Initial Seeds Research Articles

A Novel Seed Generation Approach for Vulnerability Mining Based on Generative Adversarial Networks and Attention Mechanisms

Coverage-guided fuzzing has been widely applied in software error and security vulnerability detection. The fuzzing technique based on AFL (American Fuzzy Loop) is a common coverage-guided fuzzing method. The code coverage during AFL fuzzing is highly dependent on the quality of the initial seeds. If the selected seeds’ quality is poor, the AFL may not be able to detect program paths in a targeted manner, resulting in wasted time and computational resources. To solve the problems that the seed selection strategy in traditional AFL fuzzing cannot quickly and effectively generate high-quality seed sets and the mutated test cases cannot reach deeper paths and trigger security vulnerabilities, this paper proposes an attention mechanism-based generative adversarial network (GAN) seed generation approach for vulnerability mining, which can learn the characteristics and distribution of high-quality test samples during the testing process and generate high-quality seeds for fuzzing. The proposed method improves the GAN by introducing fully connected neural networks to balance the competitive adversarial process between discriminators and generators and incorporating attention mechanisms, greatly improving the quality of generated seeds. Our experimental results show that the seeds generated by the proposed method have significant improvements in coverage, triggering unique crashes and other indicators and improving the efficiency of AFL fuzzing.

Peer-to-Peer Chain Recruitment for Enrolling Young South African Women into an HIV Pre-exposure Prophylaxis (PrEP) Intervention Study: How Did It Perform?

Peer-to-peer chain recruitment has been used for descriptive studies, but few intervention studies have employed it. We used this method to enroll sexually active women ages 18 to 25 into an online Pre-Exposure Prophylaxis (PrEP) information and motivation intervention pilot in eThekwini (Durban), South Africa. Seeds (N = 16) were recruited by study staff and randomized to Masibambane, Ladies Chat, a Gender-Enhanced group-based WhatsApp Workshop (GE), or Individual-Access (IA), a control condition that provided participants with online information/motivation materials only. Each seed could recruit up to three women to participate in the same study condition, with an incentive for each enrolled woman; participants in subsequent waves could choose to recruit or not. We evaluated if peer-to-peer recruitment was self-sustaining and resulted in enrolling women who, in subsequent waves, had less contact with the health care system and less knowledge about PrEP than the initial seeds. Over three recruitment waves beyond the seeds, 84 women were recruited. Almost 90% of women became recruiters, with each recruiting on average 1.90 women and 1.26 eligible enrolled women. The approach was successful at reaching women with less education but not women with less health system contact and PrEP knowledge across waves. IA participants had a slightly higher, though non-significantly different, percentage of individuals who became Peer Health Advocates (PHAs) than GE participants and, on average, they recruited slightly more women who enrolled. Our findings demonstrated that peer-to-peer recruitment is a feasible and self-sustaining way to recruit SA young women into a PrEP intervention study.

Performance evaluation of individual tree detection and segmentation algorithms using ALS data in Chir Pine (Pinus roxburghii) forest

The application of individual tree detection algorithms for assessing forest inventories and aiding decision-making in forestry has been a subject of research for more than two decades. Nevertheless, there is a notable research gap in the development of robust algorithms capable of automatically detecting trees of different species, ages, and varied crown sizes in dense forest environments. In this study, we conducted a comprehensive evaluation of six different individual tree detection (ITD) algorithms using airborne LiDAR data in Chir Pine (Pinus roxburghii) forests. This research represents one of the pioneering efforts in applying ITD algorithms to Chir Pine forests using ALS data. We categorized ITD routines into two groups: those reliant on local maxima as treetops and initial seeds for crown delineation, and those that do not require explicit treetop identification. To assess accuracy, we developed a special Individual Tree Matching (ITM) algorithm, enabling the matching of LiDAR-detected trees with 284 reference trees measured in the field. Our analysis involved various combinations of filtering fixed window sizes and adaptive window sizes applied to the point cloud, unsmoothed, and smoothed canopy height model (CHM). Our results highlighted the effectiveness of the 3 × 3m fixed window size method on unsmoothed CHM, achieving an overall F-score of 0.65 and a tree detection rate of 86%. Additionally, the Dalponte 2016 method proved superior for crown segmentation using identified treetops, consistently measuring mean crown radii within 0.5 m of reference field trees. Among the methods not relying on treetops, the adaptive mean shift algorithm (AMS3D) delivered strong performance, boasting an overall F-score of 0.67 and mean crown radii within 0.1 m. Our study revealed a high correlation between LiDAR-detected tree heights and field-measured tree heights across all evaluated methods. Overall, our findings underscore the potential of ITD algorithms in enhancing forest attribute measurement accuracy and facilitating climate-responsive forest management strategies.

