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Dr. Naveen Jain. "Artificial Neural Network Models for Material Classification by Photon Scattering Analysis." International Journal of New Practices in Management and Engineering 9, no. 03 (September 30, 2020): 01–04. http://dx.doi.org/10.17762/ijnpme.v9i03.88.
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This article explains the risk factors involved in a business. In each type of business, there are certain risk factors for the implementation of anything in the business. The type of risks involved can depend upon many factors. It also depends on the type of business an organisation is doing. But it is very important that the risk analyst does all the analysis of the risks that might arise in future and must take necessary actions in order to avoid those risks. The risk analyst can also try to reduce the impact of the risks on the business. Therefore, it is very important that the risk analyst should have the knowledge of how to analyse risk and then can act upon them.
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Li, Zengquan, T. J. Wong, and Gwen Yu. "Information Dissemination through Embedded Financial Analysts: Evidence from China." Accounting Review 95, no. 2 (August 1, 2019): 257–81. http://dx.doi.org/10.2308/accr-52521.
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ABSTRACT When emerging market firms disclose relationship-based transactions, they face a trade-off in which greater transparency may help lower their cost of capital at the cost of revealing proprietary information. We find that firms overcome this challenge by relying on analysts within their private networks (i.e., connected analysts) who, through repeated interaction with the firm, can better verify relationship-based transactions. Using Chinese firms, we show that firms with more connected analysts have more accurate consensus forecasts and lower forecast dispersion. When a connected analyst departs and stops covering a firm, the accuracy and informativeness of the unconnected analysts' forecasts decrease, suggesting that information spills over from the connected analyst to analysts outside the network. We find a potential mechanism for this information spillover: communication through common institutional clients, which suggests that embedded financial analysts—those sharing close connections with firms and analysts—serve as a channel for disseminating proprietary, hard-to-verify information. JEL Classifications: G14; G15; M40; N25. Data Availability: Data are available from sources identified in the text.
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Johnson, Sean W., Derrick J. A. Chambers, Michael S. Boltz, and Keith D. Koper. "Application of a convolutional neural network for seismic phase picking of mining-induced seismicity." Geophysical Journal International 224, no. 1 (October 5, 2020): 230–40. http://dx.doi.org/10.1093/gji/ggaa449.
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SUMMARY Monitoring mining-induced seismicity (MIS) can help engineers understand the rock mass response to resource extraction. With a thorough understanding of ongoing geomechanical processes, engineers can operate mines, especially those mines with the propensity for rockbursting, more safely and efficiently. Unfortunately, processing MIS data usually requires significant effort from human analysts, which can result in substantial costs and time commitments. The problem is exacerbated for operations that produce copious amounts of MIS, such as mines with high-stress and/or extraction ratios. Recently, deep learning methods have shown the ability to significantly improve the quality of automated arrival-time picking on earthquake data recorded by regional seismic networks. However, relatively little has been published on applying these techniques to MIS. In this study, we compare the performance of a convolutional neural network (CNN) originally trained to pick arrival times on the Southern California Seismic Network (SCSN) to that of human analysts on coal-mine-related MIS. We perform comparisons on several coal-related MIS data sets recorded at various network scales, sampling rates and mines. We find that the Southern-California-trained CNN does not perform well on any of our data sets without retraining. However, applying the concept of transfer learning, we retrain the SCSN model with relatively little MIS data after which the CNN performs nearly as well as a human analyst. When retrained with data from a single analyst, the analyst-CNN pick time residual variance is lower than the variance observed between human analysts. We also compare the retrained CNN to a simpler, optimized picking algorithm, which falls short of the CNN's performance. We conclude that CNNs can achieve a significant improvement in automated phase picking although some data set-specific training will usually be required. Moreover, initializing training with weights found from other, even very different, data sets can greatly reduce the amount of training data required to achieve a given performance threshold.
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Höfer, Andreas, and Andreas Oehler. "Sell-side security analysts in the nexus of principal-agent relations: An information economics perspective." Corporate Ownership and Control 10, no. 2 (2013): 267–73. http://dx.doi.org/10.22495/cocv10i2c2art6.
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In this paper we investigate in depth the contractual partner relationships between sell-side security analysts and the correspondently involved parties, where the sell-side security analyst is considered as both principal and agent. We break the activities of security analysts down into a nexus of principal-agent relationships where the most striking contractual partner relationship in this network appears among sell-side analyst and the (to be) assessed company (evaluand). By analyzing the research question in this fashion we find considerable potential for information and moral hazard risks.
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Ramaiah, CH, D. Adithya Charan, and R. Syam Akhil. "Secure automated threat detection and prevention (SATDP)." International Journal of Engineering & Technology 7, no. 2.20 (April 18, 2018): 86. http://dx.doi.org/10.14419/ijet.v7i2.20.11760.
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Secure automated threat detection and prevention is the more effective procedure to reduce the workload of analyst by scanning the network, server functions& then informs the analyst if any suspicious activity is detected in the network. It monitors the system continuously and responds according to the threat environment. This response action varies from phase to phase. Here suspicious activities are detected by the help of an artificial intelligence which acts as a virtual analyst concurrently with network intrusion detection system to defend from the threat environment and taking appropriate measures with the permission of the analyst. In its final phase where packet analysis is carried out to surf for attack vectors and then categorize supervised and unsupervised data. Where the unsupervised data will be decoded or converted to supervised data with help of analyst feedback and then auto-update the algorithm (virtual analyst). So that it evolves the algorithm (with active learning mechanism) itself by time and become more efficient, strong. So, it can able to defend form similar or same kind of attacks.
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Nocke, T., S. Buschmann, J. F. Donges, N. Marwan, H. J. Schulz, and C. Tominski. "Review: visual analytics of climate networks." Nonlinear Processes in Geophysics 22, no. 5 (September 23, 2015): 545–70. http://dx.doi.org/10.5194/npg-22-545-2015.
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Abstract. Network analysis has become an important approach in studying complex spatiotemporal behaviour within geophysical observation and simulation data. This new field produces increasing numbers of large geo-referenced networks to be analysed. Particular focus lies currently on the network analysis of the complex statistical interrelationship structure within climatological fields. The standard procedure for such network analyses is the extraction of network measures in combination with static standard visualisation methods. Existing interactive visualisation methods and tools for geo-referenced network exploration are often either not known to the analyst or their potential is not fully exploited. To fill this gap, we illustrate how interactive visual analytics methods in combination with geovisualisation can be tailored for visual climate network investigation. Therefore, the paper provides a problem analysis relating the multiple visualisation challenges to a survey undertaken with network analysts from the research fields of climate and complex systems science. Then, as an overview for the interested practitioner, we review the state-of-the-art in climate network visualisation and provide an overview of existing tools. As a further contribution, we introduce the visual network analytics tools CGV and GTX, providing tailored solutions for climate network analysis, including alternative geographic projections, edge bundling, and 3-D network support. Using these tools, the paper illustrates the application potentials of visual analytics for climate networks based on several use cases including examples from global, regional, and multi-layered climate networks.
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Nocke, T., S. Buschmann, J. F. Donges, N. Marwan, H. J. Schulz, and C. Tominski. "Review: visual analytics of climate networks." Nonlinear Processes in Geophysics Discussions 2, no. 2 (April 30, 2015): 709–80. http://dx.doi.org/10.5194/npgd-2-709-2015.
