1st INCF Workshop on Sustainability of Neuroscience Databases

The goal of the workshop was to discuss issues related to the sustainability of neuroscience databases, identify problems and propose solutions, and formulate recommendations to the INCF. The report summarizes the discussions of invited participants from the neuroinformatics community as well as from other disciplines where sustainability issues have already been approached. The recommendations for the INCF involve rating, ranking, and supporting database sustainability

‘Generation Facebook’ – A Cognitive Calculus Model of Teenage User Behavior on Social Network Sites

With the growing popularity of Facebook, the number of teenage users has significantly increased. Parents and teachersobserve this development critically as they fear that teenagers are prone to over-engage in pleasant activities and neglectthe risks connected with information revelation. This paper adopts an explorative approach in order to investigate whatmotivates and hinders teenagers to use SNS and how using this medium affects their identities. By applying GroundedTheory to analyze data obtained in interviews, we formulate a conceptual model of teenage behavior on Facebook. Wefind that teenagers behave rationally on SNS, consciously weighing the benefits against the costs and acting inaccordance with their preferences. Shared information and the diversified network structure allow teenagers to obtainsupport in school-related matters, broaden their horizon and intensify relationships with their peers. At the same time,peer and parental pressure play a significant role in this process

Poor Man's Content Centric Networking (with TCP)

A number of different architectures have been proposed in support of data-oriented or information-centric networking. Besides a similar visions, they share the need for designing a new networking architecture. We present an incrementally deployable approach to content-centric networking based upon TCP. Content-aware senders cooperate with probabilistically operating routers for scalable content delivery (to unmodified clients), effectively supporting opportunistic caching for time-shifted access as well as de-facto synchronous multicast delivery. Our approach is application protocol-independent and provides support beyond HTTP caching or managed CDNs. We present our protocol design along with a Linux-based implementation and some initial feasibility checks

Adaptive real-time predictive collaborative content discovery and retrieval in mobile disconnection prone networks

Emerging mobile environments motivate the need for the development of new distributed technologies which are able to support dynamic peer to peer content sharing, decrease high operating costs, and handle intermittent disconnections. In this paper, we investigate complex challenges related to the mobile disconnection tolerant discovery of content that may be stored in mobile devices and its delivery to the requesting nodes in mobile resource-constrained heterogeneous environments. We propose a new adaptive real-time predictive multi-layer caching and forwarding approach, CafRepCache, which is collaborative, resource, latency, and content aware. CafRepCache comprises multiple multi-layer complementary real-time distributed predictive heuristics which allow it to respond and adapt to time-varying network topology, dynamically changing resources, and workloads while managing complex dynamic tradeoffs between them in real time. We extensively evaluate our work against three competitive protocols across a range of metrics over three heterogeneous real-world mobility traces in the face of vastly different workloads and content popularity patterns. We show that CafRepCache consistently maintains higher cache availability, efficiency and success ratios while keeping lower delays, packet loss rates, and caching footprint compared to the three competing protocols across three traces when dynamically varying content popularity and dynamic mobility of content publishers and subscribers. We also show that the computational cost and network overheads of CafRepCache are only marginally increased compared with the other competing protocols

High-quality patents for emerging science and technology through external actors: community scientific experts and knowledge societies

This article explores one type of administrative mechanism to achieve high-quality patents: Article 115 of the European Patent Convention, which permits the inclusion of third parties to provide input to the prior art search and to communicate relevant information to the examiner in charge. Our empirical research analyzes the field of human genetic inventions. The empirical findings here show that third parties usually participate only after patents have been granted. Between 1999 and 2009, only a limited number of human gene patent cases made use of third-party, pre-grant interventions. There is thus an imbalance between third-party participation in the pre- and post-grant phase of patent prosecution, and we urge for greater participation of knowledge communities in the search and examination process. Europe should create a funnel for participation through advisory bodies and learned societies, which would allow judicious consideration of the search and examination, with a resultant improvement in patent quality

A Comprehensive Analysis of the Role of Artificial Intelligence and Machine Learning in Modern Digital Forensics and Incident Response

In the dynamic landscape of digital forensics, the integration of Artificial Intelligence (AI) and Machine Learning (ML) stands as a transformative technology, poised to amplify the efficiency and precision of digital forensics investigations. However, the use of ML and AI in digital forensics is still in its nascent stages. As a result, this paper gives a thorough and in-depth analysis that goes beyond a simple survey and review. The goal is to look closely at how AI and ML techniques are used in digital forensics and incident response. This research explores cutting-edge research initiatives that cross domains such as data collection and recovery, the intricate reconstruction of cybercrime timelines, robust big data analysis, pattern recognition, safeguarding the chain of custody, and orchestrating responsive strategies to hacking incidents. This endeavour digs far beneath the surface to unearth the intricate ways AI-driven methodologies are shaping these crucial facets of digital forensics practice. While the promise of AI in digital forensics is evident, the challenges arising from increasing database sizes and evolving criminal tactics necessitate ongoing collaborative research and refinement within the digital forensics profession. This study examines the contributions, limitations, and gaps in the existing research, shedding light on the potential and limitations of AI and ML techniques. By exploring these different research areas, we highlight the critical need for strategic planning, continual research, and development to unlock AI's full potential in digital forensics and incident response. Ultimately, this paper underscores the significance of AI and ML integration in digital forensics, offering insights into their benefits, drawbacks, and broader implications for tackling modern cyber threats

A Balanced Trust-Based Method to Counter Sybil and Spartacus Attacks in Chord

A Sybil attack is one of the main challenges to be addressed when securing peer-to-peer networks, especially those based on Distributed Hash Tables (DHTs). Tampering routing tables by means of multiple fake identities can make routing, storing, and retrieving operations significantly more difficult and time-consuming. Countermeasures based on trust and reputation have already proven to be effective in some contexts, but one variant of the Sybil attack, the Spartacus attack, is emerging as a new threat and its effects are even riskier and more difficult to stymie. In this paper, we first improve a well-known and deployed DHT (Chord) through a solution mixing trust with standard operations, for facing a Sybil attack affecting either routing or storage and retrieval operations. This is done by maintaining the least possible overhead for peers. Moreover, we extend the solution we propose in order for it to be resilient also against a Spartacus attack, both for an iterative and for a recursive lookup procedure. Finally, we validate our findings by showing that the proposed techniques outperform other trust-based solutions already known in the literature as well