1743872
Refining the SAT decision ordering for bounded model checking
Bounded Model Checking (BMC) relies on solving a sequence of highly correlated Boolean satisfiability (SAT) problems, each of which checks for the existence of counter-examples of a bounded length. The performance of SAT search depends heavily on the variable decision ordering. We propose an algorithm to exploit the correlation among different SAT problems in BMC, by predicting and successively refining a partial variable ordering. This ordering is based on the analysis of all previous unsatisfiable instances, and is combined with the SAT solver's existing decision heuristic to determine the final variable decision ordering. Experiments on real designs from industry show that our new method improves the performance of SAT-based BMC significantly.
18368722
Abstraction refinement in symbolic model checking using satisfiability as the only decision procedure
We present an abstraction refinement algorithm for model checking of safety properties that relies exclusively on a SAT solver for checking the abstract model, testing abstract counterexamples on the concrete model, and refinement. Model checking of the abstractions is based on bounded model checking extended with checks for the existence of simple paths that help in deciding passing properties. All minimum-length spurious counterexamples are eliminated in one refinement step by an incremental procedure that combines the analysis of the conflict dependency graph produced by the SAT solver while looking for concrete counterexamples with an effective refinement minimization procedure.
10395010
Abstraction and BDDs Complement SAT-Based BMC in DiVer
Bounded Model Checking (BMC) based on Boolean Satisfiability (SAT) procedures has recently gained popularity for finding bugs in large designs. However, due to its incompleteness, there is a need to perform deeper searches for counterexamples, or a proof by induction where possible. The DiVer verification platform uses abstraction and BDDs to complement BMC in the quest for completeness. We demonstrate the effectiveness of our approach in practice on industrial designs.
2969808
Improving Ariadne's Bundle by Following Multiple Threads in Abstraction Refinement
The authors propose a scalable abstraction-refinement method for model checking invariant properties on large sequential circuits, which is based on fine-grain abstraction and simultaneous analysis of all abstract counterexamples of the shortest length. Abstraction efficiency is introduced to measure for a given abstraction-refinement algorithm how much of the concrete model is required to make the decision. The fully automatic techniques presented in this paper can efficiently reach or come near to the maximal abstraction efficiency. First, a fine-grain abstraction approach is given to keep the abstraction granularity small by breaking down large combinational logic cones with Boolean network variables (BNVs) and then treating both state variables and BNVs as atoms in abstraction. Second, a refinement algorithm is proposed based on an improved Ariadne's bundle In the legend of Theseus, Ariadne's bundle contained one ball of thread to help Theseus navigate the labyrinth. In this paper, we work with multiple threads-hence, the "improved." of synchronous onion rings on the abstract model, through which the transitions contain all shortest abstract counterexamples. The synchronous onion rings are exploited in two distinct ways to provide global guidance to the abstraction refinement process. The scalability of our algorithm is ensured in the sense that all the analysis and computation required in our refinement algorithm are conducted on the abstract model. Finally, we derive sequential don't cares from the invisible variables and use them to constrain the behavior of the abstract model. We conducted experimental comparisons of our new method with various existing techniques. The results show that our method outperforms other counterexample-guided methods in terms of both run time and abstraction efficiency
18730116
A satisfiability-based approach to abstraction refinement in model checking
Abstract We present an abstraction refinement algorithm for model checking of safety properties that relies exclusively on a SAT solver for checking the abstract model, testing abstract counterexamples on the concrete model, and refinement. Model checking of the abstractions is based on bounded model checking extended with checks for the existence of simple paths that help in deciding passing properties. All minimum-length spurious counterexamples are eliminated in one refinement step by a procedure that combines the analysis of the conflict dependency graph produced by the SAT solver while looking for concrete counterexamples with an effective abstraction minimization procedure.
53757025
The Compositional Far Side of Image Computation
46728752
Some more remarks on domination in cubic graphs
Abstract We give sharp upper bounds on the upper signed domination number of nearly regular graphs and on the minus domination number of connected cubic graphs.
15593196
Real Time Sobel Square Edge Detector for Night Vision Analysis
Vision analysis with low or no illumination is gaining more and more attention recently, especially in the fields of security surveillance and medical diagnosis. In this paper, a real time sobel square edge detector is developed as a vision enhancer in order to render clear shapes of object in targeting scenes, allowing further analysis such as object or human detection, object or human tracking, human behavior recognition, and identification on abnormal scenes or activities. The method is optimized for real time applications and compared with existing edge detectors. Program codes are illustrated in the content and the results show that the proposed algorithm is promising to generate clear vision data with low noise.
14633874
Multi-platform Soccer Robot Development System
Robot soccer is a challenging research domain, which involves multiple agents (physical robots or "softbots") to work together in a dynamic, noisy, cooperative and adversarial environment to achieve specific objectives. The paper focuses on the design and implementation of an effective system for developing a small-size physical soccer team for the competition of RoboCup. There are three goals preset in our research, that is, robust individual behaviors, controllable cooperation and behavioral learning. The system is therefore required to collect data in real-time situations and repeat experiments accurately, which are found difficult to be achieved in pure physical or simulated case. A distinct feature of our system is that it is a close combination of three functional parts: physical test platform, simulation platform and measurement platform, which support each other to overcome many defects inherent in traditional physical system or simulation system.
7097063
Summarizing itemset patterns using probabilistic models
In this paper, we propose a novel probabilistic approach to summarize frequent itemset patterns. Such techniques are useful for summarization, post-processing, and end-user interpretation, particularly for problems where the resulting set of patterns are huge. In our approach items in the dataset are modeled as random variables. We then construct a Markov Random Fields (MRF) on these variables based on frequent itemsets and their occurrence statistics. The summarization proceeds in a level-wise iterative fashion. Occurrence statistics of itemsets at the lowest level are used to construct an initial MRF. Statistics of itemsets at the next level can then be inferred from the model. We use those patterns whose occurrence can not be accurately inferred from the model to augment the model in an iterative manner, repeating the procedure until all frequent itemsets can be modeled. The resulting MRF model affords a concise and useful representation of the original collection of itemsets. Extensive empirical study on real datasets show that the new approach can effectively summarize a large number of itemsets and typically significantly outperforms extant approaches.
14558760
A Constrained Clustering Approach to Duplicate Detection Among Relational Data
This paper proposes an approach to detect duplicates among relational data. Traditional methods for record linkage or duplicate detection work on a set of records which have no explicit relations with each other. These records can be formatted into a single database table for processing. However, there are situations that records from different sources can not be flattened into one table and records within one source have certain (semantic) relations between them. The duplicate detection issue of these relational data records/instances can be dealt with by formatting them into several tables and applying traditional methods to each table. However, as the relations among the original data records are ignored, this approach generates poor or inconsistent results. This paper analyzes the characteristics of relational data and proposes a particular clustering approach to perform duplicate detection. This approach incorporates constraint rules derived from the characteristics of relational data and therefore yields better and more consistent results, which are revealed by our experiments.