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

Context: The security goals of a software system provide a foundation for security requirements engineering. Identifying security goals is a process of iteration and refinement, leveraging the knowledge and expertise of the analyst to secure not only the core functionality but the security mechanisms as well. Moreover, a comprehensive security plan should include goals for not only preventing a breach, but also for detecting and appropriately responding in case a breach does occur. Goal: The objective of this research is to support analysts in security requirements engineering by providing a framework that supports a systematic and comprehensive discovery of security goals for a software system. Method: We develop a framework, Discovering Goals for Security (DIGS), that models the key entities in information security, including assets and security goals. We systematically develop a set of security goal patterns that capture multiple dimensions of security for assets. DIGS explicitly captures the relations and assumptions that underlie security goals to elicit implied goals. We map the goal patterns to NIST controls to help in operationalizing the goals. We evaluate DIGS via a controlled experiment where 28 participants analyzed systems from mobile banking and human resource management domains. Results: Participants considered security goals commensurate to the knowledge available to them. Although the overall recall was low given the empirical constraints, participants using DIGS identified more implied goals and felt more confident in completing the task. Conclusion: Explicitly providing the additional knowledge for the identification of implied security goals significantly increased the chances of discovering such goals, thereby improving coverage of stakeholder security requirements, even if they are unstated.

DIGS: A Framework for Discovering Goals for Security Requirements Engineering

Although much progress has been made in agent theory and practice, bottlenecks remain in the construction of complex multiagent systems. We introduce *interaction-oriented programming (IOP)* as an approach to orchestrate the interactions among agents. As envisioned, IOP is more tractable and practical than general agent programming, especially in settings such as open information environments, where the internal details of autonomously developed agents are not available. IOP facilitates multiagent system design by enabling declarative specification and enactment of agent interactions, thereby channeling the intellectual energies of designers into the most amenable and effective design tasks. We have begun the work on realizing the above vision. Our preliminary approach formally specifies certain interactions among agents, and executes them in a distributed manner. We have implemented it in an actor language.

/pdf/toward-interaction-oriented-programming-c0x0n1kzhs.pdf

Toward Interaction-Oriented Programming

We view the Internet as supporting a peer-to-peer information system whose components provide services to one another. The services could involve serving static pages, processing queries, or carrying out transactions. We model service providers and consumers as autonomous agents. Centralized indexes of the web are replaced by individual indexes kept by the agents. The agents can cooperate with one another. An agent may provide a service to another agent or give a referral that leads it in the right direction. Importantly, the agents can judge the quality of a service obtained and adaptively select their neighbors in order to improve their local performance.

Our approach enables us to address two important challenges. One, in contrast with traditional systems, finding trustworthy parties is nontrivial in open systems. Through referrals, agents can help one another find trustworthy parties. Two, recent work has studied the structure of the web as it happens to have emerged mostly through links on human-generated, static pages. Whereas existing work takes an after-the-fact look at web structure, we can study the emerging structure of an adaptive P2P system as it relates to the policies of the members.

/pdf/an-agent-based-approach-for-trustworthy-service-location-3tvx1t2j9t.pdf

An agent-based approach for trustworthy service location

Today, the Internet covers worldwide. All over the world, people prefer an E-commerce platform to buy or sell their products. Therefore, cybercrime has become the center of attraction for cyber attackers in cyberspace. Phishing is one such technique where the unidentified structure of the Internet has been used by attackers/criminals that intend to deceive users with the use of the illusory website and emails for obtaining their credentials (like account numbers, passwords, and PINs). Consequently, the identification of a phishing or legitimate web page is a challenging issue due to its semantic structure. In this paper, a phishing detection system is implemented using deep learning techniques to prevent such attacks. The system works on URLs by applying a convolutional neural network (CNN) to detect the phishing webpage. In paper [19] the proposed model has achieved 97.98% accuracy whereas our proposed system achieved accuracy of 98.00% which is better than earlier model. This system doesn’t require any feature engineering as the CNN extract features from the URLs automatically through its hidden layers. This is other advantage of the proposed system over earlier reported in [19] as the feature engineering is a very time-consuming task.

Phishing Detection from URLs Using Deep Learning Approach

The advancement in last decade in electronics & communication, computer science and information technology domain has resulted in the new computing and communication era, known as Wireless Sensor Networks. The routing protocols differ on the basis of application and network architecture. With awareness is a compulsory design criterion, many new protocols have been specifically designed for routing, power management and data dissemination. Efficient routing in a sensor network requires that routing protocol must minimize network energy dissipation and maximize network lifetime. This paper analyses the lifetime of different routing protocols for wireless sensor networks.

Munindar P. Singh

Papers

DIGS: A Framework for Discovering Goals for Security Requirements Engineering

Toward Interaction-Oriented Programming

An agent-based approach for trustworthy service location

Phishing Detection from URLs Using Deep Learning Approach

Analysis of Lifetime of Wireless Sensor Network