Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing最新文献_第4页

Quantum Enhanced Machine Learning for Unobtrusive Stress Monitoring 用于应力监测的量子增强机器学习

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549288

Anupama Padha, Anita Sahoo

{"title":"Quantum Enhanced Machine Learning for Unobtrusive Stress Monitoring","authors":"Anupama Padha, Anita Sahoo","doi":"10.1145/3549206.3549288","DOIUrl":"https://doi.org/10.1145/3549206.3549288","url":null,"abstract":"Prolonged stress can negatively impact a person's mental health leading to multiple diseases. Stress monitoring can be efficiently done with the help of artificial intelligence technology combined with the benefits of quantum computing. The main aim of the paper is to analyze quantum enhanced machine learning techniques in predicting the stress of knowledge workers at office through multiple modalities. A general overview of popular quantum enhanced machine learning methods such as Quantum Support Vector Machine (QSVM), Variational Quantum Classifier (VQC) and Quantum K-Nearest Neighbor (QKNN) methods has been presented after studying the literatures of past 10 years. Besides, these models have been implemented on multimodal SWELL-KW dataset, which contains knowledge worker's computer interaction, facial expressions, body postures, heart rate variability and skin conductance data recorded in various working conditions. Further, the impacts of Quantum Principal Component Analysis based feature reduction on their performances have been analyzed. Experimental results show that for the current dataset, QSVM model with PCA on heart rate variability and skin conductance data results in highest accuracy of 0.8.","PeriodicalId":199675,"journal":{"name":"Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122496716","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

Explainable Machine Learning For Malware Detection Using Ensemble Bagging Algorithms 使用集成装袋算法的恶意软件检测的可解释机器学习

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549284

Rajesh Kumar, Geetha Subbiah

{"title":"Explainable Machine Learning For Malware Detection Using Ensemble Bagging Algorithms","authors":"Rajesh Kumar, Geetha Subbiah","doi":"10.1145/3549206.3549284","DOIUrl":"https://doi.org/10.1145/3549206.3549284","url":null,"abstract":"Vulnerabilities in various software products can be used to attack the security systems in any organization anywhere. Malware is downloaded after a click on the hyperlink by the unsuspecting user and used as the exploitation tool for the vulnerabilities in systems for attacks. Detecting a large number of malware effectively can be possible by machine learning. However, Machine learning based systems have misclassification as false positives and false negatives. Novelty in this paper is to improve the efficiency and robustness of ensemble bagging algorithm Extra tree to detect malware effectively and robustly by explainable machine learning. The paper uses waterfall plots based on Shapley value to detect the trends in features for misclassification. The trends in the five topmost features for misclassification are used to make inductive rules. The inductive rules are applied to overcome misclassification and enhance the performance of bagging algorithms. The inductive rules can be applied to effectively detect unknown future malware known as zero-day malware preventing the attack on security systems. The accuracy for the Extra tree bagging algorithm is 98.1% for future unknown malware. Considering, that the misclassified samples are also detected by the inductive rules the accuracy is 100%. Heatmap based on Shapley value of features confirms the topmost features for all the misclassified samples in the dataset and strengthens the inductive rule.","PeriodicalId":199675,"journal":{"name":"Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131665929","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

A Sentimental Analysis System Using Zero-Shot Machine Learning Technique 基于零射击机器学习技术的情感分析系统

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549266

Shreya Ganga, A. Solanki

{"title":"A Sentimental Analysis System Using Zero-Shot Machine Learning Technique","authors":"Shreya Ganga, A. Solanki","doi":"10.1145/3549206.3549266","DOIUrl":"https://doi.org/10.1145/3549206.3549266","url":null,"abstract":"The internet has turned our lives upside down and has become a global means of communication. As the world is rapidly advancing, many new and challenging calls for humankind are associated. One of those challenges is analyzing the sentiments, i.e. opinions or feelings of the person or any user such as customers, while choosing and buying any product. For cases and situations like this, analysis of sentiments or opinion mining plays a significant role. Sentiment Analysis is vital because the customers can get an overview and understanding of reviews of the customers who have already purchased that particular product. Also, it helps them make decisions about their purchase and hence proceed forward accordingly. In comparison to the existing work, the proposed work considers all the sentiments throughout any conversation or review, whether they are good or bad, and hence classifies them further as positive and negative with their extent i.e. percentages of positivity and negativity in the statement. It also finds out the label of the review or any other conversation so that the users can get an idea about the domain of the conversation. Even though related research work has already been done, there is still a need to improve the accuracy and understandability of sentiment analysis. This work is mainly done by using the zero-shot learning technique. After classifying the reviews and predicting labels, the spaCy model is used with it to get essential keywords and phrases for the conversation. In the proposed work, this is done by discarding greetings with a score greater than 80%.","PeriodicalId":199675,"journal":{"name":"Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120986220","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: 0

