arXiv subjects

Stephanie Forrest

Publications and source records attributed to Stephanie Forrest.

At least 19 recordsLinked to original sources

arXiv2025

Strategic Analysis of Dissent and Self-Censorship

Expressions of dissent against authority are an important feature of most societies, and efforts to suppress such expressions are common. Modern digital communications, social media, and Internet surveillance and censorship technologies are changing the landscape of public speech and dissent. Especially in authoritarian settings, individuals must assess the risk of voicing their true opinions or choose self-censorship, voluntarily moderating their behavior to comply with authority. We present a model in which individuals strategically manage the tradeoff between expressing dissent and avoiding punishment through self-censorship while an authority adapts its policies to minimize both total expressed dissent and punishment costs. We study the model analytically and in simulation to derive conditions separating defiant individuals who express their desired dissent in spite of punishment from self-censoring individuals who fully or partially limit their expression. We find that for any population, there exists an authority policy that leads to total self-censorship. However, the probability and time for an initially moderate, locally-adaptive authority to suppress dissent depend critically on the population's willingness to withstand punishment early on, which can deter the authority from adopting more extreme policies.

physics.soc-ph↗

arXiv2025

Prebiotic Functional Programs: Endogenous Selection in an Artificial Chemistry

Artificial chemistry simulations produce many intriguing emergent behaviors, but they are often difficult to steer or control. This paper proposes a method for steering the dynamics of a classic artificial chemistry model, known as AlChemy (Algorithmic Chemistry), which is based on untyped lambda calculus. Our approach leverages features that are endogenous to AlChemy without constructing an explicit external fitness function or building learning into the dynamics. We demonstrate the approach by synthesizing non-trivial lambda functions, such as Church addition and succession, from simple primitives. The results provide insight into the possibility of endogenous selection in diverse systems such as autocatalytic chemical networks and software systems.

cs.FL↗

arXiv2025

Concerning the Responsible Use of AI in the US Criminal Justice System

Artificial intelligence (AI) is increasingly being adopted in most industries, and for applications such as note taking and checking grammar, there is typically not a cause for concern. However, when constitutional rights are involved, as in the justice system, transparency is paramount. While AI can assist in areas such as risk assessment and forensic evidence generation, its "black box" nature raises significant questions about how decisions are made and whether they can be contested. This paper explores the implications of AI in the justice system, emphasizing the need for transparency in AI decision-making processes to uphold constitutional rights and ensure procedural fairness. The piece advocates for clear explanations of AI's data, logic, and limitations, and calls for periodic audits to address bias and maintain accountability in AI systems.

cs.CY↗

arXiv2024

Self-Organization in Computation & Chemistry: Return to AlChemy

How do complex adaptive systems, such as life, emerge from simple constituent parts? In the 1990s Walter Fontana and Leo Buss proposed a novel modeling approach to this question, based on a formal model of computation known as $\lambda$ calculus. The model demonstrated how simple rules, embedded in a combinatorially large space of possibilities, could yield complex, dynamically stable organizations, reminiscent of biochemical reaction networks. Here, we revisit this classic model, called AlChemy, which has been understudied over the past thirty years. We reproduce the original results and study the robustness of those results using the greater computing resources available today. Our analysis reveals several unanticipated features of the system, demonstrating a surprising mix of dynamical robustness and fragility. Specifically, we find that complex, stable organizations emerge more frequently than previously expected, that these organizations are robust against collapse into trivial fixed-points, but that these stable organizations cannot be easily combined into higher order entities. We also study the role played by the random generators used in the model, characterizing the initial distribution of objects produced by two random expression generators, and their consequences on the results. Finally, we provide a constructive proof that shows how an extension of the model, based on typed $\lambda$ calculus, could simulate transitions between arbitrary states in any possible chemical reaction network, thus indicating a concrete connection between AlChemy and chemical reaction networks. We conclude with a discussion of possible applications of AlChemy to self-organization in modern programming languages and quantitative approaches to the origin of life.

