Performance Improvement in Manufacturing Shop Floor Operations of Developing Countries Based on Three Characteristics of Information Flow

The management of information flow for production improvement has always been a target in the research. In this paper, the focus is on the analysis model of the characteristics of information flow in shop floor operations based on the influence that dimension (support or medium), direction and the quality information flow have on the value of information flow using machine learning classification algorithms. The obtained results of classification algorithms used to analyze the value of information flow are Decision Trees (DT) and Random Forest (RF) with a score of 0.99% and the mean absolute error of 0.005. The results also show that the management of information flow using DT or RF shows that, the dimension of information such as digital information has the greatest value of information flow in shop floor operations when the shop floor is totally digitalized. Direction of information flow does not have any great influence on shop floor operations processes when the operations processes are digitalized or done by operators as machines.

Distributed Information Flow Verification for Secure Service Composition in Smart Sensor Network

Accelerate processor, efficient software and pervasive connections provide sensor nodes with more powerful computation and storage ability, which can offer various services to user. Based on these atomic services, different sensor nodes can cooperate and compose with each other to complete more complicated tasks for user. However, because of the regional characteristic of sensor nodes, merging data with different sensitivities become a primary requirement to the composite services, and information flow security should be intensively considered during service composition. In order to mitigate the great cost caused by the complexity of modeling and the heavy load of single-node verification to the energy-limited sensor node, in this paper, we propose a new distributed verification framework to enforce information flow security on composite services of smart sensor network. We analyze the information flows in composite services and specify security constraints for each service participant. Then we propose an algorithm over the distributed verification framework involving each sensor node to participate in the composite service verification based on the security constraints. The experimental results indicate that our approach can reduce the cost of verification and provide a better load balance.

Information Flow Security Models for Cloud Computing

Cloud computing provides services to users through Internet.This open mode not only facilitates the access by users,but also brings potential security risks.In cloud computing,the risk of data leakage exists between users and virtual machines.Whether direct or indirect data leakage,it can be regarded as illegal information flow.Methods,such as access control models can control the information flow,but not the covert information flow.Therefore,it needs to use the noninterference models to detect the existence of illegal information flow in cloud computing architecture.Typical noninterference models are not suitable to certificate information flow in cloud computing architecture.In this paper,we propose several information flow models for cloud architecture.One model is for transitive cloud computing architecture.The others are for intransitive cloud computing architecture.When concurrent access actions execute in the cloud architecture,we want that security domain and security domain do not affect each other,that there is no information flow between security domains.But in fact,there will be more or less indirect information flow between security domains.Our models are concerned with how much information is allowed to flow.For example,in the CIP model,the other domain can learn the sequence of actions.But in the CTA model,the other domain can’t learn the information.Which security model will be used in an architecture depends on the security requirements for that architecture.

Evolving a Bayesian network model with information flow for time series interpolation of multiple ocean variables

Based on Bayesian network (BN) and information flow (IF),a new machine learning-based model named IFBN is put forward to interpolate missing time series of multiple ocean variables. An improved BN structural learning algorithm with IF is designed to mine causal relationships among ocean variables to build network structure. Nondirectional inference mechanism of BN is applied to achieve the synchronous interpolation of multiple missing time series. With the IFBN,all ocean variables are placed in a causal network visually,making full use of information about related variables to fill missing data. More importantly,the synchronous interpolation of multiple variables can avoid model retraining when interpolative objects change. Interpolation experiments show that IFBN has even better interpolation accuracy,effectiveness and stability than existing methods.

Information Flow Scheduling in Concurrent Multi-Product Development Based on DSM

Multi-product collaborative development is adopted widely in manufacturing enterprise, while the present multi-project planning models don＇t take techni- cal/data interactions of multiple products into account. To decrease the influence of technical/data interactions on project progresses, the information flow scheduling models based on the extended DSM is presented. Firstly, infor- mation dependencies are divided into four types： series, parallel, coupling and similar. Secondly, different types of dependencies are expressed as DSM units, and the exten- ded DSM model is brought forward, described as a block matrix. Furthermore, the information flow scheduling methods is proposed, which involves four types of opera- tions, where partitioning and clustering algorithm are modified from DSM for ensuring progress of high-priority project, merging and converting is the specific computation of the extended DSM. Finally, the information flow scheduling of two machine tools development is analyzed with example, and different project priorities correspond to different task sequences and total coordination cost. The proposed methodology provides a detailed instruction for information flow scheduling in multi-product development, with specially concerning technical/data interactions.