Cloud Task Scheduling Algorithms using Teaching-Learning-Based Optimization and Jaya Algorithm 基于教学优化和Jaya算法的云任务调度算法

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549227

Monika Tak, Akanksha Joshi, S. K. Panda

{"title":"Cloud Task Scheduling Algorithms using Teaching-Learning-Based Optimization and Jaya Algorithm","authors":"Monika Tak, Akanksha Joshi, S. K. Panda","doi":"10.1145/3549206.3549227","DOIUrl":"https://doi.org/10.1145/3549206.3549227","url":null,"abstract":"Over the last few years, cloud computing has accelerated in the economic and scientific communities due to breakthroughs in virtualization technology. It is an emerging computing technology in which many users submit their requirements (i.e., compute, storage, network, etc.) in the form of tasks to process them through widely dispersed resources (i.e., virtual machines (VMs)) on a pay-as-you-go basis. However, it is pretty challenging to manage the submitted tasks and process them on the VMs, such that overall completion time (i.e., makespan) is minimized. Many researchers have proposed meta-heuristic algorithms to solve the above-discussed task scheduling problem. However, these algorithms are based on algorithm-specific parameters. This paper uses the concepts of well-known teaching-learning-based optimization (TLBO) and the Jaya algorithm, and model them to solve task scheduling problem individually. The rationality behind using these algorithms is that they are algorithm-specific parameter-less algorithms. We call the modeled algorithm as cloud-TLBO and cloud-Jaya algorithm. We model the candidate solutions as tasks and design variables as VMs, and consider the makespan as the objective function. We simulate both the cloud-TLBO and cloud-Jaya algorithm using five synthetic datasets and monitor their results over 50 iterations. Finally, we compare the results with the online benchmark algorithm, called minimum completion time (MCT), to show that the results of the proposed algorithms are near-optimal.","PeriodicalId":199675,"journal":{"name":"Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129494674","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: 0

An Exhaustive Investigation on Resource-aware Client Selection Mechanisms for Cross-device Federated Learning 跨设备联邦学习中资源感知客户端选择机制的详尽研究

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549222

Monalisa Panigrahi, Sourabh Bharti, Arun Sharma

{"title":"An Exhaustive Investigation on Resource-aware Client Selection Mechanisms for Cross-device Federated Learning","authors":"Monalisa Panigrahi, Sourabh Bharti, Arun Sharma","doi":"10.1145/3549206.3549222","DOIUrl":"https://doi.org/10.1145/3549206.3549222","url":null,"abstract":"Federated learning (FL) is a distributed machine learning technique in which each client (FLClient) trains a model without revealing it’s local data to the server. Appropriate client selection is a crucial step towards ensuring the quality and robustness of the global model in a cross-device FL set-up. As such, various client selection mechanisms have been proposed, however, most of the mechanism makes the assumption of clients (devices) being mobile phones with uninterrupted power and compute resources supply. On the other hand, due to growing digitization in various industries, clients in a cross-device FL set-up can be resource-constrained IoT edge devices such as single board computers. To this end, there are a few resource-aware client selection mechanisms proposed in the literature. This paper provides a comprehensive, experimental comparative analysis of these mechanisms while resource-constrained IoT edge devices as clients. The effect of varying FL specific hyper-parameters on accuracy, convergence time and client retention is observed for all resource-aware client selection mechanisms so that a cognitive choice of the client selection mechanism can be made for a given application scenario.","PeriodicalId":199675,"journal":{"name":"Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing","volume":"317 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133371384","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: 4

Mitigating Epidemics Through Vaccination, Treatment, and Awareness Programs 通过接种疫苗、治疗和提高认识项目减轻流行病

