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Federated Multimedia Recommendation systems with Privacy Protection

Federated Multimedia Recommendation Systems with Privacy Pro...

引用

IEEE international symposium on Broadband Multimedia systems and Broadcasting (BMSB)

作者： Shih Yu Chang Hsiao-Chun Wu Kun Yan Scott Chih-Hao Huang Yiyan Wu Department of Applied Data Science San Jose State University San Jose CA USA School of Electrical Engineering and Computer Science Louisiana State University Baton Rouge LA USA Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin University of Electronic Technology Guilin Guangxi China Department of Information and Telecommunication Guilin University of Electronic Technology Guilin Guangxi China Department of Electrical Engineering National Tsinghua University Hsinchu Taiwan

ISBN: (数字)9798350364262

ISBN: (纸本)9798350364279

In this big data era, distributed machine learning and global data pooling have become quite critical in practical implementation of any multimedia data-science technique. The emerging federated multimedia recommendation systems (FeMRSs) offer a promising approach to address the need of personalized recommendations subject to both users’ privacy and data security. However, there exists no quantitative method for assessing the discrepancy between the large original rating matrix stored on the server and the dimensionality-reduced rating matrix produced on each client’s equipment. Meanwhile, there lacks a systematic means to evaluate the differential privacy (DP), which is a critical security measure in many distributed systems, particularly when sensitive data are processed. In this work, we first introduce a novel personalized dimensionalityreduction algorithm utilizing the matrix sketching technique. This new algorithm effectively control the difference between the original rating matrix on the server and the dimensionality-reduced rating matrix on each client’s equipment. Moreover, a randomized DP matrix factorization algorithm is designed to be executed at each client’s equipment. The theoretical proof is also carried out to show how much DP can be attained by use of the aforementioned randomized DP matrix factorization algorithm. Finally, extensive numerical studies are presented to evaluate the effectiveness of our proposed novel algorithms using both simulated and real datasets for building the FeMRSs.

关键词： Dimensionality reduction Digital multimedia broadcasting Privacy Systematics distributed databases Filtering algorithms Servers

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StencilFlow: Mapping Large Stencil Programs to distributed Spatial Computing systems 21

StencilFlow: Mapping Large Stencil Programs to Distributed S...

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19th IEEE/ACM international symposium on Code Generation and Optimization (CGO)

作者： Licht, Johannes de Fine Kuster, Andreas De Matteis, Tiziano Ben-Nun, Tal Hofer, Dominic Hoefler, Torsten Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland MeteoSwiss Zurich Switzerland

ISBN: (纸本)9781728186139

Spatial computing devices have been shown to significantly accelerate stencil computations, but have so far relied on unrolling the iterative dimension of a single stencil operation to increase temporal locality. This work considers the general case of mapping directed acyclic graphs of heterogeneous stencil computations to spatial computing systems, assuming large input programs without an iterative component. StencilFlow maximizes temporal locality and ensures deadlock freedom in this setting, providing end-to-end analysis and mapping from a high-level program description to distributed hardware. We evaluate our generated architectures on a Stratix 10 FPGA testbed, yielding 1.31 TOp /s and 4.18 TOp/s on single-device and multi-device, respectively, demonstrating the highest performance recorded for stencil programs on FPGAs to date. We then leverage the framework to study a complex stencil program from a production weather simulation application. Our work enables productively targeting distributed spatial computing systems with large stencil programs, and offers insight into architecture characteristics required for their efficient execution in practice.

关键词： Mapping

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Lock-free Computation of PageRank in Dynamic Graphs

Lock-free Computation of PageRank in Dynamic Graphs

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Subhajit Sahu Kishore Kothapalli Hemalatha Eedi Sathya Peri International Institute of Information Technology Hyderabad India JNTUH College of Engineering Hyderabad India Indian Institute of Technology Hyderabad India

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

PageRank is a metric that assigns importance to the vertices of a graph based on its neighbors and their scores. Recently, there has been increasing interest in computing PageRank on dynamic graphs, where the graph structure evolves due to edge insertions and deletions. However, traditional barrier-based approaches for updating PageRanks encounter significant wait times on certain graph structures, leading to high overall runtimes. Additionally, the growing trend of multicore architectures with increased core counts has raised concerns about random thread delays and failures. In this study, we propose a lock-free algorithm for updating PageRank scores on dynamic graphs. First, we introduce our Dynamic Frontier (DF) approach, which identifies and processes vertices likely to change PageRanks with minimal overhead. Subsequently, we integrate DF with our lock-free and fault-tolerant PageRank (Alg. DF LF ), incorporating a helping mechanism among threads between its two phases. Experimental results demonstrate that Alg. DF LF not only eliminates waiting times at iteration barriers but also withstands random thread delays and crashes. On average, it is 4.6× faster than lock-free Naive-dynamic PageRank (Alg. ND LF ).

