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Automatically derived stateful network functions including non-field attributes

Automatically derived stateful network functions including n...

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ieee international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Bin Yuan Shengyao Sun Xianjun Deng Deqing Zou Haoyu Chen Shenghui Li Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Shenzhen Research Institute Huazhong University of Science and Technology Shenzhen China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Cluster and Grid Computing Lab

ISBN: (纸本)9781665416597

The modern network consists of thousands of network devices from different suppliers that perform distinct code-pendent functions, such as routing, switching, modifying header fields, and access control across physical and virtual networks. Because of the network complexity, the network is prone to a wide range of errors, such as false-positive configuration, software errors, or unexpected interactions across protocols. These errors can lead to loops, sub-optimal routing, path leaks, black holes, and access control violations that make services unavailable, vulnerable to exploitation, or prone to attacks (e.g., DDoS attacks). To mitigate these problems, network operators deploy many different stateful network functions, like firewalls, NATs, load balancers, and intrusion-prevention boxes. They have become an important part of networks today, so it is critical to verify that these network functions are the same as expected deployments. All static network verification tools are meant to rigorously check network software or configuration for bugs before deployment. They usually use handwritten models or limited derivation models that are error-prone and ignore the fact that even the same type of network functions (from different vendors) still have different implementation details. In this paper, we propose a tool that can automatically synthesize more realistic and high-fidelity models that include stateful network functions with non-field attributes. We design an inferring algorithm, implement the transformation between data packages and symbolic packages, and obtain a finite state machine that can accurately express the actions of black-box network functions for a given configuration.

关键词： Access control Performance evaluation Privacy Protocols Computational modeling Switches Routing

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Online Community engagement: exposing Information and Communication Technology among Rural Learners during the COVID-19 era

Online Community Engagement: Exposing Information and Commun...

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international conference on Computer Applications and Industrial electronics (ICCAIe)

作者： Shahida Sulaiman Mahirah Afifah Mohd Haliza Sh. Khayulzahri Sh. A Raof Faculty of Computing Universiti Teknologi Malaysia Johor Bahru Johor Malaysia Southeast Johor Development Authority (KEJORA) KEJORA Headquarters Jln. Dato’ Haji Hassan Yunus Bandar Penawar Johor Malaysia

Due to the COVID-19 pandemic in 2020, many sectors including education have been familiar with online activities. It has also affected community engagement (Ce) or service learning among academicians and university students. This study reports an online intervention programme under a university community project that aims to expose Information and Communication Technology (ICT) among rural students during the COVID-19 era in 2022. It focused on the use of ICT in learning Computer science and english subjects for the secondary level. A total of 47 secondary students from six rural schools in Southeast Johor region participated in the online programme known as e-THRiL from February to April 2022. The findings reflect positive impacts in students’ knowledge before and after joining the online programme that had also fulfilled their needs. In addition, it also accomplished both targeted changes and sustainability aspects through the activities and the exposed ICT. In brief, despite the key challenge in the Internet access, the online Ce could be conducted successfully among the selected rural learners who were exposed with numerous tools, applications, and methods in using ICT for learning english and Computer science. Students’ experience in online learning due to the COVID-19 pandemic since 2020 could have contributed to the acceptance of online Ce.

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efficient Implementation of Kyber on Mobile Devices 27

Efficient Implementation of Kyber on Mobile Devices

引用

27th ieee international conference on Parallel and Distributed Systems, ICPADS 2021

作者： Zhao, Lirui Zhang, Jipeng Huang, Junhao Liu, Zhe Hancke, Gerhard Nanjing University of Aeronautics and Astronautics Jiangsu China Beijing Normal University-Hong Kong Baptist University United International College Guangdong China State Key Laboratory of Cryptology Beijing100878 China City University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9781665408783

