167 research outputs found
Understanding Android Obfuscation Techniques: A Large-Scale Investigation in the Wild
In this paper, we seek to better understand Android obfuscation and depict a
holistic view of the usage of obfuscation through a large-scale investigation
in the wild. In particular, we focus on four popular obfuscation approaches:
identifier renaming, string encryption, Java reflection, and packing. To obtain
the meaningful statistical results, we designed efficient and lightweight
detection models for each obfuscation technique and applied them to our massive
APK datasets (collected from Google Play, multiple third-party markets, and
malware databases). We have learned several interesting facts from the result.
For example, malware authors use string encryption more frequently, and more
apps on third-party markets than Google Play are packed. We are also interested
in the explanation of each finding. Therefore we carry out in-depth code
analysis on some Android apps after sampling. We believe our study will help
developers select the most suitable obfuscation approach, and in the meantime
help researchers improve code analysis systems in the right direction
Identification of potential key genes associated with termination phase of rat liver regeneration through microarray analysis
Background and objective: Liver regeneration (LR) is a complex process
influenced by various genes and pathways, the majority of the of research on LR
focus on the initiation and proliferation phase while studies on termination
phase is lacking. We aimed to identify potential genes and reveal the underlying
the molecular mechanisms involved in the precise regulation of liver size during
the termination phase of LR.
Materials and methods: We obtained the rat liver tissue gene datasets
(GSE63742) collected following partial hepatectomy (PH) from the Gene Expression
Omnibus (GEO) of the National Center for Biotechnology Information (NCBI), from
which, this study screened the late stage LR samples (7 days post-PH) using the
R/Bioconductor packages for the identification of differentially expressed genes
(DEGs). Afterwards, we performed enrichment analysis using the database for
annotation visualization and integrated discovery (DAVID) online tool. Moreover,
the Search Tool for the Retrieval of Interacting proteins (STRING) database was
employed to construct protein-protein interaction (PPI) networks based on those
identified DEGs; the PPI network was then used by Cytoscape software to predict
hub genes and nodes. Animal experimentation (Rat PH model) was performed to
acquire liver tissues which were then used for western blot analysis to verify
our results.
Results: The present study identified together 74 significant DEGs,
among which, 51 showed up-regulation while 23 presented as down-regulated. As
revealed by KEGG pathway enrichment analysis, DEGs were mostly related to
pathways such as retinol metabolism, steroid hormone synthesis, transforming
growth factor-β (TGF-β) and mitogen-activated protein kinase
(MAPK) signaling. In addition, as suggested by GO enrichment analysis, DEGs were
mostly related to the cyclooxygenase P450 pathway, negative regulation of Notch
signaling pathway, aromatase activity, steroid hydroxylase activity, exosomes,
and extracellular domain. Analyses based on STRING database and Cytoscape
software identified genes like Ste2 and Btg2 as the hub genes in the termination
stage LR. The obtained results were confirmed by Western blot analysis.
Conclusions: Taken together, the microarray analysis in this study
suggests that DEGs such as Ste2 and Btg2 are the hub genes, which are associated
with the regulation of termination stage LR, while the molecular mechanisms are
possibly related to the MAPK and TGF-β signal transduction pathways
Integrated Robotics Networks with Co-optimization of Drone Placement and Air-Ground Communications
Terrestrial robots, i.e., unmanned ground vehicles (UGVs), and aerial robots,
i.e., unmanned aerial vehicles (UAVs), operate in separate spaces. To exploit
their complementary features (e.g., fields of views, communication links,
computing capabilities), a promising paradigm termed integrated robotics
network emerges, which provides communications for cooperative UAVs-UGVs
applications. However, how to efficiently deploy UAVs and schedule the
UAVs-UGVs connections according to different UGV tasks become challenging. In
this paper, we propose a sum-rate maximization problem, where UGVs plan their
trajectories autonomously and are dynamically associated with UAVs according to
their planned trajectories. Although the problem is a NP-hard mixed integer
program, a fast polynomial time algorithm using alternating gradient descent
and penalty-based binary relaxation, is devised. Simulation results demonstrate
the effectiveness of the proposed algorithm.Comment: Accepted by VTC2023-Fall, 5 pages, 4 figure
Design, synthesis and antimycobacterial activity of novel nitrobenzamide derivatives