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Abstract. Network analysis has become an important approach in studying complex spatiotemporal behaviour within geophysical observation and simulation data. This new field produces increasing amounts of large geo-referenced networks to be analysed. Particular focus lies currently on the network analysis of the complex statistical interrelationship structure within climatological fields. The standard procedure for such network analyses is the extraction of network measures in combination with static standard visualisation methods. Existing interactive visualisation methods and tools for geo-referenced network exploration are often either not known to the analyst or their potential is not fully exploited. To fill this gap, we illustrate how interactive visual analytics methods in combination with geovisualisation can be tailored for visual climate network investigation. Therefore, the paper provides a problem analysis, relating the multiple visualisation challenges with a survey undertaken with network analysts from the research fields of climate and complex systems science. Then, as an overview for the interested practitioner, we review the state-of-the-art in climate network visualisation and provide an overview of existing tools. As a further contribution, we introduce the visual network analytics tools CGV and GTX, providing tailored solutions for climate network analysis, including alternative geographic projections, edge bundling, and 3-D network support. Using these tools, the paper illustrates the application potentials of visual analytics for climate networks based on several use cases including examples from global, regional, and multi-layered climate networks.
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Marcet, Francisco. "Analyst coverage network and stock return comovement in emerging markets." Emerging Markets Review 32 (September 2017): 1–27. http://dx.doi.org/10.1016/j.ememar.2017.05.002.
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Javidi, Mohammad Masoud. "Network Attacks Detection by Hierarchical Neural Network." Computer Engineering and Applications Journal 4, no. 2 (June 18, 2015): 119–32. http://dx.doi.org/10.18495/comengapp.v4i2.108.
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Intrusion detection is an emerging area of research in the computer security and net-works with the growing usage of internet in everyday life. Most intrusion detection systems (IDSs) mostly use a single classifier algorithm to classify the network traffic data as normal behavior or anomalous. However, these single classifier systems fail to provide the best possible attack detection rate with low false alarm rate. In this paper,we propose to use a hybrid intelligent approach using a combination of classifiers in order to make the decision intelligently, so that the overall performance of the resul-tant model is enhanced. The general procedure in this is to follow the supervised or un-supervised data filtering with classifier or cluster first on the whole training dataset and then the output are applied to another classifier to classify the data. In this re- search, we applied Neural Network with Supervised and Unsupervised Learning in order to implement the intrusion detection system. Moreover, in this project, we used the method of Parallelization with real time application of the system processors to detect the systems intrusions.Using this method enhanced the speed of the intrusion detection. In order to train and test the neural network, NSLKDD database was used. Creating some different intrusion detection systems, each of which considered as a single agent, we precisely proceeded with the signature-based intrusion detection of the network.In the proposed design, the attacks have been classified into 4 groups and each group is detected by an Agent equipped with intrusion detection system (IDS).These agents act independently and report the intrusion or non-intrusion in the system; the results achieved by the agents will be studied in the Final Analyst and at last the analyst reports that whether there has been an intrusion in the system or not.Keywords: Intrusion Detection, Multi-layer Perceptron, False Positives, Signature- based intrusion detection, Decision tree, Nave Bayes Classifier
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Vieane, Alex, Gregory Funke, Vincent Mancuso, Eric Greenlee, Gregory Dye, Brett Borghetti, Brent Miller, Lauren Menke, and Rebecca Brown. "Coordinated Displays to Assist Cyber Defenders." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 60, no. 1 (September 2016): 344–48. http://dx.doi.org/10.1177/1541931213601078.
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Cyber network analysts must gather evidence from multiple sources and ultimately decide whether or not suspicious activity represents a threat to network security. Information relevant to this task is usually presented in an uncoordinated fashion, meaning analysts must manually correlate data across multiple databases. The current experiment examined whether analyst performance efficiency would be improved by coordinated displays, i.e., displays that automatically link relevant information across databases. We found that coordinated displays nearly doubled performance efficiency, in contrast to the standard uncoordinated displays, and coordinated displays resulted in a modest increase in threat detections. These results demonstrate that the benefits of coordinated displays are significant enough to recommend their inclusion in future cyber defense software.
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Scott, Craig, Kofi Nyarko, Tanya Capers, and Jumoke Ladeji-Osias. "Network Intrusion Visualization with NIVA, an Intrusion Detection Visual and Haptic Analyzer." Information Visualization 2, no. 2 (June 2003): 82–94. http://dx.doi.org/10.1057/palgrave.ivs.9500044.
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The rapid growth of malicious activities on worldwide communication networks, such as the Internet, has highlighted the need for efficient intrusion detection systems. The efficiency of traditional intrusion detection systems is limited, in part, by their inability to relay effectively relevant information due to their lack of interactive/immersive technologies. In this paper, we explore several network visualization techniques geared toward intrusion detection on small- and large-scale networks. We also examine the use of haptics in network intrusion visualization. By incorporating concepts from electromagnetics, fluid dynamics, and gravitational theory, we show that haptic technologies can provide another dimension of information critical to the efficient visualization of network intrusion data. Furthermore, we explore the applicability of these visualization techniques in conjunction with commercial network intrusion detectors. Finally, we present a network intrusion visualization application with haptic integration, NIVA, which allows the analyst to interactively investigate as well as efficiently detect structured attacks across time and space using advanced interactive three-dimensional displays.
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Araujo, Antonio, and Omar Delgado. "Manejo sostenible de residuos sólidos domiciliarios: recolección y transporte al relleno sanitario. Caso cantón Naranjal." Universidad-Verdad, no. 78 (June 23, 2021): 62–95. http://dx.doi.org/10.33324/uv.v1i1.364.
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Las rutas de recolección de residuos sólidos domiciliarios en el cantón Naranjal se han creado de manera intuitiva y no responden a un análisis técnico. En este estudio se evaluó las rutas actuales de recolección y se diseñó nuevas rutas optimizadas, mediante el uso de Sistema de Información Geográfica (SIG), con la finalidad de minimizar la distancia recorrida y aumentar la cobertura de recolección. Para la optimización de las rutas se utilizó el software ArcGis 10.3 y se aplicó la herramienta Network Analyst. Los resultados de la investigación permitieron reducir las rutas de 24 existentes a 20 rutas de recolección diferenciadas (10 en la cabecera cantonal y 10 en el área rural de Naranjal), se aumentó la cobertura de recolección e incrementó la frecuencia en el área rural.
Palabras clave: Recolección de desechos, residuos sólidos domiciliarios (RSD), optimización de rutas, ArcGis10.3, Network Analyst.
AbstractIn Naranjal, domestic solid waste collection routes have been created intuitively and do not respond to a technical analysis. The present study evaluated the current collection routes to design new optimized routes, through the use of Geographic Information Systems (GIS), in order to minimize the distance traveled and increase collection coverage. ArcGis 10.3 software was used for route optimization and the Network Analyst tool was applied. The results of the research made it possible to reduce the existing routes from 24 to 20 diferentiated collection routes (10 in the cantonal head and 10 in the rural area of Naranjal) increasing the collection coverage and the frequency in the rural area was also increased.
Keywords: waste collection, domestic solid waste (DSW), route optimization, ArcGis 10.3, Network Analyst.
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Liu, Suxia, and Xuan Zhu. "Accessibility Analyst: An Integrated GIS Tool for Accessibility Analysis in Urban Transportation Planning." Environment and Planning B: Planning and Design 31, no. 1 (February 2004): 105–24. http://dx.doi.org/10.1068/b305.
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The authors present an integrated GIS tool, Accessibility Analyst, for accessibility analysis in urban transportation planning, built as an extension to the desktop GIS software package, ArcView. Accessibility Analyst incorporates a number of accessibility measures, ranging from catchment profile analysis to cumulative-opportunity measures, gravity-type measures, and utility-based measures, contains several travel-impedance measurement tools for estimating the travel distance, time, or cost by multiple travel modes along actual travel routes, and interoperates with GIS data-management and data-integration, spatial-analysis, network-analysis, surface-modelling, and spatial-visualisation functions. Undertaking accessibility analysis with use of Accessibility Analyst allows the user to take full advantage of a GIS to produce spatial distributions of accessibility over a region. It can be applied to a wide range of issues in urban transportation planning, such as for studies on the relationship between transportation and land use, evaluation of transportation network efficiency, transportation infrastructure planning, and for impact assessments relating to transportation policies.
