The course of lectures on discipline “Intellectual property” (for the 5 year students of the specialty 8.03060101 “Management”)

Затверджено на засіданні кафедри менеджменту інноваційної діяльності та підприємнцтва. Протокол No 1 від 27 серпня 2015 р. Рекомендовано методичною комісією факультету управління і бізнесу у виробництві ТНТУ імені Івана Пулюя. Протокол No 6 від 26 лютого 2016 р.У методичних вказівках, у відповідності до робочої програми, сформовано лекційний матеріал з дисципліни “Інтелектуальна власність” для іноземних студентів спеціальності 8.03060101 “Менеджмент організацій та адміністрування”.Методичні вказівки призначені для допомоги іноземним студентам при вивченні курсу “Інтелектуальна власність”. У методичних вказівках містяться загальні теоретичні відомості, необхідні до вивчення даного курсу. Рекомендовано для іноземних студентів спеціальності 8.03060101 “Менеджмент організацій та адміністрування” з метою закріплення, поглиблення і узагальнення знань, одержаних студентами за час навчання та їх застосування до комплексного вирішення конкретного фахового завдання із дисципліни “Інтелектуальна власність”. Складено з урахуванням робочої програми вивчення курсу, методичних розробок інших вузів, а також матеріалів літературних джерел, наведених у рекомендованій літературі

Consumer Data Research

Big Data collected by customer-facing organisations – such as smartphone logs, store loyalty card transactions, smart travel tickets, social media posts, or smart energy meter readings – account for most of the data collected about citizens today. As a result, they are transforming the practice of social science. Consumer Big Data are distinct from conventional social science data not only in their volume, variety and velocity, but also in terms of their provenance and fitness for ever more research purposes. The contributors to this book, all from the Consumer Data Research Centre, provide a first consolidated statement of the enormous potential of consumer data research in the academic, commercial and government sectors – and a timely appraisal of the ways in which consumer data challenge scientific orthodoxies

National Culture: Understanding the Impact of Cross-culture on Airline Pilots\u27 Safety Performance in the Middle-East and North Africa (MENA) Region

The continuous expansion of Middle Eastern airlines has created a pilot shortage. Since the local pilot population in the Middle East is relatively small, airlines have been relying on foreign pilots to satisfy their operational requirements. Consequently, pilots with diverse cultural perspectives have been operating together. In order to manage this cultural diversity and ensure safe operations, airlines have been applying a number of training and operational strategies such as Crew Resource Management (CRM) with emphasis on adherence to Standard Operating Procedures (SOP). However, CRM was designed and implemented by North Americans as a solution for human factor intricacies among North American pilots, and thus, CRM is not culturally calibrated to accommodate pilots from other regions in the world. The analyses of Flight Operational Quality Assurance (FOQA) information acquired from a Middle Eastern airline aided in understanding the influences of cultural diversity on airline operations. This analysis helped in understanding the impact of cross-culture among airline pilots on three relevant unsafe performance events: hard landings, unstable approaches, and pilot deviations. The study was conducted using a descriptive comparative method to analyze the relationship between unsafe performance events and captain / first officer nationality combinations during flights where performance events were recorded. The flight data were retrieved from an unchanged flight data-recording environment yielding robust detailed data that was combined with administrative demographic data. Tests of associations were used to understand the relationship between unsafe performance events and nationality combinations. These associations were illustrated through multi-dimensional chi-square tests. A comparison of cross-cultural and homogeneous flight deck crew combinations from unsafe performance events was examined. Additional analyses were conducted to predict group membership through discriminant analysis and multinomial logistic regression. Several Spearman\u27s r correlation tests were conducted to assess the influence of intervening demographic variables on the association between nationality combinations and unsafe performance events. While cause-and-effect relationships between variables could not be determined in this research design, association variations between variables were made evident. ANCOVA statistical tests were conducted to control for the effect of: age of captains / first officers, airport destinations, and eligibility to command the flight on the relationship between nationality combination and unsafe performance events. The Spearman\u27s rank correlation test indicated significant weak correlation between destination airport and unsafe performance events, as well as, eligibility to command the flight and unsafe performance events. A 7 by 7 multi-dimensional chi-square test indicated that there was a relationship between certain pilot nationality combinations and unsafe performance events categories for pilot deviations and all unsafe performance events together. Moreover, the discriminant analysis test results showed that there was a significant effect of some nationality combinations on unsafe performance events. Results obtained from the analyses buttress the literature that certain cultural traits and beliefs influence pilots\u27 behavior and attitudes and may jeopardize safety levels. CRM skills may be weakened as a result of heterogeneous nationality combinations. It is recommended to conduct further research on current CRM training concepts in order to improve its effectiveness among cross-cultural crewmembers

