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Sensitive Content Detection in Social Networks Using Deep Learning Models and Explainability Techniques

Sensitive Content Detection in Social Networks Using Deep Le...

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Big Data, Cloud computing, Data Science & Engineering (BCD)

作者： Isidoros Perikos Computer Engineering and Informatics Department University of Patras Patras Greece Electrical and Computer Engineering Dept. Computer Technology Institute and Press “Diophantus” University of Peloponnese Patras Greece

ISBN: (数字)9798350394191

ISBN: (纸本)9798350394207

In the dynamic landscape of online social networks, recognizing sensitive content is essential for safeguarding user privacy, fostering inclusivity, and enhancing diversity awareness. Building on prior research, this study explores new dimensions and methodologies for detecting sensitive content. We examine the temporal evolution of sensitive content, revealing how patterns shift over time, and address cross-linguistic challenges, emphasizing cultural and contextual nuances in detection. We employ advanced machine learning techniques, including deep learning models and BERT that improve the accuracy and robustness of the detection procedure. In the experimental study, BERT transformer reported the best performance in detecting sensitive content in text. Additionally, we incorporate explainability techniques such as LIME and SHAP to provide deeper insights into the model's decision-making processes, ensuring predictions are interpretable and reliable. Our work enhances the theoretical framework of sensitive content detection in social networks and provide methods that are accurate and scalable and can facilitate the creation of user-centric interaction that prioritize privacy and user experience.

关键词： Deep learning Privacy Sensitivity Accuracy Social networking (online) Decision making Predictive models Transformers User experience Cultural differences

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Examining Platform Strategy for Influencer Marketing Using Text Mining

Examining Platform Strategy for Influencer Marketing Using T...

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Big Data, Cloud computing, Data Science & Engineering (BCD)

作者： Jin-A Choi Cyril S. Ku School of Communication and Media Montclair State University Montclair USA Department of Computer Science William Paterson University Wayne USA

Social media influencer marketing is widely accepted as an effective approach for companies and brands to reach target consumers and allows advertisers to gather consumers9; feedback in real time. there is limited ... 详细信息

ISBN: (数字)9798350394191

ISBN: (纸本)9798350394207

Social media influencer marketing is widely accepted as an effective approach for companies and brands to reach target consumers and allows advertisers to gather consumers' feedback in real time. there is limited research on the investigation of the efficacy of influencer marketing based on platform strategy, as most literature attributes the success of influencer marketing to that of the source (influencers) and message (content). To fill this gap in research, this study utilizes natural language processing to examine social media users' responses to influencers' advertisements by mining their textual comments on three different major social media platforms: Facebook, YouTube, and X (formerly Twitter). By comparatively analyzing the nature of social media user responses on three different platforms, specifically the evaluation of the advertising messages, varying insights can be gleaned on the efficacy of social media platforms for influencer marketing. the results of sentiment analysis and topic modeling indicate that Facebook yields the most positive responses to advertisements in influencer-generated content as social media users display strong fandom behavior. Moreover, social media users tend to indicate purchase intentions and leave post-purchase reviews on X while forming discussions around the contents of the advertisement on YouTube.

关键词： Text mining Sentiment analysis Video on demand Social networking (online) Reviews Companies Media Data science Real-time systems Web sites

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Recent Efficiency Gains in Deep Learning: Performance, Power, and Sustainability

Recent Efficiency Gains in Deep Learning: Performance, Power...

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9th IEEE international conference on Big Data (IEEE BigData)

作者： Hodak, Miro Dholakia, Ajay Lenovo Infrastruct Solut Grp Morrisville NC 27560 USA

ISBN: (纸本)9781665439022

Deep learning (DL) continues to develop at a rapid pace with improvements coming from both hardware and software sides. In this work we evaluate the strides made over the last 2 years, during which a new generation of GPU accelerators has been introduced and significant algorithmic progress has been made. We find a dramatic improvement in runtime and power usage for a standard AI training workload. Specifically, a 4x improvement in runtime and energy consumption is demonstrated. the improvements are about equally split between hardware and algorithms. Additionally, we examine further ways to improve AI training power consumption on data center servers and identify 3 system level tunings that make most difference. these yield up to 20% more energy savings without any changes to the user code. Implications for the field and ways to make DL more energy-efficient going forward are also discussed. (Abstract)

关键词： deep learning power efficiency distributed computing high performance energy efficiency

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Point cloud indexing using Big Data technologies

Point cloud indexing using Big Data technologies

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9th IEEE international conference on Big Data (IEEE BigData)

