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Leveraging Synergies Between AI and networking to Build Next Generation Edge Networks

Leveraging Synergies Between AI and Networking to Build Next...

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international conference on Collaborative computing: networking, Applications and Worksharing (CollaborateCom)

作者： Sen Lin Ming Shi Anish Arora Raef Bassily Elisa Bertino Constantine Caramanis Kaushik Chowdhury Eylem Ekici Atilla Eryilmaz Stratis Ioannidis Nan Jiang Gauri Joshi Jim Kurose Yingbin Liang Zhiqiang Lin Jia Liu Mingyan Liu Tommaso Melodia Aryan Mokhtari Rob Nowak Sewoong Oh Srini Parthasarathy Chunyi Peng Hulya Seferoglu Ness Shroff Sanjay Shakkottai Kannan Srinivasan Ameet Talwalkar Aylin Yener Lei Ying The Ohio State University Purdue University University of Texas Austin Northeastern University University of Illinois Urbana Champaign Carnegie Mellon University University of Massachusetts Amherst University of Michigan University of Wisconsin Madison University of Washington University of Illinois Chicago

networking and Artificial Intelligence (AI) are two of the most transformative information technologies over the last few decades. Building upon the synergies of these two powerful technologies, we envision designing next generation of edge networks to be highly efficient, reliable, robust and secure. To this end, in this paper, we delve into interesting and fundamental research challenges and opportunities that span two major broad and symbiotic areas: AI for Networks and Networks for AI. The former deals with the development of new AI tools and techniques that can enable the next generation AI-assisted networks; while the latter focuses on developing networking techniques and tools that will facilitate the vision of distributed intelligence, resulting in a virtuous research cycle where advances in one will help accelerate advances in the other. A wide range of applications will be further discussed to illustrate the importance of the foundational advances developed in these two areas.

关键词： Wireless communication Symbiosis Privacy Uncertainty Program processors Reliability engineering Security

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Application of Machine Learning in the Classification of Data over Social Media Platform

Application of Machine Learning in the Classification of Dat...

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international conference on Parallel distributed and Grid computing (PDGC)

作者： Harinder Singh Shahanawaj Ahamad G. Taviti Naidu Venkataramana Arangi Ashok Koujalagi Dharmesh Dhabliya Dept. of CS & IT Sant Baba Attar Singh Khalsa College Sandaur Punjab India College of CSE University of Hail Hail City Saudi Arabia Dept. of Management Dr. L. Bullayya College Visakhapatnam AP India Dept. of Commerce and Management Andhra University Visakhapatnam AP India Dept. of CSE Godavari Institute of Engineering and Technology (Autonomous) AP India Dept. of IT Vishwakarma Institute of Information Technology Pune Maharashtra India

ISBN: (纸本)9781665454025

Data classification operation over social media platform such as twitter, Facebook and what’s app is in demand from last two decades. There have been several approaches that are used for data classification such as machine learning and different soft computing techniques. It has been observed that machine learning has made significant contribution in data classification. There have been several research work that did work on data classification using machine learning but the issue with existing research work was limited performance and lack of accuracy. Present research work has focused on application of machine learning in classification of data over social media platform. The theme of work is to enhance the accuracy of classification model that is making use of machine learning technique. In proposed work user comments over social platform have been considered for classification that would support sentiment analysis along with opinion mining.

关键词： Training Sentiment analysis Social networking (online) Social groups Blogs Machine learning Grid computing

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Performance Considerations for Edge Blockchain Systems in Emerging 5G Data Networks 20

Performance Considerations for Edge Blockchain Systems in Em...

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21st international conference on distributed computing and networking (ICDCN)

作者： Krishnaswamy, Dilip Reliance Jio Infocomm Ltd Navi Mumbai India

ISBN: (纸本)9781450377515

As edge computing applications become increasingly relevant to society, data processing and storage requirements are expected to increase at the 5G networked edge. At the same time, with the emergence of blockchain and distributed ledger technologies, one can provide support for trust, immutability, and transparency to share information among networked entities at the edge and in the cloud in 5G networks. A performance model for a permissioned private blockchain platform is developed. Depending on the latency sensitivity and throughput requirements of a 5G edge application, different possibilities are explored to provide support for blockchain technology at the edge. In particular, applications to 5G use-cases are considered, and lazy ledger decoupling between the 5G edge and the 5G cloud is proposed to meet edge latency constraints.

关键词： Blockchain Systems distributed Ledger Technology 5G Edge Data enters Edge Processing Latency Throughput 5G Data Network

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Hidden Markov Model Spectrum Predictor for Poisson distributed Traffic 16

Hidden Markov Model Spectrum Predictor for Poisson Distribut...

