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A Contrastive Collaborative Filtering Method for Personalized Recommendation with Self-Attention

A Contrastive Collaborative Filtering Method for Personalize...

引用

international symposium on parallel and distributed Processing with Applications, ISPA

作者： Cheng Chen Hao Tian Hang Zhang Wanchun Dou State Key Laboratory for Novel Software Technology Nanjing University China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Recently, sequential and Collaborative Filtering (CF) based recommender systems have shown their research popularity in both academia and industry. Graph-based CF and Transformer-based sequential models have independently shown state-of-art recommendation performance respectively. However, each approach has its own limitation and retrieves different potential factors from users and items. CF aims to retrieve collaborative signals between users and items while Transformer models the temporal dependencies between user historical actions. The under utilization of latent factors can limit the performance and generalization of recommender systems. To address the above limitation, in this paper, we proposed a novel multi-view recommender method called Contrastive Attentive Collaborative Filtering (CACF) that combines collaborative signals and temporal dependencies. Our method leverages the strengths of both approaches by combining the temporal dependecies captured by the Transformer with the user-item collaborative signals retrieved by graph convolutional network (GCN). The Transformer learns temporal dependencies and generates embeddings of items while GCN component capture collaborative signals. By contrastivly optimizing output embeddings from both methods, our approach aims to provide more accurate and personalized recommendations. Finally, we evaluate our method on several benchmark datasets and compare its performance with state-of-the-art recommendation systems. The experimental results show that our method outperforms in several evaluation metrices.

关键词： Industries Ethics Accuracy Graph convolutional networks Collaborative filtering Collaboration Predictive models Transformers Recommender systems Optimization

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Communication-reduced Conjugate Gradient Variants for GPU-accelerated Clusters

Communication-reduced Conjugate Gradient Variants for GPU-ac...

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Euromicro Conference on parallel, distributed and Network-Based Processing

作者： Massimo Bernaschi Mauro G. Carrozzo Alessandro Celestini Giacomo Piperno Pasqua D’Ambra Institute for Applied Computing (IAC) Via dei Taurini 19 Rome Institute for Applied Computing (IAC) Via P. Castellino 111 Naples

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Linear solvers are key components in any software platform for scientific and engineering computing. The solution of large and sparse linear systems lies at the core of physics-driven numerical simulations relying on partial differential equations (PDEs) and often represents a significant bottleneck in data-driven procedures, such as scientific machine learning. In this paper, we present an efficient implementation of the preconditioned s-step Conjugate Gradient (CG) method, originally proposed by Chronopoulos and Gear in 1989, for large clusters of Nvidia GPU-accelerated computing nodes. The method, often referred to as communication-reduced or communication-avoiding CG, reduces global synchronizations and data communication steps compared to the standard approach, enhancing strong and weak scalability on parallel computers. Our main contribution is the design of a parallel solver that fully exploits the aggregation of low-granularity operations inherent to the s-step CG method to leverage the high throughput of GPU accelerators. Additionally, it applies overlap between data communication and computation in the multi-GPU sparse matrix-vector product. Experiments on classic benchmark datasets, derived from the discretization of the Poisson PDE, demonstrate the potential of the method.

关键词： Linear systems Scalability Partial differential equations Machine learning Throughput Numerical simulation software Data communication Sparse matrices Standards

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DDBO: Discrete Dung Beetle Optimizer for Optical Communication Simulation Task Allocation

DDBO: Discrete Dung Beetle Optimizer for Optical Communicati...

