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2022 International Conference on Cyber-Physical Social Intelligence, ICCSI 2022

作者： Ma, Ermeng Liu, Peisheng Guo, Xiwang Wang, Jiacun Qin, Shujin Tang, Ying Liaoning Petrochemical University Artificial Intelligence and Software Fushun China Monmouth University Information and Control Liaoning Petrochemical University United States Monmouth University Computer Science and Software Engineering West Long Branch United States Shangqiu Normal University Economics and Management Shangqiu China Roman University Electrical and Computer Engineering Glassboro United States

ISBN: (数字)9781665498357

ISBN: (纸本)9781665498357

Over the years, the disassembly and recycling of waste products have attracted more and more social attention. Nowadays, in many small companies, the cost of pre-job training is sometimes not paid back because of the high turnover rate of employees. This paper proposes on-the-job-training for disassembly employees. The mathematical model of multi-product mixed disassembly balancing problem that considers employee training target is discussed. To maximize the disassembly profit, a tabu search algorithm based on an improved multi-neighborhood search is used to solve this problem. The results show that the improved multi-neighborhood tabu search can quickly obtain the approximate optimal feasible solution. © 2022 IEEE.

关键词： Tabu search

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Machine Learning and Multiple Regression-Based Study of the Medicinal Properties of Food

Machine Learning and Multiple Regression-Based Study of the ...

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作者： Jiasheng Zhou Zhihong Fan Wenxin Zhang Fuzhou University College of Computer and Data Science/College of Software Fuzhou University College of Physics and Information Engineering

We study the relationship between food ingredients and hot and cold properties based on the idea that "medicine and food have the same origin". Firstly, we classify foods with known hot and cold properties as typical representatives of flat, warm and cold properties, and use various machine learning algorithms to classify the typical food representatives. In order to further improve the reliability and accuracy of the classification, we applied Bayesian optimization to the SVM and the SVM was able to classify the typical food representatives again with an accuracy of 96.53%. Based on the above findings, we further analysed which chemical components played a key role in the hot and cold properties. We then applied multivariate logistic regression for quantitative analysis, using stepwise forward regression to minimise complete multicollinearity and OLS + robust standard errors to eliminate the effect of heteroscedasticity. Based on the analysis of the coefficients of the regression equations and the significance test results, the conclusions reached were consistent with the qualitative analysis, with the four main components of energy, water, minerals and fat playing a major role in the chilling and heating properties of food. In conjunction with the analysis in the full text, we make recommendations for the development of functional foods where heat and cold are the principles.

关键词： Factor analysis Clustering algorithms Food classification

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Do people use social information to improve predictions about everyday events? 44

Do people use social information to improve predictions abou...

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44th Annual Meeting of the Cognitive science Society: Cognitive Diversity, CogSci 2022

作者： Payne, Stephen Jern, Alan Department of Computer Science and Software Engineering Rose-Hulman Institute of Technology United States Department of Humanities Social Sciences and the Arts Rose-Hulman Institute of Technology United States

Following Griffiths and Tenenbaum (2006), we explore whether people use relevant social information to improve their already nearly optimal predictions about quantities in everyday events. We tested this question in two experiments involving quantities in three domains: cake baking times, movie runtimes, and podcast lengths. In Experiment 1, we found that participants were sensitive to the difference between relevant and irrelevant social information. In Experiment 2, we found that people consistently used relevant social information to adjust their predictions in the expected directions. We introduce an optimal social prediction model but find that it does not consistently perform better at accounting for our participants' social predictions than an optimal non-social prediction model. We conclude by discussing whether people use social information for prediction in an optimal way. © 2022 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY)

关键词： Forecasting

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A Deep Reinforcement Learning based VM Scheduling Strategy Decreasing Data Center Communication costs

A Deep Reinforcement Learning based VM Scheduling Strategy D...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Zhijiao Xiao Xiao Liu Zhong Ming Software Engineering Research Center College of Computer Science and Software Engineering Shenzhen University Shenzhen Guangdong China

With the rapid development of cloud computing, more and more complicated services are deployed on the cloud. The frequent communication between services places a great burden on the network of the data center and leads to a decline in the quality of service. Researches have shown that the costs of communication between services vary with the placements of virtual machines on which services are running. Therefore, the virtual machine strategy should be optimized by migrating virtual machines with high communication frequency closer. Based on fat-tree network topology, a Deep Q network based online virtual machines scheduling strategy(DQN-VMS) is proposed. The objective is to obtain an optimized virtual machine scheduling strategy that brings down communication costs while guaranteeing the quality of service. The calculation model for communication costs is built for the fat-tree network topology. A reward function that takes into account the communication costs and Service Level Agreements violation(SLAV) rate is designed. The experiment shows that the proposed strategy performs about 26.81% and 40.55% better in comparison with two virtual machine scheduling strategies based on graph partition algorithm and greedy algorithm, which are commonly used in existing data centers.

