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Algorithmic Acceleration of parallel ALS for Collaborative Filtering: Speeding up distributed Big Data Recommendation in Spark

Algorithmic Acceleration of Parallel ALS for Collaborative F...

引用

International conference on parallel and distributed Systems (ICPADS)

作者： Manda Winlaw Michael B Hynes Anthony Caterini Hans De Sterck University of Waterloo ON Canada University of Waterloo Canada University of Waterloo Waterloo ON CA

Collaborative filtering algorithms are important building blocks in many practical recommendation systems. For example, many large-scale data processing environments include collaborative filtering models for which the Alternating Least Squares (ALS) algorithm is used to compute latent factor matrix decompositions. In this paper, we propose an approach to accelerate the convergence of parallel ALS-based optimization methods for collaborative filtering using a nonlinear conjugate gradient (NCG) wrapper around the ALS iterations. We also provide a parallel implementation of the accelerated ALS-NCG algorithm in the Apache Spark distributed data processing environment, and an efficient line search technique as part of the ALS-NCG implementation that requires only one pass over the data on distributed datasets. In serial numerical experiments on a linux workstation and parallel numerical experiments on a 16 node cluster with 256 computing cores, we demonstrate that the combined ALS-NCG method requires many fewer iterations and less time than standalone ALS to reach movie rankings with high accuracy on the MovieLens 20M dataset. In parallel, ALS-NCG can achieve an acceleration factor of 4 or greater in clock time when an accurate solution is desired; furthermore, the acceleration factor increases as greater numerical precision is required in the solution. Furthermore, the NCG acceleration mechanism is efficient in parallel and scales linearly with problem size on synthetic datasets with up to nearly 1 billion ratings. The acceleration mechanism is general and may also be applicable to other optimization methods for collaborative filtering.

关键词： Acceleration Motion pictures Collaboration Sparks Optimization Algorithm design and analysis Convergence

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FERI: Feature Enhancement and Relational Interaction for image-text Matching

FERI: Feature Enhancement and Relational Interaction for Ima...

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International conference on parallel and distributed Systems (ICPADS)

作者： Yu Zhang Jianqiang Zhang Gongpeng Song Qin Lu Shuo Zhao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

image-text matching is an important problem at the intersection of computer vision and natural language processing. It aims to establish the semantic link between image and text to achieve high-quality semantic alignment between the two modalities. However, the existing methods have the problem that the meaning expressed in the image or the complex narrative in the text cannot be fully understood due to insufficient feature extraction. Moreover, due to the essential modal differences between images and texts, how to effectively and accurately align the semantic contents in images and texts has become the key of research. In order to solve the above problems, this paper proposes a method based on feature enhancement and relationship interaction. When processing images, the proposed method fuses labeled features, region features and location features to represent images. When processing text, a combination of Bi-GRU and self-attention mechanism is used to represent the text. In order to further align the semantic content in images and texts accurately, this paper improves two relational interaction mechanisms by identifying connection relationships and learning association relationships. Thus, the relation enhanced embedding is obtained. Finally, it calculated the similarity of the enhanced embedding to judge the matching degree of the image and text. Extensive experiments on the public datasets Flickr30K and MSCOCO demonstrate the effectiveness of our method.

关键词： Computer vision Fuses image matching Semantics Feature extraction Natural language processing

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parallel implementations of AOS schemes: a fast way of nonlinear diffusion filtering

Parallel implementations of AOS schemes: a fast way of nonli...

引用

IEEE International conference on image processing

作者： J. Weickert K.J. Zuiderveld B.M. ter Haar Romeny W.J. Niessen Image Sciences Institute Utrecht University Hospital Utrecht Netherlands DIKU Universitetsparken Copenhagen Denmark

In most cases nonlinear diffusion filtering is implemented by means of explicit finite difference schemes. These algorithms are not very efficient, since they are only stable for small time steps. We address this problem by presenting unconditionally stable semi-implicit schemes which are based on an additive operator splitting (AOS). They are very efficient since they can be implemented by recursive filtering, and their separability allows a straightforward implementation in any dimension. We analyse their behaviour on a parallel computer and demonstrate that parallel AOS schemes on a modern shared-memory multiprocessor system with 8 processors allow a speed-up of two orders of magnitude in comparison to the widely-used explicit scheme on a single processor.