关键词： Measurement Runtime Multicore processing Instruction sets Heuristic algorithms Redundancy Fault tolerant systems

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Real-Time Visualization and Detection of Malicious Network Flows in IoT Devices using a Scalable Stream Processing Pipeline

Real-Time Visualization and Detection of Malicious Network F...

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international Conference on parallel distributed and Grid Computing (PDGC)

作者： S. Saravanan B. Uma Maheswari Kanagasundaram K Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Nokia Networks Bengaluru India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

The continuous adoption of Internet of Things (IoT) devices has established notable security risks. In recent times, IoT devices are often targeted by malicious actors as IoT devices are limited with processing and memory resources. The detection of malicious network flows in real-time is necessary to maintain the security of IoT networks. This paper addresses the challenge by presenting a scalable stream processing pipeline that identifies malicious and non-malicious flows from IoT devices in real-time. The proposed system utilizes NFStream to generate network flows from device packets. The generated network flows are then published to a Kafka topic and consumed by a Kafka consumer. A distributed random forest-based machine learning model from Apache Spark MLlib is trained to classify these flows. The classification results are stored alongside the flows in a MongoDB database. Real-time visualization of the count of malicious and non-malicious flows for each IoT device is provided via Streamlit. Experimental results demonstrate the system's capability to accurately classify network traffic in real time while maintaining scalability for large-scale IoT environments. The proposed pipeline offers a robust solution for real-time malicious flow detection in large-scale IoT networks.

关键词： Accuracy Pipelines distributed databases Data visualization Cluster computing Telecommunication traffic Real-time systems Internet of Things Object recognition Monitoring

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parallel Query Service for Object-centric Data Management systems 34

Parallel Query Service for Object-centric Data Management Sy...

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34th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Tang, Houjun Byna, Suren Dong, Bin Koziol, Quincey Lawrence Berkeley Natl Lab Berkeley CA 94720 USA

ISBN: (纸本)9781728174457

While large-scale scientific experiments and simulations produce massive amounts of data, a small fraction of data contains useful information. Efficient querying on such volume of data to extract that information increases the productivity of the scientific discovery process. Although querying has been explored extensively in relational databases, research and adoption of querying tools for scientific data that is stored in parallel file systems on high performance computing (HPC) systems are still in infancy. In this paper, we introduce a parallel query service, called PDC-Query, for an object data management systems (ODMS) on HPC systems. It operates on partitioned objects in parallel, and provides several optimization strategies for fast query evaluation. The ODMS paradigm for HPC systems is promising in reducing the burden on users in data management and in moving data transparently across the deep memory hierarchy in modern HPC systems. We propose a 'global histogram'-based approach to accelerate query evaluation, through selectivity estimation and reducing the amount of data that needs to be loaded from storage and processed. We compare querying performance and demonstrate the efficiency and scalability of different approaches PDC-Query supports, including using global histograms, bitmap indexes, sorting, and full scan, in performing various queries on top of a plasma physics dataset with 125 billion particles and an astronomy dataset with 25 million objects.

关键词： Information management

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WHW: An Efficient Data Organization Method for Fine-tuning Large Language Models

WHW: An Efficient Data Organization Method for Fine-tuning L...

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Information Science, parallel and distributed systems (ISPDS), international Conference on

作者： Lubao Wang Huaqi Zhang Haiming Shao Mingxuan Wu Wei Ren TINNOVE ChongQing China

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

There are still deficiencies in fine-tuning technology for Large Language Models (LLM) in the industrial field. Fine-tuned large language models often fail to achieve expected results, and the following problems usually occur: (1) LLM hallucination: A common problem with generative models is that the generated content is uncontrollable and limited to tasks within the vertical domain. (2) LLM does not perform well in fine-tuning on domain data: The training data of the base generative large language model is relatively common. It is difficult to make domain knowledge fully exert its influence on large models trained with general knowledge. (3) Insufficient training samples: In few-shot learning, the fine-tuning results of large language models are often worse or unable to learn domain knowledge. In order to solve the above problems encountered in the fine-tuning of large language models, we propose an efficient fine-tuning data organization method. Our data approach works by adding domain-specific task descriptions. Improves the results of vertical domain fine-tuning of large language models, reduces LLM hallucination, and uses only a small number of instructions to fine-tune data. Specifically, we provided detailed descriptions and restrictions on vertical domain tasks by manually constructing task description data WHW (What, How, Why), which improved the F1 score by 11% in the role setting task and by 3% in the classification task. While reducing the LLM hallucination by 73% compared with the prompt fine-tune method.

