Proceedings of ADCOM最新文献

LIFT- A Linear Two-Phase Commit Protocol 一个线性的两阶段提交协议

Proceedings of ADCOM Pub Date : 2019-09-05 DOI: 10.34048/adcom.2019.paper.3

Aman Pandey, Sarvesh Pandey, Udai Shanker

引用次数: 4

Link performance evaluation of Uplink Precoded Multiuser MIMO-NOMA system for 5G Communication Networks 5G通信网络上行预编码多用户MIMO-NOMA系统链路性能评估

Proceedings of ADCOM Pub Date : 2019-09-05 DOI: 10.34048/adcom.2019.paper.4

Kishore Kr, Pagadala Dinesh Kumar

引用次数: 0

acBF: A High Accuracy Membership Filter using rDBF acBF:基于rDBF的高精度成员过滤器

Proceedings of ADCOM Pub Date : 2019-09-05 DOI: 10.34048/adcom.2019.paper.1

Ripon Patgiri, Sabuzima Nayak, S. Borgohain

{"title":"acBF: A High Accuracy Membership Filter using rDBF","authors":"Ripon Patgiri, Sabuzima Nayak, S. Borgohain","doi":"10.34048/adcom.2019.paper.1","DOIUrl":"https://doi.org/10.34048/adcom.2019.paper.1","url":null,"abstract":"Bloom Filter is a data structure for membership query which is deployed in diverse research domains to boost up system’s performance and to lower on-chip memory consumption. However, there are still lacking of a high accuracy Bloom Filterwithoutcompromisingtheperformanceandmemoryspace. Moreover, the scalability causes more memory consumption as well as time complexity. Therefore, in this paper, we present a novel Bloom Filter, called accurate Bloom Filter (acBF), which features: a) an impressive guaranteed accuracy of 99.98%, b) a maximum false positive probability of 0.00015, c) lower collision probability, d) free from false negative, e) optimal insertion and membershipquerycost,andg)≤ 8−bits ofmemoryconsumption per item. acBF deploys eight multidimensional Bloom Filter. ThesemultidimensionalBloomFilterseliminatethefalsepositives at eight stages without sacriﬁcing the system performance. We have conducted rigorous experiments to validate the accuracy of acBF which is unprecedentedly high. Also, acBF is compared with Scalable Bloom Filter (SBF) and Cuckoo Filter (CF). Experiments show acBF outperforms SBF and CF in terms of accuracy, and scalability. Moreover, performance of acBF outperforms CF in lookup operation. But, CF outperforms acBF in insertion. However, accuracy of acBF is incomparable with both SBF and CF.","PeriodicalId":195065,"journal":{"name":"Proceedings of ADCOM","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122797633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

DLRCNeg: Deep Learning based Reading Comprehension by handling Negation dlrcnegg:通过处理否定来深度学习阅读理解

Proceedings of ADCOM Pub Date : 2019-09-05 DOI: 10.34048/adcom.2019.phdforumpaper.6

J. F. Lilian, K. Sundarakantham, S. Shalinie

{"title":"DLRCNeg: Deep Learning based Reading Comprehension by handling Negation","authors":"J. F. Lilian, K. Sundarakantham, S. Shalinie","doi":"10.34048/adcom.2019.phdforumpaper.6","DOIUrl":"https://doi.org/10.34048/adcom.2019.phdforumpaper.6","url":null,"abstract":"Question Answer (QA) System for Reading Comprehension (RC) is a computerized approach to retrieve relevant response to the query posted by the users. The underlined concept in developing such a system is to build a human computer interaction. The interactions will be in natural language and we tend to use negation words as a part of our expressions. During the pre-processing stage in Natural Language Processing (NLP) task these negation words gets removed and hence the semantics gets changed. This remains to be an unsolved problem in QA system. In order to maintain the semantics we have proposed a novel approach Hybrid NLP based Bi-directional Long Short Term Memory (Bi-LSTM) with attention mechanism. It deals with the negation words and maintains the semantics of the sentence. We also focus on answering any factoid query (i.e. ’what’, ’when’, ’where’, ’who’) that is raised by the user. For this purpose, the use of attention mechanism with softmax activation function has obtained superior results that matches the question type and process the context information effectively. The experimental results are performed over the SQuAD dataset for reading comprehension and the Stanford Negation dataset is used to perform the negation in the RC sentence. The accuracy of the system over negation is obtained as 93.9% and over the QA system is 87%.","PeriodicalId":195065,"journal":{"name":"Proceedings of ADCOM","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133444199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Design of Efficient Algorithms for Video Surveillance Applications using Artificial Intelligence 基于人工智能的视频监控高效算法设计

Proceedings of ADCOM Pub Date : 2019-09-05 DOI: 10.34048/adcom.2019.phdforumpaper.5

M. Mohana, RAVISH ARADHYA H V

引用次数: 0

Evaluation of Functional Splits in terms of Optimal Number of Users Served in 5G Downlink 基于5G下行链路最优用户数的功能拆分评估

Proceedings of ADCOM Pub Date : 2019-09-05 DOI: 10.34048/adcom.2019.paper.2

Pagadala Dinesh Kumar, R. KishoreK

引用次数: 0

Real-Time Headgear Detection in Videos Using Deep Learning Based Feature Extraction with A Supervised Classifier 基于深度学习特征提取和监督分类器的视频实时头饰检测

Proceedings of ADCOM Pub Date : 2018-09-05 DOI: 10.34048/adcom.2018

Rajdeep Pal, R. Seshadri, Swarnashree Mysore Sathyendra, Natarajan S