Innovation Hot Spots: the Case of the Computer Services Sector in the Region of Attica, Greece

Elaborating on the notion innovation hot spots, we examine the case of the computer services sector in the Region of Attica, Greece. Fast-growing, geographically and industrially clustered firms are becoming an increasingly important factor for innovation and regional development. As a result, innovation hot spots enjoy rapid growth, leading to job creation, knowledge expansion and, in the best cases, sustainable development. The most recent European Trend Chart Reports (2004 and 2005) present Greece as innovation leader in the computer services sector. Computer services are characterized by a high knowledge creation and knowledge diffusion intensity meaning that the hot spots exploiting such services position high on an innovation intensity scale. Consulting, implementation, operations management and support services enjoy similar growth since they are complementary industries forming the Attica IT innovation hot spot. The purpose of our research within this field is twofold. First, we present the conditions under which this innovation leadership has emerged and come to flourish. We argue that growth in the Region of Attica has been boosted by the Information Society Program, the Olympic Games and the necessity for modernizing Greek firms, which leads them to favor investments in new technologies. Moreover, the region presents a favorable macroeconomic environment, characterized by high rates of development, increase of consumption and investments. Second, we analyze and propose a framework for maintaining the dynamics in the region -and in innovation hot spots in general- as there is a significant risk of rise-and-fall patterns occurring, leading to former hot spots transforming into “blind spotsâ€, and core competencies developed turning into core rigidities and cultural lock-in.

Is "Better Data" Better than "Better Data Miners"? (On the Benefits of Tuning SMOTE for Defect Prediction)

We report and fix an important systematic error in prior studies that ranked classifiers for software analytics. Those studies did not (a) assess classifiers on multiple criteria and they did not (b) study how variations in the data affect the results. Hence, this paper applies (a) multi-criteria tests while (b) fixing the weaker regions of the training data (using SMOTUNED, which is a self-tuning version of SMOTE). This approach leads to dramatically large increases in software defect predictions. When applied in a 5*5 cross-validation study for 3,681 JAVA classes (containing over a million lines of code) from open source systems, SMOTUNED increased AUC and recall by 60% and 20% respectively. These improvements are independent of the classifier used to predict for quality. Same kind of pattern (improvement) was observed when a comparative analysis of SMOTE and SMOTUNED was done against the most recent class imbalance technique. In conclusion, for software analytic tasks like defect prediction, (1) data pre-processing can be more important than classifier choice, (2) ranking studies are incomplete without such pre-processing, and (3) SMOTUNED is a promising candidate for pre-processing.Comment: 10 pages + 2 references. Accepted to International Conference of Software Engineering (ICSE), 201

Is "Better Data" Better than "Better Data Miners"? (On the Benefits of Tuning SMOTE for Defect Prediction)

We report and fix an important systematic error in prior studies that ranked classifiers for software analytics. Those studies did not (a) assess classifiers on multiple criteria and they did not (b) study how variations in the data affect the results. Hence, this paper applies (a) multi-criteria tests while (b) fixing the weaker regions of the training data (using SMOTUNED, which is a self-tuning version of SMOTE). This approach leads to dramatically large increases in software defect predictions. When applied in a 5*5 cross-validation study for 3,681 JAVA classes (containing over a million lines of code) from open source systems, SMOTUNED increased AUC and recall by 60% and 20% respectively. These improvements are independent of the classifier used to predict for quality. Same kind of pattern (improvement) was observed when a comparative analysis of SMOTE and SMOTUNED was done against the most recent class imbalance technique. In conclusion, for software analytic tasks like defect prediction, (1) data pre-processing can be more important than classifier choice, (2) ranking studies are incomplete without such pre-processing, and (3) SMOTUNED is a promising candidate for pre-processing.Comment: 10 pages + 2 references. Accepted to International Conference of Software Engineering (ICSE), 201

Deploying rural community wireless mesh networks

Inadequate Internet access is widening the digital divide between town and countryside, degrading both social communication and business advancements in rural areas. Wireless mesh networking can provide an excellent framework for delivering broadband services to such areas. With this in mind, Lancaster University deployed a WMN in the rural village of Wray over a three-year period, providing the community with Internet service that exceeds many urban offerings. The project gave researchers a real-world testbed for exploring the technical and social issues entailed in deploying WMNs in the heart of a small community

Establishing Human Factors Programs to Mitigate Blind Spots in Cybersecurity

Most business organizations lack a human factors program and remain inattentive to human-centric issues and human-related problems that are leading to cybersecurity incidents, significant financial losses, reputational damage, and lost production. Other industries such as aviation, nuclear power, healthcare, and industrial safety leverage human factors problems as platforms to reduce human errors. The underappreciation and under-exploration of human factors in cybersecurity threatens the existence of every business. Cybersecurity operations are becoming increasingly abstruse and technologically sophisticated resulting in heightened opportunities for human errors. A human factors program can provide the foundation to address and mitigate human-centric issues, properly train the workforce, and integrate psychology-based professionals as stakeholders to remediate human factors-based problems