Greybox fuzzing is a powerful testing technique. Given a set of initial seeds, greybox fuzzing continuously generates new test inputs to execute the program under test and drives executions with code coverage as feedback. Seed prioritization is an important step of greybox fuzzing that helps greybox fuzzing choose promising seeds for input generation in priority. However, mainstream greybox fuzzers like AFL++ and Zest tend to neglect the importance of seed prioritization. They may pick seeds plainly according to the sequential order of the seeds being queued or an order produced with a random-based approach, which may consequently degrade their performance in exploring code and exposing bugs. In the meantime, existing state-of-the-art techniques like Alphuzz and K-Scheduler adopt complex strategies to schedule seeds. Although powerful, such strategies also inevitably incur great overhead and will reduce the scalability of the proposed technique. In this article, we propose a novel distance-based seed prioritization approach named DiPri to facilitate greybox fuzzing. Specifically, DiPri evaluates the queued seeds according to seed distances and chooses the outlier ones, which are the farthest from the others, in priority to improve the probabilities of discovering previously unexplored code regions. To make a profound evaluation of DiPri , we prototype DiPri on AFL++ and conduct large-scale experiments with four baselines and 24 C/C++ fuzz targets, where eight are from widely adopted real-world projects, eight are from the coverage-based benchmark FuzzBench, and eight are from the bug-based benchmark Magma. The results obtained through a fuzzing exceeding 50,000 CPU hours suggest that DiPri can (1) insignificantly influence the host fuzzer’s capability of code coverage by slightly improving the branch coverage on the eight targets from real-world projects and slightly reducing the branch coverage on the eight targets from FuzzBench, and (2) improve the host fuzzer’s capability of finding bugs by triggering five more Magma bugs. Besides the evaluation with the three C/C++ benchmarks, we integrate DiPri into the Java fuzzer Zest and conduct experiments on a Java benchmark composed of five real-world programs for more than 8,000 CPU hours to empirically study the scalability of DiPri . The results with the Java benchmark demonstrate that DiPri is pretty scalable and can help the host fuzzer find bugs more consistently.

This replicated computational results (RCR) report describes how to (1) set up DiPri and (2) replicate the experimental results. The primary artifact is the C/C++ prototype of DiPri , which is essentially an extension of the state-of-the-art greybox fuzzer AFL++ (version 4.06). Other artifacts include the Java implementation of DiPri on Zest, the materials for integrating DiPri into FuzzBench and Magma, and the scripts for running docker and processing data. All artifacts can be found at our GitHub repository 1 and Zenodo archive. 2

BACKGROUND: To investigate the association between CT signs and clinicopathological features and disease recurrence in patients with hepatoid adenocarcinoma of stomach (HAS). METHODS: Forty nine HAS patients undergoing radical surgery were retrospectively collected. Association between CT and clinicopathological features and disease recurrence was analyzed. Multivariate logistic model was constructed and evaluated for predicting recurrence by using receiver operating characteristic (ROC) curve. Survival curves between model-defined risk groups was compared using Kaplan-Meier method. RESULTS: 24(49.0%) patients developed disease recurrence. Multivariate logistic analysis results showed elevated serum CEA level, peritumoral fatty space invasion and positive pathological vascular tumor thrombus were independent factors for disease recurrence. Odds ratios were 10.87 (95%CI, 1.14-103.66), 6.83 (95%CI, 1.08-43.08) and 42.67 (95%CI, 3.66-496.85), respectively. The constructed model showed an area under ROC of 0.912 (95%CI,0.825-0.999). The model-defined high-risk group showed poorer overall survival and recurrence-free survival than the low-risk group (both P < 0.001). CONCLUSIONS: Preoperative CT appearance of peritumoral fatty space invasion, elevated serum CEA level, and pathological vascular tumor thrombus indicated poor prognosis of HAS patients.