引用

international symposium on parallel and distributed Processing with Applications, ISPA

作者： Zheng Wang Weiwei Xing Yue Cheng Weibin Liu Zhiyuan Zou Zhi Wang Genxiang Chen Wei Lu School of Software Engineering Beijing Jiaotong University Beijing China Institute of Information Science Beijing Jiaotong University Beijing China Computer School Beijing Information Science and Technology University Beijing China School of Physical Sciences and Engineering Beijing Jiaotong University Beijing China College of Science Minzu University of China Beijing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Optical Communication Simulation Task Allocation (OCSTA) constitutes an interdisciplinary quandary. This paper proposes a swarm intelligence approach harnessing a Discrete Dung Beetle Optimizer (DDBO) algorithm with a globally equilibrated strategy to offer a resolution avenue for this intricate conundrum. Firstly, we provide a comprehensive exposition of the mathematical model underpinning the OCSTA, which employs the spatial positioning of the population to articulate diverse allocation solutions. Then, a weighted random selection technique is used to generate initial solutions. Thirdly, a predator avoidance strategy is introduced to facilitate updates in the dung beetle position. Finally, the global equilibrium mechanism with the swap operator is exploited to harmonize the exploratory and exploitative capabilities, thereby further augmenting the quality of the solutions. We conducted several simulation experiments 1 across various distinct task load scenarios, and statistical tests are employed to evaluate the significant differences between the proposed algorithm and other state-of-the-art methods. The outcomes revealed that the DDBO solution yielded an improvement of approximately 15.8%, which underscores the competitiveness and robustness of solving the OCSTA.

关键词： distributed processing Heuristic algorithms Dynamic scheduling Optical fiber communication Robustness Mathematical models Resource management Spatial resolution Particle swarm optimization Optimization

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Common Mode Voltage Elimination of Dual Three-Phase Motor Driven by paralleled Three-Level Inverters

Common Mode Voltage Elimination of Dual Three-Phase Motor Dr...

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IEEE international symposium on Power Electronics for distributed Generation systems (PEDG)

作者： Yafei Ma Dong Jiang Zicheng Liu School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China

In this paper, a common-mode voltage (CMV) elimination and neutral point potential balance control algorithm is proposed for the dual three-phase motor (DTPM) driven by neutral point clamped (NPC) parallel three-level inverters. In this paper, the CMV is eliminated by pulse phase shift alignment, and the neutral-point potential (NPP) balance is controlled by zero sequence voltage. At the same time, the PWM signal of the system can be realized by sawtooth carrier, and the algorithm can adapt to DTPM load with arbitrary angle displacement (AD). Finally, simulation results verify the effectiveness and superiority of the proposed CMV elimination algorithm.

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Non-deterministic event brokered computing 2022

Non-deterministic event brokered computing

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12th international symposium on Highly Efficient Accelerators and Reconfigurable Technologies (HEART)

作者： Brown, Andrew Thomas, David Vousden, Mark Bragg, Graeme Luk, Wayne Beaumont, Jonny Todman, Tim Yakovlev, Alex Rafiev, Ashur Univ Southampton Southampton Hants England Imperial Coll London London England Univ Newcastle Newcastle Upon Tyne Tyne & Wear England

ISBN: (纸本)9781450396608

This paper reviews the massively micro-parallel compute system POETS (Partially Ordered Event Triggered System) and illustrates its potential for speeding up demanding applications. Application domains that benefit from POETS include simulations of physical systems that can be discretised as a mesh. The problem graph is distributed over a large compute mesh;each mesh vertex contains a processor - an FPGA-based RISC-V thread supporting custom instructions in our prototype - and a small amount of local problem state data. There is no central overseer of any sort and processors cannot see memory besides their own. A problem graph vertex interacts with a neighbour to send a state change by sending an asynchronous packet. The packets are fixed size and small - currently 64 bytes - and the hardware communications infrastructure is very fast. Applications can use an asynchronous 'packet storm' approach;run synchronously using a hardware idle barrier or run in a globally asynchronous, locally synchronous manner. Results show significant wallclock speedup and power consumption improvement over conventional systems: for one application we show a 40-fold speedup over a conventional CPU-based system;versus a multi-GPU system, the POETS cluster is 26% faster, 60% more power efficient, and 34% more energy efficient.