关键词： Filters Smoothing methods Filtering image edge detection Ultrasonic imaging Hospitals Concurrent computing Time sharing computer systems Multiprocessing systems parallel processing

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Network Optimized by K-L Transform for Digital Modulation Identification

Network Optimized by K-L Transform for Digital Modulation Id...

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IEEE International conference on parallel and distributed Computing, Applications and Technologies (PDCAT)

作者： Yonghong Kuo Jian Chen Xiaohua Tan School of Communication Engineering Xidian University No.2 Taibai South Road Xi'an China

It is important for digital modulation identification based on neural network to determine a suitable size of a network. This paper proposes that the number of nodes in hidden layer which is the core for network optimizing can be confirmed by K-L transform. The validity and robustness are verified by simulation. The percentage of correct identification (PCI) is almost the same before and after the network optimizing.

关键词： Digital modulation Covariance matrix Network topology Neural networks Mean square error methods Robustness Digital communication Signal processing Optimization methods Redundancy

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Concurrent techniques for developing motion video compression algorithms

Concurrent techniques for developing motion video compressio...

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Data Compression conference (DCC)

作者： J.A. Elliott P.M. Grant G.G. Sexton Department of Electrical Engineering University of Edinburgh Edinburgh UK

Summary form only given. The authors discuss parallel techniques for speeding up software simulations of video codec algorithms on a reconfigurable MIMD machine with distributed memory (multicomputer). The Edinburgh Concurrent Supercomputer, houses over 400 INMOS T800 transputer floating point microprocessors. Instead of a pipeline, the use of compact graphs in conjunction with an optimised software routing harness is advocated. They can considerably ease the development of concurrent image processing software and provide results better than those obtained from specifically tailored topologies and software. This technique scales to any number of processors with minimal effort.< >

关键词： Video compression Video codecs Concurrent computing Computational modeling parallel processing image processing Topology Code standards Standards development Signal processing

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PIC: Enable Large-Scale Privacy Preserving Content-Based image Search on Cloud

PIC: Enable Large-Scale Privacy Preserving Content-Based Ima...

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International conference on parallel processing (ICPP)

作者： Lan Zhang Taeho Jung Puchun Feng Kebin Liu Xiang-Yang Li Yunhao Liu School of Software Tsinghua University Department of Computer Science Illinois Institute of Technology

ISBN: (纸本)9781467375887

Many cloud platforms emerge to meet urgent requirements for large-volume personal image store, sharing and search. Though most would agree that images contain rich sensitive information (e.g., People, location and event) and people's privacy concerns hinder their participation into untrusted services, today's cloud platforms provide little support for image privacy protection. Facing large-scale images from multiple users, it is extremely challenging for the cloud to maintain the index structure and schedule parallel computation without learning anything about the image content and indices. In this work, we introduce a novel system PIC: a Privacy-preserving image search system on Cloud, which is a step towards feasible cloud services which provide secure content-based large-scale image search with fine-grained access control. Users can search on others' images if they are authorized by the image owners. Majority of the computationally intensive jobs are handled by the cloud, and a querier can now simply send the query and receive the result. Specially, to deal with massive images, we design our system suitable for distributed and parallel computation and introduce several optimizations to further expedite the search process. Our security analysis and prototype system evaluation results show that PIC successfully protects the image privacy at a low cost of computation and communication.

关键词： Privacy Encryption Feature extraction Servers image reconstruction Indexing

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Automated image and Video Quality Assessment for Computational Video Editing 9th

Automated Image and Video Quality Assessment for Computation...

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9th International conference on Analysis of images, Social Networks and Texts, AIST 2020

作者： Lomotin, Konstantin Makarov, Ilya HSE University Moscow Russia Samsung-PDMI Joint AI Center St. Petersburg Department of Steklov Institute of Mathematics St. Petersburg Russia

ISBN: (纸本)9783030726096

We study non-reference image and video quality assessment methods, which are of great importance for computational video editing. The object of our work is image quality assessment (IQA) applicable for fast and robust frame-by-frame multipurpose video quality assessment (VQA) for short videos. We present a complex framework for assessing the quality of images and videos. The scoring process consists of several parallel steps of metric collection with final score aggregation step. Most of the individual scoring models are based on deep convolutional neural networks (CNN). The framework can be flexibly extended or reduced by adding or removing these steps. Using Deep CNN-Based Blind image Quality Predictor (DIQA) as a baseline for IQA, we proposed improvements based on two patching strategies, such as uniform patching and object-based patching, and add intelligent pre-training step with distortion classification. We evaluated our model on three IQA benchmark image datasets (LIVE, TID2008, and TID2013) and manually collected short YouTube videos. We also consider interesting for automated video editing metrics used for video scoring based on the scale of a scene, face presence in frame and compliance of the shot transitions with the shooting rules. The results of this work are applicable to the development of intelligent video and image processing systems. © 2021, Springer Nature Switzerland AG.

