Clouds of Suspicion: Airspace Arrangements, Escalation, and Discord in U.S./NATO-Russian Relations

How have cooperative airspace arrangements contributed to cooperation and discord in the Euro-Atlantic region? This study analyzes the role of three sets of airspace arrangements developed by Euro-Atlantic states since the end of the Cold War—(1) cooperative aerial surveillance of military activity, (2) exchange of air situational data, and (3) joint engagement of theater air and missile threats—in political-military relations among neighbors and within the region. These arrangements provide insights into the integration of Central and Eastern European states into Western security institutions, and the current discord that centers on the conflict in Ukraine and Russia’s place in regional security. The study highlights the role of airspace incidents as contributors to conflict escalation and identifies opportunities for transparency- and confidence-building measures to improve U.S./NATO-Russian relations. The study recommends strengthening the Open Skies Treaty in order to facilitate the resolution of conflicts and improve region-wide military transparency. It notes that political-military arrangements for engaging theater air and missile threats created by NATO and Russia over the last twenty years are currently postured in a way that divides the region and inhibits mutual security. In turn, the U.S.-led Regional Airspace Initiatives that facilitated the exchange of air situational data between NATO and then-NATO-aspirants such as Poland and the Baltic states, offer a useful precedent for improving air sovereignty and promoting information sharing to reduce the fear of war among participating states. Thus, projects like NATO’s Air Situational Data Exchange and the NATO-Russia Council Cooperative Airspace Initiative—if extended to the exchange of data about military aircraft—have the potential to buttress deterrence and contribute to conflict prevention. The study concludes that documenting the evolution of airspace arrangements since the end of the Cold War contributes to understanding of the conflicting narratives put forward by Russia, the West, and the states “in-between” with respect to reasons for the current state of regional security. The long-term project of developing a zone of stable peace in the Euro-Atlantic must begin with the difficult task of building inclusive security institutions to accommodate the concerns of all regional actors.

Managing and utilizing information flow for new products’ periodic account sales process

The goal was to understand, document and module how information is currently flown internally in the largest dairy organization in Finland. The organization has undergone radical changes in the past years due to economic sanctions between European Union and Russia. Therefore, organization’s ultimate goal would be to continue its growth through managing its sales process more efficiently. The thesis consists of a literature review and an empirical part. The literature review consists of knowledge management and process modeling theories. First, the knowledge management discusses how data, information and knowledge are exchanged in the process. Knowledge management models and processes are describing how knowledge is created, exchanged and can be managed in an organization. Secondly, the process modeling is responsible for visualizing information flow through discussion of modeling approaches and presenting different methods and techniques. Finally, process’ documentation procedure was presented. In the end, a constructive research approach was used in order to identify process’ related problems and bottlenecks. Therefore, possible solutions were presented based on this approach. The empirical part of the study is based on 37 interviews, organization’s internal data sources and theoretical framework. The acquired data and information were used to document and to module the sales process in question with a flowchart diagram. Results are conducted through construction of the flowchart diagram and analysis of the documentation. In fact, answers to research questions are derived from empirical and theoretical parts. In the end, 14 problems and two bottlenecks were identified in the process. The most important problems are related to approach and/or standardization for information sharing, insufficient information technology tool utilization and lack of systematization of documentation. The bottlenecks are caused by the alarming amount of changes to files after their deadlines.

NETCODE: an XOR-based warning dissemination scheme for vehicular wireless networks

The next generation of vehicles will be equipped with automated Accident Warning Systems (AWSs) capable of warning neighbouring vehicles about hazards that might lead to accidents. The key enabling technology for these systems is the Vehicular Ad-hoc Networks (VANET) but the dynamics of such networks make the crucial timely delivery of warning messages challenging. While most previously attempted implementations have used broadcast-based data dissemination schemes, these do not cope well as data traffic load or network density increases. This problem of sending warning messages in a timely manner is addressed by employing a network coding technique in this thesis. The proposed NETwork COded DissEmination (NETCODE) is a VANET-based AWS responsible for generating and sending warnings to the vehicles on the road. NETCODE offers an XOR-based data dissemination scheme that sends multiple warning in a single transmission and therefore, reduces the total number of transmissions required to send the same number of warnings that broadcast schemes send. Hence, it reduces contention and collisions in the network improving the delivery time of the warnings. The first part of this research (Chapters 3 and 4) asserts that in order to build a warning system, it is needful to ascertain the system requirements, information to be exchanged, and protocols best suited for communication between vehicles. Therefore, a study of these factors along with a review of existing proposals identifying their strength and weakness is carried out. Then an analysis of existing broadcast-based warning is conducted which concludes that although this is the most straightforward scheme, loading can result an effective collapse, resulting in unacceptably long transmission delays. The second part of this research (Chapter 5) proposes the NETCODE design, including the main contribution of this thesis, a pair of encoding and decoding algorithms that makes the use of an XOR-based technique to reduce transmission overheads and thus allows warnings to get delivered in time. The final part of this research (Chapters 6--8) evaluates the performance of the proposed scheme as to how it reduces the number of transmissions in the network in response to growing data traffic load and network density and investigates its capacity to detect potential accidents. The evaluations use a custom-built simulator to model real-world scenarios such as city areas, junctions, roundabouts, motorways and so on. The study shows that the reduction in the number of transmissions helps reduce competition in the network significantly and this allows vehicles to deliver warning messages more rapidly to their neighbours. It also examines the relative performance of NETCODE when handling both sudden event-driven and longer-term periodic messages in diverse scenarios under stress caused by increasing numbers of vehicles and transmissions per vehicle. This work confirms the thesis' primary contention that XOR-based network coding provides a potential solution on which a more efficient AWS data dissemination scheme can be built.

