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Matrix completion from power-law distributed samples 09

Matrix completion from power-law distributed samples

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Proceedings of the 23rd international conference on Neural Information Processing systems

作者： Raghu Meka Prateek Jain Inderjit S. Dhillon Department of Computer Sciences University of Texas at Austin Austin TX

ISBN: (纸本)9781615679119

The low-rank matrix completion problem is a fundamental problem with many important applications. Recently, [4],[13] and [5] obtained the first non-trivial theoretical results for the problem assuming that the observed entries are sampled uniformly at random. Unfortunately, most real-world datasets do not satisfy this assumption, but instead exhibit power-law distributed samples. In this paper, we propose a graph theoretic approach to matrix completion that solves the problem for more realistic sampling models. Our method is simpler to analyze than previous methods with the analysis reducing to computing the threshold for complete cascades in random graphs, a problem of independent interest. By analyzing the graph theoretic problem, we show that our method achieves exact recovery when the observed entries are sampled from the Chung-Lu-Vu model, which can generate power-law distributed graphs. We also hypothesize that our algorithm solves the matrix completion problem from an optimal number of entries for the popular preferential attachment model and provide strong empirical evidence for the claim. Furthermore, our method is easy to implement and is substantially faster than existing methods. We demonstrate the effectiveness of our method on random instances where the low-rank matrix is sampled according to the prevalent random graph models for complex networks and present promising preliminary results on the Netflix challenge dataset.

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Combinatorial Client-Master Multiagent Deep Reinforcement Learning for Task Offloading in Mobile Edge computing 24

Combinatorial Client-Master Multiagent Deep Reinforcement Le...

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Proceedings of the 23rd international conference on Autonomous Agents and Multiagent systems

作者： Tesfay Zemuy Gebrekidan Sebastian Stein Timothy J. Norman University of Southampton Southampton United Kingdom

ISBN: (纸本)9798400704864

Deep reinforcement learning (DRL) is gaining popularity in task-offloading problems because it can adapt to dynamic changes and minimize online computational complexity. However, the various types of continuous and discrete resource constraints on user devices (UDs) and mobile edge computing (MEC) servers pose challenges to the design of an efficient DRL-based task-offloading strategy. Existing DRL-based task-offloading algorithms focus on the constraints of the UDs, assuming the availability of enough storage resources on the server. Moreover, existing multiagent DRL (MADRL)-based task-offloading algorithms are homogeneous agents and consider homogeneous constraints as a penalty in their reward function. In this work, we propose a novel combinatorial client-master MADRL (CCM_MADRL) algorithm for task offloading in mobile edge computing (CCM_MADRL_MEC) that allows UDs to decide their resource requirements and the server to make a combinatorial decision based on the UDs' requirements. CCM_MADRL_MEC is the first MADRL approach in task offloading to consider server storage capacity in addition to the constraints of the UDs. By taking advantage of the combinatorial action selection, CCM_MADRL_MEC has shown superior convergence over existing benchmark and heuristic algorithms.

关键词： client-master multiagent deep reinforcement learning combinatorial action selection distributed solution mixed constraints multiagent deep reinforcement learning

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Deep Learning-Based DDoS Attack Detection Using Adversarial Optimization

Deep Learning-Based DDoS Attack Detection Using Adversarial ...

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

作者： Dahai Yu Jianming Cui Yungang Jia Peiguo Fu Ming Liu School of Information Engineering Chang’an University Xi’an China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

