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Profiling the Arion rufus snails with computer vision

Profiling the Arion rufus snails with computer vision

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international Symposium on Computational Intelligence and Informatics

作者： Grega Vrbančič Rok Kukovec Vili Podgorelec Sancho Salcedo-Sanz Iztok Fister Faculty of Electrical Engineering and Computer Science University of Maribor Slovenia Dept. of Signal Processing and Commun. Universidad de Alcalá Alcalá de Henares Spain

ISBN: (纸本)9798350398830

Smart agriculture takes advantage of modern computational approaches that vary from IoT, cloud computing, and artificial intelligence. the primary aim is to assist the farming process. Pest detection is one of the objectives within the area of smart agriculture. It is mainly solved by computer vision approaches, usually combined with machine learning (ML) algorithms. In this paper, we propose a solution for detecting Arion rufus snails that have emerged in Central Europe and are one of the most prolific threats to agriculture in that place. Practical experiments reveal that our method is helpful in this real-world application and opens several future challenges and lines of research.

关键词： Smart agriculture Computer vision Mechatronics Machine learning algorithms Robot vision systems Machine learning object detection

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Zero-Trust Access Control Focusing on Imbalanced Distribution in Browser Clickstreams 8

Zero-Trust Access Control Focusing on Imbalanced Distributio...

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8th international conference on Software Defined Systems (SDS)

作者： Sasada, Taisho Masuda, Yuto Taenaka, Yuzo Kadobayashi, Youki Fall, Doudou Nara Inst Sci & Technol Nara Japan

ISBN: (纸本)9781665458207

the further spread of cloud computing and telework has led to an increase in borderless activities, which has expanded the demand for constructing Zero-Trust Access Control (ZTAC). In this context, preventing information leakage is an important issue, and to protect sensitive data and confidential information in a zero-trust, it is necessary to monitor the user's behavior after authentication and make sequential decisions about authorization. In this research, we monitor account's behavior such clickstreams on the browser and incorporate it to evaluation of user's trust in access control system. through the construction of behavior-based ZTAC system based on clickstreams, we have achieved access control that considers the naturalness of clicks without relying on the results of authentication. Moreover, toward solving the mismatch of click count between the system side and the end-user side, we synchronously updated the clickstream by the user-agent which observing and retrieving the click events in the DOM. As experimental evaluation, we verified that our ZTAC can deal with click count discrepancy and completely prevent various insider threat to sensitive data from even authenticated accounts. Also, we confirmed no significant differences in response time, memory usage, or CPU usage before and after the migration of zero-trust, indicating that it can be deployed in existing enterprise networks.

关键词： Zero-Trust Access Control Clickstream Imbalance enterprise Network Software-Defined System

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An Efficient End-to-End CNN Network for High-voltage Transmission Line Segmentation

An Efficient End-to-End CNN Network for High-voltage Transmi...

引用

ieee international conference on Cloud computing and Intelligence Systems (CCIS)

作者： Lei Yang Shuyi Kong Shilong Cui Hanyun Huang Yanhong Liu School of Electrical and Information Engineering Zhengzhou University China

ISBN: (纸本)9781665456579

Automation detection of power transmission lines is of great importance for intelligent power inspection, which could well serve the route programming and motion guidance of examination platforms. However, due to complex factors, such as complex natural environment, illumination change, image noise, efficient detection of transmission lines still frontages great challenges. Lately, deep learning has exhibited a good detection effect among different segmentation tasks. Nevertheless, it still has a few disadvantages in high-precision image segmentation, like inadequate detection, information loss caused by multiple pooling operations, etc. To realize automatic and accurate pixel-level extraction, an attention fusion segmentation network is put forward to provide an end-to-end segmentation module. Considering the the problem of class imbalance, a global attention model is introduced to make the module focus more on the target region and suppress the unimportant features. Meanwhile, aimed at the semantic gap, residual path is also proposed to achieve effective usage of local information. In addition, to solve information loss issue which arise from plenty of pooling processing, an attention fusion block is put forward to realize effective feature aggregation of multi-scale features and improve the detection ability of segmentation network on multi-scale objects. Experiments exhibit that the attention fusion segmentation network has a good extraction capacity compared with other classical segmentation network.

关键词： Image segmentation Power transmission lines Motion segmentation Semantics Lighting High-voltage techniques object detection

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Remote Sensing Ship Detection Based on three-way Decisions and Multi-granularity Feature Fusion

Remote Sensing Ship Detection Based on Three-way Decisions a...

