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2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)最新文献

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Probabilistic Neural Synapse Based in Quantum Mechanics 基于量子力学的概率神经突触
2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2022-12-07 DOI: 10.1109/SNPD54884.2022.10051767
H. Nieto-Chaupis
{"title":"Probabilistic Neural Synapse Based in Quantum Mechanics","authors":"H. Nieto-Chaupis","doi":"10.1109/SNPD54884.2022.10051767","DOIUrl":"https://doi.org/10.1109/SNPD54884.2022.10051767","url":null,"abstract":"The process of synaptic transmission is projected onto the territory of quantum mechanics. Thus, the electric interactions are seen as probabilistic events more than a deterministic one. In this manner, the neurotransmitter would obey quantum laws and their dynamics is governed by the evolution operator. Thus, synapse seen as an action of propagation would encompasses well to that the spatial propagator or Green function that might model the dyanamics of neurotransmitters along the cleft. This turns out to be instantaneous in coherence to the concept of neural transmission. Indeed under the quantum mechnics scenario it is found that the eigenvalues of energies of Hamiltonian consist in electric potentials between electrically charged neurotransmitters and receptors at the ion channels.","PeriodicalId":425462,"journal":{"name":"2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132763831","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
Cause-effect Graphing Technique: A Survey of Available Approaches and Algorithms 因果图技术:可用方法和算法综述
2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2022-12-07 DOI: 10.1109/SNPD54884.2022.10051799
Ehlimana Krupalija, Emir Cogo, Šeila Bećirović, Irfan Prazina, Ingmar Bešić
{"title":"Cause-effect Graphing Technique: A Survey of Available Approaches and Algorithms","authors":"Ehlimana Krupalija, Emir Cogo, Šeila Bećirović, Irfan Prazina, Ingmar Bešić","doi":"10.1109/SNPD54884.2022.10051799","DOIUrl":"https://doi.org/10.1109/SNPD54884.2022.10051799","url":null,"abstract":"Cause-effect graphs are often used as a method for deriving test case suites for black-box testing different types of systems. This paper represents a survey focusing entirely on the cause-effect graphing technique. A comparison of different available algorithms for converting cause-effect graph specifications to test case suites and problems which may arise when using different approaches are explained. Different types of graphical notation for describing nodes, logical relations and constraints used when creating cause-effect graph specifications are also discussed. An overview of available tools for creating cause-effect graph specifications and deriving test case suites is given. The systematic approach in this paper is meant to offer aid to domain experts and end users in choosing the most appropriate algorithm and, optionally, available software tools, for deriving test case suites in accordance to specific system priorities. A presentation of proposed graphical notation types should help in gaining a better level of understanding of the notation used for specifying cause-effect graphs. In this way, the most common mistakes in the usage of graphical notation while creating cause-effect graph specifications can be avoided.","PeriodicalId":425462,"journal":{"name":"2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129091237","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
Instructions with Complex Control-Flow Entailing Machine Learning 具有复杂控制流的机器学习指令
2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2022-12-07 DOI: 10.1109/SNPD54884.2022.10051797
S. Shinde, Harneet Singh Bali
{"title":"Instructions with Complex Control-Flow Entailing Machine Learning","authors":"S. Shinde, Harneet Singh Bali","doi":"10.1109/SNPD54884.2022.10051797","DOIUrl":"https://doi.org/10.1109/SNPD54884.2022.10051797","url":null,"abstract":"Reinforcement learning is when the system is allowed to make its own decisions based on what it learns. There are 2 types of observations, formative and summative. These observations have been identified as crucially important for neural network training of complicated tasks with conditional control flow. The central theme of this paper is applying reinforcement learning to follow instructions with complex control-flow. The authors study a special but important subset of multi- task reinforcement learning problems, namely instructions with complex control-flow in this work. They develop an encoding and attention architecture to achieve the research objective.","PeriodicalId":425462,"journal":{"name":"2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123260112","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
Ensemble Deep Learning Model for Damage Identification via Output-Only Signal Analysis 基于仅输出信号分析的损伤识别集成深度学习模型
2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2022-12-07 DOI: 10.1109/SNPD54884.2022.10051770
M. Sands, Jongyeop Kim, Jinki Kim, Seongsoo Kim