nlin.AO↗

arXiv2024

Automated Program Repair: Emerging trends pose and expose problems for benchmarks

Machine learning (ML) now pervades the field of Automated Program Repair (APR). Algorithms deploy neural machine translation and large language models (LLMs) to generate software patches, among other tasks. But, there are important differences between these applications of ML and earlier work. Evaluations and comparisons must take care to ensure that results are valid and likely to generalize. A challenge is that the most popular APR evaluation benchmarks were not designed with ML techniques in mind. This is especially true for LLMs, whose large and often poorly-disclosed training datasets may include problems on which they are evaluated.

cs.SE↗

arXiv2023

GEVO-ML: Optimizing Machine Learning Code with Evolutionary Computation

Parallel accelerators, such as GPUs, are key enablers for large-scale Machine Learning (ML) applications. However, ML model developers often lack detailed knowledge of the underlying system architectures, while system programmers usually do not have a high-level understanding of the ML model that runs on the specific system. To mitigate this gap between two relevant aspects of domain knowledge, this paper proposes GEVO-ML, a tool for automatically discovering optimization opportunities and tuning the performance of ML kernels, where the model and training/prediction processes are uniformly represented in a single intermediate language, the Multiple-Layer Intermediate Representation (MLIR). GEVO-ML uses multi-objective evolutionary search to find edits (mutations) to MLIR code that ultimately runs on GPUs, improving performance on desired criteria while retaining required functionality. We demonstrate GEVO-ML on two different ML workloads for both model training and prediction. GEVO-ML finds significant Pareto improvements for these models, achieving 90.43% performance improvement when model accuracy is relaxed by 2%, from 91.2% to 89.3%. For the training workloads, GEVO-ML finds a 4.88% improvement in model accuracy, from 91% to 96%, without sacrificing training or testing speed. Our analysis of key GEVO-ML mutations reveals diverse code modifications, while might be foreign to human developers, achieving similar effects with how human developers improve model design, for example, by changing learning rates or pruning non-essential layer parameters.

cs.LG↗

arXiv2022

Understanding the Power of Evolutionary Computation for GPU Code Optimization

Achieving high performance for GPU codes requires developers to have significant knowledge in parallel programming and GPU architectures, and in-depth understanding of the application. This combination makes it challenging to find performance optimizations for GPU-based applications, especially in scientific computing. This paper shows that significant speedups can be achieved on two quite different scientific workloads using the tool, GEVO, to improve performance over human-optimized GPU code. GEVO uses evolutionary computation to find code edits that improve the runtime of a multiple sequence alignment kernel and a SARS-CoV-2 simulation by 28.9% and 29% respectively. Further, when GEVO begins with an early, unoptimized version of the sequence alignment program, it finds an impressive 30 times speedup -- a performance improvement similar to that of the hand-tuned version. This work presents an in-depth analysis of the discovered optimizations, revealing that the primary sources of improvement vary across applications; that most of the optimizations generalize across GPU architectures; and that several of the most important optimizations involve significant code interdependencies. The results showcase the potential of automated program optimization tools to help reduce the optimization burden for scientific computing developers and enhance performance portability for domain-specific accelerators.

cs.SE↗

arXiv2022

Automatically Mitigating Vulnerabilities in Binary Programs via Partially Recompilable Decompilation