关键词： Event-driven Network-on-chip Massively parallel

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A Reputation Layered Coding Based Storage Strategy for Consortium Blockchain

A Reputation Layered Coding Based Storage Strategy for Conso...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Ziqian Li Maolin Pan Yang Yu School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China School of Software Engineering Sun Yat-Sen University Zhuhai China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

The improvement of consensus algorithms has greatly enhanced the performance of consortium blockchain, making it possible to be applied in large-scale network scenarios such as finance, healthcare and supply chain management. However, better performance often results in a heavier storage burden on the nodes. A storage strategy based on erasure code is a good solution, capable of reducing the storage consumption of each block to O(1). However, in large-scale consortium blockchain, this storage strategy encounters issues like poor performance and limited system dynamicity. To address the issues associated with the application of coding-based storage strategies in large-scale consortium blockchain, this paper proposes a storage strategy based on reputation-layered coding, named Level-Store. Level-Store first partitions the nodes into different storage units based on their reputations, then allocates the storage tasks of different block segments to different storage units for encoded storage. By calculating reasonable storage schemas for each storage unit, the data availability is ensured. Experiment shows that compared to existing coding-based storage strategies, Level-Store significantly improves system performance and dynamicity. It demonstrates notable advancements in key metrics such as space overhead, time consumption for storage and retrieval, the number of nodes and chunks affected by re-encoding, and the time consumption for re-encoding. Additionally, the system’s security and fairness have also been enhanced.

关键词： distributed processing Supply chain management System performance Finance Medical services Extraterrestrial measurements Encoding Blockchains Security Optimization

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Enhanced YOLOv7 Model for Aerial Drone Detection in Complex Environments

Enhanced YOLOv7 Model for Aerial Drone Detection in Complex ...

引用

international symposium on parallel and distributed Processing with Applications, ISPA

作者： Changlong Fu Yasu Wu Xin Jin Puming Wang Qian Jiang Software College Yunnan University Kunming China Software College Northwestern Polytechnical University Xi’an China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

With the growing popularity of Unmanned Aerial Vehicles (UAVs) in civilian, commercial, and military applications, the need for robust drone detection systems has become increasingly urgent. However, due to the small size of drones when observed from a distance, most traditional machine learning and two-stage deep learning detection methods currently struggle to capture effective feature information of drones in complex image backgrounds, and often fail to meet the requirements for real-time performance. To address this challenge, we have introduced an improved version of the YOLOv7-Tiny model, known as YOLOv7-ADD, which significantly enhances the detection performance of small target drones at various distances and under complex backgrounds. The model integrates the Scylla-IoU (SIoU) loss function to improve the accuracy of bounding box regression and employs the BiFormer attention mechanism, which dynamically focuses on key features of drones within the detection scene, enhancing the model’s recognition capabilities for drones. Furthermore, the introduction of the Diverse Branch Block (DBB) helps the model capture multi-scale features, optimizing the detection effect for drones of various sizes. Extensive experiments on drones dataset have demonstrated the superior performance of YOLOv7-ADD. It achieved a 2.7% improvement in mAP@0.5 and a 1.1% increase in mAP@0.5:0.95, while maintaining high FPS detection performance. This provides an efficient drone detection solution for aerial surveillance. The code is available on https://***/FuChanglong/***.

关键词： Measurement Adaptation models Accuracy Attention mechanisms Surveillance Performance gain Feature extraction Robustness Real-time systems Drones

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Lightweight Multicast Authentication in NoC-based SoCs

Lightweight Multicast Authentication in NoC-based SoCs

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IEEE international symposium on Quality Electronic Design

作者： Hansika Weerasena Prabhat Mishra Department of Computer & Information Science & Engineering University of Florida Gainesville Florida USA

ISBN: (数字)9798350309270

ISBN: (纸本)9798350309287

Network-on-Chip (NoC) is responsible for managing communication in modern SoCs. The ubiquity of NoC and its distributed nature across the chip has made it a focal point of attacks. Spoofing attacks by impersonating the nodes in SoC can lead to unauthorized information access and can also be employed to launch denial of service attacks. Modern software tends to use more parallelism among multiple cores, increasing multicast communication among cores to exchange cache coherence messages. Traditional multicast authentication solutions are not effective due to the resource-constrained nature of NoC-based SoCs. In this paper, we propose a lightweight multicast authentication mechanism that utilizes existing unicast authentication infrastructures in NoC using one-way accumulators. Experimental results demonstrate the effectiveness of our approach with required security while incurring minor area and performance overhead.