The Information Flow Analyzing Based on CPC

The information flow chart within product life cycle is given out based on collaborative production commerce （CPC） thoughts. In this chart, the separated information systems are integrated by means of enterprise knowledge assets that are promoted by CPC from production knowledge. The information flow in R＆D process is analyzed in the environment of virtual R＆D group and distributed PDM. In addition, the information flow throughout the manufacturing and marketing process is analyzed in CPC environment.

Functional connectivity and information flow in cerebral motor regions of healthy people following finger movement training

BACKGROUND：Neuro-rehabilitative training has been shown to promote motor function recovery in stroke patients,although the underlying mechanisms have not been fully clarified.OBJECTIVE：To investigate the effects of finger movement training on functional connectivity and information flow direction in cerebral motor areas of healthy people using electroencephalogram （EEG）.DESIGN,TIME AND SETTING：A self-controlled,observational study was performed at the College of Life Science and Bioengineering,Beijing University of Technology between December 2008 and April 2009.PARTICIPANTS：Nineteen healthy adults,who seldom played musical instruments or keyboards,were included in the present study.METHODS：Specific finger movement training was performed,and all subjects were asked to separately press keys with their left or right hand fingers,according to instructions.The task comprised five sessions of test train test train-test.Thirty-six channel EEG signals were recorded in different test sessions prior to and after training.Data were statistically analyzed using one-way analysis of variance.MAIN OUTCOME MEASURES：The number of effective performances,correct ratio,average response time,average movement time,correlation coefficient between pairs of EEG channels,and information flow direction in motor regions were analyzed and compared between different training sessions.RESULTS：Motor function of all subjects was significantly improved in the third test comparedwith the first test （P〈 0.01）.More than 80% of connections were strengthened in the motor-related areas following two training sessions,in particular the primary motor regions under the C4 electrode.Compared to the first test,a greater amount of information flowed from the Cz and Fcz electrodes （corresponding to supplementary motor area） to the C4 electrode in the third test.CONCLUSION：Finger task training increased motor ability in subjects by strengthening connections and changing information flow in the motor areas.These results provided a greater understanding of the mechanisms involved in motor rehabilitation.

Enhance Information Flow Tracking with Function Recognition

With the spread use of the computers, a new crime space and method are presented for criminals. Thus computer evidence plays a key part in criminal cases. Traditional computer evidence searches require that the computer specialists know what is stored in the given computer. Binary-based information flow tracking which concerns the changes of control flow is an effective way to analyze the behavior of a program. The existing systems ignore the modifications of the data flow, which may be also a malicious behavior. Thus the function recognition is introduced to improve the information flow tracking. Function recognition is a helpful technique recognizing the function body from the software binary to analyze the binary code. And that no false positive and no false negative in our experiments strongly proves that our approach is effective.

Highway Transport Development Strategy Based on Information Flow Impact

The characteristics of highway transport and the application of information technology in highway transport service are combined; the information flows＇ promotion and substitution on highway transport are analyzed; and the highway transport development strategy based on information flow impact is proposed.

A time sequence analysis on the informal information flow mechanism of microblogging

Microblog is a new Internet featured product, which has seen a rapid development in recent years. Researchers from different countries are making various technical analyses on microblogging applications. In this study, through using the natural language processing(NLP) and data mining, we analyzed the information content transmitted via a microblog, users' social networks and their interactions, and carried out an empirical analysis on the dissemination process of one particular piece of information via Sina Weibo.Based on the result of these analyses, we attempt to develop a better understanding about the rule and mechanism of the informal information flow in microblogging.

Using information flow analysis to detect implicit information leaks for web service composition

Information leak,which can undermine the compliance of web-service-composition business processes for some policies,is one of the major concerns in web service composition.We present an automated and effective approach for the detection of implicit information leaks in business process execution language(BPEL)based on information flow analysis.We introduce an adequate meta-model for BPEL representation based on a Petri net for transformation and analysis.Building on the concept of Petri net place-based noninterference,the core contribution of this paper is the application of a Petri net reachability graph to estimate Petri net interference and thereby to detect implicit information leaks in web service composition.In addition,a case study illustrates the application of the approach on a concrete workflow in BPEL notation.