关键词： Data centers Cloud computing Costs Processor scheduling Network topology Smart cities Quality of service

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Approximate Simulation for Transition Systems with Regular Expressions 2nd

Approximate Simulation for Transition Systems with Regular E...

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2nd International Conference on Artificial Intelligence Logic and Applications, AILA 2022

作者： Cui, Xinyu Li, Zhaokai Chang, Yuting Pan, Haiyu Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China School of Computer Science and Engineering North Minzu University Yinchuan750000 China

ISBN: (纸本)9789811975097

Simulation is a well-established technique for verifying wheth-er the behaviors of one labeled transition system (LTS) can mimic all behaviors of another LTS. Transition systems with regular expressions (RE-TSs) are an extension of LTSs, which are used as semantic models in modal or temporal logics to solve model checking problems. This paper presents approximate simulation, an extension of simulation of an LTS by a RE-TS, by combining general simulation and metrics, and discusses its properties. First, the notion of approximate simulation is introduced. Then, we investigate properties and an equivalent formalism of approximate simulation. On the other hand, we propose two approaches of fixed point characterization for approximate simulation, and study the relationship between them. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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Semi-supervised Few-shot Network Intrusion Detection based on Meta-learning

Semi-supervised Few-shot Network Intrusion Detection based o...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Yao Liu Le Zhou Qiao Liu Tian Lan Xiaoyu Bai Tinghao Zhou School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

Due to the rapid development of the Internet, the scale of networks is growing exponentially, and new types of attacks continue to emerge. These new attacks are becoming more threatening, and the means of attack are changing with days. Most network intrusion detection models fail to achieve the same level of detection performance in real-world network environments as in laboratory environments. This is because different types of intrusion traffic evolve over time, and new intrusion attacks continue to emerge, rendering the sample data used for model training gradually obsolete. Real-time and reliable sample data is always scarce. How to achieve accurate and effective network intrusion detection in few-shot or zero-shot scenarios is a significant challenge for researchers. To address the problem of lacking real-time labeled data for new or variant attacks in real networks, this paper proposes a few-shot intrusion detection model called SPN based on semi-supervised prototype network. In this model, the original classification problem is transformed into a metric learning problem by using prototype network in meta-learning. By redefining the construction process of prototype points through the newly designed soft K-means and soft masking mechanisms, the SPN model can incorporate a large amount of unlabeled data in training based on a small amount of labeled data. This enables the SPN model to achieve outstanding intrusion detection performance in few-shot scenarios, while also having the ability to identify unknown intrusion attacks in zero-shot scenarios. A series of experimental results on datasets CICIDS2017 and UNSW-NB15 demonstrate that the proposed SPN model can accurately detect known attacks in few-shot scenarios, achieving an accuracy rate of 96%. Furthermore, it can effectively detect unknown intrusion attacks in zero-shot scenarios with a detection rate of over 93%.

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A Gpu-Accelerated Adaptation of the Pso Algorithm for Multi-Objective Optimization Applied to Artificial Neural Networks for to Predict Energy Consumption

SSRN

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SSRN 2023年

作者： Sánchez Iruela, José Rubén Ruiz, Luis G.B. Criado-Ramón, David Pegalajar, Maria del Carmen Capel, Manuel I. Department of Computer Science and Artificial Intelligence University of Granada Spain Department of Software Engineering University of Granada Spain

Optimization research often confronts the challenge of developing time-consuming processes. This article introduces an innovative approach that leverages the computational power of Graphics Processing Units (GPUs) to speed up that optimization process. We present an innovative adaptation of Particle Swarm Optimisation (PSO) to meet the requirements of multiobjective optimization problems. This approach aims to leverage the strengths of a multi-objective approach to perform energy consumption prediction using neural networks. By employing GPU parallel techniques, our method not only speeds up the optimisation process but also enhances the efficiency of neural network training execution. Preliminary results suggest a notable enhancement in prediction accuracy of energy consumption predictions, as setting the consequence of optimizing the neural network architecture and parameters using the proposed method. This PSO adaptation stands out for its ability to address complex problems, increase efficiency and produce accurate predictions, making it a novel solution in Machine Learning/Heuristical methods for energy consumption forecasting. © 2023, The Authors. All rights reserved.

