Reverse Engineering

We describe and demonstrate an algorithm that takes as input an unorganized set of points fx1; : : : ; x n g IR 3 on or near an unknown manifold M, and produces as output a simplicial surface that approximates M. Neither the topology, the presence of boundaries, nor the geometry of M are assumed to be known in advance -all are inferred automatically from the data. This problem naturally arises in a variety of practical situations such as range scanning an object from multiple view points, recovery of biological shapes from two-dimensional slices, and interactive surface sketching.

Background: Elucidating gene regulatory networks is crucial for understanding normal cell physiology and complex pathologic phenotypes. Existing computational methods for the genomewide "reverse engineering" of such networks have been successful only for lower eukaryotes with simple genomes. Here we present ARACNE, a novel algorithm, using microarray expression profiles, specifically designed to scale up to the complexity of regulatory networks in mammalian cells, yet general enough to address a wider range of network deconvolution problems. This method uses an information theoretic approach to eliminate the majority of indirect interactions inferred by coexpression methods.

Cellular phenotypes are determined by the differential activity of networks linking coregulated genes. Available methods for the reverse engineering of such networks from genome-wide expression profiles have been successful only in the analysis of lower eukaryotes with simple genomes. Using a new method called ARACNe (algorithm for the reconstruction of accurate cellular networks), we report the reconstruction of regulatory networks from expression profiles of human B cells. The results are suggestive a hierarchical, scale-free network, where a few highly interconnected genes (hubs) account for most of the interactions. Validation of the network against available data led to the identification of MYC as a major hub, which controls a network comprising known target genes as well as new ones, which were biochemically validated. The newly identified MYC targets include some major hubs. This approach can be generally useful for the analysis of normal and pathologic networks in mammalian cells.

Explicitly stated program invariants can help programmers by identifying program properties that must be preserved when modifying code. In practice, however, these invariants are usually implicit. An alternative to expecting programmers to fully annotate code with invariants is to automatically infer invariants from the program itself. This research focuses on dynamic techniques for discovering invariants from execution traces. This paper reports two results. First, it describes techniques for dynamically discovering invariants, along with an instrumenter and an inference engine that embody these techniques. Second, it reports on the application of the engine to two sets of target programs. In programs from Gries's work on program derivation, we rediscovered predefined invariants. In a C program lacking explicit invariants, we discovered invariants that assisted a software evolution task.

This paper provides an extensive overview of existing research in the field of software refactoring. This research is compared and discussed based on a number of different criteria: the refactoring activities that are supported; the specific techniques and formalisms that are used for supporting these activities; the types of software artifacts that are being refactored; the important issues that need to be taken into account when building refactoring tool support; and the effect of refactoring on the software process. A running example is used throughout the paper to explain and illustrate the main concepts.

In many areas of industry, it is desirable to create geometric models of existing objects for which no such model is available. This paper reviews the process of reverse engineering of shapes. After identifying the purpose of reverse engineering and the main application areas, the most important algorithmic steps are outlined and various reconstruction strategies are presented. Pros and cons of various data acquisition techniques are described with related problems of boundary representation model construction.

It has become more and more common to distribute software in forms that retain most or all of the information present in the original source code. An important example is Java bytecode. Since such codes are easy to decompile, they increase the risk of malicious reverse engineering attacks. In this paper we review several techniques for technical protection of software

One of the pressing open problems of computational systems biology is the elucidation of the topology of genetic regulatory networks (GRNs) using high throughput genomic data, in particular microarray gene expression data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge aims to evaluate the success of GRN inference algorithms on benchmarks of simulated data. In this article, we present GENIE3, a new algorithm for the inference of GRNs that was best performer in the DREAM4 In Silico Multifactorial challenge. GENIE3 decomposes the prediction of a regulatory network between p genes into p different regression problems. In each of the regression problems, the expression pattern of one of the genes (target gene) is predicted from the expression patterns of all the other genes (input genes), using tree-based ensemble methods Random Forests or Extra-Trees. The importance of an input gene in the prediction of the target gene expression pattern is taken as an indication of a putative regulatory link. Putative regulatory links are then aggregated over all genes to provide a ranking of interactions from which the whole network is reconstructed. In addition to performing well on the DREAM4 In Silico Multifactorial challenge simulated data, we show that GENIE3 compares favorably with existing algorithms to decipher the genetic regulatory network of Escherichia coli. It doesn't make any assumption about the nature of gene regulation, can deal with combinatorial and non-linear interactions, produces directed GRNs, and is fast and scalable. In conclusion, we propose a new algorithm for GRN inference that performs well on both synthetic and real gene expression data. The algorithm, based on feature selection with tree-based ensemble methods, is simple and generic, making it adaptable to other types of genomic data and interactions.