关键词： Unicast Authentication Network-on-chip Coherence parallel processing Multicast communication software

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Simulation and implementation of local electrical markets in photovoltaic energy communities with PVLEM

Simulation and implementation of local electrical markets in...

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IEEE international symposium on systems engineering (ISSE)

作者： X. Moreno-Vassart F. Javier Toledo Victoria Herranz Vicente Galiano Computers Engineering Dept. Miguel Hernández University of Elche Elche Spain Operational Research Center Miguel Hernández University of Elche Elche Spain

ISBN: (数字)9798350353723

ISBN: (纸本)9798350353730

In recent decades, the increase in environmental awareness and the need to reduce greenhouse gas emissions have led to growing interest in renewable energy sources. Among them, photovoltaic solar energy has emerged as a promising option due to its abundance, sustainability, and potential for the decentralisation of energy supply. In this paper, we explore the concept of photovoltaic energy communities (PEC), an innovative model that integrates distributed photovoltaic solar energy generation with storage technologies and demand management systems. The constant increase of prosumers (producers and consumers of electrical energy) is causing a change in the rules of the electrical market. This paper presents a software architecture for a distributed energy community system based on nodes installed in each prosumer. This system is specifically designed to be implemented in embedded systems, making it suitable for deployment in residential settings and small communities. The decentralised structure and main components of the system are described, highlighting its ability to efficiently manage energy generation, storage, and consumption. In our PVLEM (PhotoVoltaic Local Electrical Market) proposal, the elements of this architecture are defined, and an implementation that allows the simulation of an energy community and its execution in a real environment is presented. Through this proposal, we facilitate the transformative potential of PECs as a sustainable approach to energy generation, capable of promoting community participation, energy resilience, and climate change mitigation.

关键词： Climate change Photovoltaic systems Power distribution distributed power generation Optimization methods Energy management Greenhouse gases Environmental monitoring Power markets Solar power generation

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Asynchronous Design of a Bitwise Elimination Argmax via High-Level Modeling in GraphRack

Asynchronous Design of a Bitwise Elimination Argmax via High...

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international symposium on Advanced Research in Asynchronous Circuits and systems

作者： Hugh Squires-Parkin Alex Chan Rishad Shafik Adrian Wheeldon Alex Yakovlev Newcastle University Newcastle upon Tyne UK Literal Labs Newcastle upon Tyne UK

ISBN: (数字)9798331503109

ISBN: (纸本)9798331503116

In this paper, we propose a novel approach for designing an asynchronous-based Bitwise Elimination Argmax (BEA) with early completion features catered for event-driven machine learning (ML) applications, and a novel software tool called GraphRack for high-level analysis of distributed, yet parallel, asynchronous designs of graph-based models like Petri nets (PNs). Typically in ML architectures, the Argmax functions to find the largest vector within an input set can be a costly operation and size inefficient, especially for bespoke low-latency ML architectures. By introducing an asynchronous BEA, we address the above with an ‘in the race’ style protocol, where vectors are competed against one another and are procedurally eliminated from the most to least significant bits, with high-confidence classification vectors winning the fastest. This asynchronous BEA component is implemented using PNs and is tested against two benchmark scenarios: randomly distributed vectors and Tsetlin Machine (TM) class sum data. We also introduce GraphRack‘s simulation platform that accelerates the analysis of PN models. Here, our results show average-case performance improvements of 1.52× over worst-case for random vectors, and a 2.06× improvement for TM classifications. With a direct, yet time-independent, design, our findings show BEA’s ability to reduce inference times in more general classification hardware.

关键词： Analytical models Protocols Petri nets Machine learning Computer architecture Vectors Hardware Integrated circuit modeling software tools Low latency communication

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