Neeraj Choudhary, Jimson Mathew, R. Behera, Zenin Easa Panthakkalakath
{"title":"ATT-LSTM: An Interpretable Deep Learning Framework for COVID Outbreak Prediction","authors":"Neeraj Choudhary, Jimson Mathew, R. Behera, Zenin Easa Panthakkalakath","doi":"10.1145/3631461.3631959","DOIUrl":"https://doi.org/10.1145/3631461.3631959","url":null,"abstract":"","PeriodicalId":368371,"journal":{"name":"International Conference of Distributed Computing and Networking","volume":"92 6s1","pages":"322-327"},"PeriodicalIF":0.0,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139630723","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}
{"title":"K Circle Formation by Swarm Robots on a Grid","authors":"Moumita Mondal, Subodh Singh, S. Chaudhuri","doi":"10.1145/3631461.3632304","DOIUrl":"https://doi.org/10.1145/3631461.3632304","url":null,"abstract":"","PeriodicalId":368371,"journal":{"name":"International Conference of Distributed Computing and Networking","volume":"36 s155","pages":"334-339"},"PeriodicalIF":0.0,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139630073","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}
The Web search ranking task has become increasingly important due to the rapid growth of the internet. With the growth of the Web and the number of Web search users, the amount of available training data for learning Web ranking models has also increased. We investigate the problem of learning to rank on a cluster using Web search data composed of 140,000 queries and approximately fourteen million URLs. For datasets much larger than this, distributed computing will become essential, due to both speed and memory constraints. We compare to a baseline algorithm that has been carefully engineered to allow training on the full dataset using a single machine, in order to evaluate the loss or gain incurred by the distributed algorithms we consider. The underlying algorithm we use is a boosted tree ranking algorithm called LambdaMART, where a split at a given vertex in each decision tree is determined by the split criterion for a particular feature. Our contributions are two-fold. First, we implement a method for improving the speed of training when the training data fits in main memory on a single machine by distributing the vertex split computations of the decision trees. The model produced is equivalent to the model produced from centralized training, but achieves faster training times. Second, we develop a training method for the case where the training data size exceeds the main memory of a single machine. Our second approach easily scales to far larger datasets, i.e., billions of examples, and is based on data distribution. Results of our methods on a real-world Web dataset indicate significant improvements in training speed. 4 Large-scale Learning to Rank using Boosted Decision Trees
{"title":"Distributed Machine Learning","authors":"Bapi Chatterjee","doi":"10.1145/3631461.3632516","DOIUrl":"https://doi.org/10.1145/3631461.3632516","url":null,"abstract":"The Web search ranking task has become increasingly important due to the rapid growth of the internet. With the growth of the Web and the number of Web search users, the amount of available training data for learning Web ranking models has also increased. We investigate the problem of learning to rank on a cluster using Web search data composed of 140,000 queries and approximately fourteen million URLs. For datasets much larger than this, distributed computing will become essential, due to both speed and memory constraints. We compare to a baseline algorithm that has been carefully engineered to allow training on the full dataset using a single machine, in order to evaluate the loss or gain incurred by the distributed algorithms we consider. The underlying algorithm we use is a boosted tree ranking algorithm called LambdaMART, where a split at a given vertex in each decision tree is determined by the split criterion for a particular feature. Our contributions are two-fold. First, we implement a method for improving the speed of training when the training data fits in main memory on a single machine by distributing the vertex split computations of the decision trees. The model produced is equivalent to the model produced from centralized training, but achieves faster training times. Second, we develop a training method for the case where the training data size exceeds the main memory of a single machine. Our second approach easily scales to far larger datasets, i.e., billions of examples, and is based on data distribution. Results of our methods on a real-world Web dataset indicate significant improvements in training speed. 4 Large-scale Learning to Rank using Boosted Decision Trees","PeriodicalId":368371,"journal":{"name":"International Conference of Distributed Computing and Networking","volume":"27 s79","pages":"4-7"},"PeriodicalIF":0.0,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139630335","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}
Md Ashikul Haque, Dali Ismail, Abusayeed Saifullah
{"title":"Scalable Real-Time Control in Industrial Cyber-Physical Systems","authors":"Md Ashikul Haque, Dali Ismail, Abusayeed Saifullah","doi":"10.1145/3631461.3631474","DOIUrl":"https://doi.org/10.1145/3631461.3631474","url":null,"abstract":"","PeriodicalId":368371,"journal":{"name":"International Conference of Distributed Computing and Networking","volume":"90 7","pages":"353-358"},"PeriodicalIF":0.0,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139630532","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}
{"title":"A Network Segmentation Architecture for Flow Aggregation and DDoS Mitigation in SDN Using RAPID Flow Rules","authors":"Himanshu, Kaushik Saha, Payali Das, Swades De","doi":"10.1145/3631461.3631561","DOIUrl":"https://doi.org/10.1145/3631461.3631561","url":null,"abstract":"","PeriodicalId":368371,"journal":{"name":"International Conference of Distributed Computing and Networking","volume":"86 5","pages":"232-241"},"PeriodicalIF":0.0,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139630638","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}
Debasish Pattanayak, Subhash Bhagat, S. Chaudhuri, A. R. Molla
{"title":"Maximal Independent Set via Mobile Agents","authors":"Debasish Pattanayak, Subhash Bhagat, S. Chaudhuri, A. R. Molla","doi":"10.1145/3631461.3631543","DOIUrl":"https://doi.org/10.1145/3631461.3631543","url":null,"abstract":"","PeriodicalId":368371,"journal":{"name":"International Conference of Distributed Computing and Networking","volume":"94 11","pages":"74-83"},"PeriodicalIF":0.0,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139630697","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}
{"title":"Integration of ROS and Gazebo Platform using MAVLINK Protocol for Real-Time UAV Applications","authors":"Ashutosh Kumar Jha, Vidyuth Sridhar, Victor Azad, Ram Narayan Yadav, Jagat Rath","doi":"10.1145/3631461.3631950","DOIUrl":"https://doi.org/10.1145/3631461.3631950","url":null,"abstract":"","PeriodicalId":368371,"journal":{"name":"International Conference of Distributed Computing and Networking","volume":"30 3","pages":"394-399"},"PeriodicalIF":0.0,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139630297","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}
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