EMMA 2--a MAGE-compliant system for the collaborative analysis and integration of microarray data

Background: Understanding transcriptional regulation by genome-wide microarray studies can contribute to unravel complex relationships between genes. Attempts to standardize the annotation of microarray data include the Minimum Information About a Microarray Experiment (MIAME) recommendations, the MAGE-ML format for data interchange, and the use of controlled vocabularies or ontologies. The existing software systems for microarray data analysis implement the mentioned standards only partially and are often hard to use and extend. Integration of genomic annotation data and other sources of external knowledge using open standards is therefore a key requirement for future integrated analysis systems. Results: The EMMA 2 software has been designed to resolve shortcomings with respect to full MAGE-ML and ontology support and makes use of modern data integration techniques. We present a software system that features comprehensive data analysis functions for spotted arrays, and for the most common synthesized oligo arrays such as Agilent, Affymetrix and NimbleGen. The system is based on the full MAGE object model. Analysis functionality is based on R and Bioconductor packages and can make use of a compute cluster for distributed services. Conclusion: Our model-driven approach for automatically implementing a full MAGE object model provides high flexibility and compatibility. Data integration via SOAP-based web-services is advantageous in a distributed client-server environment as the collaborative analysis of microarray data is gaining more and more relevance in international research consortia. The adequacy of the EMMA 2 software design and implementation has been proven by its application in many distributed functional genomics projects. Its scalability makes the current architecture suited for extensions towards future transcriptomics methods based on high-throughput sequencing approaches which have much higher computational requirements than microarrays.

The study of correlation between sea surface temperature (SST) by using the NOAA-AVHRR data and compare this data with temperature measurement in the Caspian Sea

The changes in time and location of surface temperature from a water body has an important effect on climate activities, marine biology, sea currents, salinity and other characteristics of the seas and lakes water. Traditional measurement of temperature is costly and time consumer due to its dispersion and instability. In recent years the use of satellite technology and remote sensing sciences for data acquiring and parameter and lysis of climatology and oceanography is well developed. In this research we used the NOAA’s Satellite images from its AVHRR system to compare the field surface temperature data with the satellite images information. Ten satellite images were used in this project. These images were calibrated with the field data at the exact time of satellite pass above the area. The result was a significant relation between surface temperatures from satellite data with the field work. As the relative error less than %40 between these two data is acceptable, therefore in our observation the maximum error is %21.2 that can be considered it as acceptable. In all stations the result of satellite measurements is usually less than field data that cores ponds with the global result too. As this sea has a vast latitude, therefore the different in the temperature is natural. But we know this factor is not the only cause for surface currents. The information of all satellites were images extracted by ERDAS software, and the “Surfer” software is used to plot the isotherm lines.

Estágio TAP/Megasys - Data Warehouse & Reporting

Mestrado em Gestão de Sistemas de Informação

High spatial resolution land use and land cover mapping of the Brazilian Legal Amazon in 2008 using Landsat-5/TM and MODIS data.

