Proceedings of the First International Conference on AI-ML Systems最新文献_第3页

Class-Based Order-Independent Models of Natural Language for Bayesian Auto-Complete Inference 用于贝叶斯自动完成推理的自然语言基于类的序无关模型

Proceedings of the First International Conference on AI-ML Systems Pub Date : 2021-10-21 DOI: 10.1145/3486001.3486240

Morten Hagen, Piyush Arora, Rahul Ghosh, Dawn Thomas, S. Joshi

引用次数: 0

BOND: Efficient and Frugal DL Model Co-design for Botnet detection on IoT Gateways BOND:物联网网关上僵尸网络检测的高效节俭DL模型协同设计

Proceedings of the First International Conference on AI-ML Systems Pub Date : 2021-10-21 DOI: 10.1145/3486001.3486237

Himanshu Gandhi, Misha Mehra, V. Ribeiro

引用次数: 3

Proceedings of the First International Conference on AI-ML Systems Pub Date : 2021-10-21 DOI: 10.1145/3486001.3486228

Arjun Verma, Prateksha Udhayanan, Rahul Murali Shankar, N. NikhilaK., S. Chakrabarti

{"title":"Source-Code Similarity Measurement: Syntax Tree Fingerprinting for Automated Evaluation","authors":"Arjun Verma, Prateksha Udhayanan, Rahul Murali Shankar, N. NikhilaK., S. Chakrabarti","doi":"10.1145/3486001.3486228","DOIUrl":"https://doi.org/10.1145/3486001.3486228","url":null,"abstract":"A majority of the current automated evaluation tools focus on grading a program based only on functionally testing the outputs. This approach suffers both false positives (i.e. finding errors where there are not any) and false negatives (missing out on actual errors). In this paper, we present a novel system which emulates manual evaluation of programming assignments based on the structure and not the functional output of the program using structural similarity between the given program and a reference solution. We propose an evaluation rubric for scoring structural similarity with respect to a reference solution. We present an ML based approach to map the system predicted scores to the scores computed using the rubric. Empirical evaluation of the system is done on a corpus of Python programs extracted from the popular programming platform, HackerRank, in combination with programming assignments submitted by students undertaking an undergraduate Python programming course. The preliminary results have been encouraging with the errors reported being as low as 12 percent with a deviation of about 3 percent, showing that the automatically generated scores are in high correlation with the instructor assigned scores.","PeriodicalId":266754,"journal":{"name":"Proceedings of the First International Conference on AI-ML Systems","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115907627","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: 2

On Handling Class Imbalance in Continual Learning based Network Intrusion Detection Systems 基于持续学习的网络入侵检测系统中类不平衡的处理

Proceedings of the First International Conference on AI-ML Systems Pub Date : 2021-10-21 DOI: 10.1145/3486001.3486231

Suresh Kumar Amalapuram, Thushara Tippi Reddy, Sumohana S. Channappayya, T. B. Reddy

{"title":"On Handling Class Imbalance in Continual Learning based Network Intrusion Detection Systems","authors":"Suresh Kumar Amalapuram, Thushara Tippi Reddy, Sumohana S. Channappayya, T. B. Reddy","doi":"10.1145/3486001.3486231","DOIUrl":"https://doi.org/10.1145/3486001.3486231","url":null,"abstract":"Modern-day cyber threats are growing more rapidly than ever before. To effectively defend against them, Anomaly-based Network intrusion detection systems (A-NIDS) must evolve continuously. Traditional machine learning techniques are ineffective in handling sequentially evolving tasks, and Neural Networks (NNs) in particular suffer from Catastrophic Forgetting (CF) of old tasks when trained on new ones. Continual Learning (CL) strategies help to mitigate CF by imposing constraints while training NNs on sequentially evolving data like network traffic. However, applying the CL framework in the design of A-NIDS is not straightforward due to the heavy Class Imbalance (CI) in the network traffic datasets. As a result, the performance of the system is very sensitive to the task execution order. In this work, we propose a CL based A-NIDS by applying sample replay with Class Balancing Reservoir Sampling (CBRS) to mitigate CI in a Class Incremental Setting (CIS). Using the CICIDS-2017 dataset, experiments are conducted by permuting the majority class across the different task execution orders using the proposed CL based A-NIDS. We find that using auxiliary memory with context-aware sample replacing strategies, CF can be reduced to a greater extent, as opposed to data augmentation techniques which may alter the original data distribution and increase training time (with oversampling methods).","PeriodicalId":266754,"journal":{"name":"Proceedings of the First International Conference on AI-ML Systems","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125474664","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: 3

