Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

How to Do Things With Words

What distinguishes multiagent systems from other software systems is their emphasis on the interactions among autonomous, heterogeneous agents. This paper motivates and characterizes correctness properties for multiagent systems. These properties are centered on commitments, and capture correctness at a high level. In contrast to existing approaches, commitments underlie key correctness primitives understood in terms of meaning; for example, commitment alignment maps to interoperability; commitment discharge maps to compliance. This paper gives illustrative examples and characterizations of these and other properties. The properties cover the specification of the principal artifacts—protocols, roles, and agents—of an interaction-based approach to designing multiagent systems, and thus provide the formal underpinnings of the approach.

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Correctness properties for multiagent systems

This paper proposes a deep neural network (DNN) model using the reduced input feature space of Parkinson’s telemonitoring dataset to predict Parkinson’s disease (PD) progression. PD is a chronic and progressive nervous system disorder that affects body movement. PD is assessed by using the unified Parkinson’s disease rating scale (UPDRS). In this paper, firstly, principal component analysis (PCA) is employed to the featured dataset to address the multicollinearity problems in the dataset and to reduce the dimension of input feature space. Then, the reduced input feature space is fed into the proposed DNN model with a tuned parameter norm penalty (L2) and analyses the prediction performance of it in PD progression by predicting Motor and Total-UPDRS score. The model’s performance is evaluated by conducting several experiments and the result is compared with the result of previously developed methods on the same dataset. The model’s prediction accuracy is measured by fitness parameters, mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R2). The MAE, RMSE, and R2 values are 0.926, 1.422, and 0.970 respectively for motor-UPDRS. These values are 1.334, 2.221, and 0.956 respectively for Total-UPDRS. Both the Motor and Total-UPDRS score is better predicted by the proposed method. This paper shows the usefulness and efficacy of the proposed method for predicting the UPDRS score in PD progression.

A deep learning approach for prediction of Parkinson’s disease progression

In this paper we use recursive modelling to formalize sanction-based obligations in a qualitative game theory. In particular, we formalize an agent who attributes mental attitudes such as goals and desires to the normative system which creates and enforces its obligations. The wishes (goals) of the normative system are the commands (obligations) of the agent. Since the agent is able to reason about the normative system’s behavior, our model accounts for many ways in which an agent can violate a norm believing that it will not be sanctioned. We thus propose a cognitive theory of normative reasoning which can be applied in theories requiring dynamic trust to understand when it is necessary to revise it.

/pdf/trusting-agents-for-trusting-electronic-societies-jb6yg0po4g.pdf

Trusting agents for trusting electronic societies

Web services have been gathering an increasing amount of attention lately. The raison d'etre of Web services is that we compose them to create new services. For Web services to be effectively composed, however, requires that they be trustworthy and in fact be trusted by their users and other collaborating services. In our conceptual scheme, principals interact as autonomous peers to provide services to one another. Trust is captured as a composite relationship between the trusted and the trusting principal. Principals help each other discover and locate trustworthy services and weed out untrustworthy players. The interactions of the principals combined with the needs of different applications induce interesting structures on the network.We apply multiagent systems techniques to model interactions among the principals.

By varying the requirements of different applications, the needs of different principals, the existence of special principals such as trusted authorities, and the mechanisms underlying the interactions, we draw attention to a variety of important settings where Web services would be composed. One, leading to superior methods through which trust can be evolved and managed in realistic service-composition settings. Two, studying the relationships between aspects of trust forWeb services and the evolution of Web structure.

/pdf/trustworthy-service-composition-challenges-and-research-4cmi7pv2k0.pdf

Trustworthy service composition: challenges and research questions

The sentiment captured in opinionated text provides interesting and valuable information for social media services. However, due to the complexity and diversity of linguistic representations, it is challenging to build a framework that accurately extracts such sentiment. We propose a semi-supervised framework for generating a domain-specific sentiment lexicon and inferring sentiments at the segment level. Our framework can greatly reduce the human effort for building a domainspecific sentiment lexicon with high quality. Specifically, in our evaluation, working with just 20 manually labeled reviews, it generates a domain-specific sentiment lexicon that yields weighted average FMeasure gains of 3%. Our sentiment classification model achieves approximately 1% greater accuracy than a state-of-the-art approach based on elementary discourse units.

/pdf/renew-a-semi-supervised-framework-for-generating-domain-4vjc1vs0eb.pdf

Munindar P. Singh

Papers

Correctness properties for multiagent systems

A deep learning approach for prediction of Parkinson’s disease progression

Trusting agents for trusting electronic societies

Trustworthy service composition: challenges and research questions

ReNew: A Semi-Supervised Framework for Generating Domain-Specific Lexicons and Sentiment Analysis