Estimation of the subsurface electromagnetic velocity distribution from diffraction hyperbolas by means of a novel automated picking procedure: Theory and application to glaciological ground-penetrating radar data sets

We have developed an auto-picking algorithm that is designed to automatically detect subsurface diffractors within ground-penetrating radar (GPR) data sets, to accurately track the hyperbolic diffractions originating from the identified scatterers, and to recover the subsurface electromagnetic (EM) velocity distribution, among other possible analyses. Our procedure presents several advantages with respect to other commonly applied diffraction tracking techniques because it can be applied with minimal signal preprocessing, thus making it more versatile and adaptable to local conditions; it requires only limited input from the interpreter in the form of a few thresholds for the tracking parameters, thus making the results more objective; and it does not involve pretraining as opposed to machine-learning algorithms, thus removing the need to gather a large and comprehensive image database of all possible subsurface situations, which would not necessarily be limited to only examples of diffractions. The presented algorithm starts by identifying those signals that are likely to belong to diffraction apexes, which are then used as initial seeds by the auto-tracking process. The horizontal search window used during the auto-tracking process is locally adapted through a rough preliminary estimate of the size of each diffraction. In addition, multiple seeds within the same apex can produce several acceptable hyperbolas tracking the same diffraction phase. The algorithm thus selects the best-fitting ones by assessing several signal attributes while also removing redundant hyperbolas and the expected false positives. The algorithm is applied to two glaciological GPR profiles, and it is able to accurately track the vast majority of the recorded diffractions, with very few false positives and negatives. This produces a statistically sound EM velocity distribution, which was used to assess the state of the surveyed alpine glacier.

REFLECTIONS ON THE STATE OF THE HISTORY OF ECONOMICS

The celebration of fifty years of the History of Economics Society (HES) is a wonderful achievement. It is a pity that the majority of the founding members are not alive to see this landmark. The initial seeds were planted in 1968, with a gathering run by Donald Winch at the University of Sussex, and watered the following year by A. W. “Bob” Coats at the University of Nottingham, who produced the first British History of Economic Thought newsletter. These meetings, and discussions at the annual meetings of the Allied Social Science Associations (ASSA), prompted the formation of the journal History of Political Economy (HOPE), with the first issue appearing in the spring of 1969.1 The idea of forming the HES was seriously broached in Chicago in 1973, and the first official meeting was held in Chapel Hill (North Carolina) the following year, although HES had already mounted four sessions in New York City at the 1973 ASSA meetings.

Influence of human motion patterns on epidemic spreading dynamics.

Extensive real-data indicate that human motion exhibits novel patterns and has a significant impact on the epidemic spreading process. The research on the influence of human motion patterns on epidemic spreading dynamics still lacks a systematic study in network science. Based on an agent-based model, this paper simulates the spread of the disease in the gathered population by combining the susceptible-infected-susceptible epidemic process with human motion patterns, described by moving speed and gathering preference. Our simulation results show that the emergence of a hysteresis loop is observed in the system when the moving speed is slow, particularly when humans prefer to gather; that is, the epidemic prevalence of the systems depends on the fraction of initial seeds. Regardless of the gathering preference, the hysteresis loop disappears when the population moves fast. In addition, our study demonstrates that there is an optimal moving speed for the gathered population, at which the epidemic prevalence reaches its maximum value.

Conservation of Hot Spots and Ligand Binding Sites in Protein Models by AlphaFold2.

The neural network-based program AlphaFold2 (AF2) provides high accuracy structure prediction for a large fraction of globular proteins. An important question is whether these models are accurate enough for reliably docking small ligands. Several recent papers and the results of CASP15 reveal that local conformational errors reduce the success rates of direct ligand docking. Here, we focus on the ability of the models to conserve the location of binding hot spots, regions on the protein surface that significantly contribute to the binding free energy of the protein-ligand interaction. Clusters of hot spots predict the location and even the druggability of binding sites, and hence are important for computational drug discovery. The hot spots are determined by protein mapping that is based on the distribution of small fragment-sized probes on the protein surface and is less sensitive to local conformation than docking. Mapping models taken from the AlphaFold Protein Structure Database show that identifying binding sites is more reliable than docking, but the success rates are still 5% to 10% lower than based on mapping X-ray structures. The drop in accuracy is particularly large for models of multidomain proteins. However, both the model binding sites and the mapping results can be substantially improved by generating AF2 models for the ligand binding domains of interest rather than the entire proteins and even more if using forced sampling with multiple initial seeds. The mapping of such models tends to reach the accuracy of results obtained by mapping the X-ray structures.