The inference of transcriptional networks that regulate transitions into physiological or pathological cellular states remains a central challenge in systems biology. A mesenchymal phenotype is the hallmark of tumour aggressiveness in human malignant glioma, but the regulatory programs responsible for implementing the associated molecular signature are largely unknown. Here we show that reverse-engineering and an unbiased interrogation of a glioma-specific regulatory network reveal the transcriptional module that activates expression of mesenchymal genes in malignant glioma. Two transcription factors (C/EBPβ and STAT3) emerge as synergistic initiators and master regulators of mesenchymal transformation. Ectopic co-expression of C/EBPβ and STAT3 reprograms neural stem cells along the aberrant mesenchymal lineage, whereas elimination of the two factors in glioma cells leads to collapse of the mesenchymal signature and reduces tumour aggressiveness. In human glioma, expression of C/EBPβ and STAT3 correlates with mesenchymal differentiation and predicts poor clinical outcome. These results show that the activation of a small regulatory module is necessary and sufficient to initiate and maintain an aberrant phenotypic state in cancer cells.

Inferring, or 'reverse-engineering', gene networks can be defined as the process of identifying gene interactions from experimental data through computational analysis. Gene expression data from microarrays are typically used for this purpose. Here we compared different reverseengineering algorithms for which ready-to-use software was available and that had been tested on experimental data sets. We show that reverse-engineering algorithms are indeed able to correctly infer regulatory interactions among genes, at least when one performs perturbation experiments complying with the algorithm requirements. These algorithms are superior to classic clustering algorithms for the purpose of finding regulatory interactions among genes, and, although further improvements are needed, have reached a discreet performance for being practically useful.

1 Introduction In this paper, we present a new representation for piecewise smooth surfaces of arbitrary topological type, 1 and a method for fitting such surface models to scattered range data, where neither the topolog- ical type of the surface, its geometry, nor the location of ...

We identify three types of attack on the intellectual property contained in software, and three corresponding technical defenses. A potent defense against reverse engineering is obfuscation, a process that renders software unintelligible but still functional. A defense against software piracy is watermarking, a process that makes it possible to determine the origin of software. A defense against tampering is tamper-proo ng, so that unauthorized modi cations to software (for example to remove a watermark) will result in non-functional code. We brie y survey the available technology for each type of defense.

In this paper, we give an overview of the BitBlaze project, a new approach to computer security via binary analysis. In particular, BitBlaze focuses on building a unified binary analysis platform and using it to provide novel solutions to a broad spectrum of different security problems. The binary analysis platform is designed to enable accurate analysis, provide an extensible architecture, and combines static and dynamic analysis as well as program verification techniques to satisfy the common needs of security applications. By extracting security-related properties from binary programs directly, BitBlaze enables a principled, root-cause based approach to computer security, offering novel and effective solutions, as demonstrated with over a dozen different security applications.

Systems biology aims to develop mathematical models of biological systems by integrating experimental and theoretical techniques. During the last decade, many systems biological approaches that base on genome-wide data have been developed to unravel the complexity of gene regulation. This review deals with the reconstruction of gene regulatory networks (GRNs) from experimental data through computational methods. Standard GRN inference methods primarily use gene expression data derived from microarrays. However, the incorporation of additional information from heterogeneous data sources, e.g. genome sequence and protein-DNA interaction data, clearly supports the network inference process. This review focuses on promising modelling approaches that use such diverse types of molecular biological information. In particular, approaches are discussed that enable the modelling of the dynamics of gene regulatory systems. The review provides an overview of common modelling schemes and learning algorithms and outlines current challenges in GRN modelling.