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16th international conference on Wireless and Mobile computing, networking and Communications (IEEE WiMob)

作者： Bezerra, Rodrigo F. Bordim, Jacir L. Lamar, Marcus, V Caetano, Marcos F. Univ Brasilia Dept Comp Sci Brasilia DF Brazil

ISBN: (纸本)9781728197227

Static spectrum allocation policies allied with the increasing demand for higher data rates stimulated the pursuit of alternative spectrum allocation strategies. In this context, Opportunistic Spectrum Access (OSA) has been considered an alternative to allow licensed portions of the spectrum to be shared with unlicensed users. OSA requires unlicensed users to identify unused portions of the spectrum for opportunistic access that minimizes possible interference with the licensed users. Accurate mechanisms to avoid interference and improve the spectrum usage is highly desirable. This work investigates the performance of a traditional Hidden Markov Model (HMM) predictor where the licensed traffic follows a Poisson distribution. The results show that, under the evaluated settings, traditional HMM predictor can improve spectrum usage up to 15% at the expense of a high rate (approximate to 50%) of inaccurate forecasts of idle periods. Based on these results, this paper proposes two techniques to optimize prediction performance and reduce inaccurate forecasts rate. Using the proposed enhancements inaccurate forecasts rate was reduced to only 6%. An additional benefit was observed when reducing collision with the licensed user, that is an improvement in the amount of slots effectively used by the unlicensed users from 15% to 23%.

关键词： Hidden Markov Model opportunity forecasting Poisson distributed Traffic Cognitive Radio opportunistic spectrum access

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PPSS: A Privacy-Preserving Supervision Scheme Based on Blockchain

PPSS: A Privacy-Preserving Supervision Scheme Based on Block...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Ming Chen Peichang Shi Xiang Fu Feng Jiang Fei Gao Penghui Ma Jinzhu Kong National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha China Kylin Software Co.LTD Changsha China Zhejiang Lab Hangzhou China

In JointCloud computing, multi-party participation introduces complexity and uncertainty. For all participants in JointCloud computing, both continuous supervision and necessary privacy protection are required. Traditional supervision methods usually adopt the centralized information interaction mode, which has such defects as collusion of interests, single point of failure, privacy disclosure, etc. Building a decentralized supervision mechanism has become a new research direction. In this paper, we propose PPSS, a privacy-preserving supervision scheme based on blockchain, which decentralizes the supervision of the participants in JointCloud computing, and combines the “double encryptions” and “threshold encryption” technologies to provide privacy protection. While making full use of the decentralization of the blockchain, a committee is established to carry out the analysis and decision-making tasks in terms of supervision and privacy protection. Experimental results indicate that PPSS can balance performance and security by reasonably configuring the committee.

关键词： Privacy Uncertainty Decision making Buildings Reliability engineering Blockchains Complexity theory

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A Cooperative Caching Scheme in Fog/Sensor Nodes for CCN

A Cooperative Caching Scheme in Fog/Sensor Nodes for CCN

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international Wireless Communications and Mobile computing conference, IWCMC

作者： Youssef Sellami Ghada Jaber Ahmed Lounis Hicham Lakhlef Abdelmadjid Bouabdallah Sorbonn&#x00E9 Universites Universit&#x00E9 de Technologie de Compi&#x00E8 gne Compi&#x00E8 gne Cedex France

Current Internet of Things (IoT) and Wireless Sensor Networks (WSNs) designs struggle to meet the excessive demand for content by an increasing number of smart objects, in terms of bandwidth and energy consumption. At the same time, the presence of devices with relatively high resources in the vicinity of the IoT devices, such as fog computing, may provide a balance between the cloud and end devices. In this paper, we propose to design and implement a distributed fog caching architecture for Content-Centric networking (CCN). Such an architecture consists of sensor sub-networks connected to one or more fog nodes that manage the internal caching policy and the interactions with fog nodes for efficient content caching and retrieval. Through extensive simulations, we show that our strategy achieves better results compared to existing strategies in terms of energy consumption, cache hit ratio, network congestion, and user satisfaction rate.

关键词： Wireless communication Wireless sensor networks Energy consumption Simulation Information-centric networking Computer architecture Energy states

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Context-aware Community Evolution Prediction in Online Social Networks

Context-aware Community Evolution Prediction in Online Socia...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Alexander Lercher Nishant Saurabh Radu Prodan Institute of Information Technology University of Klagenfurt Austria Department of Information and Computing Sciences Utrecht University Netherlands

Community evolution prediction enables business-driven social networks to detect customer groups modeled as communities based on similar interests by splitting them into temporal segments and utilizing ML classification to predict their structural changes. Unfortunately, existing methods overlook business contexts and focus on analyzing customer activities, raising privacy concerns. This paper proposes a novel method for community evolution prediction that applies a context-aware approach to identify future changes in community structures through three complementary features. Firstly, it models business events as transactions, splits them into explicit contexts, and detects contextualized communities for multiple time windows. Secondly, it uses novel structural metrics representing temporal features of contextualized communities. Thirdly, it uses extracted features to train ML classifiers and predict the community evolution in the same context and other dependent contexts. Experimental results on two real-world data sets reveal that traditional ML classifiers using the context-aware approach can predict community evolution with up to three times higher accuracy, precision, recall, and F1-score than other baseline classification methods (i.e., majority class, persistence).