A Multi-Flow Information Flow Tracking Approach for Proving Quantitative Hardware Security Properties

Information Flow Tracking(IFT)is an established formal method for proving security properties related to confidentiality,integrity,and isolation.It has seen promise in identifying security vulnerabilities resulting from design flaws,timing channels,and hardware Trojans for secure hardware design.However,existing IFT methods tend to take a qualitative approach and only enforce binary security properties,requiring strict non-interference for the properties to hold while real systems usually allow a small amount of information flows to enable desirable interactions.Consequently,existing methods are inadequate for reasoning about quantitative security properties or measuring the security of a design in order to assess the severity of a security vulnerability.In this work,we propose two multi-flow solutions—multiple verifications for replicating existing IFT model and multi-flow IFT method.The proposed multi-flow IFT method provides more insight into simultaneous information flow behaviors and allows for proof of quantitative information flow security properties,such as diffusion,randomization,and boundaries on the amount of simultaneous information flows.Experimental results show that our method can be used to prove a new type of information flow security property with verification performance benefits.

The impact of patent citation information flow regarding economic innovation on common stock returns:Volume vs.patent citations

This study examines whether the number of forward patent citations(along with alternative patent data)dwhen used as a proxy for the mixing variabledcould infer the aggregate amount of economic-innovation information arriving at the New York Stock Exchange(NYSE)in the United States.The results show that the number of forward patent citations,when used as a mixing variable,fails to eliminate total volatility persistence in the conditional variance equation of the exponential generalized autoregressive conditional heteroscedastic(EGARCH)model.However,the trading volume successfully eliminates total volatility persistence,thus confirming the validity of the framework used.When the volatility is modeled with an expectation of mean return,the persistence of conditional variance is deterministically increased,and the sum of the volatility coefficients exceeds unity.The inclusion of trading volume with a time trend in the variance equation rectifies the deterministic increase in the conditional volatility.These findings suggest that the form of heteroscedasticity(i.e.,as per the autoregressive conditional heteroscedastic model,ARCH model)in NYSE portfolio returns is based on the type of shocks to volatility(e.g.,deterministic vs.stochastic),which manifests as news arrivals(i.e.,new information arrivals proxied by trading volume)at the stock market.The volume therefore reflects the time dependence in the innovations to the ARCH error generation process.The response of volatility to volume persists over time when the volatility estimates are derived from the EGARCH model with an expectation for the mean of return.Backward patent citations,patent applications,and patents issued have been found to interact somewhat with trading volume,suggesting that each of these variables could play the role of an absorptive capacity variable as the new information flow associated with economic innovation(i.e.,flow of firms’stock of new knowledge)could be picked up by the trading volume.

Free Information Flow Benefits Truth Seeking

How can we approach the truth in a society？ It may depend on various factors. In this paper, using a well-established truth seeking model, the authors show that the persistent free information flow will bring us to the truth. Here the free information flow is modeled as the environmental random noise that could alter one＇s cognition. Without the random noise, the model predicts that the truth can only be captured by the truth seekers who own actively perceptive ability of the truth and their believers, while the other individuals may stick to falsehood. But under the influence of the random noise, the authors strictly prove that even there is only one truth seeker in the group, all individuals will finally approach the truth.

Effect:An Operational View Mechanism for Decentralized Information Flow Control

Flume, which implements decentralized information flow control （DIFC）, allows a high security level process to ＂pre-create＂ secret files in a low security level directory. However, the pre-create mechanism makes some normal system calls unavailable, and moreover, it needs priori knowledge to create a large quantity of objects, which is difficult to estimate in practical operating systems. In this paper, we present an extended Flume file access control mechanism, named Effect, to substitute the mechanism of pre-create, which permits write operations （create, delete, and rename a file） on directories and creates a file access virtual layer that allocates operational views for each process with noninterference properties. In the end, we further present an analysis on the security of Effect. Our work makes it easier for multi-user to share confidential information in decentralized information flow control systems.

Preserving privacy information flow security in composite service evolution

After a composite service is deployed, user privacy requirements and trust levels of component services are subject to variation. When the changes occur, it is critical to preserve privacy information flow security. We propose an approach to preserve privacy information flow security in composite service evolution. First, a privacy data item dependency analysis method based on a Petri net model is presented. Then the set of privacy data items collected by each component service is derived through a privacy data item dependency graph, and the security scope of each component service is calculated. Finally, the evolution operations that preserve privacy information flow security are defined. By applying these evolution operations, the re-verification process is avoided and the evolution efficiency is improved. To illustrate the effectiveness of our approach, a case study is presented. The experimental results indicate that our approach has high evolution efficiency and can greatly reduce the cost of evolution compared with re-verifying the entire composite service.

May Protein Be as the Genetic Information Carrier?

