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International conference on parallel distributed and Grid Computing (PDGC)

作者： Mandeep Kaur Prachi Garg Department of Computer Applications Panipat Institute of Engineering and Technology Samalkha Research Scholar DCSE Maharishi Markandeshwar University Mullana India Department of CSE Maharishi Markandeshwar University Mullana India

ISBN: (纸本)9781665454025

The changes in technologies has also changed the way we compute. Computing applications provide various types of functionalities. However, a common thing is to secure the same computing system. It requires a high level of developer skills to secure a system. Generally, verifying users before access of services, encryption of data, and techniques of parallel access of information by multiple users is done to ensure only valid users can access the services. One need to verify person, device, process, or service before it access the related service(s). In this paper, we present a review of authentication techniques used in computing computing. It elaborates methods used for traditional authentication using articles, letters, people, passwords, one-time passwords, digital certificates, two-way authentication to latest behavioural, doodles, image sequence, gestures based recognition of users using biometrics, gait-based and their behavioural analytics. It also discusses key features of various methods including gaps and scope of improvement.

关键词： Cloud computing image recognition Authentication Passwords Grid computing image sequences Security

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A parallel compression framework for fractal images using DCT block classification

A parallel compression framework for fractal images using DC...

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IEEE International Symposium on parallel and distributed processing with Applications and IEEE International conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Jiahao Pang Jie He Hui Guo Minglang Chen Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China

Fractal image compression has higher space domain compression performance compared to the mainstream image compression algorithms using discrete cosine transform, but the search time for coding fractal image compression is too long and less practical. In this paper, we propose a fractal image block classification scheme based on discrete cosine transform, and use NVIDIA CUDA architecture to achieve parallel coding, which shows that this scheme can achieve 107 times faster speedup than the basic fractal scheme, and the coding time is significantly reduced, and the PSNR decreases only about 0.4, so this scheme has greater practicality.

关键词： image coding Graphics processing units Computer architecture Ubiquitous computing Fractals Classification algorithms Discrete cosine transforms

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Secure Coded Computation for Efficient distributed Learning in Mobile IoT 18

Secure Coded Computation for Efficient Distributed Learning ...

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18th Annual IEEE International conference on Sensing, Communication, and Networking (SECON)

作者： Yang, Yilin D'Oliveira, Rafael G. L. El Rouayheb, Salim Yang, Xin Seferoglu, Hulya Chen, Yingying Rutgers State Univ New Brunswick NJ 08854 USA MIT Cambridge MA 02139 USA Univ Illinois Chicago IL USA

ISBN: (纸本)9781665441087

distributed computation plays an essential role in cloud and edge computing. Data such as images, audio, and text can be represented as matrices to facilitate efficient computation, especially in the domains of distributed machine learning, computer vision, and signal processing. Many coded computation algorithms have been proposed for big data applications to securely partition and distribute matrices to parallel worker devices. However, these proposals have yet to he adapted for mobile platforms beyond theoretical means. Mobile IoT networks can greatly benefit from secure distributed computing, however, commercial devices such as smartphones and tablets are much more limited in resources compared to platforms in data centers, requiring special design considerations. We investigate existing distribution schemes from an operational complexity and security viewpoint and study their perlbrmance in several mobile IoT networks, identifying performance bottlenecks in regards to communication and computation costs. From our findings, we propose new, scalable algorithms optimized to handle the unique constraints of mobile loT. Extensive evaluations of our proposals on publicly available image classification datasets show how distributed learning can be specially optimized to enhance runtime and battery performance on mobile loT by over 10x.

关键词： distributed computing coded computations edge device mobile IoT

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Hardware-Efficient Ultrasonic Entrance Counting: Comparing Different Machine Learning Approaches 26

Hardware-Efficient Ultrasonic Entrance Counting: Comparing D...

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26th International conference on Pattern Recognition / 8th International Workshop on image Mining - Theory and Applications (IMTA)

作者： Langer, Tim Waschneck, Bernd Partzsch, Johannes Kelber, Florian Mayr, Christian Infineon Technol Dresden GmbH Co KG Dresden Germany Fraunhofer Inst Integrated Circuits Div Engn Adapt Syst Dresden Dresden Germany Tech Univ Dresden Chair Highly Parallel VLSI Syst & Neuro Microelec Dresden Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

In this work, the classification of walking direction based on ultrasonic signals has been examined for entrance counting. Feed-forward and recurrent neural network architectures as well as simpler machine learning techniques have been investigated and compared with classical signal processing techniques. Using only a single ultrasonic receiver, the focus was set on the development of a hardware-efficient system concept. Different ultrasonic measurement methods in time and frequency domain have been compared with the perspective of a holistic energy optimization. The analysis of the system's hardware efficiency was completed by an estimation of algorithmic latency, energy and storage consumption based on the arithmetic of the classification algorithms. All algorithms showed an estimated energy consumption of less than 10 mu J for a single inference on a state-of-the-art implementation of an ARM (R) Cortex (R) M4F micro-controller, which was found to be negligible compared to the energy of the measurement principle. Compared to other sensor types and multi-sensor systems, a state-of-the-art test accuracy of 99.72% could be achieved for differentiating between the two entrance directions of a present person and the absence of a person.

