Caribbean Basin initiative : need for more reliable data on business activity resulting from the initiative /

"B-223841."--p. [1]

The Kress library of business and economics. Catalog, covering material published through 1776, with data upon cognate items in other Harvard libraries.

Arranged chronologically, with alphabetical index of authors and anonymous titles.

Selecting optimal instantiations of data models - Theory and validation of an ex ante approach

The schema of an information system can significantly impact the ability of end users to efficiently and effectively retrieve the information they need. Obtaining quickly the appropriate data increases the likelihood that an organization will make good decisions and respond adeptly to challenges. This research presents and validates a methodology for evaluating, ex ante, the relative desirability of alternative instantiations of a model of data. In contrast to prior research, each instantiation is based on a different formal theory. This research theorizes that the instantiation that yields the lowest weighted average query complexity for a representative sample of information requests is the most desirable instantiation for end-user queries. The theory was validated by an experiment that compared end-user performance using an instantiation of a data structure based on the relational model of data with performance using the corresponding instantiation of the data structure based on the object-relational model of data. Complexity was measured using three different Halstead metrics: program length, difficulty, and effort. For a representative sample of queries, the average complexity using each instantiation was calculated. As theorized, end users querying the instantiation with the lower average complexity made fewer semantic errors, i.e., were more effective at composing queries. (c) 2005 Elsevier B.V. All rights reserved.

Training emotional intelligence: Presenting the results of an experimental study

This presentation outlines the results of an eighteen month study examining the effect of an emotions focused training intervention on the emotional intelligence of employees from a large public sector organisation. Utilising an experimental methodology, 280 staff attended a two-day program focused on training emotional intelligence skills and abilities. These interventions were created around Mayer and Salovey’s four-branch model of emotional intelligence (awareness, understanding, facilitation and management of emotions). The experimental group’s emotional intelligence was tested pre and post training using the Workgroup Emotional Intelligence Profile (WEIP). In addition, a control group from the same organisation also completed the same measure at three points during the same eighteen month period. Analysis of the control and experimental group data were conducted, and whilst no changes were found in the control group, the experimental group’s overall emotional intelligence significantly improved post training. To further strengthen these findings, a measure of effect size using Cohen’s d was also conducted to assess the magnitude of the training intervention’s overall effect. Full results will be presented during the presentation, with feedback on the study and methods utilised encouraged from participants.

Cultural intelligence as a predictor of expatriate adjustment and performance in Malaysia

This research tests the linkage between cultural intelligence, expatriate adjustment to the host country's environment and expatriate performance while on international assignments. The investigation is carried out with data from 134 expatriates based in multinational corporations in Malaysia. The results highlight a direct influence of expatriates' cultural intelligence on general, interaction and work adjustments. The improved adjustments consequently have positive effects on both the expatriates' task and contextual performance. The research findings have implications for both international human resource management (IHRM) researchers and managers. © 2012 Elsevier Inc.

Application of the Artificial Intelligence Algorithms for System Analysis of Multi Dimension Physiological Data for Developing Polyparametric Information System of Public Health Diagnostics

The polyparametric intelligence information system for diagnostics human functional state in medicine and public health is developed. The essence of the system consists in polyparametric describing of human functional state with the unified set of physiological parameters and using the polyparametric cognitive model developed as the tool for a system analysis of multitude data and diagnostics of a human functional state. The model is developed on the basis of general principles geometry and symmetry by algorithms of artificial intelligence systems. The architecture of the system is represented. The model allows analyzing traditional signs - absolute values of electrophysiological parameters and new signs generated by the model – relationships of ones. The classification of physiological multidimensional data is made with a transformer of the model. The results are presented to a physician in a form of visual graph – a pattern individual functional state. This graph allows performing clinical syndrome analysis. A level of human functional state is defined in the case of the developed standard (“ideal”) functional state. The complete formalization of results makes it possible to accumulate physiological data and to analyze them by mathematics methods.

