167 research outputs found

    Understanding Android Obfuscation Techniques: A Large-Scale Investigation in the Wild

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    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

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    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

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    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

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    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

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    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

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    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

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    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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