Journal of Experimental & Theoretical Artificial Intelligence最新文献_第8页

Bi-objective web service composition problem in multi-cloud environment: a bi-objective time-varying particle swarm optimisation algorithm 多云环境下双目标web服务组合问题:一种双目标时变粒子群优化算法

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2021-03-04 DOI: 10.1080/0952813X.2020.1725652

Mirsaeid Hosseini Shirvani

{"title":"Bi-objective web service composition problem in multi-cloud environment: a bi-objective time-varying particle swarm optimisation algorithm","authors":"Mirsaeid Hosseini Shirvani","doi":"10.1080/0952813X.2020.1725652","DOIUrl":"https://doi.org/10.1080/0952813X.2020.1725652","url":null,"abstract":"ABSTRACT Cloud computing became an inevitable information technology industry. Despite its several plus points such as economy of scale and rapid elasticity, it suffers from vendor lock-in, resource limitation and cybersecurity attacks in which it leads business discontinuity or even business failure. Multi-cloud, on the other hand, can be trustable paradigm to obviate obstacles such as aforesaid unpleasant features of a single cloud. One of the biggest challenges is to know which cloud is commensurate with user’s business process with regards to security objectives. To this end, the new method is presented to quantify the amount of cloud security risk (CSR) in regards to user’s business process. Therefore, in this paper, the web service composition problem is formulated to bi-objective optimisation problem with service cost and multi-cloud risk viewpoints in ever-increasing multi-cloud environment (MCE) in which each provider has its variable pricing policy and different security level. It is obviously an NP-Hard problem. To solve the combinatorial problem, we develop a bi-objective time-varying particle swarm optimisation (BOTV-PSO) algorithm. The parameters are tuned based on elapsed time so a good balance between exploration and exploitation is achieved. To illustrate the effectiveness of proposed algorithm, we defined several scenarios and compared the performance of proposed algorithm with multi-objective GA-based (MOGA) optimiser, a single objective genetic algorithm (SOGA) that only optimises cost function and neglects CSR, and multi-objective simulated annealing algorithm (MOSA). The experimental results showed the superiority of proposed BOTV-PSO against other approaches in terms of convergence, diversity, fitness, performance, and even scalability.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"333 1","pages":"179 - 202"},"PeriodicalIF":2.2,"publicationDate":"2021-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72845726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 28

Multi-feature fusion refine network for video captioning 视频字幕的多特征融合细化网络

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2021-02-23 DOI: 10.1080/0952813X.2021.1883745

Guangbin Wang, Jixiang Du, Hongbo Zhang

{"title":"Multi-feature fusion refine network for video captioning","authors":"Guangbin Wang, Jixiang Du, Hongbo Zhang","doi":"10.1080/0952813X.2021.1883745","DOIUrl":"https://doi.org/10.1080/0952813X.2021.1883745","url":null,"abstract":"ABSTRACT Describing video content using natural language is an important part of video understanding. It needs to not only understand the spatial information on video, but also capture the motion information. Meanwhile, video captioning is a cross-modal problem between vision and language. Traditional video captioning methods follow the encoder-decoder framework that transfers the video to sentence. But the semantic alignment from sentence to video is ignored. Hence, finding a discriminative visual representation as well as narrowing the semantic gap between video and text has great influence on generating accurate sentences. In this paper, we propose an approach based on multi-feature fusion refine network (MFRN), which can not only capture the spatial information and motion information by exploiting multi-feature fusion, but also can get better semantic aligning of different models by designing a refiner to explore the sentence to video stream. The main novelties and advantages of our method are: (1) multi-feature fusion: Both two-dimension convolutional neural networks and three-dimension convolutional neural networks pre-trained on ImageNet and Kinetic respectively are used to construct spatial information and motion information, and then fused to get better visual representation. (2) Sematic alignment refiner: the refiner is designed to restrain the decoder and reproduce the video features to narrow semantic gap between different modal. Experiments on two widely used datasets demonstrate our approach achieves state-of-the-art performance in terms of BLEU@4, METEOR, ROUGE and CIDEr metrics.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"13 1","pages":"483 - 497"},"PeriodicalIF":2.2,"publicationDate":"2021-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82416243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Cellular automata-based optimised routing for secure data transmission in wireless sensor networks 基于蜂窝式自动机的无线传感器网络安全数据传输优化路由

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2021-02-10 DOI: 10.1080/0952813X.2021.1882002