关键词： Deep neural networks

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Enhancing Skin Disease Diagnosis with a Dual Vision Transformer Model 15

Enhancing Skin Disease Diagnosis with a Dual Vision Transfor...

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15th International conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Gairola, Ajay Krishan Kumar, Vidit Sahoo, Ashok Kumar Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Graphic Era deemed to be university Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350370249

An increasing number of people are experiencing skin problems, and it is never easy for a clinician to make a correct diagnosis. One potential solution to these problems is the use of deep learning for skin disease diagnosis. In this paper, a new kind of neural network is proposed for diagnosing skin diseases. We provide a new dual Vision Transformer (ViT) model that has two patches for image embedding and transformer encoding, as well as two multi-level permutation (MLP) heads to combine the two models, in light of the fact that the research datasets are images of skin diseases. To get deep characteristics from images, the proposed framework employs a ViT model that is both appropriate and effective. As a first step in pre-processing, images are first divided into uniformly sized patches, and then compressed into a string of tokens. The second step is to process the flattened patches using two transformer encoders that are connected in parallel. For the transformer encoder, there are Ni, Nj layers, where each layer has two sublayers. The first component is a self-attention unit with multiple heads, while the second component is a feedforward network with all connections enabled. After normalizing the inputs to each of the two sublayers, we evaluate the strength of the residual connections between them. The implementation of a classification block follows the concatenation of MLP heads. The block is made up of two layers: one that is flattened and one that is dense and optimized for batch processing. The experimental results of the method show classification accuracies of 88% (ISIC2016), 90% (ISIC2017), 91% (HAM10000), and 88% (Skin-disease-v1). The proposed model represents an advancement over state-of-the-art methods and signifies progress in identifying skin diseases. © 2024 IEEE.

关键词： deep learning image processing neural network skin disease classification Transformer

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A systemc TLM framework for distributed simulation of complex systems with unpredictable communication

A systemc TLM framework for distributed simulation of comple...

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conference on Design and Architectures for Signal and image processing

作者： Julien Peeters Nicolas Ventroux Tanguy Sassolas Lionel Lacassagne LIST Embedded Computing Laboratory CEA Gif-sur-Yvette France

Increasingly complex systems need parallelized simulation engines. In the context of SystemC simulation, existing proposals require predicting communication in the simulated system. However, this is often unpredictable. In order to deal with unpredictable systems, this paper presents a parallelization approach using asynchronous communication without modification of the SystemC simulation engine. Simulated system model is cut up and distributed across separate simulation engines, each part being evaluated in parallel of others. Functional consistency is preserved thanks to the simulated system write exclusive memory access policy while temporal consistency is guaranteed using explicit synchronization. Experimental results show up a speed-up up to 13× on 16 processors.

关键词： Synchronization Load modeling Adaptation models Hardware Context Engines Program processors

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Real-time range image segmentation on GPU

Real-time range image segmentation on GPU

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International conference on Control, Automation and Systems ( ICCAS)

作者： Jin Xin Hua Mun-Ho Jeong Department of Control and Instrumentation Engineering Kwangwoon University Seoul Korea

In this paper propose a GPU-based parallel processing method for real-time image segmentation with neural oscillator network. Range image segmentation methods can be divided into two categories: edge-based and region-based. Edge-base method is sensitive to noise and region-based method is hard to extracting the boundary detail between the object. However, by using LEGION (Locally Excitatory Globally Inhibitory oscillator networks) to do range image segmentation can overcome above disadvantages. The reason why LEGION is suitable for parallel processing that each oscillator calculate with its 8-neiborhood oscillators in real time when we process image segmentation by LEGION. Thus, using GPU-based parallel processing with LEGION can improve the speed to realize real-time image segmentation.

关键词： Graphics processing units Oscillators Integrated circuits image edge detection image segmentation Indexes HTML

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