In the field of cybersecurity, increasingly emerging network threats seriously affect the security of network and users. Notably, DDoS attacks represent a critical concern due to their capacity to inundate and debilitate targeted systems via distributed networks. This study introduces a DDoS attack detection framework employing a dual-detection model designed to identify both conventional and adversarial DDoS attacks. Adversarial DDoS attacks are characterized by their enhanced capability to circumvent standard DDoS detection mechanisms. The proposed framework incorporates an optimized CNN architecture for the detection of traditional DDoS attacks. This optimization involves substituting fully connected layers with global average pooling layers, an approach validated through comparative experimentation to yield the most effective base CNN model. For the detection of adversarial DDoS attacks, we propose a new detection algorithm, in which methodology initially employs the WGAN-GP data generation process to create synthetic DDoS attack data, followed by the strategic alteration of certain feature values within the generated attack data using normal traffic data. The resultant perturbed data are then utilized to train the adversarial DDoS attack detection model. Empirical assessments performed on the CIC-IDS2017 and CIC-DDoS2019 datasets confirm the efficacy of the dual-detection model in accurately identifying both traditional and adversarial DDoS attacks, achieving an accuracy exceeding 95%.

关键词： Accuracy Denial-of-service attack Generative adversarial networks Feature extraction Real-time systems Data models Convolutional neural networks Computer crime Detection algorithms Optimization

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scE(match): Privacy-Preserving Cluster Matching of Single-Cell Data

scE(match): Privacy-Preserving Cluster Matching of Single-Ce...

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

作者： Johannes Lohmöller Jannis Scheiber Rafael Kramann Klaus Wehrle Sikander Hayat Jan Pennekamp Communication and Distributed Systems RWTH Aachen University Germany Clinic for Renal and Hypertensive Disorders Rheumatological and Immunological Diseases (Medical Clinic II) RWTH Aachen University Hospital Germany Clinic for Gastroenterology Metabolic Disorders and Internal Intensive Medicine (Medical Clinic III) RWTH Aachen University Hospital Germany

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Advances in single-cell RNA sequencing (scRNA-seq) have dramatically enhanced our understanding of cellular functions and disease mechanisms. Despite its potential, scRNA-seq faces significant challenges related to data privacy, cost, and Intellectual Property (IP) protection, which hinder the sharing and collaborative use of these sensitive datasets. In this paper, we introduce a novel method, scE(match), a privacy-preserving tool that facilitates the matching of single-cell clusters between different datasets by relying on scmap as an established projection tool, but without compromising data privacy or IP. scE(match) utilizes homomorphic encryption to ensure that data and unique cell clusters remain confidential while enabling the identification of overlapping cell types for further collaboration and downstream analysis. Our evaluation shows that scE(match) performantly matches cell types across datasets with high precision, addressing both practical and ethical concerns in sharing scRNA-seq data. This approach not only supports secure data collaboration but also fosters advances in biomedical research by reliably protecting sensitive information and IP rights.

关键词： Data privacy Sequential analysis RNA Collaboration Intellectual property Reliability engineering Security Homomorphic encryption Protection Testing

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distributed Online Rollout for Multivehicle Routing in Unmapped Environments 24

Distributed Online Rollout for Multivehicle Routing in Unmap...

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Proceedings of the 23rd international conference on Autonomous Agents and Multiagent systems

作者： Jamison W. Weber Dhanush R. Giriyan Devendra R. Parkar Dimitri P. Bertsekas Andréa W. Richa School of Computing and Augmented Intelligence Arizona State University Tempe AZ USA

ISBN: (纸本)9798400704864

We consider a generalization of the well-known multivehicle routing problem: given a network, a set of agents occupying a subset of its nodes, and a set of tasks, we seek a minimum cost sequence of movements subject to the constraint that each task is visited by some agent at least once. The classical version of this problem assumes a central computational server that observes the entire state of the system perfectly and directs individual agents according to a centralized control scheme. In contrast, we assume that there is no centralized server and that each agent is an individual processor with no a priori knowledge of the underlying network, including the locations of tasks and other agents. Moreover, our agents possess strictly local communication and sensing capabilities (restricted to a fixed radius), aligning more closely with several real-world multiagent applications. We present a fully distributed, online, and scalable reinforcement learning algorithm for this problem whereby agents self-organize into local clusters to which they independently apply a multiagent rollout scheme. We demonstrate empirically via extensive simulations that there exists a critical sensing radius beyond which the distributed rollout algorithm begins to improve over a greedy base policy. This critical sensing radius grows proportionally to the log-star function of the size of the network and is therefore a small constant in practice. Our decentralized reinforcement learning algorithm achieves approximately a factor of two cost improvement over the base policy for a range of radii between two and three times the critical sensing radius. In addition, we observe in simulations that our distributed algorithm requires exponentially fewer computational resources than the centralized approach, at only a small constant factor detriment in cost. We validate our algorithm through physical robot experiments in continuous space and show that the resulting behavior reflects the discrete simul