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Paz, Alexander, Naveen Veeramisti, Indira Khanal, Justin Baker, and Hanns de la Fuente-Mella. "Development of a Comprehensive Database System for Safety Analyst." Scientific World Journal 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/636841.
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This study addressed barriers associated with the use of Safety Analyst, a state-of-the-art tool that has been developed to assist during the entire Traffic Safety Management process but that is not widely used due to a number of challenges as described in this paper. As part of this study, a comprehensive database system and tools to provide data to multiple traffic safety applications, with a focus on Safety Analyst, were developed. A number of data management tools were developed to extract, collect, transform, integrate, and load the data. The system includes consistency-checking capabilities to ensure the adequate insertion and update of data into the database. This system focused on data from roadways, ramps, intersections, and traffic characteristics for Safety Analyst. To test the proposed system and tools, data from Clark County, which is the largest county in Nevada and includes the cities of Las Vegas, Henderson, Boulder City, and North Las Vegas, was used. The database and Safety Analyst together help identify the sites with the potential for safety improvements. Specifically, this study examined the results from two case studies. The first case study, which identified sites having a potential for safety improvements with respect to fatal and all injury crashes, included all roadway elements and used default and calibrated Safety Performance Functions (SPFs). The second case study identified sites having a potential for safety improvements with respect to fatal and all injury crashes, specifically regarding intersections; it used default and calibrated SPFs as well. Conclusions were developed for the calibration of safety performance functions and the classification of site subtypes. Guidelines were provided about the selection of a particular network screening type or performance measure for network screening.
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Vieane, Alex, Gregory Funke, Eric Greenlee, Vincent Mancuso, Brett Borghetti, Brent Miller, Lauren Menke, Rebecca Brown, Cyrus K. Foroughi, and Deborah Boehm-Davis. "Task Interruptions Undermine Cyber Defense." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 61, no. 1 (September 2017): 375–79. http://dx.doi.org/10.1177/1541931213601576.
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Computer network defense analysts engage a difficult, though critical, task in cyber defense. Anecdotally, these operators complain of frequent task interruptions while they are performing their duties. The goal for the current study was to investigate the effect of a commonly reported interruption, answering email, on accuracy and completion times in a simulated network analyst task. During task trials, participants were interrupted by emails between alert investigations, during alert investigations, or not at all (control). The results indicated that email interruptions increased alert completion times regardless of when they occurred, but interruptions that occurred during an alert investigation also reduced the accuracy of subsequent judgments about alert threat. Overall, the results suggest that task interruptions can potentially undermine cyber defense, and steps should be taken to better quantify and mitigate this threat.
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Watik, N., and L. M. Jaelani. "FLOOD EVACUATION ROUTES MAPPING BASED ON DERIVED- FLOOD IMPACT ANALYSIS FROM LANDSAT 8 IMAGERY USING NETWORK ANALYST METHOD." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W8 (August 23, 2019): 455–60. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w8-455-2019.
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<p><strong>Abstract.</strong> Reported by National Disaster Response Agency of Indonesia (BPBD) as many as 94 dies, 149 injured, and more than 88 thousands homeless caused by floods in 2018. Besides bringing casualties to people and environment, the floods also affect the damages to transportation infrastructures in which vital to disaster emergency response operation e.g. evacuation process. Due to the complex impact of current disaster, the demands of providing a short-term response increases accordingly. Therefore, this research proposes a prototype of flood evacuation route utilizing network analyst method. The network analyst method particularly focus on finding alternative route based on time and distance. This research uses a flood simulation model derived from Landsat 8 imagery and terrain data. Subsequently, the simulation model divides the flood severity based on the depth which consist of < 0.3 m (slight), 0.3-0.5 m (moderate), and > 0.5 m (serious) in order to generate an impact analysis regarding the estimation of damages and casualties. In order to resemble the real situation of flood, barriers (e.g. flood area) are applied into the finding evacuation route procedure. Thereby, the estimated evacuation route can be executed considering the safest and fastest way. Moreover, some comparisons between before and after flood are conducted in order to know the effectiveness of evacuation routes. By such comparison proves that network analyst enables to support disaster management operation with respect to handling the evacuation procedure.</p>
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Weckman, Gary R., Ronald W. Dravenstott, William A. Young II, Ehsan Ardjmand, David F. Millie, and Andy P. Snow. "A Prescriptive Stock Market Investment Strategy for the Restaurant Industry using an Artificial Neural Network Methodology." International Journal of Business Analytics 3, no. 1 (January 2016): 1–21. http://dx.doi.org/10.4018/ijban.2016010101.
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Stock price forecasting is a classic problem facing analysts. Forecasting models have been developed for predicting individual stocks and stock indices around the world and in numerous industries. According to a literature review, these models have yet to be applied to the restaurant industry. Strategies for forecasting typically include fundamental and technical variables. In this research, fundamental and technical inputs were combined into an artificial neural network (ANN) stock prediction model for the restaurant industry. Models were designed to forecast 1 week, 4 weeks, and 13 weeks into the future. The model performed better than the benchmark methods, which included, an analyst prediction, multiple linear regression, trading, and Buy and Hold trading strategies. The prediction accuracy of the ANN methodology presented reached accuracy performance measures as high as 60%. The model also shown resiliency over the housing crisis in 2008.
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Amin S. Hamdi, Amin S. Hamdi. "GIS Based Analysis of Interaction between Port Accessibility and Economy in Saudi Arabia." journal of King Abdulaziz University Engineering Sciences 30, no. 1 (February 4, 2018): 21–31. http://dx.doi.org/10.4197/eng.30-1.2.
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Land transportation networks play key role in economic activities by providing access to industries and commercial centers. As integral part of transportation system ports provide essential interchange between sea and land communication to facilitate interaction with global economic hubs. Region’s accessibility to port usually determines its production and growth potential. This paper provides quantitative analysis of roadway based port accessibility to different regions in Saudi Arabia using GIS framework. It also provides analytical basis for comparing relative geographical implications of the ports in the context of regional GDP and population concentration. Using geoprocessing tools like Network Analyst and Spatial Analyst in ArcGIS framework, accessibility contours are assessed. Then spatial indicators are developed to measure interaction between accessibility and economic parameters. Using these indicators, the regions lacking in adequate port accessibility are identified. Further analysis reveals that investment to improve accessibility of these regions may provide beneficial effect on their economic growth. The proposed procedure may be applied in regional planning and economic feasibility studies.
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Hossain, Khandaker Tanvir, and Md Hafizur Rahman. "Delineating the Service Area of Cyclone Shelter and Vulnerable Households Using Network Analyst Tool: a Case Study on Southkhali Union of Bagerhat District, Bangladesh." Journal of the Asiatic Society of Bangladesh, Science 44, no. 2 (December 30, 2018): 137–48. http://dx.doi.org/10.3329/jasbs.v44i2.46556.
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In Southkhali Union of Bagerhat district in Bangladesh about 47% of the total population is living beyond the service area of cyclone shelters (CS) not only for the carrying capacity of CS but also for the accessibility. Location-allocation tool of Network Analyst tool in ArcGIS determined the service area of each CS in the study area based on its shelter capacity and accessibility. People residing outside the service area of cyclone shelter are highly vulnerable due to low elevation of land and poor housing structure. The location-allocation tool of Network Analyst provided the proper evacuation plan that would identify the shelter location for each household or cluster of population and the evacuation route also. It would also help to find the proper location to build new shelters for unserved community.