Kyber, an IND-CCA-secure key encapsulation mechanism (KeM) based on the MLWe problem, has been shortlisted for the third round evaluation of the NIST Post-Quantum Cryptography Standardization. In this paper, we explored the optimizations of Kyber in high-performance processors from the ARM Cortex-A series, which are widely used in mainstream mobile phones. To improve the performance of Kyber, we utilized the powerful SIMD instruction set NeON in an ARMv8-A to parallelize the core modules of Kyber, i.e., modular reduction and NTT. Specifically, we specially designed the optimized implementation based on the characteristic of the NeON instruction set for the Barrett and Montgomery reduction algorithms. To make full use of the computing power of NeON instructions, we proposed a novel strategy for computing the 16-bit Barrett reduction without handling the 32-bit intermediate result. Our Barrett and Montgomery reduction showed 8.52 and 8.89 times faster than the reference implementation. As for NTT/INTT, we adopted the 2+5 layer merging strategy on an ARMv8-A to implement NTT/INTT after carefully analyzing the register occupancy of various layer merging techniques. Thanks to the selected layer merging strategy, our NTT and INTT achieved 11.89 and 13.45 times speedups compared with the reference implementation. Our optimized software achieved 1.77×, 1.85×, and 2.16× speedups for key generation, encapsulation, and decapsulation compared with Kyber's reference implementation. © 2021 ieee.

关键词： Neon

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efficient Architecture Paradigm for Deep Learning Inference as a Service 39

Efficient Architecture Paradigm for Deep Learning Inference ...

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39th ieee international Performance computing and Communications conference (IPCCC)

作者： Yu, Jin Ke, Xiaopeng Xu, Fengyuan Huang, Hao Nanjing Univ State Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781728198293

Deep learning (DL) inference has been broadly used and shown excellent performance in many intelligent applications. Unfortunately, the high resource consumption and training efforts of sophisticated models present obstacles for regular users to enjoy it. Thus, Deep Learning Inference as a Service (DIaaS), offering online inference services on cloud, has earned great popularity among cloud tenants who can send their DIaaS inputs via RPCs across the internal network. However, such detached architecture paradigm is inappropriate to DIaaS because the high-dimensional inputs of DIaaS consume a lot of precious internal bandwidth and the service latency of DIaaS has to be low and stable. We therefore propose a novel architecture paradigm on cloud for DIaaS in order to address the above two problems without giving up the security and maintenance benefits. We first leverage the SGX technology, a strongly-protected user space enclave, to bring DIaaS computation to its input source as close as possible, i.e. co-locating a cloud tenant and its subscribed DIaaS in the same virtual machine. When the GPU acceleration is needed, we migrate this virtual machine to any available GPU host and transparently utilize the GPU via our backend computing stack installed on it. In this way the majority of internal bandwidth is saved compared to traditional paradigm. Furthermore, we greatly improve the efficiency of the proposed architecture paradigm, from the computation and I/O perspectives, by making the entire data flow more DL-oriented. Finally, we implement a prototype system and evaluate it in real-world scenarios. The experiments show that our locality-aware architecture achieves the average single CPU (GPU) based deep learning inference time 2.84X (4.87X) less than the traditional detached architecture on average.

关键词： Deep Learning SGX Cloud Service Bandwidth Optimization GPU computing Stack

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Visual Clustering Analysis of electricity Data Based on t-SNe 5

Visual Clustering Analysis of Electricity Data Based on t-SN...

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5th ieee international conference on Cloud computing and Big Data Analytics, ICCCBDA 2020

作者： Gong, Feixiang Bu, Fanpeng Zhang, Yufan Yan, Yong Hu, Ruoyun Dong, Mingyu China Electric Power Research Institute Co. Ltd. Beijing China Beijing University of Posts and Telecommunications Beijing China State Grid Zhejiang Electric Power Co. Ltd. Zhejiang Hangzhou China

ISBN: (数字)9781728160245

ISBN: (纸本)9781728160245

The electricity consumption data recorded by smart meters contain plenty of knowledge that contribute to consumers categorization. However, traditional unsupervised methods for electricity data clustering suffer from the following problems: 1) They are not specialized for the high-dimensional electricity data, which have high computational complexity and poor clustering effects. 2) Most of the methods need to specify the model parameters in advance, and the general enumeration methods are inefficient. To solve these problems, we propose a novel Visual Clustering Analysis framework of electricity Data (VCAeD). To improve the validity of electricity data clustering and reduce clustering complexity, the t-SNe dimensionality reduction technology is used to reduce dimensions with information loss as less as possible, and visualization also makes the parameter selection process more intuitive. Relevant experiments on the real-world electricity consumption datasets validate the effectiveness and practicability of the proposed method. © 2020 ieee.

