The cultural models in international business research: A bibliometric study of IB journals

Culture has been a widely researched topic in the International Business (IB) literature over the last decades. To better understand what culture actually means and its implication in firms? IB operations, several cultural models and taxonomies have been put forward. In this paper we seek to scrutinize the use of three well known cultural models - Hall?s (1976), Hofstede?s (1980a) and Trompenaars & Hampden-Turner?s (1993) - in the extant research. Using bibliometric techniques of the papers published in the top ranked IB journals, we performed a citation and co-citation analysis to find out the most influential model and to examine the possible linkages between models and to the issues being researched. We conclude that Hofstede?s (1980a) taxonomy is the most cited and his taxonomy has strong linkages to several streams of research. Nonetheless, we also find that there are noticeable differences on how research in different journals make use of the cultural models, probably reflecting not surprising disciplinary emphases.Cultural models, Hofstede, Trompenaars, Hall, bibliometric study

The international entrepreneurial firms' social networks

ABSTRACT This paper investigates theoretically the importance and impact of the international entrepreneurial firms? (IEFs) social networks on selected firms? strategies. We focus specifically on some core attributes of IEFs and the impact of social networks on such strategies as the choice of the foreign markets to operate and the foreign entry modes. The social networks are a major driver of the internationalization from inception and help in overcoming a variety of physical and social resource limitations as well as transactional hazards. We conclude that it is likely that both some fundamental characteristics of the IEFs and those of the foreign markets entered account for these firms reliance on their social networks.entrepreneurship, international entrepreneurial firms, social networks, internationalization

Social-media monitoring for cold-start recommendations

Generating personalized movie recommendations to users is a problem that most commonly relies on user-movie ratings. These ratings are generally used either to understand the user preferences or to recommend movies that users with similar rating patterns have rated highly. However, movie recommenders are often subject to the Cold-Start problem: new movies have not been rated by anyone, so, they will not be recommended to anyone; likewise, the preferences of new users who have not rated any movie cannot be learned. In parallel, Social-Media platforms, such as Twitter, collect great amounts of user feedback on movies, as these are very popular nowadays. This thesis proposes to explore feedback shared on Twitter to predict the popularity of new movies and show how it can be used to tackle the Cold-Start problem. It also proposes, at a finer grain, to explore the reputation of directors and actors on IMDb to tackle the Cold-Start problem. To assess these aspects, a Reputation-enhanced Recommendation Algorithm is implemented and evaluated on a crawled IMDb dataset with previous user ratings of old movies,together with Twitter data crawled from January 2014 to March 2014, to recommend 60 movies affected by the Cold-Start problem. Twitter revealed to be a strong reputation predictor, and the Reputation-enhanced Recommendation Algorithm improved over several baseline methods. Additionally, the algorithm also proved to be useful when recommending movies in an extreme Cold-Start scenario, where both new movies and users are affected by the Cold-Start problem

Chameleon: A Secure Cloud-Enabled and Queryable System with Elastic Properties

There are two dominant themes that have become increasingly more important in our technological society. First, the recurrent use of cloud-based solutions which provide infrastructures, computation platforms and storage as services. Secondly, the use of applicational large logs for analytics and operational monitoring in critical systems. Moreover, auditing activities, debugging of applications and inspection of events generated by errors or potential unexpected operations - including those generated as alerts by intrusion detection systems - are common situations where extensive logs must be analyzed, and easy access is required. More often than not, a part of the generated logs can be deemed as sensitive, requiring a privacy-enhancing and queryable solution. In this dissertation, our main goal is to propose a novel approach of storing encrypted critical data in an elastic and scalable cloud-based storage, focusing on handling JSONbased ciphered documents. To this end, we make use of Searchable and Homomorphic Encryption methods to allow operations on the ciphered documents. Additionally, our solution allows for the user to be near oblivious to our system’s internals, providing transparency while in use. The achieved end goal is a unified middleware system capable of providing improved system usability, privacy, and rich querying over the data. This previously mentioned objective is addressed while maintaining server-side auditable logs, allowing for searchable capabilities by the log owner or authorized users, with integrity and authenticity proofs. Our proposed solution, named Chameleon, provides rich querying facilities on ciphered data - including conjunctive keyword, ordering correlation and boolean queries - while supporting field searching and nested aggregations. The aforementioned operations allow our solution to provide data analytics upon ciphered JSON documents, using Elasticsearch as our storage and search engine.O uso recorrente de soluções baseadas em nuvem tornaram-se cada vez mais importantes na nossa sociedade. Tais soluções fornecem infraestruturas, computação e armazenamento como serviços, para alem do uso de logs volumosos de sistemas e aplicações para análise e monitoramento operacional em sistemas críticos. Atividades de auditoria, debugging de aplicações ou inspeção de eventos gerados por erros ou possíveis operações inesperadas - incluindo alertas por sistemas de detecção de intrusão - são situações comuns onde logs extensos devem ser analisados com facilidade. Frequentemente, parte dos logs gerados podem ser considerados confidenciais, exigindo uma solução que permite manter a confidencialidades dos dados durante procuras. Nesta dissertação, o principal objetivo é propor uma nova abordagem de armazenar logs críticos num armazenamento elástico e escalável baseado na cloud. A solução proposta suporta documentos JSON encriptados, fazendo uso de Searchable Encryption e métodos de criptografia homomórfica com provas de integridade e autenticação. O objetivo alcançado é um sistema de middleware unificado capaz de fornecer privacidade, integridade e autenticidade, mantendo registos auditáveis do lado do servidor e permitindo pesquisas pelo proprietário dos logs ou usuários autorizados. A solução proposta, Chameleon, visa fornecer recursos de consulta atuando em cima de dados cifrados - incluindo queries conjuntivas, de ordenação e booleanas - suportando pesquisas de campo e agregações aninhadas. As operações suportadas permitem à nossa solução suportar data analytics sobre documentos JSON cifrados, utilizando o Elasticsearch como armazenamento e motor de busca

