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

Fundamentals of Molecular Evolution Dan Graur, Wen ... Bio 113/244 Fundamentals of Molecular Evolution Fundamentals of Molecular Evolution | Briefings in ... Fundamentals of Molecular Evolution by Dan Graur Fundamentals of Molecular Evolution: 9780878932665 ... Fundamentals of Molecular Evolution | Dan Graur, Wen ... (PDF) Fundamentals of molecular evolution Fundamentals Of Molecular Evolution Fundamentals of Molecular Evolution Dan Graur; Wen ... eBook [PDF] Fundamentals Of Molecular Evolution Download ... Fundamentals of molecular evolution Fundamentals Of Molecular Evolution FUNDAMENTALS OF MOLECULAR EVOLUTION Fundamentals of Molecular Evolution: Amazon.co.uk: Graur D ... Fundamentals of Molecular Evolution, 2nd Edition FUNDAMENTALS OF MOLECULAR EVOLUTION Fundamentals of molecular evolution : Li, Wen-Hsiung ... Molecular evolution Wikipedia

Fundamentals of molecular evolution

High quality source code is often paired with high level summaries of the computation it performs, for example in code documentation or in descriptions posted in online forums. Such summaries are extremely useful for applications such as code search but are expensive to manually author, hence only done for a small fraction of all code that is produced. In this paper, we present the first completely datadriven approach for generating high level summaries of source code. Our model, CODE-NN , uses Long Short Term Memory (LSTM) networks with attention to produce sentences that describe C# code snippets and SQL queries. CODE-NN is trained on a new corpus that is automatically collected from StackOverflow, which we release. Experiments demonstrate strong performance on two tasks: (1) code summarization, where we establish the first end-to-end learning results and outperform strong baselines, and (2) code retrieval, where our learned model improves the state of the art on a recently introduced C# benchmark by a large margin.

/pdf/summarizing-source-code-using-a-neural-attention-model-3e5keof2ia.pdf

Summarizing Source Code using a Neural Attention Model

Research at the intersection of machine learning, programming languages, and software engineering has recently taken important steps in proposing learnable probabilistic models of source code that exploit code's abundance of patterns. In this article, we survey this work. We contrast programming languages against natural languages and discuss how these similarities and differences drive the design of probabilistic models. We present a taxonomy based on the underlying design principles of each model and use it to navigate the literature. Then, we review how researchers have adapted these models to application areas and discuss cross-cutting and application-specific challenges and opportunities.

A Survey of Machine Learning for Big Code and Naturalness

Research at the intersection of machine learning, programming languages, and software engineering has recently taken important steps in proposing learnable probabilistic models of source code that exploit the abundance of patterns of code. In this article, we survey this work. We contrast programming languages against natural languages and discuss how these similarities and differences drive the design of probabilistic models. We present a taxonomy based on the underlying design principles of each model and use it to navigate the literature. Then, we review how researchers have adapted these models to application areas and discuss cross-cutting and application-specific challenges and opportunities.

https://dl.acm.org/doi/pdf/10.1145/3212695

Use-after-free vulnerabilities are rapidly growing in popularity, especially for exploiting web browsers. Use-after-free (and double-free) vulnerabilities are caused by a program operating on a dangling pointer. In this work we propose early detection, a novel runtime approach for finding and diagnosing use-after-free and double-free vulnerabilities. While previous work focuses on the creation of the vulnerability (i.e., the use of a dangling pointer), early detection shifts the focus to the creation of the dangling pointer(s) at the root of the vulnerability. Early detection increases the effectiveness of testing by identifying unsafe dangling pointers in executions where they are created but not used. It also accelerates vulnerability analysis and minimizes the risk of incomplete fixes, by automatically collecting information about all dangling pointers involved in the vulnerability. We implement our early detection technique in a tool called Undangle. We evaluate Undangle for vulnerability analysis on 8 real-world vulnerabilities. The analysis uncovers that two separate vulnerabilities in Firefox had a common root cause and that their patches did not completely fix the underlying bug. We also evaluate Undangle for testing on the Firefox web browser identifying a potential vulnerability.