Scarecrow - Intelligent Annotation platform for Engine Health Management 稻草人-发动机健康管理的智能注释平台

Proceedings of the First International Conference on AI-ML Systems Pub Date : 2021-10-21 DOI: 10.1145/3486001.3486238

S. D. Srinivasan, R. Pruthi

引用次数: 0

VeRLPy: Python Library for Verification of Digital Designs with Reinforcement Learning VeRLPy:用于强化学习的数字设计验证的Python库

Proceedings of the First International Conference on AI-ML Systems Pub Date : 2021-08-09 DOI: 10.1145/3486001.3486236

Aebel Joe Shibu, S. Sadhana, N. Shilpa, Pratyush Kumar

引用次数: 1

Resource Constrained Neural Networks for Direction-of-Arrival Estimation in Micro-controllers 基于资源约束神经网络的微控制器到达方向估计

Proceedings of the First International Conference on AI-ML Systems Pub Date : 2021-07-23 DOI: 10.1145/3486001.3486230

Piyush Sahoo, Romesh Rajoria, Shivam Chandhok, S. Darak, D. Pau, Hem-Dutt Dabral

{"title":"Resource Constrained Neural Networks for Direction-of-Arrival Estimation in Micro-controllers","authors":"Piyush Sahoo, Romesh Rajoria, Shivam Chandhok, S. Darak, D. Pau, Hem-Dutt Dabral","doi":"10.1145/3486001.3486230","DOIUrl":"https://doi.org/10.1145/3486001.3486230","url":null,"abstract":"With the introduction of shared spectrum sensing and beam-forming based multi-antenna transceivers, 5G networks demand spectrum sensing to identify opportunities in time, frequency, and spatial domains. Narrow beam-forming makes it difficult to have spatial sensing (direction-of-arrival, DoA, estimation) in a centralized manner, and with the evolution of paradigms such as artificial intelligence of Things (AIOT), ultra-reliable low latency communication (URLLC) services and distributed networks, intelligence for edge devices (Edge-AI) is highly desirable. It helps to reduce the data-communication overhead compared to cloud-AI-centric networks and is more secure and free from scalability limitations. However, achieving desired functional accuracy is a challenge on edge devices such as microcontroller units (MCU) due to area, memory, and power constraints. In this work, we propose low complexity neural network-based algorithm for accurate DoA estimation and its efficient mapping on the off-the-self MCUs. An ad-hoc graphical-user interface (GUI) is developed to configure the STM32 NUCLEO-H743ZI2 MCU with the proposed algorithm and to validate its functionality. The performance of the proposed algorithm is analyzed for different signal-to-noise ratios (SNR), word-length, the number of antennas, and DoA resolution. In-depth experimental results show that it outperforms the conventional statistical spatial sensing approach.","PeriodicalId":266754,"journal":{"name":"Proceedings of the First International Conference on AI-ML Systems","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124971243","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

Machine Learning for Databases 面向数据库的机器学习

Proceedings of the First International Conference on AI-ML Systems Pub Date : 2021-07-01 DOI: 10.14778/3476311.3476405

Guoliang Li, Xuanhe Zhou, Lei Cao

引用次数: 36