The global maritime industry is undergoing a digital transformation driven by the Fourth Industrial Revolution, which gives rise to “smart ports” or “Port 4.0” environments. These ports leverage smart technologies, and thus increase exposure to cyber threats that have seen dramatic growth in recent years. This paper presents four key contributions to strengthen cybersecurity in smart Port ecosystems. First, it maps the current cyber-threat landscape across both Information Technology (IT) and Operational Technology (OT) systems used in the smart Port environment, highlighting critical vulnerabilities. Then, it analyzes existing regulatory and standard frameworks such as the International Maritime Organization (IMO) guidelines, ISO/IEC 27001, and the National Institute of Standards and Technology (NIST) cybersecurity framework, identifying alignment gaps with maritime operational realities. Further, this paper also provides structured, Port-specific cybersecurity recommendations tailored to the complex interplay of legacy OT systems and modern digital technologies. Finally, the paper discusses AI-assisted cybersecurity solutions available in the literature, highlighting how advanced AI-based analytics, predictive modeling, and automated incident response can be incorporated. The insights presented are intended to help Port authorities build resilient, adaptive cybersecurity postures in an increasingly interconnected maritime domain.

Scene graph generation aims to capture the semantic elements in images by modelling objects and their relationships in a structured manner, which are essential for visual understanding and reasoning tasks including image captioning, visual question answering, multimedia event processing, visual storytelling and image retrieval. The existing scene graph generation approaches provide limited performance and expressiveness for higher-level visual understanding and reasoning. This challenge can be mitigated by leveraging commonsense knowledge, such as related facts and background knowledge, about the semantic elements in scene graphs. In this paper, we propose the infusion of diverse commonsense knowledge about the semantic elements in scene graphs to generate rich and expressive scene graphs using a heterogeneous knowledge source that contains commonsense knowledge consolidated from seven different knowledge bases. The graph embeddings of the object nodes are used to leverage their structural patterns in the knowledge source to compute similarity metrics for graph refinement and enrichment. We performed experimental and comparative analysis on the benchmark Visual Genome dataset, in which the proposed method achieved a higher recall rate ( $$R@K = 29.89, 35.4, 39.12$$ for $$K = 20, 50, 100$$ ) as compared to the existing state-of-the-art technique ( $$R@K = 25.8, 33.3, 37.8$$ for $$K = 20, 50, 100$$ ). The qualitative results of the proposed method in a downstream task of image generation showed that more realistic images are generated using the commonsense knowledge-based scene graphs. These results depict the effectiveness of commonsense knowledge infusion in improving the performance and expressiveness of scene graph generation for visual understanding and reasoning tasks.

Typically a Neural Networks (NN) is trained on data centers using historic datasets, then a C source file (model as a char array) of the trained model is generated and flashed on IoT devices. This standard process impedes the flexibility of billions of deployed ML-powered devices as they cannot learn unseen/fresh data patterns (static intelligence) and are impossible to adapt to dynamic scenarios. Currently, to address this issue, Online Machine Learning (OL) algorithms are deployed on IoT devices that provide devices the ability to locally re-train themselves -continuously updating the last few NN layers using unseen data patterns encountered after deployment.In OL, catastrophic forgetting is common when NNs are trained using non-stationary data distribution. The majority of recent work in the OL domain embraces the implicit assumption that the distribution of local training data is balanced. But the fact is, the sensor data streams in real-world IoT are severely imbalanced and temporally correlated. This paper introduces Imbal-OL, a resource-friendly technique that can be used as an OL plugin to balance the size of classes in a range of data streams. When Imbal-OL processed stream is used for OL, the models can adapt faster to changes in the stream while parallelly preventing catastrophic forgetting. Experimental evaluation of Imbal-OL using CIFAR datasets over ResNet-18 demonstrates its ability to deal with imperfect data streams, as it manages to produce high-quality models even under challenging learning settings.

The concept of ML model aggregation rather than data aggregation has gained much attention as it boosts pre- diction performance while maintaining stability and preserving privacy. In a non-ideal scenario, there are chances for a base model trained on a single device to make independent but complementary errors. To handle such cases, in this paper, we implement and release the code of 8 robust ML model combining methods that achieves reliable prediction results by combining numerous base models (trained on many devices) to form a central model that effectively limits errors, built-in randomness and uncertainties. We extensively test the model combining performance by performing 15 heterogeneous devices and 3 datasets based experiments that exemplifies how a complicated collective intelligence can be derived from numerous elementary intelligence learned by distributed, ubiquitous IoT devices.

Distributed Social Networks (DSNs) are the solution to the privacy and security problems of online social networks. In DSN, a user controls their own data as it chooses personal storage for its social network data. In absence of a centralized entity with access to all social network data, information retrieval becomes difficult in DSNs. In this paper we propose to use crowd sourcing for information retrieval in a DSN. We analyze a popular information retrieval problem called expert search in a social network. In this paper, we present an algorithm for such a crowd sourcing based search process which includes solution for (a) the worker selection problem (b) the task selection problem and (c) the reward distribution problem. Using experimental evaluation, we show that, the search algorithms proposed in this paper can be as efficient as a greedy search algorithm with access to entire social network information.

User modeling of individual users on the Social Web platforms such as Twitter plays a significant role in providing personalized recommendations and filtering interesting information from social streams. Recently, researchers proposed the use of concepts (e.g., DBpedia entities) for representing user interests instead of word-based approaches, since Knowledge Bases such as DBpedia provide cross-domain background knowledge about concepts, and thus can be used for extending user interest profiles. Even so, not all concepts can be covered by a Knowledge Base, especially in the case of microblogging platforms such as Twitter where new concepts/topics emerge everyday. In this short paper, instead of using concepts alone, we propose using synsets from WordNet and concepts from DBpedia for representing user interests. We evaluate our proposed user modeling strategies by comparing them with other bag-of-concepts approaches. The results show that using synsets and concepts together for representing user interests improves the quality of user modeling significantly in the context of link recommendations on Twitter.

Although tagging is a widely accepted practice on the Social Web, it raises various issues like tags ambiguity and heterogeneity, as well as the lack of organization between tags. We believe that Semantic Web technologies can help solve many of these issues, especially considering the use of formal resources from the Web of Data in support of existing tagging systems and practices. In this article, we present the MOAT—Meaning Of A Tag—ontology and framework, which aims to achieve this goal. We will detail some motivations and benefits of the approach, both in an Enterprise 2.0 ecosystem and on the Web. As we will detail, our proposal is twofold: It helps solve the problems mentioned previously, and weaves user-generated content into the Web of Data, making it more efficiently interoperable and retrievable.

Classic argumentative discussions can be found in a variety of domains from traditional scientific publishing to today's modern social software. An interactive argumentative discussion usually consists of an initial proposition stated by a single creator, followed by supporting propositions or counter-propositions from other contributors. Thus, the actual argumentation semantics is hidden in the content created by the contributors. Although there are approaches that try to deal with this challenge, most of them focus on a particular domain, limiting the scope of the argumentation to that domain only. In this paper, we describe an abstract model for argumentation which captures the semantics independently of the domain. Following a modularized approach, we also take into account additional important aspects of the argumentation, like the provenance information or its evolution (the temporal side).

Wikis are popular collaborative hypertext authoring environments, but they neither support structured access nor information reuse. Adding semantic annotations helps to address these limitations. We present an architecture for Semantic Wikis and discuss design decisions including structured access, views, and annotation language. We present our prototype SemperWiki that implements this architecture.