Analyzing Invariants in Cyber-Physical Systems using Latent Factor Regression

A variety of real-world data and networks can be described by a heavy-tailed probability distribution of its values, vertex degrees, or other significant properties, that follows the power law. Such a scale-free data and networks can be found in both natural phenomena such as protein interaction networks and gene regulation networks and man-made structures like the Internet, language, and various social networks. An efficient analysis of large scale data and networks is often impractical and various heuristic and metaheuristc sampling techniques are deployed to select smaller subsets of the data for analysis and visualisation. A key goal of data and network sampling is to select such a subset of the original data that would accurately represent the original data with respect to selected attributes. In this work we propose a novel genetic algorithm for scale-free data and network sampling and evaluate the algorithm in a series of computational experiments.

Web 上の大規模データの解析による Moon & Spencer の色彩調和論における「美度」の妥当性の検証

Graph analytics has emerged as an important tool in the analysis of large scale data from diverse application domains such as social networks, cyber security and bioinformatics. Counting the number of triangles in a graph is a fundamental kernel with several applications such as detecting the community structure of a graph or in identifying important vertices in a graph. The ubiquity of massive datasets is driving the need to scale graph analytics on parallel systems. However, numerous challenges exist in efficiently parallelizing graph algorithms, especially on distributed-memory systems. Irregular memory accesses and communication patterns, low computation to communication ratios, and the need for frequent synchronization are some of the leading challenges. In this paper, we present TriC, our distributed-memory implementation of triangle counting in graphs using the Message Passing Interface (MPI), as a submission to the 2020 Graph Challenge competition. Using a set of synthetic and real-world inputs from the challenge, we demonstrate a speedup of up to 90 x relative to previous work on 32 processor-cores of a NERSC Cori node. We also provide details from distributed runs with up to 8192 processes along with strong scaling results. The observations presented in this work provide an understanding of the system-level bottlenecks at scale that specifically impact sparse-irregular workloads and will therefore benefit other efforts to parallelize graph algorithms.

Big data analysis technology can be used to transform massive data of oil production engineering into knowledge that can be used to guide oilfield production. Due to the lack of suitable large data analysis tools, a large data analysis software system was developed to simplify the workload of data analysis. This paper illustrates the research and application of large energy consumption data analysis through the development of big data analysis methods, process improvement and software platform, then achieves the goal of reducing costs and improving efficiency.

The detection of useful patterns in large datasets has attracted considerable interest recently. The hierarchical K-means clustering algorithm (HKM) is very efficient in large scale data analysis. It has been extensively used for building visual vocabulary in large scale image/video retrieval. However, the accuracy and speed of HKM still have room for improvement. In this paper, we propose a Parallel N-path labeling HKM clustering algorithm (PNHKM) which improves on the HKM clustering algorithm in the following ways. Firstly, we adopt a Greedy N-best Paths Labeling (GNPL) method to improve the clustering accuracy. Secondly, we focus on developing a parallel clustering algorithm for multicore processors. Our results confirm that the PNHKM is much faster and more effective.

Cyber-physical systems are a kind of emerging large-scale distributed systems that are integrations of computation, networking, and physical worlds. Cyber physical systems are increasing scale and complexity with the growing pervasiveness of ICT and the development of the smart worlds such as smart factory, smart robotics, smart grids, smart cities, smart vehicles, smart healthcare systems, and smart homes. The growing interconnection of physical and virtual worlds of complex cyber physical systems, and the development of increasingly sophisticated intelligence techniques, has given rise to the next generation of CPS, that is referred to as smart cyber-physical systems (sCPS) which can efficiently operate, manage and control physical world and virtual world, and offer broad range of novel applications and services. Thus, smart cyber physical systems are complex cyber physical systems endowed with intelligent abilities. The main characteristic of such a new generation of smart cyber physical systems is the ability to interact with the physical worlds and adapt to new working environments through distributed intelligent mechanisms that executing and operating at the unit (single device), cluster (groups of devices) and network level (the network of devices). The properties like uncertainties, safety, reliability, and performance requirement, spatial-temporal requirement, and adaptation ability are very important, modeling smart cyber-physical systems must face these challenges and satisfy these specific requiremnts. In order to specify and model such kind of systems, we propose a new paradigm for specifying and modeling smart cyber physical systems based on software defined approach using Modelicaml. Software defined approach is a new way that help abstract the real world and virtual world with software components, this approach can simplify system specification, modeling and design. The main aim of ModelicaML is to enable an efficient and effective way to use Modelica, UML and SysML models reusing notations that are also used for software modeling based on software defined approach. The effectiveness of the approach is demonstrated with a case study of Vehicular Ad-hoc NETwork.