Asiat. Soc. Bangladesh, Sci. 44(2): 137-148, December 2018
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Miao, Fajun, N. Seth Carpenter, Zhenming Wang, Andrew S. Holcomb, and Edward W. Woolery. "High-Accuracy Discrimination of Blasts and Earthquakes Using Neural Networks With Multiwindow Spectral Data." Seismological Research Letters 91, no. 3 (March 11, 2020): 1646–59. http://dx.doi.org/10.1785/0220190084.
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Abstract The manual separation of natural earthquakes from mine blasts in data sets recorded by local or regional seismic networks can be a labor-intensive process. An artificial neural network (ANN) applied to automate discriminating earthquakes from quarry and mining blasts in eastern Kentucky suggests that the analyst effort in this task can be significantly reduced. Based on a dataset of 152 local and regional earthquake and 4192 blast recordings over a three-year period in and around eastern Kentucky, ANNs of different configurations were trained and tested on amplitude spectra parameters. The parameters were extracted from different time windows of three-component broadband seismograms to learn the general characteristics of analyst-classified regional earthquake and blast signals. There was little variation in the accuracies and precisions of various models and ANN configurations. The best result used a network with two hidden layers of 256 neurons, trained on an input set of 132 spectral amplitudes and extracted from the P-wave time window and three overlapping time windows from the global maximum amplitude on all three components through the coda. For this configuration and input feature set, 97% of all recordings were accurately classified by our trained model. Furthermore, 96.7% of earthquakes in our data set were correctly classified with mean-event probabilities greater than 0.7. Almost all blasts (98.2%) were correctly classified by mean-event probabilities of at least 0.7. Our technique should greatly reduce the time required for manual inspection of blast recordings. Additionally, our technique circumvents the need for an analyst, or automatic locator, to locate the event ahead of time, a task that is difficult due to the emergent nature of P-wave arrivals induced by delay-fire mine blasts.
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Singchai, Ponglikit, and Wanchai Rivepiboon. "FRAMEWORK FOR FAST BUILDING SOFTWARE REQUIREMENTS SPECIFICATION." Jurnal Sistem Informasi 9, no. 1 (November 28, 2013): 1. http://dx.doi.org/10.21609/jsi.v9i1.339.
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Software Requirements is an important process in software project. If the information is inaccurate or delayed, the next steps may have problems. If system analyst has the initial or suggestion documents which use software requirements specification of past software projects with similar knowledge, it can help system analyst to building the software requirements specification involves gathering requirements from users more efficient and faster. Therefore, we propose a framework for fast building the relationships of the software requirements specification of past software projects from the similarity of the requirements in each project using Bayesian network model for retrieving requirements with similar knowledge to the new project.
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Vallet, Flore, Mostepha Khouadjia, Ahmed Amrani, and Juliette Pouzet. "DESIGNING A DATA VISUALISATION AND ANALYSIS TOOL FOR SUPPORTING DECISION-MAKING WITH PUBLIC TRANSPORTATION NETWORK." Proceedings of the Design Society 1 (July 27, 2021): 1093–102. http://dx.doi.org/10.1017/pds.2021.109.
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AbstractMassive data are surrounding us in our daily lives. Urban mobility generates a very high number of complex data reflecting the mobility of people, vehicles and objects. Transport operators are primary users who strive to discover the meaning of phenomena behind traffic data, aiming at regulation and transport planning. This paper tackles the question "How to design a supportive tool for visual exploration of digital mobility data to help a transport analyst in decision making?” The objective is to support an analyst to conduct an ex post analysis of train circulation and passenger flows, notably in disrupted situations. We propose a problem-solution process combined with data visualisation. It relies on the observation of operational agents, creativity sessions and the development of user scenarios. The process is illustrated for a case study on one of the commuter line of the Paris metropolitan area. Results encompass three different layers and multiple interlinked views to explore spatial patterns, spatio-temporal clusters and passenger flows. We join several transport network indicators whether are measured, forecasted, or estimated. A user scenario is developed to investigate disrupted situations in public transport.
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Trenggana, Soma. "Spatial Modeling of Cold Lava Flood Evacuation in Kali Putih, Magelang Regency, Using Network Analyst." International Journal of Environment, Agriculture and Biotechnology 3, no. 4 (2018): 1499–517. http://dx.doi.org/10.22161/ijeab/3.4.48.
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Kwag, Shinyoung, and Jinho Oh. "Development of network-based probabilistic safety assessment: A tool for risk analyst for nuclear facilities." Progress in Nuclear Energy 110 (January 2019): 178–90. http://dx.doi.org/10.1016/j.pnucene.2018.09.017.
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Happa, Jassim, Ioannis Agrafiotis, Martin Helmhout, Thomas Bashford-Rogers, Michael Goldsmith, and Sadie Creese. "Assessing a Decision Support Tool for SOC Analysts." Digital Threats: Research and Practice 2, no. 3 (July 2021): 1–35. http://dx.doi.org/10.1145/3430753.
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It is difficult to discern real-world consequences of attacks on an enterprise when investigating network-centric data alone. In recent years, many tools have been developed to help understand attacks using visualisation, but few aim to predict real-world consequences. We have developed a visualisation tool that aims to improve decision support during attacks in Security Operation Centres (SOCs). Our tool visualises propagation of risks from sensor alert data to Business Process (BP) tasks. This is an important capability gap present in many SOCs today, as most threat detection tools are technology-centric. In this article, we present a user study that assesses our tool’s usability and ability to support the analyst. Ten analysts from seven SOCs performed carefully designed tasks related to understanding risks and recovery decision-making. The study was conducted in laboratory conditions with simulated attacks and used a mixed-method approach to collect data from questionnaires, eye tracking, and semi-structured interviews. Our findings suggest that relating business tasks to network asset in visualisations can help analysts prioritise response strategies. Finally, our article also provides an in-depth discussion on user studies conducted with SOC analysts more generally, including lessons learned, recommendations and a critique of our own study.
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Baur, R., and I. Kropp. "Selecting and scheduling infrastructure rehabilitation projects." Water Supply 2, no. 4 (September 1, 2002): 43–50. http://dx.doi.org/10.2166/ws.2002.0119.
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Targets for the condition and performance of water supply networks can be formulated from quantitative needs as well as from the demand for the quality and reliability of drinking water networks. To reach these objectives, a public or semi-public utility needs to invest in system maintenance and rehabilitation. For the system analyst it is difficult to give specific action threshold values for each indicator describing the network performance. Preferences differ and interdependencies between criteria do not allow, by conventional scoring models, determination of those projects which are the most efficient. Another method is presented for ranking rehabilitation projects according to multiple criteria with respect to efficiency. The development of the procedure and its implementation in software is part of a research project funded by Deutsche Forschungsgemeinschaft (DFG).
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Jusufi, Ilir, Christian Klukas, Andreas Kerren, and Falk Schreiber. "Guiding the interactive exploration of metabolic pathway interconnections." Information Visualization 11, no. 2 (September 19, 2011): 136–50. http://dx.doi.org/10.1177/1473871611405677.
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Approaches to investigate biological processes have been of strong interest in the past few years and are the focus of several research areas, especially Systems Biology. Biochemical networks as representations of processes are very important for a comprehensive understanding of living beings. Drawings of these networks are often visually overloaded and do not scale. A common solution to deal with this complexity is to divide the complete network, for example, the metabolism, into a large set of single pathways that are hierarchically structured. If those pathways are visualized, this strategy generates additional navigation and exploration problems as the user loses the context within the complete network. In this article, we present a general solution to this problem of visualizing interconnected pathways and discuss it in context of biochemical networks. Our new visualization approach supports the analyst in obtaining an overview to related pathways if they are working within a particular pathway of interest. By using glyphs, brushing, and topological information of the related pathways, our interactive visualization is able to intuitively guide the exploration and navigation process, and thus the analysis processes too. To deal with real data and current networks, our tool has been implemented as a plugin for the VANTED system.
