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

ing WS1S Systems to Verify Parameterized Networks . . . . . . . . . . . . 188 Kai Baukus, Saddek Bensalem, Yassine Lakhnech and Karsten Stahl FMona: A Tool for Expressing Validation Techniques over Infinite State Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 J.-P. Bodeveix and M. Filali Transitive Closures of Regular Relations for Verifying Infinite-State Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220 Bengt Jonsson and Marcus Nilsson Diagnostic and Test Generation Using Static Analysis to Improve Automatic Test Generation . . . . . . . . . . . . . 235 Marius Bozga, Jean-Claude Fernandez and Lucian Ghirvu Efficient Diagnostic Generation for Boolean Equation Systems . . . . . . . . . . . . 251 Radu Mateescu Efficient Model-Checking Compositional State Space Generation with Partial Order Reductions for Asynchronous Communicating Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266 Jean-Pierre Krimm and Laurent Mounier Checking for CFFD-Preorder with Tester Processes . . . . . . . . . . . . . . . . . . . . . . . 283 Juhana Helovuo and Antti Valmari Fair Bisimulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 Thomas A. Henzinger and Sriram K. Rajamani Integrating Low Level Symmetries into Reachability Analysis . . . . . . . . . . . . . 315 Karsten Schmidt Model-Checking Tools Model Checking Support for the ASM High-Level Language . . . . . . . . . . . . . . 331 Giuseppe Del Castillo and Kirsten Winter Table of

Tools and Algorithms for the Construction and Analysis of Systems. Proc. TACAS 2009

In many places, inserted text is highlighted in red. Preface p. xx " all of which are useful in structuring concurrent applications. " Chapter 1 p. 4 In Fig. 1.1, line 4 should say for (int j = (i * block) + 1; j <= (i + 1)* block; j++) { p. 11 p. 18 Second paragraph of Mutual Exclusion bullet: " Initially the can is either up or down. Let us say it was down. Then only the pets can go in, and mutual exclusion holds. " Replace question 6 bullet 2 with:  Suppose the method M accounts for 30% of the program's computation time. Let sn be the program's speedup on n processes, assuming the rest of the program is perfectly parallelizable. Your boss tells you to double this speedup: the revised program should have speedup s'n ≥ sn/2. You advertize for a programmer to replace M with an improved version, k times faster. What value of k should you require? Chapter 2 p. 23 Figure 2.3 should be amended as shown: public long getAndIncrement() { lock.lock(); try { long temp = value; value = temp + 1; return temp; } finally { lock.unlock(); } } p. 25 Pragma 2.3.1: " We explain the reasons in Chapter 3 and Appendix B. " p. 26 In Fig. 2.5, remove all declarations of variables as volatile. Indeed as stated in Pragma 2.3.1 on page 25, one should use memory barriers when implementing these algorithms. However, in this chapter (in all code) we avoid such issues to keep the algorithms simple. Adding volatile to these variables would require more complex coding that would distract readers from the core issues at hand.

The Art of Multiprocessor Programming

Drought is a natural disaster that causes significant impact on all parts of environment and cause to reduction of the agricultural products. Other natural phenomena, for instance climate change, earthquake, storm, flood, and landslide, are also commonplace. In recent years, various techniques of artificial intelligence are used for drought prediction. The presented paper describes drought forecasting, which makes use of and compares the artificial neural network (ANN), adaptive neuro-fuzzy interface system (ANFIS), and support vector machine (SVM). The index that is used in this study is Standardized Precipitation Index (SPI). All of data from Bojnourd meteorological station (from January 1984 to December 2012) have been tested for 3-month time scales. The input parameters are as follows: temperature, humidity, and season precipitation, and the output parameter is SPI. This paper shows high accuracy of these models. The results indicated that the SVM model gives more accurate values for forecasting. On the other hand, we use the nonparametric inference to compare the proposal methods, and our results show that SVM model is more accurate than ANN and ANFIS.