Vulnerabilities are challenging to locate and repair, especially when source code is unavailable and binary patching is required. Manual methods are time-consuming, require significant expertise, and do not scale to the rate at which new vulnerabilities are discovered. Automated methods are an attractive alternative, and we propose Partially Recompilable Decompilation (PRD). PRD lifts suspect binary functions to source, available for analysis, revision, or review, and creates a patched binary using source- and binary-level techniques. Although decompilation and recompilation do not typically work on an entire binary, our approach succeeds because it is limited to a few functions, like those identified by our binary fault localization. We evaluate these assumptions and find that, without any grammar or compilation restrictions, 70-89% of individual functions are successfully decompiled and recompiled with sufficient type recovery. In comparison, only 1.7% of the full C-binaries succeed. When decompilation succeeds, PRD produces test-equivalent binaries 92.9% of the time. In addition, we evaluate PRD in two contexts: a fully automated process incorporating source-level Automated Program Repair (APR) methods; human-edited source-level repairs. When evaluated on DARPA Cyber Grand Challenge (CGC) binaries, we find that PRD-enabled APR tools, operating only on binaries, performs as well as, and sometimes better than full-source tools, collectively mitigating 85 of the 148 scenarios, a success rate consistent with these same tools operating with access to the entire source code. PRD achieves similar success rates as the winning CGC entries, sometimes finding higher-quality mitigations than those produced by top CGC teams. For generality, our evaluation includes two independently developed APR tools and C++, Rode0day, and real-world binaries.

cs.CR↗

arXiv2022

Cutting Through the Noise to Infer Autonomous System Topology

The Border Gateway Protocol (BGP) is a distributed protocol that manages interdomain routing without requiring a centralized record of which autonomous systems (ASes) connect to which others. Many methods have been devised to infer the AS topology from publicly available BGP data, but none provide a general way to handle the fact that the data are notoriously incomplete and subject to error. This paper describes a method for reliably inferring AS-level connectivity in the presence of measurement error using Bayesian statistical inference acting on BGP routing tables from multiple vantage points. We employ a novel approach for counting AS adjacency observations in the AS-PATH attribute data from public route collectors, along with a Bayesian algorithm to generate a statistical estimate of the AS-level network. Our approach also gives us a way to evaluate the accuracy of existing reconstruction methods and to identify advantageous locations for new route collectors or vantage points.

cs.NI↗

arXiv2021

Challenges in cybersecurity: Lessons from biological defense systems

We explore the commonalities between methods for assuring the security of computer systems (cybersecurity) and the mechanisms that have evolved through natural selection to protect vertebrates against pathogens, and how insights derived from studying the evolution of natural defenses can inform the design of more effective cybersecurity systems. More generally, security challenges are crucial for the maintenance of a wide range of complex adaptive systems, including financial systems, and again lessons learned from the study of the evolution of natural defenses can provide guidance for the protection of such systems.

cs.CY↗

arXiv2021

Preventing Extreme Polarization of Political Attitudes

Extreme polarization can undermine democracy by making compromise impossible and transforming politics into a zero-sum game. Ideological polarization - the extent to which political views are widely dispersed - is already strong among elites, but less so among the general public (McCarty, 2019, p. 50-68). Strong mutual distrust and hostility between Democrats and Republicans in the U.S., combined with the elites' already strong ideological polarization, could lead to increasing ideological polarization among the public. The paper addresses two questions: (1) Is there a level of ideological polarization above which polarization feeds upon itself to become a runaway process? (2) If so, what policy interventions could prevent such dangerous positive feedback loops? To explore these questions, we present an agent-based model of ideological polarization that differentiates between the tendency for two actors to interact (exposure) and how they respond when interactions occur, positing that interaction between similar actors reduces their difference while interaction between dissimilar actors increases their difference. Our analysis explores the effects on polarization of different levels of tolerance to other views, responsiveness to other views, exposure to dissimilar actors, multiple ideological dimensions, economic self-interest, and external shocks. The results suggest strategies for preventing, or at least slowing, the development of extreme polarization.