Consumer Data Research

Big Data collected by customer-facing organisations – such as smartphone logs, store loyalty card transactions, smart travel tickets, social media posts, or smart energy meter readings – account for most of the data collected about citizens today. As a result, they are transforming the practice of social science. Consumer Big Data are distinct from conventional social science data not only in their volume, variety and velocity, but also in terms of their provenance and fitness for ever more research purposes. The contributors to this book, all from the Consumer Data Research Centre, provide a first consolidated statement of the enormous potential of consumer data research in the academic, commercial and government sectors – and a timely appraisal of the ways in which consumer data challenge scientific orthodoxies

The Los Angeles Historic Resource Survey Report: A Framework for a Citywide Historic Resource Survey

Explains procedural requirements and technical components of a comprehensive survey of the city's historic buildings and neighborhoods designed to help guide planning, maintenance, and investment decisions. Discusses selected findings and best practices

Data integration for urban transport planning

Urban transport planning aims at balancing conflicting challenges by promoting more efficient transport systems while reducing negative impacts. The availability of better and more reliable data has not only stimulated new planning methodologies, but also created challenges for efficient data management and data integration. The major focus of this study is to improve methodologies for representing and integrating multi-source and multi-format urban transport data. This research approaches the issue of data integration based on the classification of urban transport data both from a functional and a representational perspective. The functional perspective considers characteristics of the urban transport system and planning requirements, and categorises data into supply, demand, performance and impact. The representational perspective considers transport data in terms of their spatial and non-spatial characteristics that are important for data representation. These two perspectives correspond to institutional and methodological data integration respectively, and are the foundation of transport data integration. This research is based on the city of Wuhan in China. The methodological issues of transport data integration are based on the representational perspective. A framework for data integration has been put forward, in which spatial data are classified as point, linear and areal types, and the non-spatial data are sorted out as values and temporal attributes. This research has respectively probed the integration of point, linear and areal transport data within a GIS environment. The locations of socio-economic activities are point-type data that need to be spatially referenced. A location referencing process requires a referencing base, source address units and referencing methods. The referencing base consists of such spatial features as streets, street addresses, points of interest and publicly known zones. These referencing bases have different levels of spatial preciseness and have to be kept in a hierarchy. Source addresses in Chinese cities are usually written as one sentence, which has to be divided into address units for automatic geo-coding. As it is difficult to separate from the sentences, the address units have to be clearly identified in survey forms. Depending on the types of address units, the referencing process makes use of either semantic name matching or address matching to link source addresses to features in the referencing base. The name-based and road-based referencing schemes constitute a comprehensive location referencing framework that is applicable to Chinese cities. The relationship between two sets of linear features can be identified with spatial overlay in the case of independent representation, or with internal linkage in a dependent representation. The bus line is such a feature that runs on the street network and can be dependently referenced by streets. In the heavily bus-oriented city of Wuhan, bus lines constitute a large public transit network that is important to transport planning and management. This research has extended conventional bus line representation to a more detailed level. Each bus line has been differentiated as two directional routes that are defined separately with reference to the street network. Accordingly, individual route stops are also represented in the database. These stop sites are spatial features with geometry that are linked to street segments and bus routes by linear location referencing methods. A data model linking base street network, bus lines and routes, line and route stops, and other bus operations data has been constructed. The benefits of the detailed model have been demonstrated in several transport applications. Zonal data transitions include three types of operations, i.e. aggregation, areal interpolation and disaggregation. This study focuses on disaggregating data from larger zones to smaller zones. In the context of Wuhan, zonal data disaggregation involves the allocation of statistical data from statistical units to smaller parcels. Given the availability of land use data, a weighted approach reflecting spatial variations has been applied in the disaggregation process. Two technical processes for disaggregation have been examined. Weighted area-weighting (WAW) is an adaptation of the classic area-weighting method, and Monte Carlo simulation (MC) is a stochastic process based on a raster data model. The MC outcome is more convenient for subsequent re-aggregation, and is also directly available for micro-simulation. An important contribution arising from this zonal integration study is that two standardised disaggregation tools have been developed within a GIS environment. The research has also explored the institutional aspect of data integration. The findings of this study show that there is generally a good institutional transport structure in the city of Wuhan and that there is also a growing awareness of using information technology. Professional cooperation exists among transport organisations, but not yet at a level for data sharing. An integrated data support framework requires data sharing. In such a framework, it should be possible to know where to get data for specific transport studies, or which kind of research an institution supports