关键词： Training Information science Large language models Training data distributed databases Organizations Data models

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SQCC: Smart Quantum Circuit Cutting

SQCC: Smart Quantum Circuit Cutting

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Betis Baheri Qiang Guan Shuai Xu Vipin Chaudhary Department of Computer Science Kent State University OH USA Department of Computer and Data Science Case Western Reserve University OH USA

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

In this paper, we are investigating the idea of cutting the original quantum circuits into multiple sub-circuits and evaluating the fidelity (correctness) amount of the sub-circuits results. We introduce an analysis method, distributed Quantum Circuits (DQC), to encounter all aspects of Quantum properties from qubit's error data to the topology of Quantum processors on cutting the quantum circuits. Finally, we share our observations and experiments to introduce a new design pattern for managing distributed Quantum circuits on smaller Quantum computers.

关键词： Computers distributed processing Program processors Conferences Qubit distributed databases Topology

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A GMM-CGABC Based Energy Replenishment Scheme for On-Demand Wireless Rechargeable Sensor Networks

A GMM-CGABC Based Energy Replenishment Scheme for On-Demand ...

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parallel Computing and distributed systems (PCDS), international symposium on

作者： S.Sudhamsu Mouli T. Veeraiah K. Mahesh M.P. Singh Department of Computer Science and Engineering Ecole Centrale School of Engineering Mahindra University Hyderabad India Department of Computer Science and Engineering National Institute of Technology Patna India

ISBN: (数字)9798350349658

ISBN: (纸本)9798350349665

Wireless Sensor Networks (WSNs) are constrained by the limited energy capacity of Sensor Nodes (SNs), which hinders their perpetual operation. The advent of Wireless Energy Transfer (WET) technology has emerged as a promising solution to this challenge, enabling the creation of Wireless Rechargeable Sensor Networks (WRSNs). Recent research has shown growing interest in integrating Wireless Mobile Chargers (WMCs) into WRSNs to replenish the energy reserves of critical SNs through WET. A key aspect of WET involves using one or more WMCs to recharge SNs, with optimizing the charging schedule (i.e., the sequence in which SNs are charged) crucial in on-demand WRSNs. In addition, spatiotemporal constraints imposed by the requesting SNs must be considered when devising the WMC's charging schedule. To address these challenges, this work proposes the integration of a Gaussian Mixture Model with a Chaotic Gaussian Artificial Bee Colony (GMM-CGABC) to develop an efficient recharge schedule that revitalizes SNs. This model is designed to optimize SN deployment, maximize network coverage, minimize energy consumption, and extend network longevity. The proposed method determines the optimal charging path for WMCs within each cluster to minimize Average Charging Latency (ACL) while incorporating a novel strategy for fitness function evaluation. Extensive simulations demonstrate that the proposed system outperforms state-of-the-art algorithms, including First Come, First Served (FCFS), Nearest Job Next with Preemption (NJNP), and the Gravitational Search Algorithm (GSA), in terms of charging latency.

关键词： Wireless communication Wireless sensor networks Schedules Energy consumption Clustering algorithms Mixture models parallel processing Spatiotemporal phenomena Optimization Gaussian mixture model

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Graph-Based Anomaly Detection and Localization in InSAR Data

Graph-Based Anomaly Detection and Localization in InSAR Data

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IEEE international symposium on Geoscience and Remote Sensing (IGARSS)

作者： Vitor R. M. Elias John Dehls Pierluigi Salvo Rossi Department of Electronic Systems Norwegian University of Science and Technology Trondheim Norway Geological Survey of Norway Trondheim Norway

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

The generation of ground-movement maps using interferometric SAR is subject to faults during the acquisition and prepossessing of SAR data. This paper addresses the task of identifying and localizing such faults specifically within the context of data provided by the European Ground Motion Service (EGMS). The lack of a regular spatial domain in geospatial data poses a challenge for signal processing and machine learning. Here, we investigate graph-based methods, that overcome the spatial irregularity of the data, to perform detection and localization of data anomalies. We demonstrate on synthetic data that graph-based frequency analysis and filtering yield superior performance at anomaly localization when compared to state-of-the-art machine-learning methods such as graph U-nets. The proposed methods are employed for anomaly detection and localization on real EGMS data.

关键词： Location awareness Training Filters Fault detection distributed databases Machine learning Geospatial analysis

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MultiGrid on FPGA Using Data parallel C++

MultiGrid on FPGA Using Data Parallel C++

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Christopher Siefert Stephen L. Olivier Gwendolyn Voskuilen Jeffrey Young Sandia National Laboratories Albuquerque NM USA Georgia Institute of Technology Atlanta GA USA

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Centered on modern C++ and the SYCL standard for heterogeneous programming, Data parallel C++ (dpc++) and Intel's oneAPI software ecosystem aim to lower the barrier to entry for the use of accelerators like FPGAs in diverse applications. In this work, we consider the usage of FPGAs for scientific computing, in particular with a multigrid solver, MueLu. We report on early experiences implementing kernels of the solver in DPC++ for execution on Stratix 10 FPGAs, and we evaluate several algorithmic design and implementation choices. These choices not only impact performance, but also shed light on the capabilities and limitations of DPC++ and oneAPI.

关键词： distributed processing Scientific computing Ecosystems distributed databases C++ languages Programming Software

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