cs.MA↗

arXiv2021

MIMOSA: Reducing Malware Analysis Overhead with Coverings

There is a growing body of malware samples that evade automated analysis and detection tools. Malware may measure fingerprints ("artifacts") of the underlying analysis tool or environment and change their behavior when artifacts are detected. While analysis tools can mitigate artifacts to reduce exposure, such concealment is expensive. However, not every sample checks for every type of artifact-analysis efficiency can be improved by mitigating only those artifacts most likely to be used by a sample. Using that insight, we propose MIMOSA, a system that identifies a small set of "covering" tool configurations that collectively defeat most malware samples with increased efficiency. MIMOSA identifies a set of tool configurations that maximize analysis throughput and detection accuracy while minimizing manual effort, enabling scalable automation to analyze stealthy malware. We evaluate our approach against a benchmark of 1535 labeled stealthy malware samples. Our approach increases analysis throughput over state of the art on over 95% of these samples. We also investigate cost-benefit tradeoffs between the fraction of successfully-analyzed samples and computing resources required. MIMOSA provides a practical, tunable method for efficiently deploying analysis resources.

cs.CR↗

arXiv2020

GEVO: GPU Code Optimization using Evolutionary Computation

GPUs are a key enabler of the revolution in machine learning and high performance computing, functioning as de facto co-processors to accelerate large-scale computation. As the programming stack and tool support have matured, GPUs have also become accessible to programmers, who may lack detailed knowledge of the underlying architecture and fail to fully leverage the GPU's computation power. GEVO (Gpu optimization using EVOlutionary computation) is a tool for automatically discovering optimization opportunities and tuning the performance of GPU kernels in the LLVM representation. GEVO uses population-based search to find edits to GPU code compiled to LLVM-IR and improves performance on desired criteria while retaining required functionality. We demonstrate that GEVO improves the execution time of the GPU programs in the Rodinia benchmark suite and the machine learning models, SVM and ResNet18, on NVIDIA Tesla P100. For the Rodinia benchmarks, GEVO improves GPU kernel runtime performance by an average of 49.48% and by as much as 412% over the fully compiler-optimized baseline. If kernel output accuracy is relaxed to tolerate up to 1% error, GEVO can find kernel variants that outperform the baseline version by an average of 51.08%. For the machine learning workloads, GEVO achieves kernel performance improvement for SVM on the MNIST handwriting recognition (3.24X) and the a9a income prediction (2.93X) datasets with no loss of model accuracy. GEVO achieves 1.79X kernel performance improvement on image classification using ResNet18/CIFAR-10, with less than 1% model accuracy reduction.

cs.NE↗

arXiv2019

Risky Business: Assessing Security with External Measurements

Security practices in large organizations are notoriously difficult to assess. The challenge only increases when organizations turn to third parties to provide technology and business services, which typically require tight network integration and sharing of confidential data, potentially increasing the organization's attack surface. The security maturity of an organization describes how well it mitigates known risks and responds to new threats. Today, maturity is typically assessed with audits and questionnaires, which are difficult to quantify, lack objectivity, and may not reflect current threats. This paper demonstrates how external measurement of an organization can be used to assess the relative quality of security among organizations. Using a large dataset from BitSight(www.bitsight.com), a cybersecurity ratings company, containing 3.2 billion measurements spanning nearly 37,000 organizations collected during calendar year 2015, we show how per-organizational "risk vectors" can be constructed that may be related to an organization's overall security posture, or maturity. Using statistical analysis, we then study the correlation between the risk vectors and botnet infections. For example, we find that misconfigured TLS services, publicly available unsecured protocols, and the use of peer-to-peer file sharing correlate with organizations that have increased rates of botnet infections. We argue that the methodology used to identify these correlations can easily be applied to other data to provide a growing picture of organizational security using external measurement.

cs.CR↗

arXiv2019

Using Dynamic Analysis to Generate Disjunctive Invariants

Program invariants are important for defect detection, program verification, and program repair. However, existing techniques have limited support for important classes of invariants such as disjunctions, which express the semantics of conditional statements. We propose a method for generating disjunctive invariants over numerical domains, which are inexpressible using classical convex polyhedra. Using dynamic analysis and reformulating the problem in non-standard "max-plus" and "min-plus" algebras, our method constructs hulls over program trace points. Critically, we introduce and infer a weak class of such invariants that balances expressive power against the computational cost of generating nonconvex shapes in high dimensions. Existing dynamic inference techniques often generate spurious invariants that fit some program traces but do not generalize. With the insight that generating dynamic invariants is easy, we propose to verify these invariants statically using k-inductive SMT theorem proving which allows us to validate invariants that are not classically inductive. Results on difficult kernels involving nonlinear arithmetic and abstract arrays suggest that this hybrid approach efficiently generates and proves correct program invariants.

