Abstract
Recent research on online censorship has provided valuable insights into common censorship strategies and censors' tolerance for collateral damage. A consistent finding across these studies is that censors tend to favour cost-effective techniques such as proxy enumeration, active probing, and deep packet inspection (DPI), rather than more complex and non-deterministic methods such as deep learning-based traffic analysis. For example, a recent study on the Snowflake censorship evasion system reinforced this finding by demonstrating that authoritarian regimes primarily relied on DPI to target the system. However, as censorship techniques continue to evolve, two critical questions arise: (1) What future attack vectors are likely to emerge based on current research and observed censor capabilities? (2) How can these emerging threats, along with previously utilised censorship methods, be effectively mitigated? In this paper, we present Obscura, a censorship evasion system designed to resist cost-effective, historically grounded censorship techniques while also defending against a class of plausible future attacks within a cost-effective threat model targeting WebRTC-based censorship evasion systems. Obscura is built upon four core features: (1) encapsulation of traffic within WebRTC media streams, (2) the use of a reliability layer, (3) support for both browser-based and Pion-based clients and proxy instances, and (4) the use of ephemeral proxies. Each feature is intended to mitigate either a known attack observed in the wild or a theoretically plausible attack consistent with the capabilities of a cost-effective censor. We provide a security analysis to justify our design choices and a performance evaluation to demonstrate that Obscura maintains reasonable throughput for typical online activities.

Abstract
The paradigm of Device as a Service (DaaS) is one where devices are used as part of a service, with the user having no ownership over them. A centralised, web-based approach can be envisioned to support such a business model, but such lacks transparency, availability, and global scalability. A blockchain-based solution is proposed to support such a business model. The concept of a blockchain-assisted DaaS is novel and, by using smart contracts to support key interactions between relevant entities, marks a shift in device ownership, management, and revenue generation.

Abstract
Adopting innovative technologies such as blockchain and additive manufacturing can help organisations promote the development of additive symbiotic networks, thus pursuing higher sustainable goals and implementing circular economy strategies. These symbiotic networks correspond to industrial symbiosis networks in which wastes and by-products from other industries are incorporated into additive manufacturing processes. The adoption of blockchain technology in such a context is still in a nascent stage. Using the case study method, this research demonstrates the adoption of blockchain technology in an additive symbiotic network of a real-life context. The requirements to use a blockchain network are identified, and an architecture based on smart contracts is proposed as an enabler of the additive symbiotic network under study. The proposed solution uses the Hyperledger Fabric Attribute-Based Access Control as the distributed ledger technology. Even though this solution is still in the proof-of-concept stage, the results show that adopting it would allow the elimination of intermediary entities, keep available tracking records of the resources exchanged, and improve trust among the symbiotic stakeholders (that do not have any trust or cooperation mechanisms established before the symbiotic relationship). This study highlights that the complexity associated with introducing a novel technology and the technology's immaturity compared to other data storage technologies are some of the main challenges related to using blockchain technology in additive symbiotic networks.

Abstract
End-users traditional ownership of devices is progressively being replaced by usage-based approaches. One of the most significant Device-as-a-Service (DaaS) challenges concerns protecting device usage outside of supplier control. In this work, we build on our previous blockchain framework for device management to propose a novel and enhanced pre-OS boot process for device validation in environments requiring both security and transparency. For that, we have customized a Unified Extensible Firmware Interface (UEFI) module to authorise device usage against the blockchain before system boot, preventing authorized manipulation at the earliest stage. Preliminary experiments show this approach is valid and effective. © 2025 IEEE.

Abstract
The proliferation of disinformation on social media platforms poses a significant challenge to the reliability of online information ecosystems and the protection of public discourse. This study investigates the role of emotional sequences in detecting intentionally misleading messages disseminated on social networks. To this end, we apply a methodological pipeline that combines semantic segmentation, automatic emotion recognition, and sequential pattern mining. Emotional sequences are extracted at the subsentence level, preserving each message's temporal order of emotional cues. Comparative analyses reveal that disinformation messages exhibit a higher prevalence of negative emotions, particularly fear, anger, and sadness, interspersed with neutral segments. Moreover, false messages frequently employ complex emotional progressions-alternating between high-intensity negative emotions and emotionally neutral passages-designed to capture attention and maximize engagement. In contrast, messages from reliable sources tend to follow simpler, more linear emotional trajectories, with a greater prevalence of positive emotions such as joy. Our dataset encompasses multiple categories of disinformation, enabling a fine-grained analysis of how emotional sequencing varies across different types of misleading content. Furthermore, we validate our approach by comparing it against a publicly available disinformation dataset, demonstrating the generalizability of our findings. The results highlight the importance of analyzing temporal emotional patterns to distinguish disinformation from verified content, reinforcing the value of integrating emotional sequences into machine learning pipelines to enhance disinformation detection. This work contributes to the growing body of research emphasizing the relationship between emotional manipulation and the virality of misleading content online.

Abstract
Automated assessment tools for programming assignments have become increasingly popular in computing education. These tools offer a cost-effective and highly available way to provide timely and consistent feedback to students. However, when evaluating a logically incorrect source code, there are some reasonable concerns about the formative gap in the feedback generated by such tools compared to that of human teaching assistants. A teaching assistant either pinpoints logical errors, describes how the program fails to perform the proposed task, or suggests possible ways to fix mistakes without revealing the correct code. On the other hand, automated assessment tools typically return a measure of the program's correctness, possibly backed by failing test cases and, only in a few cases, fixes to the program. In this paper, we introduce a tool, AsanasAssist, to generate formative feedback messages to students to repair functionality mistakes in the submitted source code based on the most similar algorithmic strategy solution. These suggestions are delivered with incremental levels of detail according to the student's needs, from identifying the block containing the error to displaying the correct source code. Furthermore, we evaluate how well the automatically generated messages provided by AsanasAssist match those provided by a human teaching assistant. The results demonstrate that the tool achieves feedback comparable to that of a human grader while being able to provide it just in time.