/pdf/undangle-early-detection-of-dangling-pointers-in-use-after-34vazd2ez5.pdf

Undangle: early detection of dangling pointers in use-after-free and double-free vulnerabilities

Interacting with computers is a ubiquitous activity for millions of people. Repetitive or specialized tasks often require creation of small, often one-off, programs. End-users struggle with learning and using the myriad of domain-specific languages (DSLs) to effectively accomplish these tasks. We present a general framework for constructing program synthesizers that take natural language (NL) inputs and produce expressions in a target DSL. The framework takes as input a DSL definition and training data consisting of NL/DSL pairs. From these it constructs a synthesizer by learning optimal weights and classifiers (using NLP features) that rank the outputs of a keyword-programming based translation. We applied our framework to three domains: repetitive text editing, an intelligent tutoring system, and flight information queries. On 1200+ English descriptions, the respective synthesizers rank the desired program as the top-1 and top-3 for 80% and 90% descriptions respectively.

/pdf/program-synthesis-using-natural-language-5eulbzbwf0.pdf

Program synthesis using natural language

Millions of computer end users need to perform tasks over tabular spreadsheet data, yet lack the programming knowledge to do such tasks automatically. This paper describes the design and implementation of a robust natural language based interface to spreadsheet programming. Our methodology involves designing a typed domain-specific language (DSL) that supports an expressive algebra of map, filter, reduce, join, and formatting capabilities at a level of abstraction appropriate for non-expert users. The key algorithmic component of our methodology is a translation algorithm for converting a natural language specification in the context of a given spreadsheet to a ranked set of likely programs in the DSL. The translation algorithm leverages the spreadsheet spatial and temporal context to assign interpretations to specifications with implicit references, and is thus robust to a variety of ways in which end users can express the same task. The translation algorithm builds over ideas from keyword programming and semantic parsing to achieve both high precision and high recall. We implemented the system as an Excel add-in called NLyze that supports a rich user interaction model including annotating the user's natural language specification and explaining the synthesized DSL programs by paraphrasing them into structured English. We collected a total of 3570 English descriptions for 40 spreadsheet tasks and our system was able to generate the intended interpretation as the top candidate for 94% (97 for the top 3) of those instances.

/pdf/nlyze-interactive-programming-by-natural-language-for-38sut1yig1.pdf

NLyze: interactive programming by natural language for spreadsheet data analysis and manipulation

Programming by Examples (PBE) has the potential to revolutionize end-user programming by enabling end users, most of whom are non-programmers, to create small scripts for automating repetitive tasks. However, examples, though often easy to provide, are an ambiguous specification of the user's intent. Because of that, a key impedance in adoption of PBE systems is the lack of user confidence in the correctness of the program that was synthesized by the system. We present two novel user interaction models that communicate actionable information to the user to help resolve ambiguity in the examples. One of these models allows the user to effectively navigate between the huge set of programs that are consistent with the examples provided by the user. The other model uses active learning to ask directed example-based questions to the user on the test input data over which the user intends to run the synthesized program. Our user studies show that each of these models significantly reduces the number of errors in the performed task without any difference in completion time. Moreover, both models are perceived as useful, and the proactive active-learning based model has a slightly higher preference regarding the users' confidence in the result.

/pdf/user-interaction-models-for-disambiguation-in-programming-by-26t94o6c0r.pdf

User Interaction Models for Disambiguation in Programming by Example

As more and more genomes are sequenced, evolutionary biologists are becoming increasingly interested in evolution at the level of whole genomes, in scenarios in which the genome evolves through insertions, deletions, and movements of genes along its chromosomes. In the mathematical model pioneered by Sankoff and others, a unichromosomal genome is represented by a signed permutation of a multi-set of genes; Hannenhalli and Pevzner showed that the edit distance between two signed permutations of the same set can be computed in polynomial time when all operations are inversions. El-Mabrouk extended that result to allow deletions and a limited form of insertions (which forbids duplications). In this paper we extend El-Mabrouk's work to handle duplications as well as insertions and present an alternate framework for computing (near) minimal edit sequences involving insertions, deletions, and inversions. We derive an error bound for our polynomial-time distance computation under various assumptions and present preliminary experimental results that suggest that performance in practice may be excellent, within a few percent of the actual distance.

/pdf/genomic-distances-under-deletions-and-insertions-ok50gk2lac.pdf

Mark Marron

Papers

Undangle: early detection of dangling pointers in use-after-free and double-free vulnerabilities

Program synthesis using natural language

NLyze: interactive programming by natural language for spreadsheet data analysis and manipulation

User Interaction Models for Disambiguation in Programming by Example

Genomic distances under deletions and insertions