Blockchain-based prosumer incentivization for peak mitigation through temporal aggregation and contextual clustering

Peak mitigation is of interest to power companies as peak periods may require the operator to over provision supply in order to meet the peak demand. Flattening the usage curve can result in cost savings, both for the power companies and the end users. Integration of renewable energy into the energy infrastructure presents an opportunity to use excess renewable generation to supplement supply and alleviate peaks. In addition, demand side management can shift the usage from peak to off-peak times and reduce the magnitude of peaks. In this work, we present a data driven approach for incentive-based peak mitigation. Understanding user energy profiles is an essential step in this process. We begin by analysing a popular energy research dataset published by the Ausgrid corporation. Extracting aggregated user energy behavior in temporal contexts and semantic linking and contextual clustering give us insight into consumption and rooftop solar generation patterns. We implement, and performance test a blockchain-based prosumer incentivization system. The smart contract logic is based on our analysis of the Ausgrid dataset. Our implementation is capable of supporting 792,540 customers with a reasonably low infrastructure footprint

The ARTICONF approach to decentralized car-sharing

Social media applications are essential for next-generation connectivity. Today, social media are centralized platforms with a single proprietary organization controlling the network and posing critical trust and governance issues over the created and propagated content. The ARTICONF project funded by the European Union's Horizon 2020 program researches a decentralized social media platform based on a novel set of trustworthy, resilient and globally sustainable tools that address privacy, robustness and autonomy-related promises that proprietary social media platforms have failed to deliver so far. This paper presents the ARTICONF approach to a car-sharing decentralized application (DApp) use case, as a new collaborative peer-to-peer model providing an alternative solution to private car ownership. We describe a prototype implementation of the car-sharing social media DApp and illustrate through real snapshots how the different ARTICONF tools support it in a simulated scenario

Security at the End of the Tunnel: The Anatomy of VPN Mental Models Among Experts and Non-Experts in a Corporate Context

With the worldwide COVID-19 pandemic in 2020 and 2021 necessitating working from home, corporate Virtual Private Networks (VPNs) have become an important item securing the continued operation of companies around the globe. However, due to their different use case, corporate VPNs and how users interact with them differ from public VPNs, which are now commonly used by end-users. In this paper, we present a first explorative study of eleven experts' and seven non-experts' mental models in the context of corporate VPNs. We find a partial alignment of these models in the high-level technical understanding while diverging in important parameters of how, when, and why VPNs are being used. While, in general, experts have a deeper technical understanding of VPN technology, we also observe that even they sometimes hold false beliefs on security aspects of VPNs. In summary, we show that the mental models of corporate VPNs differ from those for related security technology, e.g., HTTPS. Our findings allow us to draft recommendations for practitioners to encourage a secure use of VPN technology (through training interventions, better communication, and system design changes in terms of device management). Furthermore, we identify avenues for future research, e.g., into experts' knowledge and balancing privacy and security between system operators and users

A systematic literature review on trust in the software ecosystem

The worldwide software ecosystem is a trust-rich part of the world. Throughout the software life cycle, software engineers, end-users, and other stakeholders collaboratively place their trust in major hubs in the ecosystem, such as package managers, repository services, and software components. However, as our reliance on software grows, this trust is frequently violated by bad actors and crippling vulnerabilities in the software supply chain. This study aims to define software trust in the worldwide SECO, that is, to determine what signifies a trustworthy system, actor, or hub. We conduct a systematic literature review on the concept of trust in the software ecosystem. We acknowledge that trust is something between two actors in the software ecosystem, and we examine what role trust plays in the relationships between end-users and (1) software products, (2) package managers, (3) software producing organizations, and (4) software engineers. Two major findings emerged from the systematic literature review. To begin, we define trust in the software ecosystem by examining the definition and characteristics of trust. Second, we provide a list of trust factors that can be used to assemble an overview of software trust. Trust is critical in the communication between actors in the worldwide software ecosystem, particularly regarding software selection and evaluation. With this comprehensive overview of trust, software engineering researchers have a new foundation to understand and use trust to create a trustworthy software ecosystem

A Roadmap for Ethics-Aware Software Engineering

Today's software is highly intertwined with our lives, and it possesses an increasing ability to act and influence us. Besides the renown example of self-driving cars and their potential harmfulness, more mundane software such as social networks can introduce bias, break privacy preferences, lead to digital addiction, etc. Additionally, the software engineering (SE) process itself is highly affected by ethical issues, such as diversity and business ethics. This paper introduces ethics-aware SE, a version of SE in which the ethical values of the stakeholders (including developers and users) are captured, analyzed, and reflected in software specifications and in the SE processes. We propose an analytical framework that assists stakeholders in analyzing ethical issues in terms of subject (software artifact or SE process), relevant value (diversity, privacy, autonomy, ...), and threatened object (user, developer, ...). We also define a roadmap that illustrates the necessary steps for the SE research and practice community in order to fully realize ethics-aware SE

Managing the Complexity in Ethical, Social and Environmental Accounting: Engineering and Evaluating a Modelling Language

Assessing, reporting, and monitoring ethical, social and environmental is a key practice for sustainable business innovation. There are a plethora of methods that guide these assessments. Often these methods are supported by an ICT tool. In most cases, the tools are developed to support a single method only and do not allow any tailoring. Therefore, they are rigid and inflexible. To mitigate the risk of managerial problems, reporting fatigue, loss of confidence in sustainability practices, and to manage complexity in ESEA methods we offer a new model-driven approach. We have developed an open-source, model-driven, versatile tool, called openESEA. OpenESEA parses and interprets textual models, that are specified according to a domain-specific language (DSL). This article reports on a new iteration of the creation process of our modelling language, describes the most important modelling primitives of the DSL, and reports on the validation of the DSL through user testing

Benchmarking the vulnerability detection capabilities of software analysis tools

Code cloning and copy-pasting code fragments is common practice in software engineering. If security vulnerabilities exist in a cloned code segment, those vulnerabilities may spread in the related software, potentially leading to security incidents. Code similarity is one effective approach to detect vulnerabilities hidden in software projects. However, due to the complexity, size, and diversity of source code, current methods suffer from low accuracy, and poor performance. Moreover, most existing clone detection techniques focus on a limited set of programming languages in the detection process. We propose to solve these problems using SearchSECO, a software analysis tool that detects vulnerabilities in multiple programming languages

Selecting Priors for Latent Dirichlet Allocation

Latent Dirichlet Allocation (LDA) has gained much attention from researchers and is increasingly being applied to uncover underlying semantic structures from a variety of corpora. However, nearly all researchers use symmetrical Dirichlet priors, often unaware of the underlying practical implications that they bear. This research is the first to explore symmetrical and asymmetrical Dirichlet priors on topic coherence and human topic ranking when uncovering latent semantic structures from scientific research articles. More specifically, we examine the practical effects of several classes of Dirichlet priors on 2000 LDA models created from abstract and full-text research articles. Our results show that symmetrical or asymmetrical priors on the document-topic distribution or the topic-word distribution for full-text data have little effect on topic coherence scores and human topic ranking. In contrast, asymmetrical priors on the document-topic distribution for abstract data show a significant increase in topic coherence scores and improved human topic ranking compared to a symmetrical prior. Symmetrical or asymmetrical priors on the topic-word distribution show no real benefits for both abstract and full-text data