Stefan Poslad

Specifying protocols for multi-agent systems interaction

Multi-Agent-Systems or MAS represent a powerful distributed computing model, enabling agents to cooperate and complete with each other and to exchange both semantic content and a semantic context to more automatically and accurately interpret the content. Many types of individual agent and MAS models have been proposed since the mid-1980s, but the majority of these have led to single developer homogeneous MAS systems. For over a decade, the FIPA standards activity has worked to produce public MAS specifications, acting as a key enabler to support interoperability, open service interaction, and to support heterogeneous development. The main characteristics of the FIPA model for MAS and an analysis of design, design choices and features of the model is presented. In addition, a comparison of the FIPA model for system interoperability versus those of other standards bodies is presented, along with a discussion of the current status of FIPA and future directions.

An Enhanced Bag-of-Visual Word Vector Space Model to Represent Visual Content in Athletics Images

Images that have a different visual appearance may be semantically related using a higher level conceptualization. However, image classification and retrieval systems tend to rely only on the low-level visual structure within images. This paper presents a framework to deal with this semantic gap limitation by exploiting the well-known bag-of-visual words (BVW) to represent visual content. The novelty of this paper is threefold. First, the quality of visual words is improved by constructing visual words from representative keypoints. Second, domain specific “non-informative visual words” are detected which are useless to represent the content of visual data but which can degrade the categorization capability. Distinct from existing frameworks, two main characteristics for non-informative visual words are defined: a high document frequency (DF) and a small statistical association with all the concepts in the collection. The third contribution in this paper is that a novel method is used to restructure the vector space model of visual words with respect to a structural ontology model in order to resolve visual synonym and polysemy problems. The experimental results show that our method can disambiguate visual word senses effectively and can significantly improve classification, interpretation, and retrieval performance for the athletics images.

Specifying standard security mechanisms in multi-agent systems

Distributed multi-agent systems propose new infrastructure solutions to support the interoperability of electronic services. Security is a central issue for such infrastructures and is compounded by their intrinsic openness, heterogeneity and because of the autonomous and potentially self-interested nature of the agents therein. This article reviews the work that the FIPA agent standards body has undertaken to specify security in multi-agent systems. This enables a discussion about the main issues that developers have to face at different levels (i.e., intra-platform, inter-platform and application level) when developing agent-based security solutions in various domains.

Standardizing Agent Interoperability: The FIPA Approach

A prolific number of different Multi-Agent Systems (MAS) and associated applications have been developed in numerous research institutes and industrial laboratories world-wide. Perhaps the most important barrier to MAS making a successful transition form this research environment towards widespread adoption for consumer products and businesses, is the lack of interoperability between heterogeneous MA Systems. In 1996, the Foundation for Intelligent Physical Agents (FIPA) was formed to provide a forum for developing specifications for agent systems. Since its formation, FIPA has increasingly focussed more on standardizing (multi-agent system) agent interoperability. As a result, it is often said that FIPA really stands for the Foundation for InteroPerable Agents. In this article, we discuss both technical and scientific issues in defining standards for interoperability between agents in different MA systems with a particular focus on the FIPA agent interoperability standards.

Improving the Energy-Efficiency of GPS Based Location Sensing Smartphone Applications

Smartphones with an embedded GPS sensor are being increasingly used for location determination to enable Location based services (LBS) deliver location context pervasive computing services such as maps and navigation. Although a Smartphone GPS provides adequate accuracy, it has limitations such as high energy consumption and is unavailable in locations with an obscured view of GPS satellites. Use of alternate location sensors such as Wi-Fi and GSM can be used to augment GPS and to alleviate these GPS limitations, but they can increase the average localization error. The novelty of our contribution is twofold. First we present an accelerometer based architecture that reduces GPS energy-consumption without compromising on either the location accuracy or sampling rate. Evaluation of our system shows energy-savings of up to 27% in typical circumstances. Second, as a users mobility state is complex we also propose a method to not only detect that a user is non-stationary but also classify a representative set of mobility states.

Dynamic security reconfiguration for the semantic web

There is a plethora of security standards for protecting network services, specified by numerous standards consortia. These standards support different security requirements and use various syntaxes to represent the security information for different software infrastructures and applications. Open systems often require a more sophisticated security analysis and configuration to safeguard distributed services. A security framework constituting both semantic and meta-reasoning models is investigated in order to reason about the security requirements and security operation of interacting entities within open service environments. Security requirements are defined using security profiles that describe the interlinking of security policies to instances of services. Meta-reasoning refers to the reflection at a conceptual level at which the domain knowledge (ontology) is separated from the control knowledge (profiles): systems can manage and reconfigure themselves without affecting their underlying implementation. Such reasoning is particularly useful within open service infrastructures as it enables us to detect, analyse and resolve multiple-policy conflicts, to decide if a change in the environment necessitates a security reconfiguration, and to decide if a suitable level of security interoperability between heterogeneous systems is achievable. This paper describes a meta-reasoning model for semantic open service environments, an application and an evaluation of the framework and its performance.

