Secure Online Voting System using Visual Cryptography

The development of a secure online voting system using visual cryptography is highly essential for present voting systems. Based on the current requirements and design aspects of an existing online voting system, emerging technologies are required in online voting schemes, and these are examined in this work. The emerging cryptographic techniques which are suitable for secure online voting systems are analyzed. Techniques like password hashed-based schemes, visual cryptography, and threshold decryption cryptosystem are highlighted for secure online voting systems. Visual cryptography (VC) is a technique where visual information can be encrypted on the user side, with the information decrypted on the admin side, which can be helpful in allowing participation in voting systems securely and ensuring fast vote counting and monitoring of the voting process to achieve high accuracy while being scam-free. The proposed secure online voting system using visual cryptography is efficiently developed using Python and achieves better performance on minimum software and hardware configuration systems.

Applying matrix factorization to consistency-based direct diagnosis

Configuration systems must be able to deal with inconsistencies which can occur in different contexts. Especially in interactive settings, where users specify requirements and a constraint solver has to identify solutions, inconsistencies may more often arise. In inconsistency situations, there is a need of diagnosis methods that support the identification of minimal sets of constraints that have to be adapted or deleted in order to restore consistency. A diagnosis algorithm’s performance can be evaluated in terms of time to find a diagnosis (runtime) and diagnosis quality. Runtime efficiency of diagnosis is especially crucial in real-time scenarios such as production scheduling, robot control, and communication networks. However, there is a trade off between diagnosis quality and the runtime efficiency of diagnostic reasoning. In this article, we deal with solving the quality-runtime performance trade off problem of direct diagnosis. In this context, we propose a novel learning approach based on matrix factorization for constraint ordering. We show that our approach improves runtime performance and diagnosis quality at the same time.

Sustainability nudges in the context of customer co-design for consumer electronics

In this study, we shed light on the unexplored potential of customer co-design in a mass customization (MC) setting to contribute to the promotion of sustainable consumption. We theoretically derive and empirically test several opportunities for companies to improve sustainable consumption and production in a joint effort with consumers. Our research bridges between the MC and the sustainability literature and shows that MC enables consumers to cohere attitude and action, based on individual sustainability preferences. We empirically assess whether MC companies can nudge their customers successfully towards more sustainable choices by designing sustainability-based starting solutions (Study 1) and by providing transparent sustainability information (Study 2) in MC configuration systems. We do so by portraying a simulated online buying process of a customizable TV with a realistic web-based product configurator. We find that sustainable defaults can play a significant role in promoting sustainable consumption, while providing detailed sustainability information does not show an effect. To get more insights into our results, we discuss the results on a supplementary qualitative analysis based on think-aloud consumer tests (Study 3), revealing several suggestions for further research. Using these findings, we revisit sustainability information in Study 4 and find that intuitive labels significantly influence consumers to choose more sustainably.