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Shi, Da Fa. "Study on the Multi-Axis Motion Control System Based on Ether CAT." Advanced Materials Research 268-270 (July 2011): 1687–90. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.1687.
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Now, the complication and systemization of the industry is increasing day after day, which make it hard to design the motion control system. However, the technology of field control is increasing, and the motion control system based on the network is widely used. The company of beck Hoff introduced the Ether CAT network, which can be configured easily, and the speed of communication is high, and in some way, the technology has become the focus of the studying the automation. This paper analyst the application for the Ether CAT in the multi-axis motion control system.
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Karczewska, Magdalena. "Analysis of changes of the urban transport system on the example of Olsztyn." AUTOBUSY – Technika, Eksploatacja, Systemy Transportowe 23, no. 5 (May 31, 2019): 28–32. http://dx.doi.org/10.24136/atest.2019.116.
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The aim of the article is to present the assessment of the accessibility of Olsztyn as a factor determining the tourist attractiveness of the area. For this purpose, GIS tools were used. The condition of public transport before and after the construction of tram lines was compared. The Network Analyst extension was used for analysis, thanks to which we can designate a supported area
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Martins, Gabriel G., Fabrice P. Cordelières, Julien Colombelli, Rocco D’Antuono, Ofra Golani, Romain Guiet, Robert Haase, et al. "Highlights from the 2016-2020 NEUBIAS training schools for Bioimage Analysts: a success story and key asset for analysts and life scientists." F1000Research 10 (April 30, 2021): 334. http://dx.doi.org/10.12688/f1000research.25485.1.
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NEUBIAS, the European Network of Bioimage Analysts, was created in 2016 with the goal of improving the communication and the knowledge transfer among the various stakeholders involved in the acquisition, processing and analysis of biological image data, and to promote the establishment and recognition of the profession of Bioimage Analyst. One of the most successful initiatives of the NEUBIAS programme was its series of 15 training schools, which trained over 400 new Bioimage Analysts, coming from over 40 countries. Here we outline the rationale behind the innovative three-level program of the schools, the curriculum, the trainer recruitment and turnover strategy, the outcomes for the community and the career path of analysts, including some success stories. We discuss the future of the materials created during this programme and some of the new initiatives emanating from the community of NEUBIAS-trained analysts, such as the NEUBIAS Academy. Overall, we elaborate on how this training programme played a key role in collectively leveraging Bioimaging and Life Science research by bringing the latest innovations into structured, frequent and intensive training activities, and on why we believe this should become a model to further develop in Life Sciences.
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Hong, Ling, Jia Gao, and Rui Hua Xu. "Influence of Emergency Passenger Flow Distribution in Urban Rail Network." Advanced Materials Research 450-451 (January 2012): 295–301. http://dx.doi.org/10.4028/www.scientific.net/amr.450-451.295.
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The emergency disposal of urban rail transit needs to accurately estimate the emergency range and total affected passenger flow volume. The urban rail transit network could be simplified to an abstract model which is easy to be analyst based on the graph theory method. Considering the actual network back-turning lines and vehicle storage tracks of urban rail network, the emergency range could be estimated effectively. The affected passenger flow could be classified as different kinds based on the different paths of passenger flow. The classification of passenger flow mainly includes “delay passenger flow”, “detour passenger flow” and “loss passenger flow”. Considering the emergency range, the different affected passenger flows could be superposed over time based on the abstract model, then the affected passenger flow volume and virtual loss time could be calculated out. The results could provide basis for the emergency disposal in urban rail transit. The example analysis is verified the feasibility of this method.
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Wang, Mengcheng, Chuan Zhao, Alan Barr, Suihuai Yu, Jay Kapellusch, and Carisa Harris Adamson. "Hand Posture and Force Estimation using Surface Electromyography and an Artificial Neural Network." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, no. 1 (December 2020): 1247–48. http://dx.doi.org/10.1177/1071181320641296.
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Prior epidemiological studies have shown that heavy hand exertion force and hand posture (grip versus pinch) are important risk factors for distal upper extremity disorders such as wrist tendinosis and carpal tunnel syndrome (CTS). However, quantifying the magnitude of hand exertions reliably and accurately is challenging and has relied heavily upon subjective worker or analyst observations. Prior studies have used electromyography (EMG) with machine learning models to estimate hand exertion but relatively few studies have assessed whether hand posture and exertion forces can be predicted at varying levels of force exertion, duty cycle and repetition rate. Therefore, the purpose of this study was to develop an approach to estimate hand posture (pinch versus grip) and hand exertion force using forearm surface electromyography (sEMG) and artificial neural networks.
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Toyoizumi, Taro, Kamiar Rahnama Rad, and Liam Paninski. "Mean-Field Approximations for Coupled Populations of Generalized Linear Model Spiking Neurons with Markov Refractoriness." Neural Computation 21, no. 5 (May 2009): 1203–43. http://dx.doi.org/10.1162/neco.2008.04-08-757.
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There has recently been a great deal of interest in inferring network connectivity from the spike trains in populations of neurons. One class of useful models that can be fit easily to spiking data is based on generalized linear point process models from statistics. Once the parameters for these models are fit, the analyst is left with a nonlinear spiking network model with delays, which in general may be very difficult to understand analytically. Here we develop mean-field methods for approximating the stimulus-driven firing rates (in both the time-varying and steady-state cases), auto- and cross-correlations, and stimulus-dependent filtering properties of these networks. These approximations are valid when the contributions of individual network coupling terms are small and, hence, the total input to a neuron is approximately gaussian. These approximations lead to deterministic ordinary differential equations that are much easier to solve and analyze than direct Monte Carlo simulation of the network activity. These approximations also provide an analytical way to evaluate the linear input-output filter of neurons and how the filters are modulated by network interactions and some stimulus feature. Finally, in the case of strong refractory effects, the mean-field approximations in the generalized linear model become inaccurate; therefore, we introduce a model that captures strong refractoriness, retains all of the easy fitting properties of the standard generalized linear model, and leads to much more accurate approximations of mean firing rates and cross-correlations that retain fine temporal behaviors.
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Tsiliakou, E., and E. Dimopoulou. "3D NETWORK ANALYSIS FOR INDOOR SPACE APPLICATIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W2 (October 6, 2016): 147–54. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w2-147-2016.
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Indoor space differs from outdoor environments, since it is characterized by a higher level of structural complexity, geometry, as well as topological relations. Indoor space can be considered as the most important component in a building’s conceptual modelling, on which applications such as indoor navigation, routing or analysis are performed. Therefore, the conceptual meaning of sub spaces or the activities taking place in physical building boundaries (e.g. walls), require the comprehension of the building’s indoor hierarchical structure. The scope of this paper is to perform 3D network analysis in a building’s interior and is structured as follows: In Section 1 the definition of indoor space is provided and indoor navigation requirements are analysed. Section 2 describes the processes of indoor space modeling, as well as routing applications. In Section 3, a case study is examined involving a 3D building model generated in CityEngine (exterior shell) and ArcScene (interior parts), in which the use of commercially available software tools (ArcGIS, ESRI), in terms of indoor routing and 3D network analysis, are explored. The fundamentals of performing 3D analysis with the ArcGIS Network Analyst extension were tested. Finally a geoprocessing model was presented, which was specifically designed to be used to interactively find the best route in ArcScene. The paper ends with discussion and concluding remarks on Section 4.
