ExpFinder: An Ensemble Expert Finding Model Integrating NN-gram Vector Space Model and μ\muCO-HITS

Finding an expert plays a crucial role in driving successful collaborations and speeding up high-quality research development and innovations. However, the rapid growth of scientific publications and digital expertise data makes identifying the right experts a challenging problem. Existing approaches for finding experts given a topic can be categorised into information retrieval techniques based on vector space models, document language models, and graph-based models. In this paper, we propose $\textit{ExpFinder}$, a new ensemble model for expert finding, that integrates a novel $N$-gram vector space model, denoted as $n$VSM, and a graph-based model, denoted as \textit{\muCO-HITS}, that is a proposed variation of the CO-HITS algorithm. The key of $n$VSM is to exploit recent inverse document frequency weighting method for $N$-gram words and $\textit{ExpFinder}$ incorporates $n$VSM into \textit{\muCO-HITS} to achieve expert finding. We comprehensively evaluate $\textit{ExpFinder}$ on four different datasets from the academic domains in comparison with six different expert finding models. The evaluation results show that $\textit{ExpFinder}$ is a highly effective model for expert finding, substantially outperforming all the compared models in 19% to 160.2%.Comment: 15 pages, 18 figures, "for source code on Github, see https://github.com/Yongbinkang/ExpFinder", "Submitted to IEEE Transactions on Knowledge and Data Engineering

Semantic multimedia modelling & interpretation for annotation

The emergence of multimedia enabled devices, particularly the incorporation of cameras in mobile phones, and the accelerated revolutions in the low cost storage devices, boosts the multimedia data production rate drastically. Witnessing such an iniquitousness of digital images and videos, the research community has been projecting the issue of its significant utilization and management. Stored in monumental multimedia corpora, digital data need to be retrieved and organized in an intelligent way, leaning on the rich semantics involved. The utilization of these image and video collections demands proficient image and video annotation and retrieval techniques. Recently, the multimedia research community is progressively veering its emphasis to the personalization of these media. The main impediment in the image and video analysis is the semantic gap, which is the discrepancy among a user’s high-level interpretation of an image and the video and the low level computational interpretation of it. Content-based image and video annotation systems are remarkably susceptible to the semantic gap due to their reliance on low-level visual features for delineating semantically rich image and video contents. However, the fact is that the visual similarity is not semantic similarity, so there is a demand to break through this dilemma through an alternative way. The semantic gap can be narrowed by counting high-level and user-generated information in the annotation. High-level descriptions of images and or videos are more proficient of capturing the semantic meaning of multimedia content, but it is not always applicable to collect this information. It is commonly agreed that the problem of high level semantic annotation of multimedia is still far from being answered. This dissertation puts forward approaches for intelligent multimedia semantic extraction for high level annotation. This dissertation intends to bridge the gap between the visual features and semantics. It proposes a framework for annotation enhancement and refinement for the object/concept annotated images and videos datasets. The entire theme is to first purify the datasets from noisy keyword and then expand the concepts lexically and commonsensical to fill the vocabulary and lexical gap to achieve high level semantics for the corpus. This dissertation also explored a novel approach for high level semantic (HLS) propagation through the images corpora. The HLS propagation takes the advantages of the semantic intensity (SI), which is the concept dominancy factor in the image and annotation based semantic similarity of the images. As we are aware of the fact that the image is the combination of various concepts and among the list of concepts some of them are more dominant then the other, while semantic similarity of the images are based on the SI and concept semantic similarity among the pair of images. Moreover, the HLS exploits the clustering techniques to group similar images, where a single effort of the human experts to assign high level semantic to a randomly selected image and propagate to other images through clustering. The investigation has been made on the LabelMe image and LabelMe video dataset. Experiments exhibit that the proposed approaches perform a noticeable improvement towards bridging the semantic gap and reveal that our proposed system outperforms the traditional systems

Modular norm models: practical representation and analysis of contractual rights and obligations

Compliance analysis requires legal counsel but is generally unavailable in many software projects. Analysis of legal text using logic-based models can help developers understand requirements for the development and use of software-intensive systems throughout its lifecycle. We outline a practical modeling process for norms in legally binding agreements that include contractual rights and obligations. A computational norm model analyzes available rights and required duties based on the satisfiability of situations, a state of affairs, in a given scenario. Our method enables modular norm model extraction, representation, and reasoning. For norm extraction, using the theory of frame semantics, we construct two foundational norm templates for linguistic guidance. These templates correspond to Hohfeld’s concepts of claim-right and its jural correlative, duty. Each template instantiation results in a norm model, encapsulated in a modular unit which we call a super-situation that corresponds to an atomic fragment of law. For hierarchical modularity, super-situations contain a primary norm that participates in relationships with other norm models. Norm compliance values are logically derived from its related situations and propagated to the norm’s containing super-situation, which in turn participates in other super-situations. This modularity allows on-demand incremental modeling and reasoning using simpler model primitives than previous approaches. While we demonstrate the usefulness of our norm models through empirical studies with contractual statements in open source software and privacy domains, its grounding in theories of law and linguistics allows wide applicability

