Wireless sensor networks based on modular arithmetic

Chen, Jingliang; Yatskiv, Vasyl; Sachenko, Anatoly; Su, Jun

doi:10.3103/s073527271705003x

Cited by 14 publications

(5 citation statements)

References 8 publications

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“…For modular operations such as addition, subtraction, multiplication, and exact division, the computations with residues are independent with each other, which provides parallel, carry-free, and high-speed computer arithmetic. Thanks to these properties, RNS is now applied in many resource-intensive applications, such as blockchain [3], neural networks [4]- [6], cryptography [7]- [11], digital signal processing [12]- [14], wireless sensor networks [15], and digital image processing [16].…”

Section: Introductionmentioning

confidence: 99%

Using Floating-Point Intervals for Non-Modular Computations in Residue Number System

Isupov

2020

IEEE Access

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The residue number system (RNS) provides parallel, carry-free, and high-speed arithmetic and is therefore a good tool for high-performance computing. However, operations such as magnitude comparison, sign computation, overflow detection, scaling, and division are difficult to perform in RNS, since it is problematic to determine the magnitude of an RNS number. In order to resolve this problem, we propose to compute the interval evaluation of the fractional representation of an RNS number in floating-point arithmetic of limited precision. No matter what the size n of the moduli set and dynamic range, only small arithmetic operations are required, and most of the computations are performed in parallel with n threads, which allows for efficient implementation of our method on many general-purpose computing platforms. Using this method, we propose new algorithms for magnitude comparison and general division in RNS and implement them for GPUs using the CUDA platform. We evaluate the performance of our algorithms on an NVIDIA GTX 1080 GPU using sets of 4 to 256 RNS moduli that provide dynamic ranges from 64 to 4096 bits. Experimental results show that the proposed new algorithms are efficient for large moduli sets and clearly outperform the existing RNS magnitude comparison and division algorithms in terms of execution time. INDEX TERMS Residue number system, floating-point arithmetic, non-modular operations, magnitude comparison, division, high performance, parallel algorithms, graphics processing unit, CUDA.

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Section: Introductionmentioning

confidence: 99%

Using Floating-Point Intervals for Non-Modular Computations in Residue Number System

Isupov

2020

IEEE Access

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“…Here, B d is the set of bi-terms which is extracted from the service document d, and each bi-term b ∈ B d contains two unordered words (w i , w j ), D is the documents and d is the biterms, respectively. BTM can construct the generation of bi-term with the latent topic structure, according to [26]. The extended service feature is used as training corpus B.…”

Section: Btm Topic Modelmentioning

confidence: 99%

A Web Service Clustering Method with Semantic Enhancement Based on RGPS and BTM

Xie¹,

Xie²,

Chen³

et al. 2023

Journal of Internet Technology

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<p>In order to overcome the data sparsity problem in service description text and to improve the web service clustering quality, we propose a web service clustering method with semantic enhancement based on RGPS (Role-Goal-Process-Service) Framework and Bi-term Topic Model (BTM). First, we extend service description text’s feature according to RGPS meta-model framework. Also, we generate the service latent feature by BTM. Then, we employ K-means on the generated features. The results of experiments on service registry PWeb show that this method can get better clustering performance in purity and entropy. It is proved that this method has great efficiency compared with the baseline methods K-means, Agglomerative and LDA (Latent Dirichlet Allocation). This paper enhances the service clustering performance and creates foundation work for service organization and recommendation. </p> <p> </p>

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“…The major knowledge representation learning methods include the neural network model [26][27][28], matrix decomposition [29], and translation model [30,31]. TransR [32] is a kind of translation model for knowledge graph. It embeds heterogeneous entities and relations within the same vector space, and represents these entities in the distinct semantic space bridged by relation-specific metrics.…”

Section: Knowledge Graph Embeddingmentioning

confidence: 99%

A Knowledge Graph Embedding Based Service Recommendation Method for Service-Based System Development

et al. 2023

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Web API is an efficient way for Service-based Software (SBS) development, and mashup is a key technology which merges several web services to deal with the increasing complexity of software requirements and expedite the service-based system development. The efficient service recommendation method is vital for the software development. However, the existing methods often suffer from data sparsity or cold start issues, which should lead to bad effects. Currently, this paper starts with SBS development, and proposes a service recommendation method based on knowledge graph embedding and collaborative filtering (CF) technology. In our model, we first construct a refined knowledge graph using SBS-service co-invocation record and SBS and service related information to mine the potential semantics relationship between SBS and service. Then, we learn the SBS and service entities in the knowledge graph. These heterogeneous entities (SBS and service, etc.) are embedded into the low-dimensional space through the representation learning algorithms of Word2vec and TransR, and the distances between SBS and service vectors are calculated. The input of recommendation model is SBS requirement (target SBS), the similarities functional SBS set is extracted from knowledge graph, which can relieve the cold start problem. Meanwhile, the recommendation model uses CF to recommend service to target SBS. Finally, this paper verifies the effectiveness of method on the real-word dataset. Compared with the several state-of-the-art methods, our method has the best service hit rate and ranking quality.

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Wireless sensor networks based on modular arithmetic

Cited by 14 publications

References 8 publications

Using Floating-Point Intervals for Non-Modular Computations in Residue Number System

Using Floating-Point Intervals for Non-Modular Computations in Residue Number System

A Web Service Clustering Method with Semantic Enhancement Based on RGPS and BTM

A Knowledge Graph Embedding Based Service Recommendation Method for Service-Based System Development

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