We propose a scheme to reverse-engineer gene networks on a genome-wide scale using a relatively small amount of gene expression data from microarray experiments. Our method is based on the empirical observation that such networks are typically large and sparse. It uses singular value decomposition to construct a family of candidate solutions and then uses robust regression to identify the solution with the smallest number of connections as the most likely solution. Our algorithm has O(log N) sampling complexity and O(N 4 ) computational complexity. We test and validate our approach in a series of in numero experiments on model gene networks. W ith recent advances in cDNA and oligonucleotide microarray technologies (1), it has become possible to measure mRNA expression levels on a genome-wide scale. Data thus collected provide valuable descriptions of gene activities under various biochemical (2) and physiological (3) circumstances and allow one to reverse-engineer the gene networks, i.e., to infer the underlying network structures from experimental measurements. However, naturally occurring gene regulatory networks are embedded in genomes that typically consist of thousands of genes. To extract the topology of such networks and hence isolate the functional subnetworks represents a computationally daunting task; it also requires a very large amount of experimental data, which are expensive to obtain.

Software engineers often use high-level models (for instance, box and arrow sketches) to reason and communicate about an existing software system. One problem with high-level models is that they are almost always inaccurate with respect to the system's source code. We have developed an approach that helps an engineer use a high-level model of the structure of an existing software system as a lens through which to see a model of that system's source code. In particular, an engineer denes a high-level model and species how the model maps to the source. A tool then computes a software reexion model that shows where the engineer's high-level model agrees with and where it diers from a model of the source.

Foreign direct investment (FDI) can benefit innovation activity in the host country via spillover channels such as reverse engineering, skilled labor turnovers, demonstration effects, and suppliercustomer relationships. Using provincial data from 1995 to 2000, we find positive effects of FDI on the number of domestic patent applications in China. This finding is robust under both pooled timeseries and cross-section data estimation and panel data analysis and for different types of patent applications (invention, utility model, and external design). The spillover effect is the strongest for minor innovation such as external design patent, highlighting a ''demonstration effect'' of FDI. D .hk (P. Lin).

Advances in computer speed, memory capacity and hardware graphics acceleration have made the interactive manipulation and visualization of complex, detailed (and therefore large) three-dimensional models feasible. These models are painstakingly designed either through an elaborate CAD process or reverse engineered from sculpted prototypes using modern scanning technologies and integration methods. The availability of detailed data describing the shape of an object offers the computer vision practitioner new ways to recognize and localize free-form objects. This survey reviews recent literature on both the 3D model building process and techniques used to match and identify free-form objects from imagery.

This paper describe s a c ollection of algorithms that we developed and implemented to facilitate the automatic recovery of the modular structure of a software system from its source c ode. We treat automatic modularization as an optimization problem. Our algorithms make use of traditional hillclimbing and genetic algorithms.

A good object-oriented design does not necessarily make a good component-based design, and vice versa. What design principles do components introduce? This paper examines component-based programming and how it expands the design space in the context of an event-based component architecture. We present a conceptual model for addressing new design issues these components afford, and we identify fundamental design decisions in this model that are not a concern in conventional object-oriented design. We use JavaBeans-based examples to illustrate concretely how expertise in component-based design, as embodied in a component taxonomy and implementation space, impacts both design and the process of design. The results are not exclusive to JavaBeans-they can apply to any comparable component architecture.

A method for concise, faithful approximation of complex 3D datasets is key to reducing the computational cost of graphics applications. Despite numerous applications ranging from geometry compression to reverse engineering, efficiently capturing the geometry of a surface remains a tedious task. In this paper, we present both theoretical and practical contributions that result in a novel and versatile framework for geometric approximation of surfaces. We depart from the usual strategy by casting shape approximation as a variational geometric partitioning problem. Using the concept of geometric proxies, we drive the distortion error down through repeated clustering of faces into best-fitting regions. Our approach is entirely discrete and error-driven, and does not require parameterization or local estimations of differential quantities. We also introduce a new metric based on normal deviation, and demonstrate its superior behavior at capturing anisotropy.