关键词： Smart power grids

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An Introduction to Some Methods for Soft computing in Fluid Dynamics 14th

An Introduction to Some Methods for Soft Computing in Fluid ...

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14th international conference on Soft computing Models in Industrial and environmental Applications (SOCO)

作者： Le Clainche, Soledad Univ Politecn Madrid Sch Aerosp Engn E-28040 Madrid Spain

ISBN: (纸本)9783030200558;9783030200541

In recent years the terms big data, soft computing and machine learning have been widely extended in several research fields, where the analysis of data (mainly time series) provides useful information to solve different types of engineering problems. Turbulent flows and reduced order models (ROMs) are two terms related to the field of fluid dynamics that can be compared to the previous expressions, which are generally used in data science. This paper presents a short introduction to some of the methodologies generally used in fluid dynamics for the analysis of complex flows to extract spatio-temporal features. These methods are also used to construct ROMs to predict spatio-temporal events using a small number of variables. The paper intends to connect the field of computer and data science with the classical terms and methodologies used in fluid dynamics for soft computing and machine learning, known as ROMs.

关键词： Soft computing Machine learning Reduced order model Fluid dynamics Big data CFD Data science POD DMD

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Prediction Performance and explainability of COVID-19 Classification Models 25

Prediction Performance and Explainability of COVID-19 Classi...

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25th international Computer science and engineering conference, ICSeC 2021

作者： Phongchit, Natthawat Taeprasartsit, Pinyo Silpakorn University Department of Computing Nakorn Pathom Thailand

ISBN: (纸本)9781665411974

The COVID-19 pandemic has been causing the need for better lung-Analysis tools to detect disease lesions and evaluate severity of lung damage. However, responsible use of AI in a clinical workflow requires healthcare workers review outputs from AI to judiciously agree or disagree with it. For a classification task, we can examine explanation of its prediction by the use of an explainer such as Grad-CAM. In this work, competent, well-known models, along with their ensemble counterparts, were studied to evaluate their prediction performance and explainability. There are three findings that are main contributions of this work: (1) models with higher prediction performance (e.g., better accuracy) could be outperformed in terms of explainability by models with lower prediction performance, (2) two models producing the same COVID-19 classification result might rely on considerably different features, and (3) while ensemble models created by our approach gained significant improvements in some metrics, they could also get performance degradation in others, and their explainability could be better or worse than their underlying single models. This implication suggests that although we tend to select the model that performs best in metrics that matter the most in a target application, in a clinical environment, it may be better if we employ multiple models so that we can assess explanations from them altogether and prevents incorrect decision from human errors. This is essentially important for a positive case detected by AI, as an accurate model may provide poor explanation, and the expert falsely rejects its correct prediction. © 2021 ieee.

关键词： COVID-19

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Coupling Characteristics of Noise Radiation and Tank Wall Vibrations of Typical Distribution Transformers Based on Field Measurements

Coupling Characteristics of Noise Radiation and Tank Wall Vi...