Using a Smart City IoT to Incentivise and Target Shifts in Mobility Behaviour—Is It a Piece of Pie?

Whilst there is an increasing capability to instrument smart cities using fixed and mobile sensors to produce the big data to better understand and manage transportation use, there still exists a wide gap between the sustainability goals of smart cities, e.g., to promote less private car use at peak times, with respect to their ability to more dynamically support individualised shifts in multi-modal transportation use to help achieve such goals. We describe the development of the tripzoom system developed as part of the SUNSET—SUstainable social Network SErvices for Transport—project to research and develop a mobile and fixed traffic sensor system to help facilitate individual mobility shifts. Its main novelty was its ability to use mobile sensors to classify common multiple urban transportation modes, to generate information-rich individual and group mobility profiles and to couple this with the use of a targeted incentivised marketplace to gamify travel. This helps to promote mobility shifts towards achieving sustainability goals. This system was trialled in three European country cities operated as Living Labs over six months. Our main findings were that we were able to accomplish a level of behavioural shifts in travel behaviour. Hence, we have provided a proof-of-concept system that uses positive incentives to change individual travel behaviour.

A Semantic IoT Early Warning System for Natural Environment Crisis Management

An early warning system (EWS) is a core type of data driven Internet of Things (IoTs) system used for environment disaster risk and effect management. The potential benefits of using a semantic-type EWS include easier sensor and data source plug-and-play, simpler, richer, and more dynamic metadata-driven data analysis and easier service interoperability and orchestration. The challenges faced during practical deployments of semantic EWSs are the need for scalable time-sensitive data exchange and processing (especially involving heterogeneous data sources) and the need for resilience to changing ICT resource constraints in crisis zones. We present a novel IoT EWS system framework that addresses these challenges, based upon a multisemantic representation model. We use lightweight semantics for metadata to enhance rich sensor data acquisition. We use heavyweight semantics for top level W3C Web Ontology Language ontology models describing multileveled knowledge-bases and semantically driven decision support and workflow orchestration. This approach is validated through determining both system related metrics and a case study involving an advanced prototype system of the semantic EWS, integrated with a deployed EWS infrastructure.

A Multi-Modal Incompleteness Ontology model (MMIO) to enhance information fusion for image retrieval

A significant effort by researchers has advanced the ability of computers to understand, index and annotate images. This entails automatic domain specific knowledge-base (KB) construction and metadata extraction from visual information and any associated textual information. However, it is challenging to fuse visual and textual information and build a complete domain-specific KB for image annotation due to several factors such as: the ambiguity of natural language to describe image features; the semantic gap when using image features to represent visual content and the incompleteness of the metadata in the KB. Typically the KB is based upon a domain specific Ontology. However, it is not an easy task to extract the data needed from annotations and images, and then to automatically process these and transform them into an integrated Ontology model, because of the ambiguity of terms and because of image processing algorithm errors. As such, it is difficult to construct a complete KB covering a specific domain of knowledge. This paper presents a Multi-Modal Incompleteness Ontology-based (MMIO) system for image retrieval based upon fusing two derived indices. The first index exploits low-level features extracted from images. A novel technique is proposed to represent the semantics of visual content, by restructuring visual word vectors into an Ontology model by computing the distance between the visual word features and concept features, the so called concept range. The second index relies on a textual description which is processed to extract and recognise the concepts, properties, or instances that are defined in an Ontology. The two indexes are fused into a single indexing model, which is used to enhance the image retrieval efficiency. Nonetheless, this rich index may not be sufficient to find the desired images. Therefore, a Latent Semantic Indexing (LSI) algorithm is exploited to search for similar words to those used in a query. As a result, it is possible to retrieve images with a query using (similar) words that do not appear in the caption. The efficiency of the KB is validated experimentally with respect to three criteria, correctness, multimodality, and robustness. The results show that the multi-modal metadata in the proposed KB could be utilised efficiently. An additional experiment demonstrates that LSI can handle an incomplete KB effectively. Using LSI, the system can still retrieve relevant images when information in the Ontology is missing, leading to an enhanced retrieval performance.

Exploiting hashtags for adaptive microblog crawling

Researchers have capitalized on microblogging services, such as Twitter, for detecting and monitoring real world events. Existing approaches have based their conclusions on data collected by monitoring a set of pre-defined keywords. In this paper, we show that this manner of data collection risks losing a significant amount of relevant information. We then propose an adaptive crawling model that detects emerging popular hashtags, and monitors them to retrieve greater amounts of highly associated data for events of interest. The proposed model analyzes the traffic patterns of the hashtags collected from the live stream to update subsequent collection queries. To evaluate this adaptive crawling model, we apply it to a dataset collected during the 2012 London Olympic Games. Our analysis shows that adaptive crawling based on the proposed Refined Keyword Adaptation algorithm collects a more comprehensive dataset than pre-defined keyword crawling, while only introducing a minimum amount of noise.