Online voting for independent elections is generally supported by trusted election providers. Typically these providers do not offer any way in which a voter can verify their vote, and hence the providers are trusted with ballot privacy and in ensuring correctness. Despite the desire to offer online voting for political elections, this lack of transparency and verifiability is often seen as a significant barrier to the large-scale adoption of online elections. Adding verifiability to an online election increases transparency and integrity, as well as allowing voters to verify that the vote they cast has been recorded correctly and included in the tally. However, replacing existing online systems with those that provide verifiable voting requires new algorithms and code to be deployed, and this presents a significant business risk to commercial election providers, as well as the societal risk for official elections selecting for public office. In this paper we present the first step in an incremental approach which minimises the business risk but demonstrates the advantages of verifiability, by developing an implementation of key elements of a Selene-based verifiability layer and adding it to an operational online voting system. Selene is a verifiable voting protocol that publishes votes in plaintext alongside a voter's tracker. These trackers enable voters to confirm that their votes have been captured correctly by the system, such that the election provider does not know which tracker has been allocated to which voter. This results in a system where even a “dishonest but cautious” election authority running the system cannot be sure of changing the result in an undetectable way, and hence gives stronger guarantees on the integrity of the election than were previously present. We explore the challenges presented by adding a verifiability layer to an operational system. The system was used in two initial trials conducted within real contested elections. We conclude by outlining the further steps in the road-map towards the deployment of a fully trustworthy online voting system.

The digital advancement of an industrial environment where human and robot collaborate to achieve specific tasks, can be vulnerable to cyber threats. These industries use the current risk management frameworks (for example CVSS-v3) to manage potential vulnerabilities. However, the current frameworks do not consider the modes of work when rating the severity. This paper introduces a novel risk driven security framework (RDSF) which evaluates potential cyber security vulnerabilities to determine their effect/impact in relation to robot in industrial environments based on robot modes of work. The paper also describes how to use RDSF as a component of the implementation of security management in autonomous robot industries.

Bitcoin is a digital currency based on a peer-to-peer network to propagate and verify transactions. Bitcoin is gaining wider adoption than any previous crypto-currency. However, the mechanism of peers randomly choosing logical neighbours without any knowledge about the underlying physical topology can cause a delay overhead in information propagation which makes the system vulnerable to double spend attacks. Aiming at alleviating the propagation delay problem, this paper introduces a proximity-aware extension to the current Bitcoin protocol, named Master Node Based Clustering (MNBC). The ultimate purpose of the proposed protocol, which is based on how clusters are formulated and how nodes can define their membership, is to improve the information propagation delay in the Bitcoin network. In the MNBC protocol , physical internet connectivity increases as well as the number of hops between nodes decreases through assigning nodes to be responsible for maintaining clusters based on physical Internet proximity. Furthermore, a reputation-based blockchain protocol is integrated with MNBC protocol in order to securely assign a master node for every cluster. We validate our proposed methods through a set of simulation experiments and the findings show how the proposed methods run and their impact in optimising the transaction propagation delay.

Designing an Intelligent Tutoring System (ITS) that simulates human learning with regard to different knowledge levels is a challenge as it reflects an accurate way of estimating the students’ performance level. Most developed ITSs typically focus on the normal cognitive factors such as the students’ prior success and failure scores without paying appropriate consideration to the sensitive cognitive factors that have a great impact on the student performance prediction such as the integration of the human current skills and given items skills, particularly when the learning items require multiple skills, which thus reduce student’s learning efficiency due to an incomplete representation of the student’s knowledge. This paper presents a modified student modeling approach, called modified Performance Factor Analysis (ModPFA), based on a previously developed model called Performance Factor Analyses (PFA). ModPFA was developed by adding the hinting parameter to the original PFA formula. This extension has scoring procedure and knowledge level estimation for each student. Results have shown great improvement in terms of performance estimation in the ModPFA compared to PFA.

This paper discusses an approach for incremental change to an online voting system, introducing a verifiability layer based on the Selene protocol to a trusted-third-party-based system, resulting in a fully verifiable and transparent e-voting system. The paper also describes how to use Distributed Ledger Technology as a component of the implementation of Selene to manage the verifiability data in a distributed way for resilience and trust.

This paper proposes a proximity-aware extensions to the current Bitcoin protocol, named Master Node Based Clustering (MNBC). The ultimate purpose of the proposed protocol is to evaluate the security and performance of grouping nodes based on physical proximity. In MNBC protocol, physical internet connectivity increases as well as the number of hops between nodes decreases through assigning nodes to be responsible for propagating based on physical internet proximity.

In the Bitcoin system, a peer-to-peer electronic currency system, the delay overhead in transaction verification prevents the Bitcoin from gaining increasing popularity nowadays as it makes the system vulnerable to double spend attacks. This paper introduces a proximity-aware extension to the current Bitcoin protocol, named Bitcoin Clustering Based Ping Time protocol (BCBPT). The ultimate purpose of the proposed protocol, that is based on how the clusters are formulated and the nodes define their membership, is to improve the transaction propagation delay in the Bitcoin network. In BCBPT, the proximity of connectivity in the Bitcoin network is increased by grouping Bitcoin nodes based on ping latencies between nodes. We show, through simulations, that the proximity base ping latency defines better clustering structures that optimize the performance of the transaction propagation delay. The reduction of the communication link cost measured by the information propagation time between nodes is mainly considered as a key reason for this improvement. Bitcoin Clustering Based Ping Time protocol is more effective at reducing the transaction propagation delay compared to the existing clustering protocol (LBC) that we proposed in our previous work.

The delay overhead of transaction verification in Bitcoin, a peer-to-peer electronic currency system, is a complicated issue which makes the system vulnerable to double spend attacks. In this paper, we propose a new approach, that is based on how the clusters are formulated and the nodes define their membership, to improve the transaction propagation delay in the Bitcoin network. In this approach, the locality of connectivity in the Bitcoin network is increased by grouping Bitcoin nodes based on their geographical location. We show, through simulations, that location based-distance better defines clustering structures that optimize the performance of the transaction propagation delay. A key reason behind this improvement is mainly due to the reduction of the communication link cost measured by the distance between nodes. Compared to the existing clustering protocol (BCBSN) that we proposed in our previous work, location based clustering is more effective at reducing the transaction propagation delay.