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ieee international conference on Cloud computing and Intelligence Systems (CCIS)

作者： Li Ying Duoqian Miao Zhifei Zhang Qixian Zhang Department of Computer Science and Technology Tongji University Shanghai China Project Management Office of China National Scientific Seafloor Observatory Tongji University Shanghai China

ISBN: (纸本)9781665456579

With the development of remote sensing (RS), ship detection not only plays a vital role in military and civilian applications but also is full of challenges. there are some problems in detecting RS ships, such as image blurring and ship size differences, which seriously affect the accuracy of RS ship detection. To solve the above problems, based on the notion of three-way decisions (3WD) and multi-granularity feature fusion, this paper proposes an efficient RS ship detection network (namely 3WM-Net) consisting of two modules: a fuzzy classification and deblurring (FCD) module and a multi-granularity feature extraction enhancement (MFE 2 ) module. First, the FCD module aims to solve the problem of unreasonable image blur degree classification and deblurring. It combines 3WD and SVM classifiers to achieve image blur classification and uses the BlurGAN algorithm to deblur the image. then the MFE 2 module is designed to solve the feature extraction problem for ship objects of various sizes. the feature fusion of multi-granularity layers is performed by adding bottom-up paths and feature enhancement structures. Experiments with FGSD2021 and HRSC2016 datasets demonstrate that our 3WM-Net is superior to other RS ship detection methods and can well solve the problems of image blur and various ship sizes in RS ship detection.

关键词： Support vector machines Cloud computing Detectors object detection Feature extraction Classification algorithms Marine vehicles

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Real-Time Sperm Detection Using Lightweight YOLOv5

Real-Time Sperm Detection Using Lightweight YOLOv5

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international conference on Computer and Communications (ICCC)

作者： Zebin Zhang Bolin Qi Shimin Ou Chenjian Shi Shenyang Institute of Computing Technology Chinese Academy of Sciences Shengyang China University of Chinese Academy of Sciences Beijing China Department of Pubulic Credit Information Technology Xiamen Information Center Xiamen China Department of Computer and Information Science Linköping University Sweden

Malformed sperm is an important cause of male infertility, and sperm morphology analysis (SMA) is an effective means to diagnose sperm morphology. Deep learning assists to enhance performance on precise SMA; however, existing deep learning based SMA methods mostly focus on single cell scale, which presents a challenge for obtaining single-sperm-image datasets (one sperm cell per image). It is also challenging to integrate current object detection models on low-performance devices. this paper presents a lightweight model for sperm detection. By removing 50% of convolutional kernels cutting the large-object-detecting head from YOLOv5s, our model got a similar precision to the original YOLOv3 (mAP.5 of 0.957 and 0.947, respectively), but with a model size of only 2.8 MB (123.6 MB of YOLOv3). there is a slight loss in precision compared to YOLOv5s (mAP.5:.95 of 0.604 with 14.4MB model size); however, our model still shows a significant advantage in reducing the number of parameters. Experimental results also indicated that MS COCO pre-training is helpful in sperm detection tasks, and the mosaic augmentation strongly enhances the precision for all YOLO models.

关键词： Deep learning Head Computational modeling Morphology Real-time systems Complexity theory Electron microscopy

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Intelligent distributed Method to Secure Stored Data in Cloud computing

Intelligent Distributed Method to Secure Stored Data in Clou...

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ieee UP Section conference on Electrical Computer and Electronics (UPCON)

作者： Siddharth Pokhriyal Rashmi Sharma Krishna Engineering College Ghaziabad U.P India

ISBN: (纸本)9781665409636

computing using cloud services is a new and different paradigm that is manufacturing nice changes to enterprise hardware and code style additionally opening a massive path to the web-based computing services for meeting the varied wants because the cloud is gaining huge advancement cloud knowledge security and privacy of information is turning into an essential issue. If we have a tendency to take into account all the safety problems associated with the cloud then one amongst the main issues is that cloud operators have an opportunity to succeed in the cloud user’s sensitive knowledge. this concern dramatically decreases the usability of cloud computing in several areas, like monetary organizations. this paper concentrates on the difficulty that cloud operators cannot reach the sensitive info of cloud users and thus proposes a new cryptography method by which cloud administrators cannot gain access to the entire information. the proposed approach divides the data into three parts and thus the data is stored in three different distributed clouds due to which cloud operators cannot reach the complete data, they can only gain access to the partial data which is of no use. In this research we will present a solution to the above- stated problem and will try to develop a model which focuses on secure cloud data model with acceptable computation time.

关键词： Cloud computing Processor scheduling Computational modeling distributed databases Turning Data models Scheduling

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Dental Detection and Classification of YOLOv3-SPP based on Convolutional Block Attention Module

Dental Detection and Classification of YOLOv3-SPP based on C...