The Long-Term impact of Business Support? - Exploring the Role of Evaluation Timing using Micro Data

The original contribution of this work is threefold. Firstly, this thesis develops a critical perspective on current evaluation practice of business support, with focus on the timing of evaluation. The general time frame applied for business support policy evaluation is limited to one to two, seldom three years post intervention. This is despite calls for long-term impact studies by various authors, concerned about time lags before effects are fully realised. This desire for long-term evaluation opposes the requirements by policy-makers and funders, seeking quick results. Also, current ‘best practice’ frameworks do not refer to timing or its implications, and data availability affects the ability to undertake long-term evaluation. Secondly, this thesis provides methodological value for follow-up and similar studies by using data linking of scheme-beneficiary data with official performance datasets. Thus data availability problems are avoided through the use of secondary data. Thirdly, this thesis builds the evidence, through the application of a longitudinal impact study of small business support in England, covering seven years of post intervention data. This illustrates the variability of results for different evaluation periods, and the value in using multiple years of data for a robust understanding of support impact. For survival, impact of assistance is found to be immediate, but limited. Concerning growth, significant impact centres on a two to three year period post intervention for the linear selection and quantile regression models – positive for employment and turnover, negative for productivity. Attribution of impact may present a problem for subsequent periods. The results clearly support the argument for the use of longitudinal data and analysis, and a greater appreciation by evaluators of the factor time. This analysis recommends a time frame of four to five years post intervention for soft business support evaluation.

The long-term impact of business support? - Exploring the role of evaluation timing using micro data

The original contribution of this work is threefold. Firstly, this thesis develops a critical perspective on current evaluation practice of business support, with focus on the timing of evaluation. The general time frame applied for business support policy evaluation is limited to one to two, seldom three years post intervention. This is despite calls for long-term impact studies by various authors, concerned about time lags before effects are fully realised. This desire for long-term evaluation opposes the requirements by policy-makers and funders, seeking quick results. Also, current ‘best practice’ frameworks do not refer to timing or its implications, and data availability affects the ability to undertake long-term evaluation. Secondly, this thesis provides methodological value for follow-up and similar studies by using data linking of scheme-beneficiary data with official performance datasets. Thus data availability problems are avoided through the use of secondary data. Thirdly, this thesis builds the evidence, through the application of a longitudinal impact study of small business support in England, covering seven years of post intervention data. This illustrates the variability of results for different evaluation periods, and the value in using multiple years of data for a robust understanding of support impact. For survival, impact of assistance is found to be immediate, but limited. Concerning growth, significant impact centres on a two to three year period post intervention for the linear selection and quantile regression models – positive for employment and turnover, negative for productivity. Attribution of impact may present a problem for subsequent periods. The results clearly support the argument for the use of longitudinal data and analysis, and a greater appreciation by evaluators of the factor time. This analysis recommends a time frame of four to five years post intervention for soft business support evaluation.

Bizalom az üzleti kapcsolatokban. A diadikus adatelemzés egy alkalmazása = Trust in business relations - an application of dyadic data analysis

A tanulmány arra a feltevésre épül, hogy minél erősebb a bizalomra méltóság szintje egy adott üzleti kapcsolatban, annál inkább igaz, hogy nagy kockázatú tevékenységek mennek végbe benne. Ilyen esetekben a bizalomra méltóság a kapcsolatban zajló események, cselekvések irányítási eszközévé válik, és az üzleti kapcsolatban megjelenik a cselekvési hajlandóságként értelmezett bizalom. A tanulmány felhívja a figyelmet a bizalom és a bizalomra méltóság fogalmai közötti különbségre, szisztematikus különválasztásuk fontosságára. Bemutatja az úgynevezett diadikus adatelemzés gazdálkodástudományi alkalmazását. Empirikus eredményei is igazolják, hogy ezzel a módszerrel az üzleti kapcsolatok társas jellemzőinek (köztük a bizalomnak) és a közöttük lévő kapcsolatoknak mélyebb elemzésére nyílik lehetőség. ____ The paper rests on the behavioral interpretation of trust, making a clear distinction between trustworthiness (honesty) and trust interpreted as willingness to engage in risky situations with specific partners. The hypothesis tested is that in a business relation marked by high levels of trustworthiness as perceived by the opposite parties, willingness to be involved in risky situations is higher than it is in relations where actors do not believe their partners to be highly trustworthy. Testing this hypothesis clearly calls for dyadic operationalization, measurement, and analysis. The authors present the first economic application of a newly developed statistical technique called dyadic data analysis, which has already been applied in social psychology. It clearly overcomes the problem of single-ended research in business relations analysis and allows a deeper understanding of any dyadic phenomenon, including trust/trustworthiness as a governance mechanism.