关键词： Neural networks

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MPNAS: Multimodal Sentiment Analysis Pruning via Neural Architecture Search

MPNAS: Multimodal Sentiment Analysis Pruning via Neural Arch...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Binyan Zhang Ao Ren Zihao Zhang Moming Duan Duo Liu Yujuan Tan Kan Zhong College of Computer Science Chongqing University Chongqing China School of Big Data Software & Engineering Chongqing University Chongqing China Institute of Data Science National University of Singapore Singapore

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

With the rapid development of social media, sentiment analysis from multimodal posts has garnered significant attention in recent years. However, the substantial size of these models impedes their deployment on resource-constrained embedded devices. Although pruning has been extensively studied to reduce the size of unimodal models, specific challenges remain for Multimodal Sentiment Analysis (MSA) models. First, existing techniques prune fixed original models into sparse models, while our findings indicate that different model architectures of identical size yield varying performance outcomes. Second, prior studies fail to explore the unique characteristics of MSA models, resulting in suboptimal pruning performance. To address these challenges, we propose MPNAS, a unified pruning framework via Neural Architecture Search (NAS) for MSA models. Specifically, we formulate pruning as a NAS problem and analyze MSA model characteristics to guide the subnet search. We conduct an initial coarse-grained NAS on the original model, expanding the search space slightly to identify suitable subnets that enhance pruning rates and accuracy. Subsequently, we refine coarse-grained subnets in a fine-grained NAS stage, where MSA model characteristics guide the search process. Extensive experiments on three representative datasets demonstrate the superiority of our approach over existing methods.

关键词： Performance evaluation Analytical models Sentiment analysis Accuracy Social networking (online) Signal processing Search problems Acoustics Neural architecture search Speech processing

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Deep Learning Based Matrix Factorization for Collaborative Filtering 18

Deep Learning Based Matrix Factorization for Collaborative F...

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18th International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2021

作者： Tegene, Abebe Tamrat Liu, Qiao Muhammed, Seid Belay Leka, Habte Lejebo University of Electronic Science and Technology of China School of Computer Science and Engineering China University of Electronic Science and Technology of China School of Information and Software Engineering China

ISBN: (纸本)9781665413640

Collaborative Filtering based on matrix factorization (MF) has shown tremendous success in the field recommender system. However, MF has difficulty in handling sparsity and scalability. These resulted in low quality of recommendations. In this regard, deep learning has shown immense success in different application areas including recommender systems. To address the limitations, we incorporate deep learning architecture to matrix factorization and develop a novel mode. The core idea of the method is to map users and items input vector to two well-structured deep neural network architectures separately for factorization. Then, we incorporate inner product to the output layers of the network to predict the rating scores. The use of this structure significantly improve the quality of recommendation. The experimental result on real data sets shows that our proposed model outperformed state of the art methods. © 2021 IEEE.

关键词： Collaborative filtering

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Sustainability of Machine Learning-based Android Malware Detection Using API calls and Permissions 5

Sustainability of Machine Learning-based Android Malware Det...

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5th IEEE International Conference on Artificial Intelligence and Knowledge engineering, AIKE 2022

作者： Cho, Woosang Lee, Hojun Han, Sangchul Hwang, Youngsup Cho, Seong-Je Dept. of Software Science Dankook University Gyeonggi-do Yongin-si16890 Korea Republic of Dept. of Computer Engineering Konkuk University Chungju Korea Republic of Dept. of Computer Science and Engineering Sun Moon University Asan Korea Republic of Dept. of Sofware Science Dankook University Yongin Korea Republic of

ISBN: (数字)9781665471206

ISBN: (纸本)9781665471206

As the Android platform and malicious apps continue to evolve, most existing Android malware detection techniques using machine learning are turning out to be unsustainable. In this paper, we propose machine learning-based Android malware detection techniques which uses both API calls and permissions as a feature set. These features are complementary and are often used to detect malicious apps. We first analyze whether a 'yearly dataset-based trained classifier' (YDataC) is sustainable or not. The 'yearly dataset-based trained classifier' refers to the classifier that learns from 80% of the dataset of a specific year from 2014 to 2021, and is tested with 20% of the datasets of every year between 2014 and 2021. Through experiments, we discovered that the classification rate has dropped significantly since 2019, and something big has changed. Therefore, the 'yearly dataset-based trained classifier' is judged to be unsustainable. Next, we present and evaluate two incremental learning methods for gradual training: an incrementally trained Random Forest (RF) and an incrementally trained Neural Network (NN). Evaluation results show that two incremental learning classifiers have better sustainability than the 'yearly dataset-based trained classifier'. The incrementally trained RF has better sustainability than the incrementally trained NN in terms of given metrics such as f1 and AUT (Area under Time). © 2022 IEEE.

关键词： Android malware

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