关键词： distributed computing multivehicle routing reinforcement learning

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An Automated PM2.5 Analysis and Prediction System with Encoder-Decoder Architecture and Continual Learning Mechanism

An Automated PM2.5 Analysis and Prediction System with Encod...

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

作者： Le Anh Duc Vu Minh Hai Vu Bao Ngoc Tran Minh Tung Hoang Duc Anh Nguyen Minh Quan Hoang Phi Le Nguyen Hanoi - Amsterdam High School for the Gifted School of Information and Communication Technology Hanoi University of Science and Technology

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Over recent decades, air pollution, particularly fine particulate matter (PM2.5), has garnered significant attention due to its detrimental effects on human health. As a result, accurately forecasting air quality has become a critical objective to inform and guide policy-making. However, in developing countries, due to their financial constraints, the prediction ability mostly depends on sparsely distributed monitoring devices, which are insufficient. Various prediction approaches utilizing deep learning techniques, such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models, have been developed to address this gap. Despite their promise, these models encounter challenges, including slow preprocessing times and limited adaptability to changing air quality conditions. This paper proposes a simple Encoder-Decoder model for predicting hourly and daily PM2.5 concentrations. In addition, this paper implements an automated system that collects and processes real-time data, continuously updating the model to respond to emerging trends and variations in pollution levels. The model’s performance is evaluated in Vietnam’s two largest cities, Hanoi and Ho Chi Minh City, demonstrating prediction accuracy in various scenarios.

关键词： Continuing education Adaptation models Atmospheric modeling Computational modeling Urban areas Predictive models Real-time systems Data models Convolutional neural networks Long short term memory

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Towards Privacy-First Security Enablers for 6G Networks: The PRIVATEER Approach 23rd

Towards Privacy-First Security Enablers for 6G Networks: The...

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23rd international conference on Embedded Computer systems: Architectures, Modeling, and Simulation, SAMOS 2023

作者： Masouros, Dimosthenis Soudris, Dimitrios Gardikis, Georgios Katsarou, Victoria Christopoulou, Maria Xilouris, George Ramón, Hugo Pastor, Antonio Scaglione, Fabrizio Petrollini, Cristian Pinto, António Vilela, João P. Karamatskou, Antonia Papadakis, Nikolaos Angelogianni, Anna Giannetsos, Thanassis Villalba, Luis Javier García Alonso-López, Jesús A. Strand, Martin Grov, Gudmund Bikos, Anastasios N. Ramantas, Kostas Santos, Ricardo Silva, Fábio Tsampieris, Nikolaos National Technical University of Athens Athens Greece R&D Department Space Hellas S.A Athens Greece NCSR "Demokritos" Institute of Informatics and Telecommunications Agia Paraskevi Greece Telefonica I+D Madrid Spain RHEA Group Wavre Belgium INESC TEC Porto Portugal Infili Technologies S.A Zografou Greece Ubitech Ltd Digital Security & Trusted Computing Group Chalandri Greece Universidad Complutense de Madrid Madrid Spain Kjeller Norway Iquadrat Informatica S.L Barcelona Spain Polytechnic of Porto Porto Portugal ERTICO-ITS Europe Brussels Belgium