{"title":"Ensemble Deep Learning Model for Damage Identification via Output-Only Signal Analysis","authors":"M. Sands, Jongyeop Kim, Jinki Kim, Seongsoo Kim","doi":"10.1109/SNPD54884.2022.10051770","DOIUrl":"https://doi.org/10.1109/SNPD54884.2022.10051770","url":null,"abstract":"Vibration-based methods have received considerable attention in structural condition monitoring applications. We have proposed a model to detect damaged points of a target structure using the GRU model and classify the 0.84 overall accuracy. To increase the model's accuracy in this research, we propose an ensemble deep learning model using LSTM and bi-directional LSTM incorporated with GRU. Each model predicted its RMSE trend and combined the damage estimation results from both models, which are mostly close to the true damage locations. As a result of synthesizing the three algorithms, the damage point of the cantilever beam was found with an accuracy of 0.88 and a misclassification rate of 0.12. The results indicate that the proposed combined approach provides enhanced reliability than a single algorithm.","PeriodicalId":425462,"journal":{"name":"2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125772998","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
Combining OpenPose with BiLSTM for Violence Detection in Long-Term Care 结合OpenPose和BiLSTM在长期护理中的暴力检测
2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2022-12-07 DOI: 10.1109/SNPD54884.2022.10051807
Shao-Wei Chu, Chuin-Mu Wang
{"title":"Combining OpenPose with BiLSTM for Violence Detection in Long-Term Care","authors":"Shao-Wei Chu, Chuin-Mu Wang","doi":"10.1109/SNPD54884.2022.10051807","DOIUrl":"https://doi.org/10.1109/SNPD54884.2022.10051807","url":null,"abstract":"The Ministry of Health and Welfare's Statistics reports present the incidence of domestic care violence becomes higher annually. However, there is no efficient method to get rid of physical abuse. After being ill-treated of violence, someone is assessed injure by official organization. Then, the victims take legal actions to damage after the events. The deep learning motion recognized violence to family care in advance. To analyst that the images from complex data sets on the internet is important. The key part of images that are recognized as physical abuse is ambiguous and distorted in many pictures. The solution of ambiguity is to label joint points of human skeleton by OpenPose, and to train the marked joint point features in Bi-directional Long Short-Term Memory (BiLSTM). The accuracy is about to 96%, that can effectively detect physical abuse in time in the experimental results.","PeriodicalId":425462,"journal":{"name":"2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"212 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132228736","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
Machine Learning and Bayes Probability For Detecting Camouflaged Mini Pandemic at the Waves of Covid-19 机器学习和贝叶斯概率在Covid-19浪潮中检测伪装的迷你大流行
2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2022-12-07 DOI: 10.1109/SNPD54884.2022.10051812
H. Nieto-Chaupis
{"title":"Machine Learning and Bayes Probability For Detecting Camouflaged Mini Pandemic at the Waves of Covid-19","authors":"H. Nieto-Chaupis","doi":"10.1109/SNPD54884.2022.10051812","DOIUrl":"https://doi.org/10.1109/SNPD54884.2022.10051812","url":null,"abstract":"This paper present a methodology based at Machine Learning and a theory backed by the Bayes probability to identify rare strains that might not be in coherence with the corona virus. By using the criteria of Tom Mitchell applied on the data belonging to 2021–2022 period, the distributions of infections registered at the beginning of 2022 would not be in accordance to waves of pandemic as seen at 2020 and 2021. Therefore, algorithm of Machine Learning has yielded that the so-called Omicron variant would no be coherent with known mutations neither exhibiting same pattern of previous waves of pandemic. This creates a space to speculate about the origin of new strains that are camouflaged to central corona virus. From the results of this work, it is observed that Omicron might have nothing to do with Covid-19 pandemic, instead it have triggered a small pandemic of short duration as validated by global data.","PeriodicalId":425462,"journal":{"name":"2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130077076","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
Multi-Agent Reinforcement Learning Reward Engineering via Stochastic Game Evaluation 基于随机博弈评价的多智能体强化学习奖励工程
2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2022-12-07 DOI: 10.1109/SNPD54884.2022.10051783
A. Kattepur
{"title":"Multi-Agent Reinforcement Learning Reward Engineering via Stochastic Game Evaluation","authors":"A. Kattepur","doi":"10.1109/SNPD54884.2022.10051783","DOIUrl":"https://doi.org/10.1109/SNPD54884.2022.10051783","url":null,"abstract":"With the proliferation of Reinforcement Learning (RL) algorithms across multiple applications, the design of appropriate reward mechanisms that elicit desired behaviours becomes crucial. Reward setting is made more difficult in the multi-agent case where cooperative, competitive or mixed interactions between agents may lead to differing outcomes. In this paper, we formulate the reward engineering of multi-agent reinforcement learning approaches via game theoretic models. This approach is used to analyze the overall team reward when choosing one game theoretic structure over another. An empirical analysis is provided over game theoretic simulators that demonstrate co-operative game rewards improve the rewards by upto 30%. This formulation may be applied to a variety of use cases within logistics, transport and telecommunications domains that employ multi-agent techniques.","PeriodicalId":425462,"journal":{"name":"2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131844212","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