Context -aware data caching for mobile computing environments

The deployment of wireless communications coupled with the popularity of portable devices has led to significant research in the area of mobile data caching. Prior research has focused on the development of solutions that allow applications to run in wireless environments using proxy based techniques. Most of these approaches are semantic based and do not provide adequate support for representing the context of a user (i.e., the interpreted human intention.). Although the context may be treated implicitly it is still crucial to data management. In order to address this challenge this dissertation focuses on two characteristics: how to predict (i) the future location of the user and (ii) locations of the fetched data where the queried data item has valid answers. Using this approach, more complete information about the dynamics of an application environment is maintained. ^ The contribution of this dissertation is a novel data caching mechanism for pervasive computing environments that can adapt dynamically to a mobile user's context. In this dissertation, we design and develop a conceptual model and context aware protocols for wireless data caching management. Our replacement policy uses the validity of the data fetched from the server and the neighboring locations to decide which of the cache entries is less likely to be needed in the future, and therefore a good candidate for eviction when cache space is needed. The context aware driven prefetching algorithm exploits the query context to effectively guide the prefetching process. The query context is defined using a mobile user's movement pattern and requested information context. Numerical results and simulations show that the proposed prefetching and replacement policies significantly outperform conventional ones. ^ Anticipated applications of these solutions include biomedical engineering, tele-health, medical information systems and business. ^

Analysis of the Bovespa Futures and Spot Indexes With High Frequency Data. Chinese Business Review

Data from the World Federation of Exchanges show that Brazil’s Sao Paulo stock exchange is one of the largest worldwide in terms of market value. Thus, the objective of this study is to obtain univariate and bivariate forecasting models based on intraday data from the futures and spot markets of the BOVESPA index. The interest is to verify if there exist arbitrage opportunities in Brazilian financial market. To this end, three econometric forecasting models were built: ARFIMA, vector autoregressive (VAR), and vector error correction (VEC). Furthermore, it presents the results of a Granger causality test for the aforementioned series. This type of study shows that it is important to identify arbitrage opportunities in financial markets and, in particular, in the application of these models on data of this nature. In terms of the forecasts made with these models, VEC showed better results. The causality test shows that futures BOVESPA index Granger causes spot BOVESPA index. This result may indicate arbitrage opportunities in Brazil.

Applied web traffic analysis for numerical encoding of SQL Injection attack features.

SQL Injection Attack (SQLIA) remains a technique used by a computer network intruder to pilfer an organisation’s confidential data. This is done by an intruder re-crafting web form’s input and query strings used in web requests with malicious intent to compromise the security of an organisation’s confidential data stored at the back-end database. The database is the most valuable data source, and thus, intruders are unrelenting in constantly evolving new techniques to bypass the signature’s solutions currently provided in Web Application Firewalls (WAF) to mitigate SQLIA. There is therefore a need for an automated scalable methodology in the pre-processing of SQLIA features fit for a supervised learning model. However, obtaining a ready-made scalable dataset that is feature engineered with numerical attributes dataset items to train Artificial Neural Network (ANN) and Machine Leaning (ML) models is a known issue in applying artificial intelligence to effectively address ever evolving novel SQLIA signatures. This proposed approach applies numerical attributes encoding ontology to encode features (both legitimate web requests and SQLIA) to numerical data items as to extract scalable dataset for input to a supervised learning model in moving towards a ML SQLIA detection and prevention model. In numerical attributes encoding of features, the proposed model explores a hybrid of static and dynamic pattern matching by implementing a Non-Deterministic Finite Automaton (NFA). This combined with proxy and SQL parser Application Programming Interface (API) to intercept and parse web requests in transition to the back-end database. In developing a solution to address SQLIA, this model allows processed web requests at the proxy deemed to contain injected query string to be excluded from reaching the target back-end database. This paper is intended for evaluating the performance metrics of a dataset obtained by numerical encoding of features ontology in Microsoft Azure Machine Learning (MAML) studio using Two-Class Support Vector Machines (TCSVM) binary classifier. This methodology then forms the subject of the empirical evaluation.