cs.SE↗

arXiv2019

Connecting Program Synthesis and Reachability: Automatic Program Repair using Test-Input Generation

We prove that certain formulations of program synthesis and reachability are equivalent. Specifically, our constructive proof shows the reductions between the template-based synthesis problem, which generates a program in a pre-specified form, and the reachability problem, which decides the reachability of a program location. This establishes a link between the two research fields and allows for the transfer of techniques and results between them. To demonstrate the equivalence, we develop a program repair prototype using reachability tools. We transform a buggy program and its required specification into a specific program containing a location reachable only when the original program can be repaired, and then apply an off-the-shelf test-input generation tool on the transformed program to find test values to reach the desired location. Those test values correspond to repairs for the original programm. Preliminary results suggest that our approach compares favorably to other repair methods.

cs.PL↗

arXiv2018

The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities

Biological evolution provides a creative fount of complex and subtle adaptations, often surprising the scientists who discover them. However, because evolution is an algorithmic process that transcends the substrate in which it occurs, evolution's creativity is not limited to nature. Indeed, many researchers in the field of digital evolution have observed their evolving algorithms and organisms subverting their intentions, exposing unrecognized bugs in their code, producing unexpected adaptations, or exhibiting outcomes uncannily convergent with ones in nature. Such stories routinely reveal creativity by evolution in these digital worlds, but they rarely fit into the standard scientific narrative. Instead they are often treated as mere obstacles to be overcome, rather than results that warrant study in their own right. The stories themselves are traded among researchers through oral tradition, but that mode of information transmission is inefficient and prone to error and outright loss. Moreover, the fact that these stories tend to be shared only among practitioners means that many natural scientists do not realize how interesting and lifelike digital organisms are and how natural their evolution can be. To our knowledge, no collection of such anecdotes has been published before. This paper is the crowd-sourced product of researchers in the fields of artificial life and evolutionary computation who have provided first-hand accounts of such cases. It thus serves as a written, fact-checked collection of scientifically important and even entertaining stories. In doing so we also present here substantial evidence that the existence and importance of evolutionary surprises extends beyond the natural world, and may indeed be a universal property of all complex evolving systems.

cs.NE↗

arXiv2013

Software Mutational Robustness

Neutral landscapes and mutational robustness are believed to be important enablers of evolvability in biology. We apply these concepts to software, defining mutational robustness to be the fraction of random mutations that leave a program's behavior unchanged. Test cases are used to measure program behavior and mutation operators are taken from genetic programming. Although software is often viewed as brittle, with small changes leading to catastrophic changes in behavior, our results show surprising robustness in the face of random software mutations. The paper describes empirical studies of the mutational robustness of 22 programs, including 14 production software projects, the Siemens benchmarks, and 4 specially constructed programs. We find that over 30% of random mutations are neutral with respect to their test suite. The results hold across all classes of programs, for mutations at both the source code and assembly instruction levels, across various programming languages, and are only weakly related to test suite coverage. We conclude that mutational robustness is an inherent property of software, and that neutral variants (i.e., those that pass the test suite) often fulfill the program's original purpose or specification. Based on these results, we conjecture that neutral mutations can be leveraged as a mechanism for generating software diversity. We demonstrate this idea by generating a population of neutral program variants and showing that the variants automatically repair unknown bugs with high probability. Neutral landscapes also provide a partial explanation for recent results that use evolutionary computation to automatically repair software bugs.

cs.SE↗