作者： Kocon, Kevin Bormann, Pascal Tech Univ Darmstadt Fraunhofer IGD Darmstadt Germany

ISBN: (纸本)9781665439022

We present a system for scalable indexing of very large point cloud data in the cloud using common Big Data technologies. We adjusted the procedure for building the common modifiable-nested-octree data-structure to the Map-Reduce paradigm using Morton-Codes for fast point grouping and sampling. Our system requires only two shuffle operations independently of the size of the point cloud, thus enabling linear scalability with the number of cluster nodes. Combining the modifiable-nested-octree with a hash-based grid also allows our system to efficiently deal with data updates. Based on a series of experiments with freely available point cloud datasets of up to 82 billion points we show that our system outperforms the state-of-the-art by up to a factor of 3. As our system is cloud-native, even the largest point cloud datasets can be indexed efficiently. Storing the resulting data-structure in a distributed database enables fast range queries supporting level of detail for web-based rendering.

关键词： Point cloud indexing Cloud computing Map-Reduce

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Tomato Leaf Disease Classification with Lightweight Federated Learning using Identically distributed Images

Tomato Leaf Disease Classification with Lightweight Federate...

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international conference on Signal Processing and Communication (ICSC)

作者： Naresh Kumar Trivedi Himani Maheshwari Raj Gaurang Tiwari Ambuj Kumar Agarwal Vinay Gautam Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computing Graphic Era Hill University Dehradun Uttrakhand India Department of Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Greater Noida India

Tomatoes grown and consumed extensively in Asia are an essential source of protein (22% per Tomato) and energy (18% per Tomato). Bacterial, fungal, and other microbial diseases severely impact plant health and productivity for tomato farmers. Manual diagnosis of many diseases is challenging, especially in places that need access to crop protection experts. Automated disease identification is crucial for Tomato leaf protection and reduced crop loss. Unfortunately, no one method of categorizing leaf diseases in tomatoes has proven to be consistently accurate enough for widespread usage. the present study offers a streamlined federated deep learning framework to classify tomato leaf diseases while ensuring the preservation of sensitive data. this framework safeguards client information with a decentralized client-server architecture and verifies federated deep learning models with IID. the researchers piloted the framework with a single and several clients in both conventional and federated learning environments. After extracting features from numerous pre-trained models, AlexNet is selected as baseline because of its superior 99.53% accuracy. Next, IID datasets were put through their paces using federal learning (FL). the extra capabilities of the framework make it the ideal option for early Tomato leaf disease categorization.

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thE DEEP distributed LEARNING IMPLEMENTATIONS with A LARGE DATASET on the DESKTOP GRID 9

THE DEEP DISTRIBUTED LEARNING IMPLEMENTATIONS with A LARGE D...

引用

9th international conference "distributed computing and Grid Technologies in Science and Education", GRID 2021

作者： Kurochkin, Ilya Institute for Information Transmission Problems Russian Academy of Sciences Bolshoy Karetny per. 19 build.1 Moscow127051 Russia

Machine learning methods and in particular deep neural networks are often used to solve the problem of image classification. there is a trend towards an increase in training data and an increase in the size of neural network architectures. the process of training a deep neural network with millions of parameters can take thousands of hours on modern computing devices. distributed computing systems can be used to reduce training time. the wide scalability of grid systems and the ease of connecting new computational nodes can significantly reduce the training time for deep neural networks. But at the same time, you should take into account the peculiarities of data exchange between the nodes of the desktop grid system. Methods of organizing distributed deep learning are proposed using the example of the image classification problem. the technique of random formation of a local dataset is proposed. the results of using synchronous and asynchronous approaches to distributed learning are shown. Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Deep neural networks

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PARTICIPATION of Russian INSTITUTES in the PROCESSING and STORAGE of ALICE DATA 9

PARTICIPATION of Russian INSTITUTES in the PROCESSING and ST...

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9th international conference "distributed computing and Grid Technologies in Science and Education", GRID 2021

作者： Kiryanov, Andrey Zarochentsev, Andrey Petersburg Nuclear Physics Institute of NRC "Kurchatov Institute" 1 Orlova Rocha Gatchina 188300 Russia Saint Petersburg State University 7-9 Universitetskaya emb. Saint Petersburg199034 Russia

this report presents the results of the work of Russian institutes in the processing of ALICE experiment data during the last 3 years of the operation of the Large Hadron Collider (LHC) including the end of the LHC Run2 and the 1st year of the COVID-19 pandemic. the main problems and tasks facing both ALICE Grid computing and its Russian segment before the LHC Run3 including the problems of support and modernization of existing resources are considered. Also, plans for the preparation for the operation of the LHC in the HL (High Luminosity) mode are presented. Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Grid computing

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High Dynamic Collaborative Team Query via Multi-Fuzzy-Constrained Graph Pattern Matching

High Dynamic Collaborative Team Query via Multi-Fuzzy-Constr...