关键词： Entrance Counting Ultrasound Machine Learning Edge Computing Low Power TinyML Smart Buildings

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STL-SLAM: A Structured-Constrained RGB-D SLAM Approach to Texture-Limited Environments

STL-SLAM: A Structured-Constrained RGB-D SLAM Approach to Te...

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IEEE/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Juan Dong Maobin Lu Chen Chen Fang Deng Jie Chen School of Automation Beijing Institute of Technology Beijing China Beijing Institute of Technology Chongqing Innovation Center Chongqing China Beijing Institute of Technology Beijing China Tongji University Shanghai China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Most RGB-D-based SLAM methods assume texture-rich environments, making them susceptible to significant tracking errors or complete failures in the absence of texture features. Moreover, many existing methods encounter substantial rotation estimation errors, leading to long-term drift in tracking. This paper proposes a novel structured-constrained RGB-D SLAM method (STL-SLAM) for texture-limited environments. Compared to the existing methods, STL-SLAM can deal with environments without abundant texture information and significantly reduce long-term drift caused by rotation estimation errors. We assess the distribution complexity of pixels in an image by calculating the information entropy and pre-processing accordingly. We also present an efficient Manhattan Frames (MF) detection strategy based on orthogonal planes and lines. If MF is detected, we decouple rotation and translation, estimate drift-free rotation based on the Manhattan World (MW) coordinate system, and then estimate translation by minimizing the re-projection error of point, line, and plane features. In non-Manhattan Frames, the 6-DoF pose estimation is performed holistically, with the incorporation of structural constraints of parallel and perpendicular planes, as well as parallel and vertical lines, into the optimization process. Finally, we evaluate our method on public datasets and in real-world environments, which shows that our proposed method achieves superior performance compared to its counterparts.

关键词： Estimation error Simultaneous localization and mapping Translation Pose estimation Lighting parallel processing Indoor environment Information entropy Optimization Intelligent robots

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Quick and Efficient Large Scale Approximate Nearest Neighbor Search on High-Dimensional Data

Quick and Efficient Large Scale Approximate Nearest Neighbor...

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International conference on Wavelet Active Media Technology and Information processing (ICWAMTIP)

作者： Hu Zhibing University Of Electronic Science and Technology Of China

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

Large scale approximate nearest neighbor search on high-dimensional data is always a critical research question, including recommendation systems and image search. Graph-based ANNS algorithms have received more and more attention due to its accuracy and efficiency. Nevertheless, the graph-based ANNS algorithms faces the following challenges: computation latency and memory requirements. In this work, we present a novel graph-based similarity search algorithm called HNSWQ, which optimizes the HNSW algorithm by building quick and comprehensive uppers without using compression. Those uppers can bring high-quality entry point to accelerate query processing by reducing the number of points to be accessed during the search and shorten the whole computation latency. At the same time, we consider the parallelism of GPU and take advantage of GPU to shorten the build and search time of the novel HNSWQ algorithm. Finally, we verify the presented algorithm on several representative datasets. Compared with the state-of-the-art graph-based algorithm, our novel method can acquire great accuracy and search speed.

关键词： Accuracy High dimensional data Query processing Memory management Graphics processing units Nearest neighbor methods parallel processing Media Approximation algorithms Recommender systems

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Utilizing Correlation Analysis for Validation of image Datasets in Lower Spine Diagnosis: A Study on MRI image Dataset from Multiple Labs in Punjab

Utilizing Correlation Analysis for Validation of Image Datas...

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Blockchain and distributed Systems Security (ICBDS), IEEE International conference on

作者： Ruchi Dalwinder Singh Manik Rakhra Arun Singh Department of Computer Science & Engineering Lovely Professional University Jalandhar Phagwara India

For the purposes of making a diagnosis of a condition affecting the lower spine, the collection of data is an obligatory requirement. It's possible that the data that was obtained was in the form of text, but it may also have been an image. The gathering of data in the work that is being suggested is, to a primary extent, dependent upon picture datasets. MRI image dataset compiled by travelling to a number of different labs in Punjab (Pathankot) and making use of the internet as a method of gathering. The utilisation of correlation analysis is what enables the validation of datasets to become a reality. The data that has been obtained has inconsistencies that need to be fixed in order to be useful. The pre-processing mechanism that comprises edge detection and contrast stretching is also illustrated through the use of this proposed study's use of the procedure, along with the result of employing the technique. A comparison of the outcomes achieved by utilising methods such as histogram equivalence and slicing in conjunction with contrast stretching gives the largest possible result of contrast stretching. This conclusion was reached after doing the comparison. Only 3032 of the dataset's 5000 samples exhibit a positive connection; hence, only those samples were maintained to indicate decreased spine detection. The dataset contains a total of 5000 samples. The dataset contains samples that were collected from a wide range of laboratories.