The issue of data protection and data security in the (pre-Lisbon) EU third pillar

The key functional operability in the pre-Lisbon PJCCM pillar of the EU is the exchange of intelligence and information amongst the law enforcement bodies of the EU. The twin issues of data protection and data security within what was the EU’s third pillar legal framework therefore come to the fore. With the Lisbon Treaty reform of the EU, and the increased role of the Commission in PJCCM policy areas, and the integration of the PJCCM provisions with what have traditionally been the pillar I activities of Frontex, the opportunity for streamlining the data protection and data security provisions of the law enforcement bodies of the post-Lisbon EU arises. This is recognised by the Commission in their drafting of an amending regulation for Frontex , when they say that they would prefer “to return to the question of personal data in the context of the overall strategy for information exchange to be presented later this year and also taking into account the reflection to be carried out on how to further develop cooperation between agencies in the justice and home affairs field as requested by the Stockholm programme.” The focus of the literature published on this topic, has for the most part, been on the data protection provisions in Pillar I, EC. While the focus of research has recently sifted to the previously Pillar III PJCCM provisions on data protection, a more focused analysis of the interlocking issues of data protection and data security needs to be made in the context of the law enforcement bodies, particularly with regard to those which were based in the pre-Lisbon third pillar. This paper will make a contribution to that debate, arguing that a review of both the data protection and security provision post-Lisbon is required, not only in order to reinforce individual rights, but also inter-agency operability in combating cross-border EU crime. The EC’s provisions on data protection, as enshrined by Directive 95/46/EC, do not apply to the legal frameworks covering developments within the third pillar of the EU. Even Council Framework Decision 2008/977/JHA, which is supposed to cover data protection provisions within PJCCM expressly states that its provisions do not apply to “Europol, Eurojust, the Schengen Information System (SIS)” or to the Customs Information System (CIS). In addition, the post Treaty of Prüm provisions covering the sharing of DNA profiles, dactyloscopic data and vehicle registration data pursuant to Council Decision 2008/615/JHA, are not to be covered by the provisions of the 2008 Framework Decision. As stated by Hijmans and Scirocco, the regime is “best defined as a patchwork of data protection regimes”, with “no legal framework which is stable and unequivocal, like Directive 95/46/EC in the First pillar”. Data security issues are also key to the sharing of data in organised crime or counterterrorism situations. This article will critically analyse the current legal framework for data protection and security within the third pillar of the EU.

Ethical Perspectives on Big Data in Agri-food: Ownership and Governance for Safety

Big data are reshaping the way we interact with technology, thus fostering new applications to increase the safety-assessment of foods. An extraordinary amount of information is analysed using machine learning approaches aimed at detecting the existence or predicting the likelihood of future risks. Food business operators have to share the results of these analyses when applying to place on the market regulated products, whereas agri-food safety agencies (including the European Food Safety Authority) are exploring new avenues to increase the accuracy of their evaluations by processing Big data. Such an informational endowment brings with it opportunities and risks correlated to the extraction of meaningful inferences from data. However, conflicting interests and tensions among the involved entities - the industry, food safety agencies, and consumers - hinder the finding of shared methods to steer the processing of Big data in a sound, transparent and trustworthy way. A recent reform in the EU sectoral legislation, the lack of trust and the presence of a considerable number of stakeholders highlight the need of ethical contributions aimed at steering the development and the deployment of Big data applications. Moreover, Artificial Intelligence guidelines and charters published by European Union institutions and Member States have to be discussed in light of applied contexts, including the one at stake. This thesis aims to contribute to these goals by discussing what principles should be put forward when processing Big data in the context of agri-food safety-risk assessment. The research focuses on two interviewed topics - data ownership and data governance - by evaluating how the regulatory framework addresses the challenges raised by Big data analysis in these domains. The outcome of the project is a tentative Roadmap aimed to identify the principles to be observed when processing Big data in this domain and their possible implementations.