BackgroundIn many parts of the world, salt marshes play a key ecological role as the interface between the marine and the terrestrial environments. Salt marshes are also exceedingly important for public health as larval habitat for mosquitoes that are vectors of disease and significant biting pests. Although grid ditching and pesticides have been effective in salt marsh mosquito control, marsh degradation and other environmental considerations compel a different approach. Targeted habitat modification and biological control methods known as Open Marsh Water Management (OMWM) had been proposed as a viable alternative to marsh-wide physical alterations and chemical control. However, traditional larval sampling techniques may not adequately assess the impacts of marsh management on mosquito larvae. To assess the effectiveness of integrated OMWM and marsh restoration techniques for mosquito control, we analyzed the results of a 5-year OMWM/marsh restoration project to determine changes in mosquito larval production using GIS and geostatistical methods.MethodsThe following parameters were evaluated using "Before-After-Control-Impact" (BACI) design: frequency and geographic extent of larval production, intensity of larval production, changes in larval habitat, and number of larvicide applications. The analyses were performed using Moran's I, Getis-Ord, and Spatial Scan statistics on aggregated before and after data as well as data collected over time. This allowed comparison of control and treatment areas to identify changes attributable to the OMWM/marsh restoration modifications.ResultsThe frequency of finding mosquito larvae in the treatment areas was reduced by 70% resulting in a loss of spatial larval clusters compared to those found in the control areas. This effect was observed directly following OMWM treatment and remained significant throughout the study period. The greatly reduced frequency of finding larvae in the treatment areas led to a significant decrease (~44%) in the number of times when the larviciding threshold was reached. This reduction, in turn, resulted in a significant decrease (~74%) in the number of larvicide applications in the treatment areas post-project. The remaining larval habitat in the treatment areas had a different geographic distribution and was largely confined to the restored marsh surface (i.e. filled-in mosquito ditches); however only ~21% of the restored marsh surface supported mosquito production.ConclusionThe geostatistical analysis showed that OMWM demonstrated considerable potential for effective mosquito control and compatibility with other natural resource management goals such as restoration, wildlife habitat enhancement, and invasive species abatement. GPS and GIS tools are invaluable for large scale project design, data collection, and data analysis, with geostatistical methods serving as an alternative or a supplement to the conventional inference statistics in evaluating the project outcome.

BackgroundRecent advances in experimental and computational technologies have fueled the development of many sophisticated bioinformatics programs. The correctness of such programs is crucial as incorrectly computed results may lead to wrong biological conclusion or misguide downstream experimentation. Common software testing procedures involve executing the target program with a set of test inputs and then verifying the correctness of the test outputs. However, due to the complexity of many bioinformatics programs, it is often difficult to verify the correctness of the test outputs. Therefore our ability to perform systematic software testing is greatly hindered.ResultsWe propose to use a novel software testing technique, metamorphic testing (MT), to test a range of bioinformatics programs. Instead of requiring a mechanism to verify whether an individual test output is correct, the MT technique verifies whether a pair of test outputs conform to a set of domain specific properties, called metamorphic relations (MRs), thus greatly increases the number and variety of test cases that can be applied. To demonstrate how MT is used in practice, we applied MT to test two open-source bioinformatics programs, namely GNLab and SeqMap. In particular we show that MT is simple to implement, and is effective in detecting faults in a real-life program and some artificially fault-seeded programs. Further, we discuss how MT can be applied to test programs from various domains of bioinformatics.ConclusionThis paper describes the application of a simple, effective and automated technique to systematically test a range of bioinformatics programs. We show how MT can be implemented in practice through two real-life case studies. Since many bioinformatics programs, particularly those for large scale simulation and data analysis, are hard to test systematically, their developers may benefit from using MT as part of the testing strategy. Therefore our work represents a significant step towards software reliability in bioinformatics.

Automated video and image analysis can be a very efficient tool to behavior study, especially in hard access environments for researchers. The understanding of this social behavior can play a key role in the sustainable design of control policies of many species. This paper proposes the use of computer vision algorithms to identify and track, the Norway lobster, Nephrops norvegicus, a burrowing decapod with relevant commercial value which is captured by trawling. These animals can only be captured when are engaged in seabed excursions, which are strongly related with their social behavior. This emergent behavior is modulated by the day-night cycle, but social interactions remain unknown to the scientific community. The paper introduces an identification scheme made of four distinguishable black and white tags (geometric shapes). The project has recorded 15-day experiments in laboratory, under monochromatic blue light (472 nm.) and darkness conditions (recorded using Infra Red light). Using this massive image set, we propose a comparative of state-of-the-art computer vision algorithms to distinguish and track the different animals' movements. We evaluate the robustness to the high noise presence in the infrared video signals and free out-of-plane rotations due to animal movement. The experiments show promising accuracies under a cross-validation protocol, being adaptable to the automation and analysis of large scale data. In a second contribution, we created an extensive dataset of shapes (46027 different shapes) from four daily experimental video recordings, which will be available to the community.

The processes in complex cyber-physical system are always interrelated, which means different processes share the same information. Such sharing requires the robust approach to create, update and maintain the ontologies of processes and cyber-physical system as a whole in order to create well-organized data structure and to avoid duplication of information. The resulting complete ontology of cyber-physical system should be a convenient and flexible basis for modeling processes and ensuring the functioning of information-analytical and system processes. Metagraphs are considered as the basis for building an applied system ontology and extracting the ontologies of processes. A modification of the metagraph is proposed to include events and system methods in the ontology. This approach to the construction of a metagraph allows to include the process component in the ontological model of the system as an integral part of it, which allows to flexibly and with lower time expenses design, update and maintain process models based on segments of the general ontological model. As a basis for modeling, it is proposed to use a temporal neuro-fuzzy variety of Petri nets. An example of the implementation of a software-instrumental environment for ontological engineering and further construction of models of processes of a complex cyber-physical system is presented.