First Detection of an Overmassive Black Hole Galaxy UHZ1: Evidence for Heavy Black Hole Seed Formation from Direct Collapse

The recent Chandra-JWST discovery of a quasar in the z ≈ 10.1 galaxy UHZ1 reveals that accreting supermassive black holes were already in place 470 million years after the Big Bang. The Chandra X-ray source detected in UHZ1 is a Compton-thick quasar with a bolometric luminosity of L bol ∼ 5 × 1045 erg s−1, which corresponds to an estimated black hole (BH) mass of ∼4 × 107 M ⊙, assuming accretion at the Eddington rate. JWST NIRCAM and NIRSpec data yield a stellar mass estimate for UHZ1 comparable to its BH mass. These characteristics are in excellent agreement with prior theoretical predictions for a unique class of transient, high-redshift objects, overmassive black hole galaxies (OBGs) by Natarajan et al., that harbor a heavy initial black hole seed that likely formed from the direct collapse of the gas. Given the excellent agreement between the observed multiwavelength properties of UHZ1 and theoretical model template predictions, we suggest that UHZ1 is the first detected OBG candidate. Our assertion rests on multiple lines of concordant evidence between model predictions and the following observed properties of UHZ1: its X-ray detection and the estimated ratio of the X-ray flux to the IR flux, which is consistent with theoretical expectations for a heavy initial BH seed; its high measured redshift of z ≈ 10.1, as predicted for the transient OBG stage (9 < z < 12); the amplitude and shape of the detected JWST spectral energy distribution (SED) between 1 and 5 μm, which is in very good agreement with simulated template SEDs for OBGs; and the extended JWST morphology of UHZ1, which is suggestive of a recent merge and is also expected for the formation of transient OBGs. As the first OBG candidate, UHZ1 provides compelling evidence for the formation of heavy initial seeds from direct collapse in the early Universe.

Understanding the Influence of Genre-Specific Music Using Network Analysis and Machine Learning Algorithms

This study analyzes a network of musical influence using machine learning and network analysis techniques. A directed network model is used to represent the influence relations between artists as nodes and edges. Network properties and centrality measures are analyzed to identify influential patterns. In addition, influence within and outside the genre is quantified using in-genre and out-genre weights. Regression analysis is performed to determine the impact of musical attributes on influence. We find that speechiness, acousticness, and valence are the top features of the most influential artists. We also introduce the IRDI, an algorithm that provides an innovative approach to quantify an artist’s influence by capturing the degree of dominance among their followers. This approach underscores influential artists who drive the evolution of music, setting trends and significantly inspiring a new generation of artists. The independent cascade model is further employed to open up the temporal dynamics of influence propagation across the entire musical network, highlighting how initial seeds of influence can contagiously spread through the network. This multidisciplinary approach provides a nuanced understanding of musical influence that refines existing methods and sheds light on influential trends and dynamics.

Deep Open-Curve Snake for Discriminative 3D Neuron Tracking.

Open-Curve Snake (OCS) has been successfully used in three-dimensional tracking of neurites. However, it is limited when dealing with noise-contaminated weak filament signals in real-world applications. In addition, its tracking results are highly sensitive to initial seeds and depend only on image gradient-derived forces. To address these issues and boost the canonical OCS tracker to a new level of learnable deep learning algorithms, we present Deep Open-Curve Snake (DOCS), a novel discriminative 3D neuron tracking framework that simultaneously learns a 3D distance-regression discriminator and a 3D deeply-learned tracker under the energy minimization, which can promote each other. In particular, the open curve tracking process in DOCS is formed as convolutional neural network prediction procedures of new deformation fields, stretching directions, and local radii and iteratively updated by minimizing a tractable energy function containing fitting forces and curve length. By sharing the same deep learning architectures in an end-to-end trainable framework, DOCS is able to fully grasp the information available in the volumetric neuronal data to address segmentation, tracing, and reconstruction of complete neuron structures in the wild. We demonstrated the superiority of DOCS by evaluating it on both the BigNeuron and Diadem datasets where consistently state-of-the-art performances were achieved for comparison against current neuron tracing and tracking approaches. Our method improves the average overlap score and distance score about 1.7% and 17% in the BigNeuron challenge data set, respectively, and the average overlap score about 4.1% in the Diadem dataset.