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549294

Kundan Kandhway

引用次数: 0

Achieving multilevel elasticity for distributed stream processing systems in the cloud environment: A review and conceptual framework 在云环境中实现分布式流处理系统的多级弹性:回顾和概念框架

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549224

Riddhi Thakkar, Madhuri D. Bhavsar

{"title":"Achieving multilevel elasticity for distributed stream processing systems in the cloud environment: A review and conceptual framework","authors":"Riddhi Thakkar, Madhuri D. Bhavsar","doi":"10.1145/3549206.3549224","DOIUrl":"https://doi.org/10.1145/3549206.3549224","url":null,"abstract":"Recent awareness and advances in technology have triggered excessive use of social media, IoT devices, remote sensing devices, mobile applications, web applications, and gaming more than ever before in time. Such platforms are hosting their applications on the cloud as it provides various services on a pay-per-use basis. A Cloud Service Provider (CSP) should deliver all its services very swiftly to process real-time applications on time. Real-time stream computations are characteristically long-lived and receive data in an unpredictable form, requiring a fair amount of resources for their processing in constrained time. Such a dynamic nature of applications demands resource elasticity at runtime. The cloud architecture is stacked with different types of resources, each having a discrete adaption process with distinct elasticity properties. Scaling the absolute amount of resources leads to performance boosting. Recent literature landscapes the elasticity at Virtual Machine (VM) level, describing various techniques for scaling VMs. Each technique targets a distinct aspect with specific assumptions. However, the literature lacks a comprehensive survey at the operator level, where actual processing takes place and has a higher impact on the performance of the system. Compared to other works in the literature, this work presents a detailed analysis of various approaches targeting elasticity at the operator level of cloud architecture for stream processing applications, along with the conceptual framework, scaling at the operator, VM, and server levels. We have also discussed the various elastic approaches for scaling the resources at multilevel: VM and operator-level concurrently, for Distributed Stream Processing (DSP) applications running on the cloud. Conceptually, with the proposed framework, we can attain maximum resource utilization at each layer. In future work, we will evaluate the proposed framework with real-world application.","PeriodicalId":199675,"journal":{"name":"Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116735532","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: 0

Min-Hop Foremost Paths in Interval Temporal Graphs 区间时间图中的最小跳最优路径

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549314

Anuj Jain, S. Sahni

引用次数: 0

Cryptocurrency Price Prediction Using Twitter and News Articles Analysis 使用Twitter和新闻文章分析进行加密货币价格预测

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549248

P. Bansal, Shikha Jain

引用次数: 0

Implementation of Zero-Phase Zero Frequency Resonator Algorithm on FPGA 零相位零频率谐振器算法的FPGA实现

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549217

Syed Abdul Jabbar, Purva Sharma, K. Gurugubelli, Syed Azeemuddin, A. Vuppala

{"title":"Implementation of Zero-Phase Zero Frequency Resonator Algorithm on FPGA","authors":"Syed Abdul Jabbar, Purva Sharma, K. Gurugubelli, Syed Azeemuddin, A. Vuppala","doi":"10.1145/3549206.3549217","DOIUrl":"https://doi.org/10.1145/3549206.3549217","url":null,"abstract":"Epoch location is an important parameter for analysis of excitation source information from the speech signals. From several years lot of research have been done to find accurate locations of epochs. Epochs are instants at which vocal tract system are excited significantly. The prominent location of epochs can be found during the production of speech signals. However, due to the time varying nature of excitation source and the vocal tract system it is difficult to find accurate location of epochs. From various epoch extraction methods, Zero Frequency Filtering (ZFF) is the simplest and most widely used method to find accurate locations of epochs due to its highest identification rate and lowest false alarm rate among other algorithms. ZFF uses Infinite Impulse Response (IIR) filter followed by trend removal blocks however, the filter used in ZFF method is unstable which makes it unsuitable for practical implementation. In the literature many stable implementations of ZFF algorithms have been proposed. Compared to other stable algorithms of ZFF, Zero-phase Zero Frequency Resonator has simple design and gives the highest identification rate among other epoch extraction algorithms including ZFF. In this paper, the implementation of ZFF and ZP-ZFR has been proposed on FPGA board using Verilog which is Hardware Description Language (HDL).","PeriodicalId":199675,"journal":{"name":"Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128651876","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