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Skyline Detection Method Based on Offset Heatmap Network

Skyline Detection Method Based on Offset Heatmap Network

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Electronic Technology, Communication and Information (ICETCI), IEEE International conference on

作者： Dong Li Zhongjian Wu Zhiyong Zuo Han Wen Pengxin Kang Chaoze Zhong Zhenbao Luo Southwest Institute of Technical Physics Chengdu China

ISBN: (数字)9798350361643

ISBN: (纸本)9798350361650

Skyline detection plays a crucial role in fields such as reconnaissance and photoelectric guidance. This paper presents a deep learning-based skyline detection method utilizing heatmaps as the output of the skyline neural network. To address the downsampling issue prevalent in heatmap-based methods, we propose the use of offset compensation heatmaps to restore the downsampled points. Additionally, we enhance the YOLO network to improve the resolution of the output heatmaps. Experimental results demonstrate that our method is highly adaptable to complex scenes and performs at inference speeds suitable for edge deployment.

关键词： Heating systems YOLO image edge detection Neural networks Reconnaissance parallel processing Feature extraction

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Unsupervised Graph-Sequence Anomaly Detection for 5G Core Network Control Plane Traffic

Unsupervised Graph-Sequence Anomaly Detection for 5G Core Ne...

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International conference on parallel and distributed Systems (ICPADS)

作者： Min Wang Peng Li Zhang Cheng Wenmao Liu Lei Nie Haizhou Bao Qin Liu Kai Zhang School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System China NSFOCUS Technologies Group Co. Ltd School of Cyber Science and Engineering Wuhan University China

5G Core network (5GC) employs a Service Based Architecture (SBA). This architecture decomposes the control plane into multiple independent Network Functions (NFs). NFs open interfaces to provide services to other NFs, which makes the control plane more susceptible to external malicious attacks. However, existing anomaly detection methods focus more on traffic statistics features and are difficult to apply to the 5GC control plane. In this paper, we proposed GSAD, a Graph-Sequence analysis-based Anomaly Detection method for 5GC control plane traffic. We model control plane traffic as a directed graph to depict topological and NF interaction information. Further, we use the normalizing flows with temporal dependencies to mine the sequential information in the traffic. GSAD combines the topological and sequential information to provide fine-grained detection. We evaluate our proposed framework on the 5GC testbed using Free5GC and UERANSIM in various scenarios. Experimental results demonstrate that our framework outperforms the baselines significantly in terms of detection performance.

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Bigflow: A General Optimization Layer for distributed Computing Frameworks

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Journal of Computer Science & Technology 2020年第2期35卷 453-467页

作者： Yun-Cong Zhang Xiao-Yang Wang Cong Wang Yao Xu Jian-Wei Zhang Xiao-Dong Lin Guang-Yu Sun Gong-Lin Zheng Shan-Hui Yin Xian-Jin Ye Li Li Zhan Song Dong-Dong Miao Baidu Inc. Beijing 100193China Center for Energy-Efficient Computing and Applications Peking UniversityBeijing 100871China

As data volumes grow rapidly, distributed computations are widely employed in data-centers to provide cheap and efficient methods to process large-scale parallel datasets. Various computation models have been proposed to improve the abstraction of distributed datasets and hide the details of parallelism. However, most of them follow the single-layer partitioning method, which limits developers to express a multi-level partitioning operation succinctly. To overcome the problem, we present the NDD (Nested distributed Dataset) data model. It is a more compact and expressive extension of Spark RDD (Resilient distributed Dataset), in order to remove the burden on developers to manually write the logic for multi-level partitioning cases. Base on the NDD model, we develop an open-source framework called Bigflow, which serves as an optimization layer over computation engines from most widely used processing frameworks. With the help of Bigflow, some advanced optimization techniques, which may only be applied by experienced programmers manually, are enabled automatically in a distributed data processing job. Currently, Bigflow is processing about 3 PB data volumes daily in the data-centers of Baidu. According to customer experience, it can significantly save code length and improve performance over the intuitive programming style.

关键词： distributed computing programming model optimization technique

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