End-to-end GUI agents for real desktop environments require large amounts of highquality interaction data, yet collecting human demonstrations is expensive and existing synthetic pipelines often suffer from limited task diversity or noisy, goal-drifting trajectories.We present a trajectory expansion framework ANCHOR that bootstraps scalable desktop supervision from a small set of verified seed demonstrations.Starting from each seed, we identify branch points that correspond to meaningful state changes and propose new, stategrounded task variants conditioned on the current GUI context.An executing agent then follows the proposed instructions to generate new trajectories, while a verifier enforces task completion via state-aware checks and trajectorylevel consistency.To improve supervision quality, we further apply task-conditioned steplevel filtering to remove ungrounded actions and denoise post-branch segments to maintain coherent intent.Experiments on standard desktop benchmarks, OSWorld and Win-dowsAgentArena, show that models fine-tuned on our expanded corpus achieve consistent improvements over zero-shot agents and representative synthesis baselines, and generalize across applications and operating systems.

Recent work has demonstrated the utility of machine learning (ML) in carrying out highly accurate predictions at runtime. One of the major challenges with using ML, however, is that the predictions lack certain guarantees. For such approaches to become practicable in security settings involving debloating and dynamic control flow monitoring, one must distinguish between mispredictions vs. attacks.

In multi-core machines, applications execute in a complex-co-execution environment in which the number of concurrently executing applications typically exceed the number of available cores. In order to fairly and efficiently utilize cores, modern operating systems (OS) such as Linux migrate threads between cores during execution. Although such thread migrations alleviate the problem of stalling and load balancing yielding better core utilization, they also tend to destroy data locality, resulting in fewer cache hits, TLB hits, and thus performance loss for the group of applications collectively. This problem is especially severe in embedded servers which execute media and vision applications that exhibit high data locality. One one hand, mitigating this problem across a group of applications based on OS only solution is infeasible since OS treats applications as blackboxes and has no knowledge of its locality and other behavior. On the other hand, to-date, compiler optimization have focused on analysis, transformations and performance enhancement of applications in isolation ignoring the problem of optimizing performance for applications as a group. This is because of the infeasibility of global-compiler analysis across applications as well as due to the dynamic nature of inter-application interactions which is statically unknown.

Traditional profiling techniques typically identify performance hot-spots. Other specialized techniques such as WCET analysis cater to safety critical real-time systems with hard constraints and thus make conservative assumptions. However, several domains motivate the need to systematically characterize and represent differential timing properties such as the variance in execution time of application artifacts such as the functions. Such domains include, vulnerability analysis of applications with regard to differential timing attacks, and the optimization of soft-real-time applications to reduce frame-rate fluctuations. In this paper, we motivate the need for execution variance as a performance measure and propose a variance-based analysis scheme. We introduce a new program representation called Variance Characterization Graph (VCG) that is used both as the intermediate representation for the variance-based analysis, and as the final representation that provides concise actionable information to programmers and optimization frameworks. We develop a methodology based on statistical pattern matching to summarize the dominant patterns of application behavior into a very compact VCG representation useful for tuning application behavior such as the soft-real-time properties.

SUMMARY As a key part of reverse engineering, decompilation plays a very important role in software security and maintenance. A number of tools, such as Boomerang and IDA Hex_rays, have been developed to translate executable programs into source code in a relatively high‐level language. Unfortunately, most existing decompilation tools suffer from low accuracy in identifying variables, functions, and composite structures, resulting in poor readability. To address these limitations, we present a practical decompiler called C‐Decompiler for Windows C programs that (i) uses a shadow stack to perform refined data flow analysis, (ii) adopts inter‐basic‐block register propagation to reduce redundant variables, and (iii) recognizes library (i.e., Standard Template Library) functions by signatures. We evaluate and compare the decompilation quality of C‐Decompiler with two existing tools, Boomerang and IDA Hex_rays, considering four aspects: function analysis, variable expansion rate, total percentage reduction, and cyclomatic complexity. Our experimental results show that on average, C‐Decompiler has the highest total percentage reduction of 55.91%, lowest variable expansion rate of 55.79%, and the same cyclomatic complexity as the original source code for each considered application. Furthermore, in our experiments, C‐Decompiler is able to recognize functions with a lower false positive and false negative rate than the other decompilers. A case study and our evaluation results confirm that C‐Decompiler is a practical tool to produce highly readable C‐style code. Copyright © 2012 John Wiley & Sons, Ltd.