Enterprise Search in the European Union: A Techno-economic Analysis

This Report contributes to the work being carried out by IPTS on the potential of Search, discussing, in particular, the prospects of Enterprise search as well as the main challenges and opportunities. It is part of CHORUS+, an initiative supported by the Directorate General Information Society and Media. Information about CHORUS+ is available at http://avmediasearch.euJRC.J.3-Information Societ

Mining complex trees for hidden fruit : a graph–based computational solution to detect latent criminal networks : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Information Technology at Massey University, Albany, New Zealand.

The detection of crime is a complex and difficult endeavour. Public and private organisations – focusing on law enforcement, intelligence, and compliance – commonly apply the rational isolated actor approach premised on observability and materiality. This is manifested largely as conducting entity-level risk management sourcing ‘leads’ from reactive covert human intelligence sources and/or proactive sources by applying simple rules-based models. Focusing on discrete observable and material actors simply ignores that criminal activity exists within a complex system deriving its fundamental structural fabric from the complex interactions between actors - with those most unobservable likely to be both criminally proficient and influential. The graph-based computational solution developed to detect latent criminal networks is a response to the inadequacy of the rational isolated actor approach that ignores the connectedness and complexity of criminality. The core computational solution, written in the R language, consists of novel entity resolution, link discovery, and knowledge discovery technology. Entity resolution enables the fusion of multiple datasets with high accuracy (mean F-measure of 0.986 versus competitors 0.872), generating a graph-based expressive view of the problem. Link discovery is comprised of link prediction and link inference, enabling the high-performance detection (accuracy of ~0.8 versus relevant published models ~0.45) of unobserved relationships such as identity fraud. Knowledge discovery uses the fused graph generated and applies the “GraphExtract” algorithm to create a set of subgraphs representing latent functional criminal groups, and a mesoscopic graph representing how this set of criminal groups are interconnected. Latent knowledge is generated from a range of metrics including the “Super-broker” metric and attitude prediction. The computational solution has been evaluated on a range of datasets that mimic an applied setting, demonstrating a scalable (tested on ~18 million node graphs) and performant (~33 hours runtime on a non-distributed platform) solution that successfully detects relevant latent functional criminal groups in around 90% of cases sampled and enables the contextual understanding of the broader criminal system through the mesoscopic graph and associated metadata. The augmented data assets generated provide a multi-perspective systems view of criminal activity that enable advanced informed decision making across the microscopic mesoscopic macroscopic spectrum