Realization of Laser Object Vaporization Locating Based on Low-Cost 2D LiDAR 基于低成本二维激光雷达的激光目标汽化定位实现
2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2022-12-07 DOI: 10.1109/SNPD54884.2022.10051809
Meng-Sheng Tsai, Yuan-Hong Guan, You-Xuan Lin, Meng-Hua Yen
{"title":"Realization of Laser Object Vaporization Locating Based on Low-Cost 2D LiDAR","authors":"Meng-Sheng Tsai, Yuan-Hong Guan, You-Xuan Lin, Meng-Hua Yen","doi":"10.1109/SNPD54884.2022.10051809","DOIUrl":"https://doi.org/10.1109/SNPD54884.2022.10051809","url":null,"abstract":"This research focuses on integrating low-cost 2D-Lidar into a multi-split laser vaporization system that can incinerate wastes and general objects to improve the problem of manual alignment of the laser vaporization machine. In the experiment, we proposed a custom angle range and a distance threshold screening method for 2D-Lidar to distinguish whether there is an object in the scanning area and object locating. Through the law of cosine, the 2D-Lidar scanning angle is evenly distributed to the multi-split laser vaporization system. The object scanning method uses the Modbus TCP communication mode to enable the 2D-Lidar on the PC side to handshaking with the PLC on the central control side of the system. Scanning and size calculation of the object can obtain the position point of the object and hence improving the efficiency of the multi-split laser vaporization system. In addition, 2D lidar is readily available and inexpensive, in which making it more cost-effective than 3D-Lidar.","PeriodicalId":425462,"journal":{"name":"2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114642173","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
Small Probability of Fatality from Theorem of Bayes at the Monkeypox Pandemic 从贝叶斯定理看猴痘大流行的小死亡概率
2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2022-12-07 DOI: 10.1109/SNPD54884.2022.10051808
H. Nieto-Chaupis
{"title":"Small Probability of Fatality from Theorem of Bayes at the Monkeypox Pandemic","authors":"H. Nieto-Chaupis","doi":"10.1109/SNPD54884.2022.10051808","DOIUrl":"https://doi.org/10.1109/SNPD54884.2022.10051808","url":null,"abstract":"Why the number of fatalities is very low at the ongoing Monkeypox pandemic, this question can be answered through the theory of Bayes that would anticipate a posterior probability of fatality that applies to the cases of high risk, from a prior probability of infection. The probability of event is derived from the diffusion equation by which is assumed that the infections as well as the associated virus can flux along the continental and intercontinental countries. The resulting probabilities have been small fact that support the idea that the attained diffusion coefficient is large demonstrating that the diffusion is large but the high risk cases are attenuated.","PeriodicalId":425462,"journal":{"name":"2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115775705","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
Design and Implementation of AI aided Fruit Grading Using Image Recognition 基于图像识别的人工智能辅助水果分级设计与实现
2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2022-12-07 DOI: 10.1109/SNPD54884.2022.10051810
H. Kuo, Daiby Sunandan Barik, Jun You Zhou, Yi Kai Hong, Jun-Juh Yan, Meng-Hua Yen
{"title":"Design and Implementation of AI aided Fruit Grading Using Image Recognition","authors":"H. Kuo, Daiby Sunandan Barik, Jun You Zhou, Yi Kai Hong, Jun-Juh Yan, Meng-Hua Yen","doi":"10.1109/SNPD54884.2022.10051810","DOIUrl":"https://doi.org/10.1109/SNPD54884.2022.10051810","url":null,"abstract":"This research is based on the framework of a fully automated smart fruit factory that builds a small and simple fruit grading device, and spots defects in the three fruit models of apples, lemons and oranges which were used as test target, and the entire process of detection is performed in a dark box. There is a ring-shaped LED light to regulate the light source inside the dark box. The fruits to be identified are moved into the dark box by a conveyor belt, an infrared sensor is used to judge whether the fruits are within the shooting range of the image capture area, and then the photos are sent to the SSD (Single Shot Multi Box Detector) neural network model to identify defects. This system screens the surface of apples, lemons and oranges for defects like damage, pest damage, dryness, bruises, etc. and removes them to preserve the fruits that are good in quality. It has been verified by several experiments that the identification accuracy rate can reach upto more than 95%.","PeriodicalId":425462,"journal":{"name":"2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122665677","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
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