We report herein the design and synthesis of a series of novel nitrobenzamide derivatives. Results reveal that many of them display considerable in vitro antitubercular activity. Four N-benzyl or N-(pyridine-2-yl)methyl 3,5-dinitrobenzamides A6, A11, C1 and C4 have not only the same excellent MIC values of 1500), opening a new direction for further development
Design, synthesis and antitubercular evaluation of benzothiazinones containing a piperidine moiety
We herein report the design and synthesis of benzothiazinones containing a piperidine moiety as new antitubercular agents based on the structure feature of IMB-ZR-1 discovered in our lab. Some of them were found to have good in vitro activity (MIC < 1 μg/mL) against drug-susceptible Mycobacterium tuberculosis H37RV strain. After two set of modifications, compound 2i were found to display comparable in vitro anti-TB activity (MIC < 0.016 μg/mL) to PBTZ169 against drug-sensitive and resistant mycobacterium tuberculosis strains. Compound 2i also showed acceptable PK profiles. Studies to determine PK profiles in lung and in vivo efficacy of 2i are currently under way
Iterative Robust Visual Grounding with Masked Reference based Centerpoint Supervision
Visual Grounding (VG) aims at localizing target objects from an image based
on given expressions and has made significant progress with the development of
detection and vision transformer. However, existing VG methods tend to generate
false-alarm objects when presented with inaccurate or irrelevant descriptions,
which commonly occur in practical applications. Moreover, existing methods fail
to capture fine-grained features, accurate localization, and sufficient context
comprehension from the whole image and textual descriptions. To address both
issues, we propose an Iterative Robust Visual Grounding (IR-VG) framework with
Masked Reference based Centerpoint Supervision (MRCS). The framework introduces
iterative multi-level vision-language fusion (IMVF) for better alignment. We
use MRCS to ahieve more accurate localization with point-wised feature
supervision. Then, to improve the robustness of VG, we also present a
multi-stage false-alarm sensitive decoder (MFSD) to prevent the generation of
false-alarm objects when presented with inaccurate expressions. The proposed
framework is evaluated on five regular VG datasets and two newly constructed
robust VG datasets. Extensive experiments demonstrate that IR-VG achieves new
state-of-the-art (SOTA) results, with improvements of 25\% and 10\% compared to
existing SOTA approaches on the two newly proposed robust VG datasets.
Moreover, the proposed framework is also verified effective on five regular VG
datasets. Codes and models will be publicly at
https://github.com/cv516Buaa/IR-VG
Enhancing Human Activity Recognition in Wrist-Worn Sensor Data Through Compensation Strategies for Sensor Displacement
Human Activity Recognition (HAR) using wearable sensors, particularly wrist-worn devices, has garnered significant research interest. However, challenges such as sensor displacement and variations in wearing habits can affect the accuracy of HAR systems. Two compensation stratigies for sensor displacemnt are proposed to address these issues. The first strategy is hybrid data fusion, which involves merging sensor data collected from different displacement locations on the wrist. This technique aims to mitigate the discrepancies in data distribution that result from the multiple wearing positions along the wrist, thereby enhancing the overall accuracy of HAR models. The second strategy is cross-location transfer fine-tuning, which involves pretraining a model with data from typical wrist locations and then fine-tuning it with data from a new sensor location. This approach improves the model’s ability to adapt and perform accurately when the sensor is placed in a different position, significantly enhancing its performance and generalization capabilities. To verify the effectiveness of these proposed compensation strategies, we built an LSTM baseline model and introduce a new Multi-stage Feature Extraction (MSFE) model that integrates 1D CNN and attention. Experiments on common activities such as walking, standing, using stairs, and lying down, with data recorded at multiple locations along the wrist, have shown that both hybrid data fusion and cross-location transfer fine-tuning strategies notably improve the recognition accuracy of HAR models. The proposed MSFE model achieves higher recognition accuracies than the LSTM model in all six experimental scenarios, particularly in Scenario 5 involving sensor displacement, with an improvement of up to 31.65%. Additionally, thecross-location transfer fine-tuning strategy enhances the recognition accuracy by 9.19% for Subject 3 with sensor displacement at the right wrist location. These advancements in handling sensor displacement and wearing variations are crucial for developing more reliable and versatile wearable technologies
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