Mining Data Correlation from Multi-Faceted Sensor Data in Internet of Things

Sensors are ubiquitous in the Internet of Things for measuring and collecting data. Analyzing these data derived from sensors is an essential task and can reveal useful latent information besides the data. Since the Internet of Things contains many sorts of sensors, the measurement data collected by these sensors are multi-type data, sometimes contai- ning temporal series information. If we separately deal with different sorts of data, we will miss useful information. This paper proposes a method to dis- cover the correlation in multi-faceted data, which contains many types of data with temporal informa- tion, and our method can simultaneously deal with multi-faceted data. We transform high-dimensional multi-faeeted data into lower-dimensional data which is set as multivariate Gaussian Graphical Models, then mine the correlation in multi-faceted data by discover the structure of the multivariate Gausslan Graphical Models. With a real data set, we verifies our method, and the experiment demonstrates that the method we propose can correctly fred out the correlation among multi-faceted meas- urement data.

Towards Multi-Faceted Test Cases Optimization

The target of software engineering is to produce high quality software product at low cost. Software testing is labour-intensive, ambiguous and error prone activity of software development. How to provide cost-effective strategies for software test cases optimization problem such as classification, minimization, selection, and prioritization has been one of the research focuses in software testing for a long time. Many researchers and academicians have addressed the effectiveness/fitness and optimization of test cases, and obtained many interesting results. However, one issue of paramount importance in software testing i.e. the intrinsic imprecise and uncertainty of test cases fitness, fitness parameters, multi-objective optimization, is left unaddressed. Test cases fitness depends on several parameters. Vagueness of fitness of test cases and their fitness parameters have created the uncertainty in test cases optimization. Cost and adequacy values are incorporated into multi-faceted optimization of test cases. This paper argues test cases optimization requires multi-faceted optimization in order to adequately cater realistic software testing. In this paper, authors have identified several parameters for test cases fitness and multiple objectives for test cases optimization. In addition above, authors have formulated the test cases optimization problem in three different ways using multi-faceted concept. These formulations can be used in future by authors and researchers.

Multi-faceted spatio-temporal network for weather-aware air traffic flow prediction in multi-airport system

As one of the core modules for air traffic flow management,Air Traffic Flow Prediction(ATFP)in the Multi-Airport System(MAS)is a prerequisite for demand and capacity balance in the complex meteorological environment.Due to the challenge of implicit interaction mechanism among traffic flow,airspace capacity and weather impact,the Weather-aware ATFP(Wa-ATFP)is still a nontrivial issue.In this paper,a novel Multi-faceted Spatio-Temporal Graph Convolutional Network(MSTGCN)is proposed to address the Wa-ATFP within the complex operations of MAS.Firstly,a spatio-temporal graph is constructed with three different nodes,including airport,route,and fix to describe the topology structure of MAS.Secondly,a weather-aware multi-faceted fusion module is proposed to integrate the feature of air traffic flow and the auxiliary features of capacity and weather,which can effectively address the complex impact of severe weather,e.g.,thunderstorms.Thirdly,to capture the latent connections of nodes,an adaptive graph connection constructor is designed.The experimental results with the real-world operational dataset in Guangdong-Hong Kong-Macao Greater Bay Area,China,validate that the proposed approach outperforms the state-of-the-art machine-learning and deep-learning based baseline approaches in performance.

桃文化概说

本文探究"桃文化"的根源性、实用性、累积性、象征性等特点。

数学价值的多面性和高职数学教学改革研究

数学是一门逻辑严密、简洁严谨的科学语言。它也是其他自然科学研究工作的基本工具和提供思维方式的理论知识。因此,数学的学习对于高职学生以后的职业发展有着重要作用。然而,现有的高职教育只是针对教育而教育,忽视了数学价值的多面性。有鉴于此,在高职数学教学改革过程中,还应重视数学的多元价值,采取有效的教学方法。

Long-term and Cross-sectoral Management of Interconnected Events： The Case of the Fukushima Nuclear Accident

In a world-shocking nuclear disaster occurred at Fukushima in 2011, multi-faceted consequences have manifested in not only direct and indirect but also tangible and intangible way in social, political, and economic domains. At present six year later, original risk issues, such as health, environmental, and financial risks, were complexly connected to each other, and have transformed to the wicked or complicated problems. This paper addresses the following four problems that we are faced with： prolonged evacuation and return to hometown, Tokyo Electric Power Company Holdings issues, nuclear regulatory issues, and nuclear energy policy and business. The authors discuss the reasons why above-noted situations arise from nuclear disaster in terms of endogenous factors embedded in socio-technical nuclear system in Japan and some common causes across the wicked problems. The wicked problems are also closely connected with each other, and become super-wicked problem. Among others, Japan＇s energy transition policy aiming at low carbon society tends to deviate politically and now at crossroad. Finally, the authors describe some perspectives and challenges required to govern interconnected events, as lessons learned from the Fukushima nuclear disaster.

Effective and Efficient Multi-Facet Web Image Annotation

The vast amount of images available on the Web request for an effective and efficient search service to help users find relevant images. The prevalent way is to provide a keyword interface for users to submit queries. However, the amount of images without any tags or annotations are beyond the reach of manual efforts. To overcome this, automatic image annotation techniques emerge, which are generally a process of selecting a suitable set of tags for a given image without user intervention. However, there are three main challenges with respect to Web-scale image annotation： scalability, noise- resistance and diversity. Scalability has a twofold meaning： first an automatic image annotation system should be scalable with respect to billions of images on the Web; second it should be able to automatically identify several relevant tags among a huge tag set for a given image within seconds or even faster. Noise-resistance means that the system should be robust enough against typos and ambiguous terms used in tags. Diversity represents that image content may include both scenes and objects, which are further described by multiple different image features constituting different facets in annotation. In this paper, we propose a unified framework to tackle the above three challenges for automatic Web image annotation. It mainly involves two components： tag candidate retrieval and multi-facet annotation. In the former content-based indexing and concept-based eodebook are leveraged to solve scalability and noise-resistance issues. In the latter the joint feature map has been designed to describe different facets of tags in annotations and the relations between these facets. Tag graph is adopted to represent tags in the entire annotation and the structured learning technique is employed to construct a learning model on top of the tag graph based on the generated joint feature map. Millions of images from Flickr are used in our evaluation. Experimental results show that we have achieved 33% performance improvements compared with those single facet approaches in terms of three metrics： precision, recall and F1 score.

Toward flexible visual analytics augmented through smooth display transitions

Visualizing big and complex multivariate data is challenging.To address this challenge,we propose flexible visual analytics(FVA)with the aim to mitigate visual complexity and interaction complexity challenges in visual analytics,while maintaining the strengths of multiple perspectives on the studied data.At the heart of our proposed approach are transitions that fluidly transform data between userrelevant views to offer various perspectives and insights into the data.While smooth display transitions have been already proposed,there has not yet been an interdisciplinary discussion to systematically conceptualize and formalize these ideas.As a call to further action,we argue that future research is necessary to develop a conceptual framework for flexible visual analytics.We discuss preliminary ideas for prioritizing multi-aspect visual representations and multi-aspect transitions between them,and consider the display user for whom such depictions are produced and made available for visual analytics.With this contribution we aim to further facilitate visual analytics on complex data sets for varying data exploration tasks and purposes based on different user characteristics and data use contexts.