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IEEE international conference on Cloud computing and Big Data Analysis (ICCCBDA)

作者： Tao Hu Zan Zhang Chenyang Bu Lei Li Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (数字)9798350373554

ISBN: (纸本)9798350373561

Graph pattern matching is a technique widely used in various fields such as protein structure analysis, social group querying, and expert localization. this technique involves finding matching subgraphs in large social networks that align with the patterns specified in the pattern graph. In this paper, we focus on a specific sub-problem in social group querying, known as the cooperative team query, which arises from practical applications, where the nodes in the pattern graph and the data graph represent team member entities, while the edges represent their social relationships. We note that the requirements of many teams in the real world are dynamic, necessitating iterative computation for graph pattern matching using traditional methods. To address this challenge in highly dynamic systems, we propose a graph pattern matching method based on core pattern graph matching cache. this approach involves extracting the core pattern graph, and comprising core team members based on the characteristics of cooperative teams. the core graph-based matching cache enables the second half of the algorithm to operate on an order-of-magnitude smaller graph, significantly improving efficiency. Additionally, the multi-threaded approach fully leverages hardware resources, synchronizing multiple matching result of the core pattern graph to reduce matching time. Experimental results on three real social network datasets demonstrate that our proposed algorithm, Core Pattern Graph Matching Cache-based Multi-threaded Exploration (CCMTE), significantly outperforms existing methods in terms of efficiency.

关键词： Proteins Location awareness Social networking (online) Social groups Heuristic algorithms Knowledge graphs Hardware

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VERIFIABLE APPLICATION-LEVEL CHECKPOINT and RESTART FRAMEWORK for PARALLEL computing 9

VERIFIABLE APPLICATION-LEVEL CHECKPOINT and RESTART FRAMEWOR...

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9th international conference "distributed computing and Grid Technologies in Science and Education", GRID 2021

作者： Gankevich, Ivan Petriakov, Ivan Gavrikov, Anton Tereshchenko, Dmitrii Mozhaiskii, Gleb Saint Petersburg State University 13B Universitetskaya Emb. St Petersburg199034 Russia

Fault tolerance of parallel and distributed applications is one of the concerns that becomes topical for large computer clusters and large distributed systems. For a long time the common solution to this problem was checkpoint and restart mechanisms implemented on operating system level, however, they are inefficient for large systems and now application-level checkpoint and restart is considered as a more efficient alternative. In this paper we implement application-level checkpoint and restart manually for the well-known parallel computing benchmarks to evaluate this alternative approach. We measure the overheads introduced by creating and restarting from a checkpoint, and the amount of effort that is needed to implement and verify the correctness of the resulting programme. Based on the results we propose generic framework for application-level checkpointing that simplifies the process and allows to verify that the application gives correct output when restarted from any checkpoint. Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Message passing

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distributed beamforming in multi-user multi-relay cooperative networks with direct links 2021

Distributed beamforming in multi-user multi-relay cooperativ...

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9th international conference on Communications and Broadband networking, ICCBN 2021

作者： Ding, Xinyao Wang, Yan Southeast University National Mobile Communications Research Laboratory Nanjing Jiangsu China

ISBN: (纸本)9781450389174

We study the distributed optimal beamforming problem of a wireless amplify-and-forward (AF) multi-user and multi-relay network with minimal receive signal-to-noise ratio (SNR) limitations and per-relay transmit power budgets. In this network, direct links between sources and destinations always present. By jointly exploiting the distributed AF relay-link and direct-link signals, cooperative diversity is obtained. To achieve the above goal, we resort to minimizing the maximum relay transmit power subject to the receive SNR and per-relay transmit power constraints. Since the SNR constraints are non-convex, the problem is non-convex as well. Instead of dealing with its Lagrange dual problem just as what conventional methods usually do, we present a method which can transform the original non-convex problem into a second-order cone program (SOCP) problem. By utilizing this method, there is no need to prove the strong duality or to solve its dual problem. thus, the optimal solution can be obtained directly. through numerical simulations, the effects of the relay number on the min-max relay transmit power are analyzed. © 2021 ACM.

关键词： Signal to noise ratio

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