Surrogate-based Optimization is a useful approach when the objective function is computationally expensive to evaluate, compared to Simulation-based Optimization. In the surrogate-based method, analytically tractable “surrogate models” (also known as “Response Surface Models - RSMs” or “metamodels”), are constructed and validated for each optimization objective and constraint at relatively low computational cost. They are useful for replacing the time-consuming simulations during the optimization; quickly locating the area where the optimum is expected to be for further search; and gaining insight into the global behavior of the system. Nevertheless, there are still concerns about the surrogate model accuracy and the number of simulations necessary to get a reasonably accurate surrogate model. This paper aims to unveil: 1) the possible impacts of problem scale and sampling strategy on the surrogate model accuracy; and 2) the potential of Surrogate based Optimization in finding high quality solutions for building envelope design optimization problems. For this purpose, a series of multi-objective optimization test cases that mainly consider daylight and energy performance were conducted within the same time frame. Then, the results were compared, in pair, based on which discussions were made. Finally, the corresponding conclusions were obtained after the comparative study.

A definition of least cyclostationary order (LCO) is proposed based on the inner cyclic period of single carrier and orthogonal frequency division multiplexing (OFDM) signals. The LCOs of single carrier and OFDM signals are derived. Since the LCO has a relationship with the carrier frequency offset (CFO), a blind CFO estimation scheme for single carrier and OFDM signals can be proposed. The novel scheme is entirely blind without any priori information and is proved to be the same as cumulants estimation when the LCO is known. However, the new scheme is superior to the estimation based on cumulant when the LCO is unknown. The mean‐square error of M ‐phase shift keying signals approaches the Cramér–Rao bound in additive white Gaussian noise (AWGN) channel. Simulation results are given to verify the effectiveness of the proposed blind estimation scheme in Rician fading channel and in AWGN channel.

Sports building envelopes are complex systems involving multiple architectural and engineering performance requirements that are sometimes in conflict with each other. Typically, daylight usage and energy efficiency, as two primary concerns in building envelope design, are of those conflicting aspects. To improve overall performance (including daylight and energy performance) by changing the geometries of the envelope, windows and shading elements as well as the selection of construction materials, Multi-objective Optimization (MOO) is a natural choice. Based on the generated Pareto front, trade-off decisions between competing performance objectives can be made. However, as the number of design variables from different disciplines increases, the huge design space and the specialization of disciplines make the optimization process less efficient. Therefore, two possible Multidisciplinary Design Optimization (MDO) frameworks, namely Individual Disciplinary Feasible (i.e. IDF, a single-level MDO framework) and Collaborative Optimization (i.e. CO, a bi-level MDO framework), are investigated to combine with MOO. Resorting to the capability of MDO in decomposition and coordination between different disciplines, parallel disciplinary simulations and/or bi-level optimizations can be realized, which compresses design cycle time and achieves better overall performance. Through the combination of MOO and MDO, Multi-objective Multidisciplinary Design Optimization (M-MDO or multi-objective MDO) problems are expected to be solved more effectively and efficiently. The whole process of the proposed method consists of three phases (i.e. preprocessing, solution and post-processing phases), in which variable screening, multi-objective MDO solving and Pareto front comparison are performed respectively. An ongoing real project located in China, is used as a case study to test the proposed method. For now, the research work is in the preprocessing phase. Preliminary observations and results are obtained, and future research is discussed.

Traditional symbol rate estimators based on wavelet transform (WT) suffer from difficulties in choosing wavelet scale. Additionally, the most estimators are only adapted to limited signal types. Furthermore, those estimators are susceptible to noise, which is impractical to implement. In this paper, to overcome shortages of those traditional estimators, a self- adapting symbol rate estimator is proposed. The proposed estimator has advantages in reliably working under low Eb-to-N0. More importantly, it can be widely used to estimate various types of signal. The numerical simulations show that the proposed estimator has superiority in performance compared with the improved estimators based on wavelet transform.