Studying software logging using topic models

Software developers insert logging statements in their source code to record important runtime information; such logged information is valuable for understanding system usage in production and debugging system failures. However, providing proper logging statements remains a manual and challenging task. Missing an important logging statement may increase the difficulty of debugging a system failure, while too much logging can increase system overhead and mask the truly important information. Intuitively, the actual functionality of a software component is one of the major drivers behind logging decisions. For instance, a method maintaining network communications is more likely to be logged than getters and setters. In this paper, we used automatically-computed topics of a code snippet to approximate the functionality of a code snippet. We studied the relationship between the topics of a code snippet and the likelihood of a code snippet being logged (i.e., to contain a logging statement). Our driving intuition is that certain topics in the source code are more likely to be logged than others. To validate our intuition, we conducted a case study on six open source systems, and we found that i) there exists a small number of “log-intensive” topics that are more likely to be logged than other topics; ii) each pair of the studied systems share 12% to 62% common topics, and the likelihood of logging such common topics has a statistically significant correlation of 0.35 to 0.62 among all the studied systems; and iii) our topic-based metrics help explain the likelihood of a code snippet being logged, providing an improvement of 3% to 13% on AUC and 6% to 16% on balanced accuracy over a set of baseline metrics that capture the structural information of a code snippet. Our findings highlight that topics contain valuable information that can help guide and drive developers’ logging decisions.     

SharkDB: an in-memory column-oriented storage for trajectory analysis

The last decade has witnessed the prevalence of sensor and GPS technologies that produce a high volume of trajectory data representing the motion history of moving objects. However some characteristics of trajectories such as variable lengths and asynchronous sampling rates make it difficult to fit into traditional database systems that are disk-based and tuple-oriented. Motivated by the success of column store and recent development of in-memory databases, we try to explore the potential opportunities of boosting the performance of trajectory data processing by designing a novel trajectory storage within main memory. In contrast to most existing trajectory indexing methods that keep consecutive samples of the same trajectory in the same disk page, we partition the database into frames in which the positions of all moving objects at the same time instant are stored together and aligned in main memory. We found this column-wise storage to be surprisingly well suited for in-memory computing since most frames can be stored in highly compressed form, which is pivotal for increasing the memory throughput and reducing CPU-cache miss. The independence between frames also makes them natural working units when parallelizing data processing on a multi-core environment. Lastly we run a variety of common trajectory queries on both real and synthetic datasets in order to demonstrate advantages and study the limitations of our proposed storage.     

Land Change Patterns and Driving Mechanism in Beijing since 1990 based on GEE Platform

Land-cover and land-use dynamics is a key component for global change,and it is a significant form of the impact of human activities on physical environment.Basing Google Earth Engine platform and Classification And Regression Tree method,selected seven types of cultivated land,forest,grassland,wetland,water body,artificial surface and bare land as classification system,the paper used Landsat 5 TM and Landsat 8 OLI images to interpret the land\|cover and land\|use since 1990 of Beijing.Simultaneously,the paper analyzed and summarized the character of land\|use changing and driving force.The results show that：(1) GEE has outstanding advantages in remote sensing data analysis and processing at regional scales.(2) The CART method has high accuracy of remote sensing classification,and the overall accuracy of validation of 6 land cover products is above 93%.The spatial consistency of 2010 products and GlobeLand30\|2010 data showed that the spatial consistency ratios of woodland,water body and cultivated land were 84.28%,74.75%and 73.56% respectively.The spatial consistency of the distribution is 74.0%.(3) The main land types in Beijing were cultivated land,woodland and artificial surface,and the area accounted for about 90%.During the period from 1990 to 2016,the artificial surface and woodland area increased,and the cultivated land and water were shrinking.The artificial surface area increase of 1 371 km²,and cultivated land shrinkage 40%;On Beijing plain area,artificial surface by the circle of “spread pie” expansion trend to “blossom everywhere” expansion trend;The expansion of the artificial surface is mainly achieved through the encroachment of cultivated land.We constructed a multidimensional stepwise linear equation model to analyze the driving force of land type change,indicated that rapid population growth,rapid economic development,government\|related policies and other socio\|economic development factors jointly drive the Beijing land-cover/land-use evolution process.     

一种改进聚合通道特征的行人检测方法

行人检测是计算机视觉和模式识别领域的研究热点与难点,针对聚合通道特征（Aggregate channel feature,ACF）算法应用于行人检测时,出现检测精度较低、平均对数漏检率（Log-average miss rate,LAMR）较高的情况,提出一种改进的ACF行人检测算法。首先结合objectness方法对ACF算法低得分区域进行进一步验证,可以在一定程度上减少算法的误检数;其次结合检测窗口的得分及位置信息,对非极大值抑制算法（Non-maximum suppression,Nms）进行改进,平均精度（Average precision,AP）提升了0.41%,LAMR降低了1.49%;最后采用星型可形变部件模型（Star-cascade DPM,casDPM）对一定阈值下的得分检测窗口进行级联检测,AP提升了0.65%,LAMR降低了2.06%。在INRIA数据集上实验表明,满足实时检测的条件下,极大地降低了误检数,具有较好的行人检测效果。     