Введение in vitro и регенерационная способность реликтового эндемика недзвецкия семиреченская Niedzwedzkia semiretschenskia B. Fedtsch

В статье приведены экспериментальные данные по получению асептической культуры редкого реликтового вида недзвецкия (инкарвиллея) семиреченская Niedzwedzkia semiretschenskia от исходных семян из природных популяций на юге Казахстана. Изучено влияние гормонального состава питательной среды Мурасиге и Скуга на всхожесть семян, адвентивное побегообразование и укоренение in vitro. Проведена оценка коэффициента размножения в ходе многократного микрочеренкования асептических побегов. Установлено, что in vitro всхожесть семян не превышала 25 % на среде Кнопа, максимальная всхожесть отмечалась при их посадке в почвенный субстрат. Внесение в среду цитокинина 6-бензиламинопурина (БАП) приводило к образованию каллуса из тканей семядолей, к адвентивному побегообразованию у апикальных эксплантов проростков с настоящими листьями. Выявлено, что оптимальной средой для микроклонального размножения является среда с внесением 0,5 мг/л БАП с максимальным коэффициентом размножения 7,84 на втором пассаже. Установлено, что при депонировании в условиях низкой положительной температуры в течение 6 месяцев пробирочная культура инкарвиллеи сохраняет жизнеспособность и восстаналивает рост при переносе в стационарные условия световой комнаты. В результате работы проведено введение в культуру тканей, микроклональное размножение и создана депонированная коллекция in vitro реликтового вида N. semiretschenskia.

Overcoming vaccine hesitancy by multiplex social network targeting: an analysis of targeting algorithms and implications

Incorporating social factors into disease prevention and control efforts is an important undertaking of behavioral epidemiology. The interplay between disease transmission and human health behaviors, such as vaccine uptake, results in complex dynamics of biological and social contagions. Maximizing intervention adoptions via network-based targeting algorithms by harnessing the power of social contagion for behavior and attitude changes largely remains a challenge. Here we address this issue by considering a multiplex network setting. Individuals are situated on two layers of networks: the disease transmission network layer and the peer influence network layer. The disease spreads through direct close contacts while vaccine views and uptake behaviors spread interpersonally within a potentially virtual network. The results of our comprehensive simulations show that network-based targeting with pro-vaccine supporters as initial seeds significantly influences vaccine adoption rates and reduces the extent of an epidemic outbreak. Network targeting interventions are much more effective by selecting individuals with a central position in the opinion network as compared to those grouped in a community or connected professionally. Our findings provide insight into network-based interventions to increase vaccine confidence and demand during an ongoing epidemic.

An Efficient and Secure DNA based Image Encryption Technique

In this paper, an efficient and secure DNA based image encryption technique using hyper chaos Lorenz function is proposed. The technique employs SHA-512 hash of gray scale image and primary key to alter the initial seeds of hyper chaos Lorenz function. Initially, chaotic sequences are generated and redesigned. Then, an image is divided into blocks and randomly shuffled according to the primary key. Further perform circular shift operation over the rows of the shuffled image matrix and index based scrambling operation over the columns. After that, perform dynamic encoding and diffusion based DNA algebraic operation over the encoded DNA sequence and DNA based Key sequence using chaotic sequence. Finally, DNA decoding is employed to convert the diffused dynamic matrix into a ciphered image. The dynamicity of the chaotic sequences makes its extraordinary and contributes to the enhanced security and robustness of the technique. Implementation, security and performance analysis demonstrate that the technique has not only an outstanding encryption effect, but also posses large key space, less pixel correlation, low computation time and successfully resilient to the statistical, differential and brute-force attack. Comparative analysis with other references proves its efficiency and practicability for real time applications.