The identification of design patterns as part of the reengineering process can convey important information to the designer.
However, existing pattern detection methodologies generally have problems in dealing with one or more of the following issues:
Identification of modified pattern versions, search space explosion for large systems and extensibility to novel patterns. In this paper, a design pattern detection methodology is proposed that is based on similarity scoring between graph vertices. Due to the nature of the underlying graph algorithm, this approach has the ability to also recognize patterns that are modified from their standard representation. Moreover, the approach exploits the fact that patterns reside in one or more inheritance hierarchies, reducing the size of the graphs to which the algorithm is applied. Finally, the algorithm does not rely on any pattern-specific heuristic, facilitating the extension to novel design structures. Evaluation on three open-source projects demonstrated the accuracy and the efficiency of the proposed method.

In this paper, we describe a hierarchical face clustering algorithm for triangle meshes based on fitting primitives belonging to an arbitrary set. The method proposed is completely automatic, and generates a binary tree of clusters, each of which is fitted by one of the primitives employed. Initially, each triangle represents a single cluster; at every iteration, all the pairs of adjacent clusters are considered, and the one that can be better approximated by one of the primitives forms a new single cluster. The approximation error is evaluated using the same metric for all the primitives, so that it makes sense to choose which is the most suitable primitive to approximate the set of triangles in a cluster. Based on this approach, we have implemented a prototype that uses planes, spheres and cylinders, and have experimented that for meshes made of 100 K faces, the whole binary tree of clusters can be built in about 8 s on a standard PC. The framework described here has natural application in reverse engineering processes, but it has also been tested for surface denoising, feature recovery and character skinning.

3D imaging sensors for the acquisition of three dimensional (3D) shapes have created, in recent years, a considerable degree of interest for a number of applications. The miniaturization and integration of the optical and electronic components used to build them have played a crucial role in the achievement of compactness, robustness and flexibility of the sensors. Today, several 3D sensors are available on the market, even in combination with other sensors in a "sensor fusion" approach. An importance equal to that of physical miniaturization has the portability of the measurements, via suitable interfaces, into software environments designed for their elaboration, e.g., CAD-CAM systems, virtual renders, and rapid prototyping tools. In this paper, following an overview of the state-of-art of 3D imaging sensors, a number of significant examples of their use are presented, with particular reference to industry, heritage, medicine, and criminal investigation applications.

At the conclusion of the second DREAM conference [10], a few voices of reason suggested that reverse-engineering challenges should not be solely focused on the network inference. As the argument goes, only that which can be measured should be

We present a novel graphical Gaussian modeling approach for reverse engineering of genetic regulatory networks with many genes and few observations. When applying our approach to infer a gene network for isoprenoid biosynthesis in Arabidopsis thaliana, we detect modules of closely connected genes and candidate genes for possible cross-talk between the isoprenoid pathways. Genes of downstream pathways also fit well into the network. We evaluate our approach in a simulation study and using the yeast galactose network.

Systems biology approaches are extensively used to model and reverse engineer gene regulatory networks from experimental data. Conversely, synthetic biology allows ''de novo'' construction of a regulatory network to seed new functions in the cell. At present, the usefulness and predictive ability of modeling and reverse engineering cannot be assessed and compared rigorously. We built in the yeast Saccharomyces cerevisiae a synthetic network, IRMA, for in vivo ''benchmarking'' of reverse-engineering and modeling approaches. The network is composed of five genes regulating each other through a variety of regulatory interactions; it is negligibly affected by endogenous genes, and it is responsive to small molecules. We measured time series and steady-state expression data after multiple perturbations. These data were used to assess state-of-the-art modeling and reverse-engineering techniques. A semiquantitative model was able to capture and predict the behavior of the network. Reverse engineering based on differential equations and Bayesian networks correctly inferred regulatory interactions from the experimental data.