关键词： Measurement Privacy Social networking (online) Predictive models Feature extraction Context modeling Business

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Poster: CarML: distributed machine learning in vehicular 26

Poster: CarML: Distributed machine learning in vehicular

引用

26th Annual international conference on Mobile computing and networking, MobiCom 2020

作者： Du, Anran Shen, Yicheng Tseng, Lewis Boston College MA United States

This paper presents CarML, a distributed machine learning platform built on top of an emerging computing paradigm, vehicular clouds. We discuss our design and technical challenges, followed by our preliminary solutions. We verify the efficacy of our solutions using a customized simulator based on Python and SUMO. © 2020 Owner/Author.

关键词： Fault tolerance

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Age Prediction on social media using Machine Learning and AI

Age Prediction on social media using Machine Learning and AI

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Advances in computing, Communication Control and networking (ICAC3N), international conference on

作者： Ojasw Khare Kadambari Agrawal Aastha Sharma Sandhya Awasthi Ayushi Prakash Department of CSE ABES Engineering College Ghaziabad UP India

ISBN: (数字)9798350330861

ISBN: (纸本)9798350330878

Personalized marketing, age-based demographic analysis, and facial recognition systems are just a few of the many uses for accurate age prediction from facial photos. With the FG-NET dataset, a benchmark dataset frequently used for age estimation tasks, we investigate the usage of deep learning approaches for age prediction in this research. Convolutional neural networks (CNNs) are used in our method to extract meaningful representations from facial photos and forecast the related age labels. We upgrade the pretrained model on the FG-NET dataset using a cutting-edge architecture, such as VGGNet or ResNet, to enhance age prediction accuracy. We adhere to a strict experimental setup to achieve reliable evaluation. We have divided the FG-NET dataset into training, validation, and test sets, making sure that the age groups in each set were distributed equally. To lessen overfitting and increase generalization, we preprocess the photos by aligning the faces, pixel-value normalization, and data augmentation. We use appropriate loss functions, like mean squared error or cross-entropy loss, during the training phase to optimize the network’s parameters. In order to improve the performance of the model, we additionally investigate the impact of other hyper parameters, including learning rate, batch size, and regularization *** the model has been trained, we assess its effectiveness using measures like mean absolute error (MAE) and mean squared error (MSE) on the test set. To confirm the efficacy of our strategy, we compare our findings with those of other cutting-edge approaches for age prediction using the FG-NET dataset. Our tests show that age prediction tasks can be successfully completed by deep learning models trained on the FG-NET dataset. Our method advances facial analysis research by reliably determining an individual’s age from facial photos, and it creates opportunities for real-world applications in numerous fields.

关键词： Training Social networking (online) Face recognition Predictive models Real-time systems Face detection Reliability

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News Sentiment and Company Reports Impact on Stock Returns

News Sentiment and Company Reports Impact on Stock Returns

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IEEE international conference on Intelligent Engineering Systems (INES)

作者： Nikita Pashkov Ilya Makarov HSE University Moscow Russia AIRI HSE University ISP RAS Moscow Russia

ISBN: (数字)9798350367591

ISBN: (纸本)9798350367607

Stock market returns prediction is a complicated topic with ever-growing interest within the past 20 years. Recent studies have been constantly trying to find more useful predictors, which quite often were found to be extracted from text sources such as social networks or discussion forums. In this study, we propose to take a closer look at the information contained in Securities and Exchange Committee (SEC) reports in the form of 10-K and 10-Q. In particular, we focus on analyzing the Management Analysis and Discussion (MD&A) *** perform our analysis, we collected and processed almost 300,000 reports following the best practices of distributed computing on PySpark. It is worth noting that, unlike many other research studies in the same field, we consider not only S&P 500 companies but a broader range of companies as well. Based on the MD&A sections, we created a Bag-of-Words (BOW) corpus, and then utilized the TF-IDF technique to determine the weight of each *** evaluate the sentiment of the text, we utilized an exhaustive dictionary of words allocated to different emotional categories. This allowed us to compute sentiment scores for each category. Our findings indicate that certain emotions have a significant correlation with stock returns in the following quarter and can improve the performance of price movement direction classification models. Interestingly, commonly extracted Positive and Negative sentiment scores were found to have little importance, while the Weak and Understatement categories emerged as the top features.

关键词： Discussion forums Dictionaries Correlation Social networking (online) Natural languages Companies Feature extraction

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