On transforming model‐based tests into code: A systematic literature review

SummaryModel‐based test design is increasingly being applied in practice and studied in research. Model‐based testing (MBT) exploits abstract models of the software behaviour to generate abstract tests, which are then transformed into concrete tests ready to run on the code. Given that abstract tests are designed to cover models but are run on code (after transformation), the effectiveness of MBT is dependent on whether model coverage also ensures coverage of key functional code. In this article, we investigate how MBT approaches generate tests from model specifications and how the coverage of tests designed strictly based on the model translates to code coverage. We used snowballing to conduct a systematic literature review. We started with three primary studies, which we refer to as the initial seeds. At the end of our search iterations, we analysed 30 studies that helped answer our research questions. More specifically, this article characterizes how test sets generated at the model level are mapped and applied to the source code level, discusses how tests are generated from the model specifications, analyses how the test coverage of models relates to the test coverage of the code when the same test set is executed and identifies the technologies and software development tasks that are on focus in the selected studies. Finally, we identify common characteristics and limitations that impact the research and practice of MBT: (i) some studies did not fully describe how tools transform abstract tests into concrete tests, (ii) some studies overlooked the computational cost of model‐based approaches and (iii) some studies found evidence that bears out a robust correlation between decision coverage at the model level and branch coverage at the code level. We also noted that most primary studies omitted essential details about the experiments.

Associahedra for finite‐type cluster algebras and minimal relations between g‐vectors

AbstractWe show that the mesh mutations are the minimal relations among the ‐vectors with respect to any initial seed in any finite‐type cluster algebra. We then use this algebraic result to derive geometric properties of the ‐vector fan: we show that the space of all its polytopal realizations is a simplicial cone, and we then observe that this property implies that all its realizations can be described as the intersection of a high‐dimensional positive orthant with well‐chosen affine spaces. This sheds a new light on and extends earlier results of Arkani‐Hamed, Bai, He, and Yan in type and of Bazier‐Matte, Chapelier‐Laget, Douville, Mousavand, Thomas, and Yıldırım for acyclic initial seeds. Moreover, we use a similar approach to study the space of polytopal realizations of the ‐vector fans of another generalization of the associahedron: nonkissing complexes (also known as support ‐tilting complexes) of gentle algebras. We show that the space of realizations of the nonkissing fan is simplicial when the gentle bound quiver is brick and 2‐acyclic, and we describe in this case its facet‐defining inequalities in terms of mesh mutations. Along the way, we prove algebraic results on 2‐Calabi–Yau triangulated categories, and on extriangulated categories that are of independent interest. In particular, we prove, in those two setups, an analogue of a result of Auslander on minimal relations for Grothendieck groups of module categories.

On the variability of grain boundary mobility in the isoconfigurational ensemble

Recent grain growth experiments have revealed that the same type of grain boundary can have very different mobilities depending on its local microstructure. In this work, we use molecular dynamics simulations to quantify uncertainty in the reduced mobility of curved grain boundaries for different types of boundary conditions and over a range of initial velocity seeds. We consider cylindrical island grains in Ni with a [001] tilt axis as a model system. Unexpectedly, we find large variation in the reduced mobility of curved grain boundaries depending on the imposed constraints, the initial velocity distribution and grain size. We perform a dynamic propensity analysis inspired by studies of glass forming liquids to analyze sources of variation in reduced mobility. Our work highlights the significant impact of initial velocity distributions on grain boundary motion which has not been analyzed in prior work.

A new community-based algorithm based on a “peak-slope-valley” structure for influence maximization on social networks

Influence Maximization (IM) is a key algorithmic problem that has been extensively studied in social influence analysis, but most of existing researches either make sacrifices in solution accuracy or suffer high computational complexity. In this paper, we propose a new Community-based Influence Maximization (CIM) algorithm for identifying a set of seed spreaders in a social network to maximize the expected number of influenced nodes. In CIM, the initial candidate seeds are first selected based on the proposed topological potential “peak-slope-valley” structure framework. Then, we propose a recursive clustering approach and a similarity indicator based on local resource allocation to partition communities. Finally, we design a community-based regional influence indicator to select seed nodes without using any prior knowledge. Experiment datasets include three artificial benchmarks with varying community strengths, as well as nine representative networks drawn from various fields. Extensive numerical simulations on both artificial and real networks indicate that (i) community-based techniques enrich the toolbox for addressing the IM problem and (ii) the derivative algorithm outperforms recent high-performing influence maximization algorithms in terms of influence propagation and coverage redundancy of the seed set with an acceptable complexity. Furthermore, our algorithm exhibits good stability on networks of varying scales and structural characteristics.