Understanding spatial patterns of land use and land cover is essential for studies addressing biodiversity, climate change and environmental modeling as well as for the design and monitoring of land use policies. The aim of this study was to create a detailed map of land use land cover of the deforested areas of the Brazilian Legal Amazon up to 2008. Deforestation data from and uses were mapped with Landsat-5/TM images analysed with techniques, such as linear spectral mixture model, threshold slicing and visual interpretation, aided by temporal information extracted from NDVI MODIS time series. The result is a high spatial resolution of land use and land cover map of the entire Brazilian Legal Amazon for the year 2008 and corresponding calculation of area occupied by different land use classes. The results showed that the four classes of Pasture covered 62% of the deforested areas of the Brazilian Legal Amazon, followed by Secondary Vegetation with 21%. The area occupied by Annual Agriculture covered less than 5% of deforested areas; the remaining areas were distributed among six other land use classes. The maps generated from this project ? called TerraClass - are available at INPE?s web site (http://www.inpe.br/cra/projetos_pesquisas/terraclass2008.php)

Integrating internet-of-things with the power of cloud computing and the intelligence of big Data analytics- a three layered approach

This paper is written through the vision on integrating Internet-of-Things (IoT) with the power of Cloud Computing and the intelligence of Big Data analytics. But integration of all these three cutting edge technologies is complex to understand. In this research we first provide a security centric view of three layered approach for understanding the technology, gaps and security issues. Then with a series of lab experiments on different hardware, we have collected performance data from all these three layers, combined these data together and finally applied modern machine learning algorithms to distinguish 18 different activities and cyber-attacks. From our experiments we find classification algorithm RandomForest can identify 93.9% attacks and activities in this complex environment. From the existing literature, no one has ever attempted similar experiment for cyber-attack detection for IoT neither with performance data nor with a three layered approach.

The SEC's retail investor 2.0: interactive data and the rise of calculative accountability

The Securities and Exchange Commission (SEC) in the United States mandated a new digital reporting system for US companies in late 2008. The new generation of information provision has been dubbed by Chairman Cox, ‘interactive data’ (SEC, 2006a). Despite the promise of its name, we find that in the development of the project retail investors are invoked as calculative actors rather than engaged in dialogue. Similarly, the potential for the underlying technology to be applied in ways to encourage new forms of accountability appears to be forfeited in the interests of enrolling company filers.We theorise the activities of the SEC and in particular its chairman at the time, Christopher Cox, over a three year period, both prior to and following the ‘credit crisis’. We argue that individuals and institutions play a central role in advancing the socio-technical project that is constituted by interactive data. We adopt insights from ANT (Callon, 1986, Latour, 1987 and Latour, 2005b) and governmentality (Miller, 2008 and Miller and Rose, 2008) to show how regulators and the proponents of the technology have acted as spokespersons for the interactive data technology and the retail investor. We examine the way in which calculative accountability has been privileged in the SEC's construction of the retail investor as concerned with atomised, quantitative data (Kamuf, 2007, Roberts, 2009 and Tsoukas, 1997). We find that the possibilities for the democratising effects of digital information on the Internet has not been realised in the interactive data project and that it contains risks for the very investors the SEC claims to seek to protect.

Cooperative data scheduling in hybrid vehicular ad hoc networks: VANET as a software defined network

This paper presents the first study on scheduling for cooperative data dissemination in a hybrid infrastructure-to-vehicle (I2V) and vehicle-to-vehicle (V2V) communication environment. We formulate the novel problem of cooperative data scheduling (CDS). Each vehicle informs the road-side unit (RSU) the list of its current neighboring vehicles and the identifiers of the retrieved and newly requested data. The RSU then selects sender and receiver vehicles and corresponding data for V2V communication, while it simultaneously broadcasts a data item to vehicles that are instructed to tune into the I2V channel. The goal is to maximize the number of vehicles that retrieve their requested data. We prove that CDS is NP-hard by constructing a polynomial-time reduction from the Maximum Weighted Independent Set (MWIS) problem. Scheduling decisions are made by transforming CDS to MWIS and using a greedy method to approximately solve MWIS. We build a simulation model based on realistic traffic and communication characteristics and demonstrate the superiority and scalability of the proposed solution. The proposed model and solution, which are based on the centralized scheduler at the RSU, represent the first known vehicular ad hoc network (VANET) implementation of software defined network (SDN) concept.

Nonparametric discovery and analysis of learning patterns and autism subgroups from therapeutic data

The spectrum nature and heterogeneity within autism spectrum disorders (ASD) pose as a challenge for treatment. Personalisation of syllabus for children with ASD can improve the efficacy of learning by adjusting the number of opportunities and deciding the course of syllabus. We research the data-motivated approach in an attempt to disentangle this heterogeneity for personalisation of syllabus. With the help of technology and a structured syllabus, collecting data while a child with ASD masters the skills is made possible. The performance data collected are, however, growing and contain missing elements based on the pace and the course each child takes while navigating through the syllabus. Bayesian nonparametric methods are known for automatically discovering the number of latent components and their parameters when the model involves higher complexity. We propose a nonparametric Bayesian matrix factorisation model that discovers learning patterns and the way participants associate with them. Our model is built upon the linear Poisson gamma model (LPGM) with an Indian buffet process prior and extended to incorporate data with missing elements. In this paper, for the first time we have presented learning patterns deduced automatically from data mining and machine learning methods using intervention data recorded for over 500 children with ASD. We compare the results with non-negative matrix factorisation and K-means, which being parametric, not only require us to specify the number of learning patterns in advance, but also do not have a principle approach to deal with missing data. The F1 score observed over varying degree of similarity measure (Jaccard Index) suggests that LPGM yields the best outcome. By observing these patterns with additional knowledge regarding the syllabus it may be possible to observe the progress and dynamically modify the syllabus for improved learning.

