Secure electronic bills of lading: blind counts and digital signatures

Electronic documents used in the framework of the goods delivery industry—i.e. electronic bills of lading (e-BOLs)—are the enablers of any payment, and therefore exposed to frauds. As of today, e-BOLs are handled by special private companies, which provide paperless trading services through their trade chains. This paper contributes a zero-knowledge open solution to the problem of designing secure electronic bills of lading, in the framework of a shipper-carrier-buyer transmission model. The suggested solution is a cryptographic protocol based on digital signatures and blind merchandise counts—that is, counts that do not reveal any information about actually counted quantities. The model is designed to mitigate a number of security threats and assumes the existence of both a trusted third party and a bank in charge of payment procedures. The paper discusses the drawbacks of the existing proprietary solutions and shows how the suggested open protocol addresses them.     

Agens Faber: Toward a Theory of Artefacts for MAS

Human intelligence has evolved along with the use of more and more sophisticated tools, allowing Homo Faber (from Homo Habilis to Homo Sapiens Sapiens) to cope with environment changes, as well as to adapt the environment to his needs. Analogously, in this seminal paper we introduce the notion of Agens Faber, conveying the idea that agent intelligence should not be considered as separated by the agent ability to perceive and affect the environment—and so, that agent intelligence is strictly related to the artefacts that enable, mediate and govern any agent (intelligent) activity.

Along this line, we first discuss the notion of artefact for MAS in general, then we try to devise out the admissible / required / desirable features of an artefact for MAS. We elaborate on the many sorts of possible relations between agents and artefacts, focusing in particular on the issue of the rational exploitation of artefacts, and also rough out a possible taxonomy of artefacts for MAS.     

An Evolutionary Model of Endogenous Business Cycles

In this paper, we present an evolutionary model of industry dynamics yielding endogenous business cycles with ‘Keynesian’ features. The model describes an economy composed of firms and consumers/workers. Firms belong to two industries. The first one performs R&D and produces heterogeneous machine tools. Firms in the second industry invest in new machines and produce a homogenous consumption good. Consumers sell their labor and fully consume their income. In line with the empirical literature on investment patterns, we assume that the investment decisions by firms are lumpy and constrained by their financial structures. Moreover, drawing from behavioral theories of the firm, we assume boundedly rational expectation formation. Simulation results show that the model is able to deliver self-sustaining patterns of growth characterized by the presence of endogenous business cycles. The model can also replicate the most important stylized facts concerning micro- and macro-economic dynamics. Indeed, we find that investment is more volatile than GDP; consumption is less volatile than GDP; investment, consumption and change in stocks are procyclical and coincident variables; employment is procyclical; unemployment rate is anticyclical; firm size distributions are skewed but depart from log-normality; firm growth distributions are tent-shaped. JEL Classifications: C15, C22, C49, E17, E22, E32.     

Good Enough is Good Enough: Overcoming Disaster Response Organizations’ Slow Social Media Data Adoption

Organizations that respond to disasters hold unreasonable standards for data arising from technology-enabled citizen contributions. This has strong negative potential for the ability of these responding organizations to incorporate these data into appropriate decision points. We argue that the landscape of the use of social media data in crisis response is varied, with pockets of use and acceptance among organizations. In this paper we present findings from interviews conducted with representatives from large international disaster response organizations concerning their use of social media data in crisis response. We found that emergency responders already operate with less than reliable, or “good enough,” information in offline practice, and that social media data are useful to responders, but only in specific crisis situations. Also, responders do use social media, but only within their known community and extended network. This shows that trust first begins with people and not data. Lastly, we demonstrate the barriers used by responding organizations have gone beyond discussions of trustworthiness and data quality to that of more operational issues.     

Abnormal behavior detection using dominant sets

Smart surveillance systems are increasingly being used to detect potentially dangerous situations. To do so, the common and easier way is to model normal human behaviors and consider as abnormal any new strange behavior in the scene. In this article, Dominant Sets is adapted to model most frequent behaviors and to detect any unknown event to trigger an alarm. It is proved that after an unsupervised training, Dominant Sets can robustly detect abnormal behaviors. The method is tested in several different cases and compared to other usual clusterization methods such as KNN, mixture of Gaussians or Fuzzy \(K\) -Means to confirm its robustness and performance. The overall performance of abnormal behavior detection based on Dominant Sets is better, being the error ratio at least \(1.5\) points lower than the others.     

Erfolgsfaktoren bei der Einführung von Wissensmanagementsystemen in die Praxis

Neben der Gestaltung technischer Systeme zur Unterstützung des Wissensmanagements ist die Ausgestaltung organisatorischer Ma?nahmen gleichbedeutend wichtig.     

PIN OLEDs — Improved structures and materials to enhance device lifetime

Abstract— Currently, three issues are identified that decide upon the commercial success of organic light‐emitting diodes (OLEDs), both in display and lighting applications: power efficiency, lifetime, and price competitiveness. PIN OLEDs are widely seen as the preferred way to maximize power efficiency. Here, it is reported that this concept also delivers the world longest lifetimes. For a highly efficient deep‐red PIN OLED, a half‐lifetime of 25,000 hours for a starting brightness of 10,000 cd/m² and a minimal voltage increase over lifetime is reported. This value corresponds to more than 1 × 10⁶ hours at 1000 cd/m² using an exponent of n = 1.7, which was measured by driving the OLEDs at different starting luminances. Because there is no initial luminance drop, these PIN OLEDs also exhibit a very high 80% lifetime (>300,000 hours at 1000 cd/m²). New record lifetime values for blue and green will be reported as well. Additionally, further topics that have impact on the production yield and cost such as the newly developed air‐stable organic n‐doping material NDN‐26 and top‐emitting structures will be discussed.