Adaptive Business Intelligence platform and its contribution as a support in the evolution of Hospital 4.0

For many years there has been debate about what healthcare systems will look like in the future. Covid-19 has caused all Healthcare organizations to quickly adopt new solutions and evolution in this sector is a certainty. This research looks at the role that an Adaptive Business Intelligence (ABI) system can play in the evolution to a Hospital 4.0 and how it needs to evolve to achieve full integration between hospital services and the technological solutions. Thus, the first version of this system is explained and that will serve as a basis for the development of a more robust platform, with a view to a more effective environment, both for the professionals and for the main beneficiary of this type of service, the patient.FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/202

Applying optimization models in the scheduling of medical exams

The management of waiting lists in hospitals is a topic with relevance given its direct implication in the quality of healthcare services provided to the patients in the good management of human, material and financial resources. Ministry of Health in Portugal stipulates a guaranteed maximum response time for the execution of Complementary Means of Diagnosis and Therapeutics (CMDT), surgeries and outpatient appointments. This paper addresses an investigation conducted at the Centro Hospitalar e Universitário do Porto (CHUP) with the goal of optimizing decisions in the management of waiting lists for CMDT. This objective will be achieved through the development of hill climbing and simulated annealing models. With this study, it was possible to optimize the way these exams can be scheduled, reducing waiting lists, associated costs and waste, improving the quality of service provided to patients.FCT -Fundação para a Ciência e a Tecnologia(UIDB/00319/2020

Pervasive decision support to predict football corners and goals by means of data mining

Football is considered nowadays one of the most popular sports. In the betting world, it has acquired an outstanding position, which moves millions of euros during the period of a single football match. The lack of profitability of football betting users has been stressed as a problem. This lack gave origin to this research proposal, which it is going to analyse the possibility of existing a way to support the users to increase their profits on their bets. Data mining models were induced with the purpose of supporting the gamblers to increase their profits in the medium/long term. Being conscience that the models can fail, the results achieved by four of the seven targets in the models are encouraging and suggest that the system can help to increase the profits. All defined targets have two possible classes to predict, for example, if there are more or less than 7.5 corners in a single game. The data mining models of the targets, more or less than 7.5 corners, 8.5 corners, 1.5 goals and 3.5 goals achieved the pre-defined thresholds. The models were implemented in a prototype, which it is a pervasive decision support system. This system was developed with the purpose to be an interface for any user, both for an expert user as to a user who has no knowledge in football games.Future work will pass for adding new variables to these models, to try different scenar ios in order to obtain models with even greater precision to be added later to the prototype. In parallel, the prototype will be converted in to a system able to disseminate all the probabilities anywhere and anytime in mobile or situated devices. This prot otype also will incorporate the other predictions made in this field related to the final result [22, 23, 24]. Acknowledgments This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/201

Texture segmentation based on fuzzy grammar for cork parquet quality control

This paper presents an approach for image texture segmentation based on the wavelets transform and on a fuzzy grammar inference system. It was developed for the Portuguese cork industry, specifically for the quality control in the cork parquet sector. The main purpose was to deal with major quality issues related with texture features. The segmentation procedure reveals a good performance indicated by high classification rates. This approach was integrated in a vision system leading to an industrial prototype that has already been tested, revealing good perspectives of full industrialization