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2020 ieee international conference on Power, Intelligent computing and Systems, ICPICS 2020

作者： Zhu, Yunxiang Tu, Feng Wang, Zhiyong Hu, Jingyu Shen, Chong Xu, Jianfeng Min, Hequn Cao, Meigen State Grid Zhejiang Electric Power Co. Ltd. Hangzhou310007 China School of Architecture Southeast University Nanjing210096 China Daxing Power Construction Company Shaoxing Jianyuan Power Group Co. Ltd Shaoxing312000 China School of Civil Engineering North China University of Technology Beijing100144 China

ISBN: (数字)9781728198743

ISBN: (纸本)9781728198736

Distribution transformer noise is one of the main factors affecting residents near substations. Studying the relationship between vibration and sound radiation characteristics of tank wall surfaces of typical distribution transformers is of great significance for corresponding noise and vibration control. In this paper, field measurements are conducted to study the coupling between tank wall vibration of 35kV and 110kV distribution transformers and their corresponding near-field and far-field sound radiation. Results show that, vibration and noise radiation of two typical surfaces of distribution transformer are mainly concentrated between 200 Hz and 500 Hz. Coherence between vibration and sound signals proves the strong coupling between the tank wall vibration and noise radiation of distribution transformers. © 2020 ieee.

关键词： Acoustic properties

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Analysing Business Process Anomalies Using Discrete-Time Markov chains 22

Analysing Business Process Anomalies Using Discrete-Time Mar...

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22nd ieee international conference on High Performance computing and Communications, 18th ieee international conference on Smart City and 6th ieee international conference on Data science and Systems, HPCC-SmartCity-DSS 2020

作者： Yang, Lingkai McClean, Sally Donnelly, Mark Khan, Kashaf Burke, Kevin Ulster University School of Computing Jordanstown United Kingdom British Telecom Ipswich United Kingdom University of Limerick Macsi Limerick Ireland

ISBN: (纸本)9781728176499

Within a business context, anomalies can be viewed as indicators for inefficiencies or fraud, which impact upon product quality and customer satisfaction. The development of approaches to monitor, detect and predict anomalous business processes remains an important research topic. In this paper, we propose a method, combining Discrete-Time Markov chains (DTMCs) and hitting probabilities (HP), for detecting anomalies occurring in the execution of business processes. Our method extends standard DTMCs to be able to estimate the probability of occurring for a process instance even though it is partially recorded (i.e., the initial executions are missing). The proposed method, denoted as HPDTMC, does not rely on prior knowledge about anomalies and the business process and can be trained on datasets already consisting of anomalies. A Šidák correction is applied to balance the probability of instances of varying length since naturally, process instances with more executions have lower sequence probability and more likely to be detected as anomalies by using DTMCs. We demonstrate the effectiveness of the method by evaluating it on two artificial datasets and one real-life dataset against seven classic anomaly detection methods. In the experiments, our approach reached an F1 score of 0.904 on average. Moreover, the proposed method outperforms competitors under noisy conditions. The main contribution of this paper is the proposed noise-robust method which is able to detect fully or partially recorded process instances of varying lengths. © 2020 ieee.

关键词： Anomaly detection

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A Pattern for Features on a Hierarchical Spatial grid 21

A Pattern for Features on a Hierarchical Spatial Grid

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Proceedings of the 10th international Joint conference on Knowledge Graphs

作者： Cogan Shimizu Rui Zhu Gengchen Mai Colby Fisher Ling Cai Mark Schildhauer Krzysztof Janowicz Pascal Hitzler Lu Zhou Shirly Stephen Kansas State University United States University of California United States Stanford University United States Hydronos Labs LLC United States UC Santa Barbara United States National Center Ecological Analysis & Synthesis United States Arizona State University United States

ISBN: (纸本)9781450395656

The integration of data along a common spatial component remains an obstacle in many problem spaces. One promising method for integrating data in such a way is through the use of a common, underlying spatial reference system, such as a Discrete Global grid (e.g., the S2 grid System), and pre-computing spatial relations between features and the constituent components at a spatial resolution appropriate for the data and use case. That is, by emphasizing the notion of the cell, we can examine what is in a cell, predict contents of its parent and child cells, and quickly get an overview of spatially co-located features and regions of interest without having to directly compute spatial interactions. This paper provides an ontology design pattern, to be used as a structural template, for modeling how features or regions map onto a hierarchical grid system and addresses how the attributes of these features may be inherited upwards or downwards through the hierarchy. We furthermore provide a motivating example and implementation.

关键词： ontology engineering geoinformation science ontology design pattern

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