The 'central dogma 'of molecular biology indicated that the direction of the genetic information flow is from DNA - RNA - protein. However, up to now, the central dogma has not obtained a sufficient theoretical support from cybernetics and information theory. In addition, some special cases in biology, such as, although the scrapie prion is irreversibly inactivated by alkali, five procedures with more specificity for modifying nucleic acids failed to cause inactivation and when a resting cell is activated by some factors and division occurs, protein synthesis has begun before DNA synthesis etc., are also very difficult to explain clearly by the central dogma. A broad outline of a mechanism for reverse translation can easily be 'designed', based on the normal translation process, and this serves both to prove that there is no fundamental theoretical reason for the central dogma, and to illustrate why the redundancy of genetic code is not a problem.This paper, based on some previous research work of authors, from the view of cybernetics, information theory and theoretical biology, explored the possibility of protein as a genetic information carrier, the probable pairing ways between ammo acids-codons, and the direction of genetic information flows etc., at theory, by comparing and analyzing theoretically the characteristics of information carriers existing in DNA and protein. The authors inferred that perhaps protein may join the informational transferring as a genetic information carrier; the direction of genetic information flows, besides the way described by the central dogma, seem also to have another type, that is, genetic information flowing from protein - DNA (RNA) - protein, which also includes the genetic information flow in the central dogma. Undoubtedly, the research on problems about the position and roles of protein during the genetic information transferring will have an important effect on the investigation and development of molecular biology, molecular genetics and gene engineering.

DuFNet:Dual Flow Network of Real-Time Semantic Segmentation for Unmanned Driving Application of Internet of Things

The application of unmanned driving in the Internet of Things is one of the concrete manifestations of the application of artificial intelligence technology.Image semantic segmentation can help the unmanned driving system by achieving road accessibility analysis.Semantic segmentation is also a challenging technology for image understanding and scene parsing.We focused on the challenging task of real-time semantic segmentation in this paper.In this paper,we proposed a novel fast architecture for real-time semantic segmentation named DuFNet.Starting from the existing work of Bilateral Segmentation Network(BiSeNet),DuFNet proposes a novel Semantic Information Flow(SIF)structure for context information and a novel Fringe Information Flow(FIF)structure for spatial information.We also proposed two kinds of SIF with cascaded and paralleled structures,respectively.The SIF encodes the input stage by stage in the ResNet18 backbone and provides context information for the feature fusionmodule.Features from previous stages usually contain rich low-level details but high-level semantics for later stages.Themultiple convolutions embed in Parallel SIF aggregate the corresponding features among different stages and generate a powerful global context representation with less computational cost.The FIF consists of a pooling layer and an upsampling operator followed by projection convolution layer.The concise component provides more spatial details for the network.Compared with BiSeNet,our work achieved faster speed and comparable performance with 72.34%mIoU accuracy and 78 FPS on Cityscapes Dataset based on the ResNet18 backbone.

Debris flow susceptibility analysis based on the combined impacts of antecedent earthquakes and droughts： a case study for cascade hydropower stations in the upper Yangtze River, China

The upper Yangtze River region is one of the most frequent debris flow areas in China. The study area contains a cascade of six large hydropower stations located along the river with total capacity of more than 70 million kilowatts. The purpose of the study was to determine potential and dynamic differences in debris flow susceptibility and intensity with regard to seasonal monsoon events. We analyzed this region's debris flow history by examining the effective peak acceleration of antecedent earthquakes,the impacts of antecedent droughts, the combined effects of earthquakes and droughts, with regard to topography, precipitation, and loose solid material conditions. Based on these factors, we developed a debris flow susceptibility map. Results indicate that the entire debris flow susceptibility area is 167,500 km^2, of which 26,800 km^2 falls within the high susceptibility area, with 60,900 km^2 in medium and 79,800 km^2 are in low susceptibility areas. Three of the six large hydropower stations are located within the areas with high risk of debris flows. The synthetic zonation map of debris flow susceptibility for the study area corresponds with both the investigation data and actual distribution of debris flows. The results of debris flow susceptibility provide base-line data for mitigating, assessing, controlling and monitoring of debris flows hazards.

Measuring the Evolution and Influence in Society’s Information Networks

We develop a series of mathematical models to describe flow of information in different periods of time and the relationship between flow of information and inherent value. We optimize the diffusion mechanism of information based on model SEIR and improve the diffusion mechanism. In order to explore how inherent value of the information affects the flow of information, we simulate the model by using Matalab. We also use the data that the number of people is connected to Internet in Canada from the year 2009 to 2014 to analysis the model’s reliability. Then we use the model to predict the communication networks’ relationships and capacities around the year 2050. Last we do sensitivity analysis by making small changes in parameters of simulation experiment. The result of the experiment is helpful to model how public interest and opinion can be changed in complex network.