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Кулик, В. В., О. Б. Бурикін, and В. М. Пірняк. "КОМПЛЕКСНЕ ОЦІНЮВАННЯ ЕФЕКТИВНОСТІ ВСТАНОВЛЕННЯ ДОДАТКОВИХ ДЖЕРЕЛ РЕАКТИВНОЇ ПОТУЖНОСТІ У РОЗПОДІЛЬНИХ ЕЛЕКТРИЧНИХ МЕРЕЖАХ." Bulletin of the Kyiv National University of Technologies and Design. Technical Science Series 124, no. 4 (November 2, 2018): 103–11. http://dx.doi.org/10.30857/1813-6796.2018.4.11.
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To develop a method for assessing the effectiveness of reactive power sources (RPS) in electrical grids. The efficiency indicator was obtained as a result of combining the method of assessing the quality of the functioning of electrical grids on the basis of Markov networks and the method of interval analysis of energy losses. The efficiency index improves the formulation of the problem of optimizing the connection of the RPS to electrical networks. It improves the quality of this task. A new method of forming an indicator of the guaranteed effectiveness of the installing the RPS is obtained. It provides an unambiguous comprehensive evaluation of efficiency, takes into account the reliability of the electrical network, the quality of the voltage in its nodes, the accuracy of calculating the released and lost electricity. The method makes it possible to construct more efficient algorithms for optimizing the connection of RPS. They allow excluding from the search area optimal solutions "indefinite" fragments of the network without the help of an analyst. Estimation of the real effect is difficult or impossible for such fragments due to frequent failures or lack of monitoring devices. This correction of the search area improves the quality of the solution.
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Wu, Xue, Yuan Li, and Baoan Chen. "Integrated Analysis of Key Genes for FGFR1 Knockdown in Mantle Cell Lymphoma Cell Line (Z-138)." Blood 136, Supplement 1 (November 5, 2020): 32. http://dx.doi.org/10.1182/blood-2020-143311.
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Background:Mantle cell lymphoma (MCL) is the secondary common B cell lymphoma subtype that comprises 6 to 8% of non-Hodgkin's lymphoma, and is closely related to the poor clinical outcomes. Previous studies have focussed on the mechanisms mediating ibrutinib resistance, the first-in-class oral covalent inhibitor of Bruton's tyrosine kinase (BTK). The aims of the this study is to identify key genes related to the FGFR1 Knockdown in Mantle cell lymphoma cell line (Z-138) that has been proved to play a vital role in MCL progression. Methods:GSE138127 mRNA microarray datasets from Gene Expression Omnibus (GEO) were analysed to obtain differentially expressed genes (DEGs) between vector control and the Knockdown of FGFR1 charactered by ibrutinib resistance. The GO and GSEA were carried out by WEB-based GEne SeT AnaLysis Toolkit (WebGestalt) to do the functional enrichment analysis. The network analysis of protein-protein interactions (PPIs), TF-gene interaction were carried out by Network Analyst 3.0 to identify hub genes. And the main hub gene was probed using Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway analysis. Results:In total, 175 DEGs were obtained, of which 87 and 88 were up- and down-regulated, respectively. Three hub genes (CDK1, CCND1) were identified and associated to cell cycle and DNA replication. FOXC1 were identified as the potential Transcription factors in the biological process. Conclusion:CDK1, CCND1 may affect the cell cycle regulated by FOXC1, and represent the new candidate molecular markers of the occurrence of ibrutinib resistance. Keywords: Mantle cell lymphoma (MCL), ibrutinib resistance, Network Analyst, Microarray, Protein-protein interactions, Molecular markers Disclosures No relevant conflicts of interest to declare.
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Rahmad, Nur Azmina, Nur Anis Jasmin Sufri, Nurul Hamizah Muzamil, and Muhammad Amir As'ari. "Badminton player detection using faster region convolutional neural network." Indonesian Journal of Electrical Engineering and Computer Science 14, no. 3 (June 1, 2019): 1330. http://dx.doi.org/10.11591/ijeecs.v14.i3.pp1330-1335.
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Nowadays, coaches and sport analyst are concerning about sport performance analysis through sport video match. However, they still used conventional method which is through manual observation of the full video that is very troublesome because they might miss some meaningful information presence in the video. Several previous studies have discussed about tracking ball movements, identification of player based on jersey color and number as well as player movement detection in various type of sport such as soccer and volleyball but not in badminton. Therefore, this study focused on developing an automated system using Faster Region Convolutional Neural Network (Faster R-CNN) to track the position of the badminton player from the sport broadcast video. In preparing the dataset for training and testing, several broadcast videos were converted into image frames before labelling the region which indicate the players. After that, several different trained Faster R-CNN detectors were produced from the dataset before tested with different set of videos to evaluate the detector performance. In evaluating the performance of each detector model, the average precision was obtained from precision recall graph. As a result, this study revealed that the detector successfully detects the player when the detector is being fed with more generalized dataset.
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Shashank, A., V. V. Sajithvariyar, V. Sowmya, K. P. Soman, R. Sivanpillai, and G. K. Brown. "IDENTIFYING EPIPHYTES IN DRONES PHOTOS WITH A CONDITIONAL GENERATIVE ADVERSARIAL NETWORK (C-GAN)." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-M-2-2020 (November 17, 2020): 99–104. http://dx.doi.org/10.5194/isprs-archives-xliv-m-2-2020-99-2020.
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Abstract. Unmanned Aerial Vehicle (UAV) missions often collect large volumes of imagery data. However, not all images will have useful information, or be of sufficient quality. Manually sorting these images and selecting useful data are both time consuming and prone to interpreter bias. Deep neural network algorithms are capable of processing large image datasets and can be trained to identify specific targets. Generative Adversarial Networks (GANs) consist of two competing networks, Generator and Discriminator that can analyze, capture, and copy the variations within a given dataset. In this study, we selected a variant of GAN called Conditional-GAN that incorporates an additional label parameter, for identifying epiphytes in photos acquired by a UAV in forests within Costa Rica. We trained the network with 70%, 80%, and 90% of 119 photos containing the target epiphyte, Werauhia kupperiana (Bromeliaceae) and validated the algorithm’s performance using a validation data that were not used for training. The accuracy of the output was measured using structural similarity index measure (SSIM) index and histogram correlation (HC) coefficient. Results obtained in this study indicated that the output images generated by C-GAN were similar (average SSIM = 0.89–0.91 and average HC 0.97–0.99) to the analyst annotated images. However, C-GAN had difficulty to identify when the target plant was away from the camera, was not well lit, or covered by other plants. Results obtained in this study demonstrate the potential of C-GAN to reduce the time spent by botanists to identity epiphytes in images acquired by UAVs.
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Pooja R. Kotwal, Miss, Prof Mangesh M. Ghonge, and Dr Amol D. Potgantwar. "Efficiently Verifiable Computations on User Data for Behavior Analysis with Privacy Preservation." International Journal of Engineering & Technology 7, no. 3.8 (July 7, 2018): 87. http://dx.doi.org/10.14419/ijet.v7i3.8.15227.
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Along with development of internet and web, online social network are becoming important information propagation platform with hundreds of million users worldwide. Online social network attract thousands of million users to use it every day for different purpose. So that tons of user behavior data is generated on internet. Developing endeavors have been committed to mining the inexhaustible behavior data to extract significant information for research purposes to inquire about that, or analyst to develop better ecommerce strategies for business purpose. However the concern arises with this data is security, which is going to be presented to third parties. The most recent decade has seen an assortment of look into works endeavoring to perform information conglomeration in a privacy protecting manner. Most by far of existing techniques give protection to users information yet at the cost of very limited data aggregation operations like calculating sum and mean of particular query, which barely fulfill the requirement of behavior analysis. So that, proposed system mainly focuses on privacy preservation and behavior analysis of online user data. In this paper we use general accumulation and specific collection for behavior analysis. Using cryptographic algorithm we prevent privacy disclosure from both third party data aggregator and analyst. We have executed our technique and assessed its execution utilizing a relational dataset. The results of the experiment shows that this research scheme handle both overall queries and various selective aggregate queries with acceptable computation, privacy, and overheads of the communication effectively.