This study proposes content and interaction analysis techniques for a large repository created from social media content. Though we have presented our study for a large platform dedicated to discussions around financial topics, the proposed methods are generic and applicable to all platforms. Along with an extension of topic extraction method using Latent Dirichlet Allocation, we propose a few measures to assess user participation, influence and topic affinities specifically. Our study also maps user-generated content to components of behavioral finance. While these types of information are usually gathered through surveys, it is obvious that large scale data analysis from social media can reveal many potentially unknown or rare insights. Characterising users based on their platform behavior to provide critical insights about how communities are formed and trust is established in these platforms using graphical analysis is also studied.

Accurate prediction of cycle time (CT) plays an important role in the promises of a good delivery-time for semiconductor manufacturers. However, the CTs of wafer lots are difficult to forecast since they are influent by a lot factors (e.g., workload for every machine). The identification of key factors (called CT-related) can not only improve the prediction performance but also facilitate the control of CT, which is of particular significance. This paper focuses on gathering subtle variables into the candidate variables set along with the further analysis which is required in the correlation analysis between various candidate factors and CTs of wafer lots. The candidates set is first constructed to collect all factors that may affect wafer lots’ CT. Then, a regression-based model is proposed to select CT-related variables, which consists of three parts: 1) discretization; 2) adaptive logistic regression-based correlation analysis; and 3) CT-related factor selection. Subsequently, a parallel computation model is implemented to forecast the CTs of wafer lots. In the numerical experiments, 108 CT-related factors stood out from 774 candidates and replaced six global factors (used as reference) to predict CTs of wafer lots. The results indicated that the proposed approach had higher accuracy than linear regression and back propagation network in CT forecasting in large scale data analysis.

The purpose of the workis to explore the current problems and prospects of mining solution, big web data in real time, as well as the possibility of practical implementation of Web Mining technology for big web data on a practical example.Materials and methods.The study included a review of bibliographic sources on big data mining. We used Web Mining technology for associative analysis of large web data, as well as computer modeling of the practical task of transaction analysis using a general-purpose scripting language (PHP).Results.During the work, the specifics of the Data Mining technology were described, and a modern approach to the analysis of large web data –Web Mining was analyzed. A brief classification of tasks solved using Web Mining technology is given. The problem of data mining of large web data in a general-purpose scripting language (PHP) has been solved: the lack of libraries for data mining, the difficult normalization of data to the form necessary for associative analysis, interaction with the database management system. Also, an example showing an approach to the mining of large web data was implemented. Based on the understanding of Web Mining technology and the described difficulties of analyzing web data in the PHP language, methods for effectively solving the practical problem of analyzing web data based on transactions committed in a dynamic web application have been proposed. A module for associative analysis of customer transactions in the programming language PHP was developed. The module includes an intelligent data processing class. The structural scheme of the module and system architecture were developed. The constructed module allows us to solve the main part of the problem of associative analysis of large web data using Web Mining technology in order to solve the problem of identifying patterns in a large array of web data. Associative analysis of web data is much faster because of the combination of a general-purpose scripting language and an object-oriented approach.Conclusion.According to the results of the study, it can be argued that the current state of the technology for the analysis of large web data allows efficiently process data objects, identify patterns, obtain hidden data and receive complete statistical data in real time. The results can be used both for the purpose of the initial research of technologies for analyzing large web data, and as an addition to the content management system for the intelligent analysis of web data. The usage of the technology of associative analysis and the created universal handler class makes the created module flexible, while the possibility of manual integration makes this module universal. With manual integration, the database management system is not important. Algorithm methods work with selected data. This factor greatly simplifies the further development of program code.

Guest Editorial

Nowadays, the power system is evolving into a complex cyber physical system with the closely merged physical system, information system, and communication network. It is critical to understand the connections between the power and cyber systems, and the potential impact of cyber vulnerability. In this study, a flexible hardware‐in‐the‐loop (HIL) testbed is proposed for studying the cyber physical power system. By using the flexible interface, various co‐simulation systems for different purposes are generated. Based on this testbed, three sample co‐simulators are built as proofs. First, a HIL power and communication co‐simulator with non‐real‐time synchronisation mechanism is introduced, and a case of false data injection attack on automation voltage control is studied. Then, a real‐time power and communication HIL co‐simulator is introduced, and a case considering the impact of communication bit error on the stability control system is simulated to demonstrate the performance of stability control equipment. Finally, another co‐simulator for simulating the actual cyber‐attack on the stability control system is introduced, and a case of a man‐in‐the‐middle attack on the data link is simulated to demonstrate the impact of cyber‐attack on the stability control system.