MULTIMEDIA QUESTION ANSWERING AND CONTENT-BASED PRODUCT ANNOTATION AND SEARCH

Ph.DDOCTOR OF PHILOSOPH

A novel service discovery model for decentralised online social networks.

Online social networks (OSNs) have become the most popular Internet application that attracts billions of users to share information, disseminate opinions and interact with others in the online society. The unprecedented growing popularity of OSNs naturally makes using social network services as a pervasive phenomenon in our daily life. The majority of OSNs service providers adopts a centralised architecture because of its management simplicity and content controllability. However, the centralised architecture for large-scale OSNs applications incurs costly deployment of computing infrastructures and suffers performance bottleneck. Moreover, the centralised architecture has two major shortcomings: the single point failure problem and the lack of privacy, which challenges the uninterrupted service provision and raises serious privacy concerns. This thesis proposes a decentralised approach based on peer-to-peer (P2P) networks as an alternative to the traditional centralised architecture. Firstly, a self-organised architecture with self-sustaining social network adaptation has been designed to support decentralised topology maintenance. This self-organised architecture exhibits small-world characteristics with short average path length and large average clustering coefficient to support efficient information exchange. Based on this self-organised architecture, a novel decentralised service discovery model has been developed to achieve a semantic-aware and interest-aware query routing in the P2P social network. The proposed model encompasses a service matchmaking module to capture the hidden semantic information for query-service matching and a homophily-based query processing module to characterise user’s common social status and interests for personalised query routing. Furthermore, in order to optimise the efficiency of service discovery, a swarm intelligence inspired algorithm has been designed to reduce the query routing overhead. This algorithm employs an adaptive forwarding strategy that can adapt to various social network structures and achieves promising search performance with low redundant query overhead in dynamic environments. Finally, a configurable software simulator is implemented to simulate complex networks and to evaluate the proposed service discovery model. Extensive experiments have been conducted through simulations, and the obtained results have demonstrated the efficiency and effectiveness of the proposed model.University of Derb

Improving Interaction in Visual Analytics using Machine Learning

Interaction is one of the most fundamental components in visual analytical systems, which transforms people from mere viewers to active participants in the process of analyzing and understanding data. Therefore, fast and accurate interaction techniques are key to establishing a successful human-computer dialogue, enabling a smooth visual data exploration. Machine learning is a branch of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. It has been utilized in a wide variety of fields, where it is not straightforward to develop a conventional algorithm for effectively performing a task. Inspired by this, we see the opportunity to improve the current interactions in visual analytics by using machine learning methods. In this thesis, we address the need for interaction techniques that are both fast, enabling a fluid interaction in visual data exploration and analysis, and also accurate, i.e., enabling the user to effectively select specific data subsets. First, we present a new, fast and accurate brushing technique for scatterplots, based on the Mahalanobis brush, which we have optimized using data from a user study. Further, we present a new solution for a near-perfect sketch-based brushing technique, where we exploit a convolutional neural network (CNN) for estimating the intended data selection from a fast and simple click-and-drag interaction and from the data distribution in the visualization. Next, we propose an innovative framework which offers the user opportunities to improve the brushing technique while using it. We tested this framework with CNN-based brushing and the result shows that the underlying model can be refined (better performance in terms of accuracy) and personalized by very little time of retraining. Besides, in order to investigate to which degree the human should be involved into the model design and how good the empirical model can be with a more careful design, we extended our Mahalanobis brush (the best current empirical model in terms of accuracy for brushing points in a scatterplot) by further incorporating the data distribution information, captured by kernel density estimation (KDE). Based on this work, we then provide a detailed comparison between empirical modeling and implicit modeling by machine learning (deep learning). Lastly, we introduce a new, machine learning based approach that enables the fast and accurate querying of time series data based on a swift sketching interaction. To achieve this, we build upon existing LSTM technology (long short-term memory) to encode both the sketch and the time series data in two networks with shared parameters. All the proposed interaction techniques in this thesis were demonstrated by application examples and evaluated via user studies. The integration of machine learning knowledge into visualization opens further possible research directions.Doktorgradsavhandlin