FUIR: Fusing user and item information to deal with data sparsity by using side information in recommendation systems

Recommendation systems adopt various techniques to recommend ranked lists of items to help users in identifying items that fit their personal tastes best. Among various recommendation algorithms, user and item-based collaborative filtering methods have been very successful in both industry and academia. More recently, the rapid growth of the Internet and E-commerce applications results in great challenges for recommendation systems as the number of users and the amount of available online information have been growing too fast. These challenges include performing high quality recommendations per second for millions of users and items, achieving high coverage under the circumstance of data sparsity and increasing the scalability of recommendation systems. To obtain higher quality recommendations under the circumstance of data sparsity, in this paper, we propose a novel method to compute the similarity of different users based on the side information which is beyond user-item rating information from various online recommendation and review sites. Furthermore, we take the special interests of users into consideration and combine three types of information (users, items, user-items) to predict the ratings of items. Then FUIR, a novel recommendation algorithm which fuses user and item information, is proposed to generate recommendation results for target users. We evaluate our proposed FUIR algorithm on three data sets and the experimental results demonstrate that our FUIR algorithm is effective against sparse rating data and can produce higher quality recommendations.

Big privacy: challenges and opportunities of privacy study in the age of big data

One of the biggest concerns of big data is privacy. However, the study on big data privacy is still at a very early stage. We believe the forthcoming solutions and theories of big data privacy root from the in place research output of the privacy discipline. Motivated by these factors, we extensively survey the existing research outputs and achievements of the privacy field in both application and theoretical angles, aiming to pave a solid starting ground for interested readers to address the challenges in the big data case. We first present an overview of the battle ground by defining the roles and operations of privacy systems. Second, we review the milestones of the current two major research categories of privacy: data clustering and privacy frameworks. Third, we discuss the effort of privacy study from the perspectives of different disciplines, respectively. Fourth, the mathematical description, measurement, and modeling on privacy are presented. We summarize the challenges and opportunities of this promising topic at the end of this paper, hoping to shed light on the exciting and almost uncharted land.

Interoperable Resource Brokering with Policy-based Provisioning and Job Allocation

The increasing needs for computational power in areas such as weather simulation, genomics or Internet applications have led to sharing of geographically distributed and heterogeneous resources from commercial data centers and scientific institutions. Research in the areas of utility, grid and cloud computing, together with improvements in network and hardware virtualization has resulted in methods to locate and use resources to rapidly provision virtual environments in a flexible manner, while lowering costs for consumers and providers. However, there is still a lack of methodologies to enable efficient and seamless sharing of resources among institutions. In this work, we concentrate in the problem of executing parallel scientific applications across distributed resources belonging to separate organizations. Our approach can be divided in three main points. First, we define and implement an interoperable grid protocol to distribute job workloads among partners with different middleware and execution resources. Second, we research and implement different policies for virtual resource provisioning and job-to-resource allocation, taking advantage of their cooperation to improve execution cost and performance. Third, we explore the consequences of on-demand provisioning and allocation in the problem of site-selection for the execution of parallel workloads, and propose new strategies to reduce job slowdown and overall cost.

User interaction monitoring and analysis framework

An overview is given of a user interaction monitoring and analysis framework called BaranC. Monitoring and analysing human-digital interaction is an essential part of developing a user model as the basis for investigating user experience. The primary human-digital interaction, such as on a laptop or smartphone, is best understood and modelled in the wider context of the user and their environment. The BaranC framework provides monitoring and analysis capabilities that not only records all user interaction with a digital device (e.g. smartphone), but also collects all available context data (such as from sensors in the digital device itself, a fitness band or a smart appliances). The data collected by BaranC is recorded as a User Digital Imprint (UDI) which is, in effect, the user model and provides the basis for data analysis. BaranC provides functionality that is useful for user experience studies, user interface design evaluation, and providing user assistance services. An important concern for personal data is privacy, and the framework gives the user full control over the monitoring, storing and sharing of their data.