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Husin, Nur Diana Izzati, and Nur Atiqah Sia Abdullah. "Overlapping issues and solutions in data visualization techniques." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 3 (December 1, 2019): 1600. http://dx.doi.org/10.11591/ijeecs.v16.i3.pp1600-1608.
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<span>The tremendous growth of big data has caused the data visualization process becomes more complex and challenging, and yet, data is expected to be increased from time to time. With these massive and complex data, it is getting harder for the data analyst to interpret or read the data in order to gain new knowledge or information. Therefore, it is important to visualize these data using different techniques. However, there are many remaining issues in data visualization techniques. These issues make the data visualization a big challenge to the data analyst. The most common issue in data visualization techniques is the overlapping issue. This paper reviews the overlapping issues in multidimensional and network data visualization techniques. The existing solutions are also reviewed and discussed in term of advantages and disadvantages. This paper concludes the advantages of the overlapping issues and solutions, before discussing their drawbacks. This paper suggests the color-based approach, relocation, and reduction of data sets to solve the overlapping issues.</span>
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CHEN, LIANG-HSUAN, and CHENG-HSIUNG CHIANG. "MULTI-OBJECTIVE OPTIMIZATION IN RELIABILITY SYSTEM USING GENETIC ALGORITHM AND NEURAL NETWORK." Asia-Pacific Journal of Operational Research 25, no. 05 (October 2008): 649–72. http://dx.doi.org/10.1142/s0217595908001936.
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To optimize the design of reliability systems, an analyst is frequently faced with the demand of achieving several targets (i.e., maximization of system reliability, minimizations of cost, volume, and weight), some of which may be in conflict with each other. This paper presents a novel hybrid approach, combining a multi-objective genetic algorithm and a neural network, for multi-objective optimization of a reliability system, namely GANNRS (Genetic Algorithm and Neural Network for Reliability System optimization). The multi-objective genetic algorithm's evolutionary strategy is based on the modified neighborhood design, and is presented to find the Pareto optimal solutions so as to provide a variety of compromise solutions to the decision makers. The purpose of the neural network is to generate a good initial population in order to speed up the searching by genetic algorithm. For demonstrating the feasibility of the proposed approach, four multi-objective optimization problems of reliability system are used, and the outcomes are compared with those from other methods. The evidence shows that the proposed GANNRS is more efficient in computation, and the results from the objectives are appealing.
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Levin, E., W. Roland, R. Habibi, Z. An, and R. Shults. "RAPID VISUAL PRESENTATION TO SUPPORT GEOSPATIAL BIG DATA PROCESSING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2020 (August 25, 2020): 463–70. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2020-463-2020.
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Abstract. Given the limited number of human GIS/image analysts at any organization, use of their time and organizational resources is important, especially in light of Big Data application scenarios when organizations may be overwhelmed with vast amounts of geospatial data. The current manuscript is devoted to the description of experimental research outlining the concept of Human-Computer Symbiosis where computers perform tasks, such as classification on a large image dataset, and, in sequence, humans perform analysis with Brain-Computer Interfaces (BCIs) to classify those images that machine learning had difficulty with. The addition of the BCI analysis is to utilize the brain’s ability to better answer questions like: “Is the object in this image the object being sought?” In order to determine feasibility of such a system, a supervised multi-layer convolutional neural network (CNN) was trained to detect the difference between ‘ships’ and ‘no ships’ from satellite imagery data. A prediction layer was then added to the trained model to output the probability that a given image was within each of those two classifications. If the probabilities were within one standard deviation of the mean of a gaussian distribution centered at 0.5, they would be stored in a separate dataset for Rapid Serial Visual Presentations (RSVP), implemented with PsyhoPy, to a human analyst using a low cost EMOTIV “Insight” EEG BCI headset. During the RSVP phase, hundreds of images per minute can be sequentially demonstrated. At such a pace, human analysts are not capable of making any conscious decisions about what is in each image; however, the subliminal “aha-moment” still can be detected by the headset. The discovery of these moments are parsed out by exposition of Event Related Potentials (ERPs), specifically the P300 ERPs. If a P300 ERP is generated for detection of a ship, then the relevant image would be moved to its rightful designation dataset; otherwise, if the image classification is still unclear, it is set aside for another RSVP iteration where the time afforded to the analyst for observation of each image is increased each time. If classification is still uncertain after a respectable amount of RSVP iterations, the images in question would be located within the grid matrix of its larger image scene. The adjacent images to those of interest on the grid would then be added to the presentation to give an analyst more contextual information via the expanded field of view. If classification is still uncertain, one final expansion of the field of view is afforded. Lastly, if somehow the classification of the image is indeterminable, the image is stored in an archive dataset.
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Benichou, Charaf eddine, Mokhlis Derkaoui Alaoui, and Ali Faleh. "Approche SIG Pour La Modélisation Du Réseau Routier Et La Mesure De L’accessibilité Aux Équipements Publics. Cas De La Ville d’Agadir." European Scientific Journal, ESJ 14, no. 2 (January 31, 2018): 38. http://dx.doi.org/10.19044/esj.2018.v14n2p38.
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The study of road networks and accessibility to public facilities in Morocco doesn’t receive enough importance in regional planning. Most of the work carried out in the field is limited to diagnosis, relying on traditional tools of analysis and description. The goal of this article is to propose a thorough analysis of public facilities accessibility using isochronous maps, which will not only make it possible to evaluate the quality of urban transport system, but also guarantee the equity of the geographical distribution of public services. The study area is the urban commune of Agadir city on Morocco’s southwest coast. The approach used for this purpose is based on urban road network modeling, travel time calculations and geographic information software (GIS). Therefore, a Geographical Information System dedicated to Transport (GIST) was built, supplied with structured data in a geodatabase, and analyzed by using ArcGIS Network Analyst extension. The results demonstrate that Access to public facilities in the city of Agadir differs in quality according to districts and the types of services. However, the most remote marginal areas remain the most affected areas, both in terms of equipment distribution. the availability of transport infrastructure, or the quality of service in urban public transport.
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Bosurgi, Gaetano, Orazio Pellegrino, and Giuseppe Sollazzo. "Road Functional Classification Using Pattern Recognition Techniques." Baltic Journal of Road and Bridge Engineering 14, no. 3 (September 26, 2019): 360–83. http://dx.doi.org/10.7250/bjrbe.2019-14.448.
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The existing international standards suggest a methodology to assign a specific functional class to a road, by the values of some features, both geometrical and use-related. Sometimes, these characteristics are in contrast with each other and direct the analyst towards conflicting classes for a road or, worse, one or more of these features vary heterogeneously along the road. In these conditions, the analyst assigns the class that, by his capability and experience, he retains the most appropriate, in a very subjective way. On the contrary, the definition of an automatic procedure assuring an objective identification of the most appropriate functional class for each road would be desirable. Such a solution would be useful, especially when the road belongs to the existing infrastructure network or when it was not realised by out of date standards. The proposed procedure regards the definition of a classification model based on Pattern Recognition techniques, considering 13 input variables that, depending on their assumed value, direct the analyst towards one of the four functional classes defined by the Italian standards. In this way, it is possible to classify a road even when its characteristics are heterogeneous and conflicting. Moreover, the authors analysed the model limitations, in terms of errors and dataset size, considering observation and variable numbers. This approach, representing a beneficial decision support tool for the decision-maker, is exploitable for both planned and existing roads and becomes particularly advantageous for road agencies aiming to optimally allocate their limited funds for specific interventions assuring the achievement of a fixed functional class.