ISBN: (纸本)9783031460760

The advent of 6G networks is anticipated to introduce a myriad of new technology enablers, including heterogeneous radio, RAN softwarization, multi-vendor deployments, and AI-driven network management, which is expected to broaden the existing threat landscape, demanding for more sophisticated security controls. At the same time, privacy forms a fundamental pillar in the EU development activities for 6G. This decentralized and globally connected environment necessitates robust privacy provisions that encompass all layers of the network stack. In this paper, we present PRIVATEER’s approach for enabling "privacy-first" security enablers for 6G networks. PRIVATEER aims to tackle four major privacy challenges associated with 6G security enablers, i.e., i) processing of infrastructure and network usage data, ii) security-aware orchestration, iii) infrastructure and service attestation and iv) cyber threat intelligence sharing. PRIVATEER addresses the above by introducing several innovations, including decentralised robust security analytics, privacy-aware techniques for network slicing and service orchestration and distributed infrastructure and service attestation mechanisms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Queueing networks

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ArtFL: Exploiting Data Resolution in Federated Learning for Dynamic Runtime Inference via Multi-Scale Training

ArtFL: Exploiting Data Resolution in Federated Learning for ...

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international Symposium on Information Processing in Sensor Networks (IPSN)

作者： Siyang Jiang Xian Shuai Guoliang Xing The Chinese University of Hong Kong Hong Kong SAR China

ISBN: (数字)9798350362015

ISBN: (纸本)9798350362022

Federated Learning (FL) has emerged as a prominent paradigm for distributed machine learning, crucial for mission-critical applications such as autonomous driving and smart health. However, existing FL systems have not adequately addressed the dynamic real-time requirements of these applications due to stringent inference deadlines and resource limitations on edge devices. In this paper, we propose ArtFL, a novel federated learning system designed to support dynamic runtime inference through multi-scale training. The key idea of ArtFL is to utilize the data resolution, i.e., frame resolution of videos, as a knob to accommodate dynamic inference latency requirements. Specifically, we initially propose data-utility-based multi-scale training, allowing the trained model to process data of varying resolutions during inference. Subsequently, we introduce an innovative strategy for frame resolution selection in inference, based on the similarity of adjacent frames. Finally, leveraging latency-based dynamic data dropping, we propose a systematic scheme to reduce the overall training time by shortening the waiting time in FL. For evaluation, we build two real-world FL testbeds for smart vehicles and healthcare applications, utilizing a heterogeneous edge platform. Extensive experiments across our testbeds and three public datasets show that ArtFL outperforms state-of-the-art baselines in overall accuracy and system performance up to 36.36% and 47.81%, respectively. A demo video of ArtFL on our smart vehicle testbed is available at https://***/eeK6yRVEG3U, and our code is available at https://***/siyang-jiang/*** CONCEPTS• computing methodologies → Machine learning.

关键词： Training Runtime Systematics Federated learning System performance Smart healthcare Real-time systems

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Towards the Development of WellFix: A Mobile-based Multi-Job Platform 19

Towards the Development of WellFix: A Mobile-based Multi-Job...

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Proceedings of the 2019 3rd international conference on Software and e-Business

作者： Rex P. Bringula Josiah Eleazar T. Regencia Rosula S. J. Reyes Ateneo de Manila University Philippines

ISBN: (纸本)9781450376495

We envision a community of well-maintained houses through the assistance of service providers (i.e., highly skilled individuals). In social entrepreneurship, it is desirable to empower local highly skilled workers within the community, thereby creating jobs not bound by the corporate environments. Toward this goal, this study determined the design considerations of WellFix -- a mobile-based system that allows posting of a wide range of on-demand services by the customers. An online content-validated survey form was distributed to 282 respondents to determine the initial design considerations of the mobile-based system. It was found that the client mostly needed house cleaning (59%), laundry (50%), carpentry (24%), electrical wiring (23%), and plumbing (15%) services. Respondents also reported that their walls (34%), roofs (33%), electrical outlets (30%), sinks (26%), and faucets (22%) need repairs. It is therefore concluded that the system must be able to handle different job requests and have potential contributions to the community. Future works were also discussed.

关键词： interface design entrepreneurship mobile computing mobile services information systems

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