Protocol reverse engineering, the process of extracting the application-level protocol used by an implementation, without access to the protocol specification, is important for many network security applications. Recent work has proposed protocol reverse engineering by using clustering on network traces. That kind of approach is limited by the lack of semantic information on network traces. In this paper we propose a new approach using program binaries. Our approach, shadowing, uses dynamic binary analysis and is based on a unique intuition-the way that an implementation of the protocol processes the received application data reveals a wealth of information about the protocol message format. We have implemented our approach in a system called Polyglot and evaluated it extensively using real-world implementations of five different protocols: DNS, HTTP, IRC, Samba and ICQ. We compare our results with the manually crafted message format, included in Wireshark, one of the state-of-the-art protocol analyzers. The differences we find are small and usually due to different implementations handling fields in different ways. Finding such differences between implementations is an added benefit, as they are important for problems such as fingerprint generation, fuzzing, and error detection.

Due to globalh:ation of Integrated Circuit (!C) design flmv, rogue elements in the supply ch<:\in nm pirate ICs, overbuild ICs. a.ml insert ha.rdware trojaus. EPIC [1] obfuscates the design by randomly inserting additional gates; only a correct key makes the design to produce correct outputs. vVe demonstrate that an attacker can decipher the obfuscated net.list, in a time linear to the number of keys, by sensitizing the key values to the output. \Ve then develop techniques to fix this vulnerability and make obfiu:;ca.tion truly exponential in the number of inserted keys.

Dissection of regulatory networks that control gene transcription is one of the greatest challenges of functional genomics. Using human genomic sequences, models for binding sites of known transcription factors, and gene expression data, we demonstrate that the reverse engineering approach, which infers regulatory mechanisms from gene expression patterns, can reveal transcriptional networks in human cells. To date, such methodologies were successfully demonstrated only in prokaryotes and low eukaryotes. We developed computational methods for identifying putative binding sites of transcription factors and for evaluating the statistical significance of their prevalence in a given set of promoters. Focusing on transcriptional mechanisms that control cell cycle progression, our computational analyses revealed eight transcription factors whose binding sites are significantly overrepresented in promoters of genes whose expression is cell-cycle-dependent. The enrichment of some of these factors is specific to certain phases of the cell cycle. In addition, several pairs of these transcription factors show a significant co-occurrence rate in cell-cycle-regulated promoters. Each such pair indicates functional cooperation between its members in regulating the transcriptional program associated with cell cycle progression. The methods presented here are general and can be applied to the analysis of transcriptional networks controlling any biological process.

Today, even a moderately sized corporate intranet con-tains multiple firewalls and routers, which are all used to enforce various aspects of the global corporate security pol-icy. Configuring these devices to work in unison is difficult, especially if they are made by different vendors. Even ...

1. Reverse engineering: Starting from a rough published schematic, we determined the complete functional details of the cipher underpinning the challenge-response protocol in the DST. We accomplished this with only &quot;oracle&quot; or &quot;black-box&quot; access to an ordinary DST, that is, by ...

The position-specific weight matrix (PWM) model, which assumes that each position in the DNA site contributes independently to the overall protein-DNA interaction, has been the primary means to describe transcription factor binding site motifs. Recent biological experiments, however, suggest that there exists interdependence among positions in the binding sites. In order to exploit this interdependence to aid motif discovery, we extend the PWM model to include pairs of correlated positions and design a Markov chain Monte Carlo algorithm to sample in the model space. We then combine the model sampling step with the Gibbs sampling framework for de novo motif discoveries. Results: Testing on experimentally validated binding sites, we find that about 25% of the transcription factor binding motifs show significant within-site position correlations, and 80% of these motif models can be improved by considering the correlated positions. Using both simulated data and real promoter sequences, we show that the new de novo motif-finding algorithm can infer the true correlated position pairs accurately and is more precise in finding putative transcription factor binding sites than the standard Gibbs sampling algorithms.

We argue that essential facets of web services, and especially those useful to understand their interaction, are best described using process-algebraic notations. Web service description and execution languages such as BPEL are essentially process description languages; they are based on primitives for behaviour description and message exchange which can also be found in more abstract languages such as process algebras. One legitimate question is therefore whether the web services community can benefit from the sophisticated languages and tools developed in the process algebra area. Our investigations suggest a positive answer, and we claim that process algebras provide solutions to a number of challenges raised by the web services paradigm, among which are central issues of orchestration and choreography. We show on a case study that readily available tools based on process algebra are effective at verifying that compositions of services obtained by choreography and orchestration conform their requirements and respect properties. We suggest a general framework based on a mapping between process algebra and web services written in BPEL, and illustrate both the modelling of services by process algebra and the use of reasoning tools.