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WHITE, ARTHUR, and THOMAS BRENDAN MURPHY. "Mixed-membership of experts stochastic blockmodel." Network Science 4, no. 1 (December 16, 2015): 48–80. http://dx.doi.org/10.1017/nws.2015.29.
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AbstractSocial network analysis is the study of how links between a set of actors are formed. Typically, it is believed that links are formed in a structured manner, which may be due to, for example, political or material incentives, and which often may not be directly observable. The stochastic blockmodel represents this structure using latent groups which exhibit different connective properties, so that conditional on the group membership of two actors, the probability of a link being formed between them is represented by a connectivity matrix. The mixed membership stochastic blockmodel extends this model to allow actors membership to different groups, depending on the interaction in question, providing further flexibility.Attribute information can also play an important role in explaining network formation. Network models which do not explicitly incorporate covariate information require the analyst to compare fitted network models to additional attributes in a post-hoc manner. We introduce the mixed membership of experts stochastic blockmodel, an extension to the mixed membership stochastic blockmodel which incorporates covariate actor information into the existing model. The method is illustrated with application to the Lazega Lawyers dataset. Model and variable selection methods are also discussed.
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Kusuma, Angga Budi. "Pemanfaatan Sistem Informasi Geografis Dalam Evaluasi Kinerja Penyediaan Air Minum Perpipaan (Studi Kasus Sistem Lendah Kabupaten Kulon Progo)." JURNAL GEOGRAFI 10, no. 1 (January 2, 2018): 14. http://dx.doi.org/10.24114/jg.v10i1.8629.
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Abstrak Evaluasi jaringan perpipaan merupakan bagian dari evaluasi kinerja penyediaan air minum. Sistem Informasi Geografis (SIG) memberikan informasi akurat mengenai informasi kebumian dan integrasinya dengan Epanet memberikan efisiensi dalam evaluasi jaringan perpipaan. Jaringan Perpipaan Sistem Lendah merupakan jaringan distribusi air minum dengan sistem pompa-gravitasi dengan sumber air baku Sungai Progo. Hasil analisis menunjukan bahwa Sistem Lendah mampu menyediakan kebutuhan air minum selama 24 jam. Sisa tekanan air di beberapa lokasi sangat tinggi diatas persyaratan menyebebabkan rentan kebocoran. Kecepatan aliran di beberapa ruas pipa masih dibawah kecepatan yang dipersyaratkan menyebabkan potensi pengendapan dalam pipa. Beberapa solusi dapat digunakan untuk mengatasi permasalahan tersebut dengan mengganti jenis pipa, mengganti diameter pipa sesuai dengan kriteria, menambahkan bak pelepas tekanan (BTP) atau pressure reducing valve (valve) untuk mengurangi sisa tekanan di beberapa titik. Kata Kunci: Sistem Informasi Geografis, Epanet, Sistem Lendah, Jaringan PerpipaanAbstractPipe network evaluation is a part of evaluation of drinking water supply performance. Geographic Information System (GIS) provides accurate information about earth and its integration with Epanet gives eficiency in pipe network evaluation. Lendah system pipe network is drinking water distribution networks with pump-gravitation system and water of Progo River as the water source. The analyst shows that Lendah System is capable of providing drinking water needs 24 hours daily.The remaining water pressure in several locations are exceeded standard causing leakage vulnerability. Water velocity of several pipe segments is below required velocity. Several solutions could be taken to solve those problems they change pipe type, change pipe diameter suited to standard, add pressure release tube (PRT) or pressure reducing valve (valve) to reduce remaining pressure in several nodes. Keywords: Geographic Information Systems, Epanet, Lendah System, Pipe Network
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Zaidi, Taskeen, and Rampratap Rampratap. "Virtual Machine Allocation Policy in Cloud Computing Environment using CloudSim." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 1 (February 1, 2018): 344. http://dx.doi.org/10.11591/ijece.v8i1.pp344-354.
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Cloud computing has been widely accepted by the researchers for the web applications. During the past years, distributed computing replaced the centralized computing and finally turned towards the cloud computing. One can see lots of applications of cloud computing like online sale and purchase, social networking web pages, country wide virtual classes, digital libraries, sharing of pathological research labs, supercomputing and many more. Creating and allocating VMs to applications use virtualization concept. Resource allocates policies and load balancing polices play an important role in managing and allocating resources as per application request in a cloud computing environment. Cloud analyst is a GUI tool that simulates the cloud-computing environment. In the present work, the cloud servers are arranged through step network and a UML model for a minimization of energy consumption by processor, dynamic random access memory, hard disk, electrical components and mother board is developed. A well Unified Modeling Language is used for design of a class diagram. Response time and internet characteristics have been demonstrated and computed results are depicted in the form of tables and graphs using the cloud analyst simulation tool.
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Strain, Tom, R. Eddie Wilson, and Roger Littleworth. "Computer Vision for Rapid Updating of the Highway Asset Inventory." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 9 (July 9, 2020): 245–55. http://dx.doi.org/10.1177/0361198120928348.
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In this paper, a decision support system is proposed to assist an analyst in updating the highway roadside asset inventory. The feasibility of the system is tested with assets along an 8 km section of the A27 highway on the south coast of England, UK. Survey data from a vehicle equipped with a single forward-facing camera and a GPS-enabled inertial measurement unit, aerial imagery of the highway, and the asset inventory are fused to develop the system. The camera on the vehicle is calibrated so that assets may be automatically located within the survey images. The assets are then classified by a state-of-the-art convolutional neural network. Therefore, those assets recorded correctly in the inventory and those needing further manual inspection are automatically identified. Three different asset types are considered (traffic signs, matrix signs, and reference marker posts), and overall 91% of the assets in a withheld test set are verified automatically. Thus the analyst is presented with a much smaller set of assets for which the inventory is incorrect and which require further inspection. We therefore demonstrate the value in fusing multiple data sources to develop decision support systems for transportation asset monitoring.
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Leifeld, Philip, and Skyler J. Cranmer. "A theoretical and empirical comparison of the temporal exponential random graph model and the stochastic actor-oriented model." Network Science 7, no. 1 (March 2019): 20–51. http://dx.doi.org/10.1017/nws.2018.26.
Full textAbstract:
AbstractThe temporal exponential random graph model (TERGM) and the stochastic actor-oriented model (SAOM, e.g., SIENA) are popular models for longitudinal network analysis. We compare these models theoretically, via simulation, and through a real-data example in order to assess their relative strengths and weaknesses. Though we do not aim to make a general claim about either being superior to the other across all specifications, we highlight several theoretical differences the analyst might consider and find that with some specifications, the two models behave very similarly, while each model out-predicts the other one the more the specific assumptions of the respective model are met.
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LIN, CHAU-YOUNG IVAN, and CHENG-SEEN HO. "A generic ontology-based approach for requirement analysis and its application in network management software." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 13, no. 1 (January 1999): 37–61. http://dx.doi.org/10.1017/s0890060499131044.
Full textAbstract:
This paper describes a generic ontology-based approach that eases the requirement analysis (RA) work. The approach enables the user to derive method-specific RA tools for different applications. The derivation process is based on a unified framework that contains a software methodology ontology and a knowledge acquisition ontology. The former contains a library of software RA methods and a set of modeling support entities, which use the library to construct methodological knowledge. The latter contains a library of knowledge acquisition techniques and a set of acquisition support entities, which work on the library, guided by the generated methodological knowledge, to extract domain knowledge. The generated methodological knowledge, coupled with the domain knowledge, forms the RA tools. We have demonstrated the use of the approach by deriving an RA tool to assist the system analyst to acquire and formalize a requirement specification for a network management system. This approach facilitates the integrating, sharing, and reuse of software methodologies and knowledge acquisition techniques and alleviates the problems associated with the correct generation of requirement specification for different domains.