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international conference on Computer and Communications (ICCC)

作者： Ning Li Ruifeng Guo Xiaozhou Liu Lin Wu Hongliang Wang Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Shenyang China Liaoning Provience Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Liaoning Provincial Key Laboratory of Oral Diseases School and Hospital of Stomatology China Medical University Shenyang China

the purpose of this research paper is to implement the tooth target detection task by deep convolutional neural networks. In order to solve the problems of low accuracy of target detection due to the high similarity between teeth and complex tooth textures, an improved tooth detection method with YOLOv3-SPP model is proposed in this paper. this method incorporates the convolutional block attention module (CBAM) in the YOLOv3-SPP algorithm framework, and improves the performance of the network features by adding the channel attention mechanism and spatial attention mechanism to the feature extraction network to enhance the saliency of the tooth target region in the image. Secondly, CIoU border regression loss is introduced to improve the localization accuracy. In addition, a Non-maximum suppression (NMS) method is used to solve the candidate frame overlap problem. through experiments, it is shown that the mAP of the modified YOLOv3-SPP target detection model is improved to 86.8 percent, which indicates that the improved model can be applied to the detection of teeth.

关键词： Location awareness Training Teeth object detection Feature extraction Dentistry Classification algorithms

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Syzballer: Kernel Fuzzing Based on Basic Block Weight and Multi-armed Bandit

Syzballer: Kernel Fuzzing Based on Basic Block Weight and Mu...

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international conference on Computer and Communications (ICCC)

作者： Zhengyang Huang Xuyan Song Yifan Luo Jun Yang Baojiang Cui School of Computing Beijing University of Posts and Telecommunications Beijing China School of Cyber Security Beijing University of Posts and Telecommunications Beijing China

the Linux operating system is now extensively used on personal computers, cloud platforms, and enterprise servers. the security of the Linux kernel has also increased in importance. Several techniques, such as symbolic execution, data flow analysis, and reinforcement learning, have been adapted for vulnerability discovery in recent years. Among these techniques, fuzzing is the most widely used one. However, the ease of accessing each kernel code basic block has not been considered in previous research. this means many high-risk vulnerabilities cannot be detected. To solve this problem, we present Syzballer, a hybrid fuzzer that combines multi-armed bandits with basic block weight, which is calculated by traversing the control flow graph generated by the kernel source code. First, we compile the kernel source code into LLVM bitcode and use the static analysis tool SVF to compute the weight of each basic block. then we launched the fuzzer and loaded the weight file. Finally, a multi-armed bandit machine model is used to dynamically alter the task and seed selection. To verify the effectiveness of Syzballer, we compared it with the two most popular kernel fuzzers, Syzkaller and Syzvegas. Experiments have demonstrated that our Syzballer has improved in terms of code coverage and vulnerability detection.

关键词： Codes Source coding Linux Static analysis Reinforcement learning Fuzzing Servers

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Massively Parallel Processing Database for Sequence and Graph Data Structures Applied to Rapid-Response Drug Repurposing 8

Massively Parallel Processing Database for Sequence and Grap...

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8th ieee international conference on Big Data (Big Data)

作者： Rickett, Christopher D. Maschhoff, Kristyn J. Sukumar, Sreenivas R. Hewlett Packard Enterprise Spring TX 77389 USA

ISBN: (纸本)9781728162515

In this paper, we present the application of a massively parallel-processing graph database for rapid-response drug repurposing. the novelty of our approach is that the scalable graph database is able to host a knowledge graph of medically relevant facts integrated from multiple knowledge sources and also act as a computational engine capable of in-database protein sequence analytics. We demonstrate the performance of the graph database on a real-world use-case to hypothesize cures for COVID-19, leveraging its built-in accelerated protein-sequence matching capabilities at unprecedented scale (to simultaneously handle data size and query latency requirements for interactive research). Based on supporting evidence from medical literature, we show that results generated by computing similarity of COVID-19 virus proteins across 4 million other open-science sequences and intelligently traversing over a 150 billion facts from open-science medical knowledge produces biologically insightful results. By presenting sample queries and extending application to use-cases beyond COVID-19, we demonstrate the use and value of the novel database for hypotheses generation in reducing the time-to-insight and increasing researcher productivity with interactivity.

关键词： graph database graph analytics in-database analytics distributed processing parallel processing sequence analytics

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the Adoption of enterprise Architecture: An Institutional theory Perception 8

The Adoption of Enterprise Architecture: An Institutional Th...

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8th international conference on Information Technology and Multimedia, ICIMU 2020

作者： Ahmad, Nor Azizah Drus, Sulfeeza Mohd. Kasim, Hairoladenan College of Computing and Informatics Universiti Tenaga Nasional Informatics Department Putrajaya Malaysia

ISBN: (数字)9781728173108

ISBN: (纸本)9781728173108

Numerous models are suggested in the extant literature to explain the adoption of technologies in which pressure considerations of the adoption are not discussed. Based on institutional theory, this study suggests a model for analyzing the three (3) pressures of normative, coercive, and mimetic factors within the scope of enterprise architecture (EA). the model is examined by 255 respondents using the survey questionnaires. the results indicate that normative, coercive, and mimetic pressures have a significant impact on the decision to adopt EA. the study leads an operational review in the context of EA at the organizational level. the results also provide valuable insights into the management of the EA process for decision-makers and EA initiators. © 2020 ieee.

关键词： Surveys

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