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

According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

Out of the Crisis

The concept of an agent has become important in both Artificial Intelligence (AI) and mainstream computer science. Our aim in this paper is to point the reader at what we perceive to be the most important theoretical and practical issues associated with the design and construction of intelligent agents. For convenience, we divide these issues into three areas (though as the reader will see, the divisions are at times somewhat arbitrary). Agent theory is concerned with the question of what an agent is, and the use of mathematical formalisms for representing and reasoning about the properties of agents. Agent architectures can be thought of as software engineering models of agents;researchers in this area are primarily concerned with the problem of designing software or hardware systems that will satisfy the properties specified by agent theorists. Finally, agent languages are software systems for programming and experimenting with agents; these languages may embody principles proposed by theorists. The paper is not intended to serve as a tutorial introduction to all the issues mentioned; we hope instead simply to identify the most important issues, and point to work that elaborates on them. The article includes a short review of current and potential applications of agent technology.

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Intelligent Agents: Theory and Practice

物件導向軟體之架構(Object-Oriented Software Construction)探討

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

We present Prioni, a tool that integrates model checking and theorem proving for relational reasoning. Prioni takes as input formulas written in Alloy, a declarative language based on relations. Prioni uses the Alloy Analyzer to check the validity of Alloy formulas for a given scope that bounds the universe of discourse. The Alloy Analyzer can refute a formula if a counterexample exists within the given scope, but cannot prove that the formula holds for all scopes. For proofs, Prioni uses Athena, a denotational proof language. Prioni translates Alloy formulas into Athena proof obligations and uses the Athena tool for proof discovery and checking.

Integrating model checking and theorem proving for relational reasoning

A system and method that enables a plurality of lay users to collaborate on automating computer tasks is disclosed. In one embodiment, the system automatically performs these tasks, rather than just documenting how to perform them. The system allows a database of solutions to be built for every important computer task. A key characteristic of this system is that users contribute to this database by simply performing the task. The system records the graphical user interface (GUI) actions as the user performs the task. It aggregates GUI traces from multiple users into a canonical sequence of GUI actions parameterized by user-environment that will successfully accomplish the task on a variety of different configurations. A classifier is used to predict which steps are likely to be misinterpreted and requests human intervention to properly perform them. This process can be done iteratively until the translation is believed to be correct.

Translating text to, merging, and optimizing graphical user interface tasks

We present a new technique and system, DIODE, for auto- matically generating inputs that trigger overflows at memory allocation sites. DIODE is designed to identify relevant sanity checks that inputs must satisfy to trigger overflows at target memory allocation sites, then generate inputs that satisfy these sanity checks to successfully trigger the overflow. DIODE works with off-the-shelf, production x86 binaries. Our results show that, for our benchmark set of applications, and for every target memory allocation site exercised by our seed inputs (which the applications process correctly with no overflows), either 1) DIODE is able to generate an input that triggers an overflow at that site or 2) there is no input that would trigger an overflow for the observed target expression at that site.

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Targeted Automatic Integer Overflow Discovery Using Goal-Directed Conditional Branch Enforcement

In this position paper, we describe our vision of the future of machine programming through a categorical examination of three pillars of research. Those pillars are: (i) intention, (ii) invention, and (iii) adaptation. Intention emphasizes advancements in the human-to-computer and computer-to-machine-learning interfaces. Invention emphasizes the creation or refinement of algorithms or core hardware and software building blocks through machine learning (ML). Adaptation emphasizes advances in the use of ML-based constructs to autonomously evolve software.

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The three pillars of machine programming

We have developed a system that accepts a specification of key data structure constraints, then dynamically detects and repairs violations of these constraints, enabling the program to recover from otherwise crippling errors to continue to execute productively. We present our experience using our system to repair violated constraints in a simplified version of the ext2 file system and in the CTAS air-traffic control program. Our experience indicates that the specifications are relatively straightforward to develop and that our technique enables the applications to effectively recover from data structure corruption errors.

/pdf/automatic-data-structure-repair-for-self-healing-systems-q99vqnw2a1.pdf

Martin Rinard

Papers

Integrating model checking and theorem proving for relational reasoning

Translating text to, merging, and optimizing graphical user interface tasks

Targeted Automatic Integer Overflow Discovery Using Goal-Directed Conditional Branch Enforcement

The three pillars of machine programming

Automatic Data Structure Repair for Self-Healing Systems