonlinear systems assigning a desired (Port‐Controlled Hamiltonian) structure to the closed‐loop. The aim of this paper is to give a survey on different PBC of IM. The originality of this work is that the author proves that the well known field oriented control of IM is a particular case of the IDA‐PBC with disturbance.