1,337 research outputs found
The Progress, Challenges, and Perspectives of Directed Greybox Fuzzing
Most greybox fuzzing tools are coverage-guided as code coverage is strongly
correlated with bug coverage. However, since most covered codes may not contain
bugs, blindly extending code coverage is less efficient, especially for corner
cases. Unlike coverage-guided greybox fuzzers who extend code coverage in an
undirected manner, a directed greybox fuzzer spends most of its time allocation
on reaching specific targets (e.g., the bug-prone zone) without wasting
resources stressing unrelated parts. Thus, directed greybox fuzzing (DGF) is
particularly suitable for scenarios such as patch testing, bug reproduction,
and specialist bug hunting. This paper studies DGF from a broader view, which
takes into account not only the location-directed type that targets specific
code parts, but also the behaviour-directed type that aims to expose abnormal
program behaviours. Herein, the first in-depth study of DGF is made based on
the investigation of 32 state-of-the-art fuzzers (78% were published after
2019) that are closely related to DGF. A thorough assessment of the collected
tools is conducted so as to systemise recent progress in this field. Finally,
it summarises the challenges and provides perspectives for future research.Comment: 16 pages, 4 figure
Extenics-based Study on Evaluation of Urban Community Home-care Service for the Elderly
AbstractThis paper tries to introduce extenics theory into the evaluation of the urban community home-care service for the elderly. The paper analyzes the feasibility of using extenics to evaluate the service, uses analytic hierarchy process to decide the weight of index and constructs a comprehensive evaluation model for the service on the basis of extenics. Based on the case study of communities in Ningbo, the paper has completed the evaluation of home-care service in operation and put forward countermeasures to the existing problems
An empirical analysis of regional disparity of influence factors on financial exclusion based on types and levels of financial institution in China
We make an empirical analysis of regional characteristics of influence factors on financial exclusion by using the data of types and levels of financial institutions in the nation from 1998 to 2012. The study shows the number of financial institution nodes continues to increase, but with uneven distributions; the density of financial institutions increases steadily and the gap between the eastern and the Midwest is huge; the commercial banks of the eastern region account for the highest but the rate of the commercial bank basic outlets is low and the rate of the rural institution bank basic outlets is relatively higher, in addition the proportion of the Midwest rural banks is high. Even though the financial exclusion eases, large differences still exist in different areas and the situation is rather critical in rural areas, especially the financial exclusion in western rural areas is the wors
Focal Inverse Distance Transform Maps for Crowd Localization and Counting in Dense Crowd
In this paper, we propose a novel map for dense crowd localization and crowd
counting. Most crowd counting methods utilize convolution neural networks (CNN)
to regress a density map, achieving significant progress recently. However,
these regression-based methods are often unable to provide a precise location
for each person, attributed to two crucial reasons: 1) the density map consists
of a series of blurry Gaussian blobs, 2) severe overlaps exist in the dense
region of the density map. To tackle this issue, we propose a novel Focal
Inverse Distance Transform (FIDT) map for crowd localization and counting.
Compared with the density maps, the FIDT maps accurately describe the people's
location, without overlap between nearby heads in dense regions. We
simultaneously implement crowd localization and counting by regressing the FIDT
map. Extensive experiments demonstrate that the proposed method outperforms
state-of-the-art localization-based methods in crowd localization tasks,
achieving very competitive performance compared with the regression-based
methods in counting tasks. In addition, the proposed method presents strong
robustness for the negative samples and extremely dense scenes, which further
verifies the effectiveness of the FIDT map. The code and models are available
at https://github.com/dk-liang/FIDTM.Comment: The code and models are available at
https://github.com/dk-liang/FIDT
The Impact of User Heterogeneity on Knowledge Collaboration Performance
Existing research showed that the social Q & A community is centred on user participation and can improve knowledge collaboration performance by means of heterogeneous knowledge, but it did not reveal the process and mechanism. This paper takes the user behaviours of the Zhihu which is the largest social Q & A community in China as the research object. From an interactive perspective, it builds a research model of the relationship between user heterogeneity and knowledge collaboration performance by establishing measurement indicators for relationship analysis according to the user behaviours data of Zhihu. The results show that there is an inverted Ushaped relationship between user heterogeneity and knowledge collaboration performance, and the interpersonal interaction of users plays a mediating role in the relationship between user heterogeneity and knowledge collaboration performance, while the mediating effect of machine interaction is not significant. This paper, based on this, puts forward some suggestions for better development of social Q & A community
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