Background In breast cancer, overexpression of the transmembrane tyrosine kinase ERBB2 is an adverse prognostic marker, and occurs in almost 30% of the patients. For therapeutic intervention, ERBB2 is targeted by monoclonal antibody trastuzumab in adjuvant settings; however, de novo resistance to this antibody is still a serious issue, requiring the identification of additional targets to overcome resistance. In this study, we have combined computational simulations, experimental testing of simulation results, and finally reverse engineering of a protein interaction network to define potential therapeutic strategies for de novo trastuzumab resistant breast cancer. Results First, we employed Boolean logic to model regulatory interactions and simulated single and multiple protein loss-of-functions. Then, our simulation results were tested experimentally by producing single and double knockdowns of the network components and measuring their effects on G1/S transition during cell cycle progression. Combinatorial targeting of ERBB2 and EGFR did not affect the response to trastuzumab in de novo resistant cells, which might be due to decoupling of receptor activation and cell cycle progression. Furthermore, examination of c-MYC in resistant as well as in sensitive cell lines, using a specific chemical inhibitor of c-MYC (alone or in combination with trastuzumab), demonstrated that both trastuzumab sensitive and resistant cells responded to c-MYC perturbation. Conclusion In this study, we connected ERBB signaling with G1/S transition of the cell cycle via two major cell signaling pathways and two key transcription factors, to model an interaction network that allows for the identification of novel targets in the treatment of trastuzumab resistant breast cancer. Applying this new strategy, we found that, in contrast to trastuzumab sensitive breast cancer cells, combinatorial targeting of ERBB receptors or of key signaling intermediates does not have potential for treatment of de novo trastuzumab resistant cells. Instead, c-MYC was identified as a novel potential target protein in breast cancer cells.

Source model extraction-the automated extraction of information from system artifacts-is a common phase in reverse engineering tools. One of the major challenges of this phase is creating extractors that can deal with irregularities in the artifacts that are typical for the reverse engineering domain (for example, syntactic errors, incomplete source code, language dialects and embedded languages).

By the early 1990s the need for reengineering legacy systems was already acute, but recently the demand has increased significantly with the shift toward web-based user interfaces. The demand by all business sectors to adapt their information systems to the Web has created a tremendous need for methods, tools, and infrastructures to evolve and exploit existing applications efficiently and cost-effectively. Reverse engineering has been heralded as one of the most promising technologies to combat this legacy systems problem. This paper presents a roadmap for reverse engineering research for the first decade of the new millennium, building on the program comprehension theories of the 1980s and the reverse engineering technology of the 1990s.

A methodology for extracting an extended Entity-Relationship (EER) model from a relational database is presented. Through a combination of data schema and data instance analysis, an EER model is derived which is semantically richer and more comprehensible for maintenance and design purposes than the original database. Classification schemes for relations and attributes necessary for the EER model extraction are derived and justified. These have been demonstrated to be implementable in a knowledge-based system; a working prototype system which does so is briefly discussed. In addition, cases in which human input is required are also clearly identified. This research also illustrates that the database reverse engineering process can be implemented at a high level of automation.

A fundamental issue in theoretical computer science is that of establishing unambiguous formal criteria for algorithmic output. This paper does so within the domain of computeraided geometric modeling. For practical geometric modeling algorithms, it is often desirable to create piecewise linear approximations to compact manifolds embedded in , and it is usually desirable for these two representations to be "topologically equivalent". Though this has traditionally been taken to mean that the two representations are homeomorphic, such a notion of equivalence suffers from a variety of technical and philosophical difficulties; we adopt the stronger notion of ambient isotopy. It is shown here, that for any , compact, 2-manifold without boundary, which is embedded in , there exists a piecewise linear ambient isotopic approximation. Furthermore, this isotopy has compact support, with specific bounds upon the size of this compact neighborhood. These bounds may be useful for practical application in computer graphics and engineering design simulations. The proof given relies upon properties of the medial axis, which is explained in this paper.