Speedup of the k-Means Algorithm for Partitioning Large Datasets of Flat Points by a Preliminary Partition and Selecting Initial Centroids

Abstract A problem of partitioning large datasets of flat points is considered. Known as the centroid-based clustering problem, it is mainly addressed by the k-means algorithm and its modifications. As the k-means performance becomes poorer on large datasets, including the dataset shape stretching, the goal is to study a possibility of improving the centroid-based clustering for such cases. It is quite noticeable on non-sparse datasets that the resulting clusters produced by k-means resemble beehive honeycomb. It is natural for rectangular-shaped datasets because the hexagonal cells make efficient use of space owing to which the sum of the within-cluster squared Euclidean distances to the centroids is approximated to its minimum. Therefore, the lattices of rectangular and hexagonal clusters, consisting of stretched rectangles and regular hexagons, are suggested to be successively applied. Then the initial centroids are calculated by averaging within respective hexagons. These centroids are used as initial seeds to start the k-means algorithm. This ensures faster and more accurate convergence, where at least the expected speedup is 1.7 to 2.1 times by a 0.7 to 0.9 % accuracy gain. The lattice of rectangular clusters applied first makes rather rough but effective partition allowing to optionally run further clustering on parallel processor cores. The lattice of hexagonal clusters applied to every rectangle allows obtaining initial centroids very quickly. Such centroids are far closer to the solution than the initial centroids in the k-means++ algorithm. Another approach to the k-means update, where initial centroids are selected separately within every rectangle hexagons, can be used as well. It is faster than selecting initial centroids across all hexagons but is less accurate. The speedup is 9 to 11 times by a possible accuracy loss of 0.3 %. However, this approach may outperform the k-means algorithm. The speedup increases as both the lattices become denser and the dataset becomes larger reaching 30 to 50 times.

Hubungan Tingkat Pengetahuan Remaja Terhadap Kejadian Pernikahan Dini di Wilayah Kelurahan Kereng Bangkirai RT 01/ RW 01 Kota Palangka Raya Tahun 2022

Adolescents are the initial seeds of a nation to become a better nation. However, teenagers drop out of school due to marriage at a young age. Based on data from the Central Statistics Agency (BPS) Central Kalimantan ranks second in cases of early marriage with a percentage of 20.2%. Factors causing early marriage that are often encountered are economic factors, education, parents, local customs and marriage due to accidents. The purpose of this study was to determine the relationship between the level of knowledge of adolescents on the incidence of early marriage. This study uses an analytical survey research design, the type of analytical survey research design using the Cross Sectional method, the sample in this study amounted to 59 teenagers in Kereng Bangkirai Village RT 01/RW 01 Palangka Raya City. Sampling by purposive sampling using inclusion criteria. Data analysis using Chi Square test. The results of the Chi Square test (P Value = 0.000 &lt;0.05), which means there is a relationship. The conclusion in this study is that there is a relationship between the level of knowledge of adolescents on the incidence of early marriage. Therefore, to reduce early childhood, the quality of education should be improved.

Improve Model Testing by Integrating Bounded Model Checking and Coverage Guided Fuzzing

Eectromechanical systems built by Simulink or Ptolemy have been widely used in industry fields, such as autonomous systems and robotics. It is an urgent need to ensure the safety and security of those systems. Test case generation technologies are widely used to ensure the safety and security. State-of-the-art testing tools employ model-checking techniques or search-based methods to generate test cases. Traditional search-based techniques based on Simulink simulation are plagued by problems such as low speed and high overhead. Traditional model-checking techniques such as symbolic execution have limited performance when dealing with nonlinear elements and complex loops. Recently, coverage guided fuzzing technologies are known to be effective for test case generation, due to their high efficiency and impressive effects over complex branches of loops. In this paper, we apply fuzzing methods to improve model testing and demonstrate the effectiveness. The fuzzing methods aim to cover more program branches by mutating valuable seeds. Inspired by this feature, we propose a novel integration technology SPsCGF, which leverages bounded model checking for symbolic execution to generate test cases as initial seeds and then conduct fuzzing based upon these worthy seeds. Over the evaluated benchmarks which consist of industrial cases, SPsCGF could achieve 8% to 38% higher model coverage and 3x-10x time efficiency compared with the state-of-the-art works.