Drought forecasting by ANN, ANFIS, and SVM and comparison of the models

pdf/snow-avalanche-hazard-prediction-using-machine-learning-29i8wjebul.pdf

Snow avalanche hazard prediction using machine learning methods

In this paper we study empirical metrics for software source code, which can predict the performance of verification tools on specific types of software. Our metrics comprise variable usage patterns, loop patterns, as well as indicators of control-flow complexity and are extracted by simple data-flow analyses. We demonstrate that our metrics are powerful enough to devise a machine-learning based portfolio solver for software verification. We show that this portfolio solver would be the (hypothetical) overall winner of both the 2014 and 2015 International Competition on Software Verification (SV-COMP). This gives strong empirical evidence for the predictive power of our metrics and demonstrates the viability of portfolio solvers for software verification.

https://link.springer.com/content/pdf/10.1007%2F978-3-319-21690-4_39.pdf

Empirical Software Metrics for Benchmarking of Verification Tools

We study empirical metrics for software source code, which can predict the performance of verification tools on specific types of software. Our metrics comprise variable usage patterns, loop patterns, as well as indicators of control-flow complexity and are extracted by simple data-flow analyses. We demonstrate that our metrics are powerful enough to devise a machine-learning based portfolio solver for software verification. We show that this portfolio solver would be the (hypothetical) overall winner of the international competition on software verification (SV-COMP) in three consecutive years (2014---2016). This gives strong empirical evidence for the predictive power of our metrics and demonstrates the viability of portfolio solvers for software verification. Moreover, we demonstrate the flexibility of our algorithm for portfolio construction in novel settings: originally conceived for SV-COMP'14, the construction works just as well for SV-COMP'15 (considerably more verification tasks) and for SV-COMP'16 (considerably more candidate verification tools).

/pdf/empirical-software-metrics-for-benchmarking-of-verification-19yklqaukj.pdf

Empirical software metrics for benchmarking of verification tools

We present a thread-modular proof method for complexity and resource bound analysis of concurrent, shared-memory programs, lifting Jones’ rely-guarantee reasoning to assumptions and commitments capable of expressing bounds. We automate reasoning in this logic by reducing bound analysis of concurrent programs to the sequential case. Our work is motivated by its application to lock-free data structures, fine-grained concurrent algorithms whose time complexity has to our knowledge not been inferred automatically before.

Rely-Guarantee Reasoning for Automated Bound Analysis of Lock-Free Algorithms

We present a thread-modular proof method for complexity and resource bound analysis of concurrent, shared-memory programs. To this end, we lift Jones’ rely-guarantee reasoning to assumptions and commitments capable of expressing bounds. The compositionality (thread-modularity) of this framework allows us to reason about parameterized programs, i.e., programs that execute arbitrarily many concurrent threads. We automate reasoning in our logic by reducing bound analysis of concurrent programs to the sequential case. As an application, we automatically infer time complexity for a family of fine-grained concurrent algorithms, lock-free data structures, to our knowledge for the first time.

/pdf/rely-guarantee-bound-analysis-of-parameterized-concurrent-c23rqe1ugc.pdf

Rely-guarantee bound analysis of parameterized concurrent shared-memory programs

In this work, we conduct a systematic study of loops in C programs We describe static analyses capable of efficiently identifying definite iteration in C code Our experiments show that over one third of loops in our benchmarks take this form To cover further loops, we systematically weaken our definition of definite iteration and derive a family of loop classes that are heuristics for definite iteration We then measure the occurrence of these classes on real-world C code and investigate which statements are used to express them Finally, we empirically show that our classification is meaningful -- (a) it describes the majority of loops in our benchmarks, (b) the classes are good heuristics for termination, and (c) they can be used as software metrics to characterize benchmarks for software verification

/pdf/loop-patterns-in-c-programs-2lun1jcakk.pdf

Loop Patterns in C Programs

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