逐级细化的交通标志识别算法

针对交通标志识别中存在的识别精度和实时应用之间的矛盾,根据中国交通标志的特点,提出一种逐级细化的交通标志识别算法。首先进行粗分类,构建颜色属性-梯度直方图（Color name-histogram of gradient,CN-HOG）描述子表示每类标志的形状和颜色特征,采用线性支持向量机（Support vector machine,SVM）将交通标分为禁令标志、警告标志、指示标志、解除禁令标志和其他标志5大类;然后进行细分类,采用词袋模型中颜色和形状特征早融合的方式将颜色属性（Color name,CN）和尺度不变特征变换（Scale-invariant feature transform,SIFT）描述子相结合、利用高斯核SVM得到交通标志区域的最终类别标记。在公开数据集上的实验表明本文算法在满足实时应用的同时取得了99.15%的识别精度。     

Stabilization of a Timoshenko Beam With Disturbance Observer‐Based Time Varying Boundary Controls

This paper is concerned with the boundary feedback stabilization for a Timoshenko beam with external disturbances in the boundary inputs. Based on the idea of active disturbance rejection controls, extended state observers with the time‐varying gains are designed to estimate disturbances and then a control strategy is presented by canceling the disturbances via the feedback channels. The well‐posedness of the resulting closed‐loop system is proved by the dual theory and admissibility theory, and the relationship between the stability and the disturbance is interpreted by Lyapunov's second method. At the end, the numerical experiment illustrate the effectiveness of the proposed control strategy.     

Reconfiguration point decision method based on dynamic complexity for reconfigurable manufacturing system (RMS)

To address the problem of how to identify the best time to implement reconfiguration for the reconfigurable manufacturing system (RMS), a dynamic complexity-based RMS reconfiguration point decision method is proposed. This method first identifies factors that affect RMS dynamic complexity (including both positive and negative complexity) at the machine tool and manufacturing cell levels. Next, based on information entropy theory, a quantitative model for RMS dynamic complexity is created, which is solved via state probability analysis for processing capability and the processing function. This model is combined with cusp catastrophe theory to establish an RMS reconfiguration decision model. Both positive and negative complexity are control variables for cusp catastrophe. Cusp catastrophe’s state condition is used to identify RMS state catastrophe at the final stage of production. This catastrophe point is the RMS reconfiguration point. Finally, the case study result shows that this method can effectively identify the RMS state catastrophe moment so that system reconfiguration is implemented promptly to improve RMS’s responsiveness to the market.     

经验模态分解结合包络谱LSSVM的滚动轴承故障诊断

针对滚动轴承故障诊断问题,提出了经验模态分解(EMD)包络谱分析结合最小二乘支持向量机(LSSVM)方法用于滚动轴承的故障诊断。EMD具有自适应性,可以有效分析非平稳,非线性信号。利用EMD将轴承信号分解为由高频到低频的固有模态函数(IMF),选取前4个主要包含轴承故障频率的IMF函数进行包络谱分析,采用每个分量函数的特征频率和轴承的时域特征作为轴承故障的识别特征,结合LSSVM方法建立轴承故障诊断定性识别模型。结果表明：单独采用频率特征的LSSVM判别模型无法实现轴承故障的准确识别,其正确识别率仅为82.5%;采用频率特征结合时域特征的LSSVM模型可以实现轴承故障类型的100%准确识别。EMD包络谱分析结合模式识别的方法可以实现轴承故障类型的有效识别。     

Towards Long Lifetime Battery: AI-Based Manufacturing and Management

Technologies that accelerate the delivery of reliable battery-based energy storage will not only contribute to decarbonization such as transportation electrification, smart grid, but also strengthen the battery supply chain. As battery inevitably ages with time, losing its capacity to store charge and deliver it efficiently. This directly affects battery safety and efficiency, making related health management necessary. Recent advancements in automation science and engineering raised interest in AI-based solutions to prolong battery lifetime from both manufacturing and management perspectives. This paper aims at presenting a critical review of the state-of-the-art AI-based manufacturing and management strategies towards long lifetime battery. First, AI-based battery manufacturing and smart battery to benefit battery health are showcased. Then the most adopted AI solutions for battery life diagnostic including state-of-health estimation and ageing prediction are reviewed with a discussion of their advantages and drawbacks. Efforts through designing suitable AI solutions to enhance battery longevity are also presented. Finally, the main challenges involved and potential strategies in this field are suggested. This work will inform insights into the feasible, advanced AI for the health-conscious manufacturing, control and optimization of battery on different technology readiness levels.      

Cucurbiturils-Mediated Noble Metal Nanoparticles for Applications in Sensing,SERS, Theranostics,and Catalysis

Noble metal nanoparticles (NMNPs), which spring up like mushrooms, are gaining momentum owing to their unique physicochemical characteristics. Cucurbiturils, a class of synthetic macrocycles with intriguing and peculiar host–guest properties, have stimulated tremendous research interest in recent years. The marriage of NMNPs with cucurbiturils is expected to integrate and enhance the excellent characteristics of both components, e.g., precisely controlled particle size, stability, assembly, surface functionality, biocompatibility, tunable optical properties, and high catalytic activities. This review systematically outlines the recent progress on the fabricating strategies and important applications of cucurbiturils-mediated NMNPs in sensing, surface-enhanced Raman scattering, theranostics, and catalysis. A brief outlook on the future development of cucurbiturils-mediated NMNPs is also presented.