P. S. Khot, Udaykumar Naik

{"title":"Cellular automata-based optimised routing for secure data transmission in wireless sensor networks","authors":"P. S. Khot, Udaykumar Naik","doi":"10.1080/0952813X.2021.1882002","DOIUrl":"https://doi.org/10.1080/0952813X.2021.1882002","url":null,"abstract":"ABSTRACT Sensor nodes encompass the capability of controlling and monitoring the environment by measuring the factors, like temperature, pressure, and humidity. Ensuring secure routing in the network is an important and significant task in the wireless sensor network (WSN). Even though various routing protocols are adopted in the network, providing security and maintaining the energy is still a challenging issue in the research community. Thus, an effective particle-based spider monkey optimisation (P-SMO) algorithm is proposed in this research to select the optimal route to manage secure communication in WSNs. Accordingly, the network is simulated and the cluster heads (CHs) are chosen based on the efficient learning automata-based cell clustering algorithm (ELACCA) such that the routing path is established based on the chosen CHs. Thus, the proposed P-SMO algorithm establishes the secure routing path based on the factors, such as energy, delay, consistency factor, and trust. The proposed P-SMO algorithm is the integration of particle swarm optimisation (PSO) and spider monkey optimisation (SMO) algorithm in such a way that the optimal routes are defined effectively. The analysis of the proposed routing protocol proved the effective performance with the maximal number of nodes alive, coverage, energy balancing index, and the average remaining energy of 67, 93.407%, 0.7555, and 0.3402 J, respectively.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"73 1","pages":"431 - 449"},"PeriodicalIF":2.2,"publicationDate":"2021-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86913902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

A tuned feed-forward deep neural network algorithm for effort estimation 一种校正前馈深度神经网络算法

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2021-02-05 DOI: 10.1080/0952813X.2021.1871664

M. Öztürk

{"title":"A tuned feed-forward deep neural network algorithm for effort estimation","authors":"M. Öztürk","doi":"10.1080/0952813X.2021.1871664","DOIUrl":"https://doi.org/10.1080/0952813X.2021.1871664","url":null,"abstract":"ABSTRACT Software effort estimation (SEE) is a software engineering problem that requires robust predictive models. To establish robust models, the most feasible configuration of hyperparameters of regression methods is searched. Although only a few works, which include hyperparameter optimisation (HO), have been done so far for SEE, there is not any comprehensive study including deep learning models. In this study, a feed-forward deep neural network algorithm (FFDNN) is proposed for software effort estimation. The algorithm relies on a binary-search-based method for finding hyperparameters. FFDNN outperforms five comparison algorithms in the experiment that uses two performance parameters. The results of the study suggest that: 1) Employing traditional methods such as grid and random search increases tuning time remarkably. Instead, sophisticated parameter search methods compatible with the structure of regression method should be developed; 2) The performance of SEE is enhanced when associated hyperparameter search method is devised according to the essentials of chosen deep learning approach; 3) Deep learning models achieve in competitive CPU time compared to the tree-based regression methods such as CART_DE8.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"181 1","pages":"235 - 259"},"PeriodicalIF":2.2,"publicationDate":"2021-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83019660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Collaborative filtering based on multiple attribute decision making 基于多属性决策的协同过滤

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2021-02-02 DOI: 10.1080/0952813X.2021.1882000

Yajun Leng, Zong-Yu Wu, Qing Lu, Shuping Zhao

{"title":"Collaborative filtering based on multiple attribute decision making","authors":"Yajun Leng, Zong-Yu Wu, Qing Lu, Shuping Zhao","doi":"10.1080/0952813X.2021.1882000","DOIUrl":"https://doi.org/10.1080/0952813X.2021.1882000","url":null,"abstract":"ABSTRACT To address the sparsity problem, a novel collaborative filtering approach based on multiple attribute decision making (MADM-CF) is proposed. In MADM-CF, users in collaborative filtering are treated as decision alternatives, items are treated as attributes. The weight of each item is determined, and the preference similarities between the active user and other users are computed. The preference similarity means that how the users’ preferences are similar on positive ratings and negative ratings. According to the preference similarities, the candidate neighbourhood of the active user is determined. A method to compute overall assessment value is designed, the overall assessment value of each user in the candidate neighbourhood is computed, and users with the smallest overall assessment values are selected as the active user’s nearest neighbours. Finally, the most frequent item recommendation method (MFIR) is used to provide top-N recommendations to the active user. Experimental results based on MovieLens and Netflix datasets show that the proposed approach is superior to existing alternatives.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"98 1","pages":"387 - 397"},"PeriodicalIF":2.2,"publicationDate":"2021-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74971112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Detection of AAC compression using MDCT-based features and supervised learning 使用基于mdct的特征和监督学习检测AAC压缩

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2021-01-31 DOI: 10.1080/0952813X.2021.1882003

José Juan García-Hernández, W. Gómez-Flores

{"title":"Detection of AAC compression using MDCT-based features and supervised learning","authors":"José Juan García-Hernández, W. Gómez-Flores","doi":"10.1080/0952813X.2021.1882003","DOIUrl":"https://doi.org/10.1080/0952813X.2021.1882003","url":null,"abstract":"ABSTRACT Audio files are frequent targets of malicious users who seek illegal profit trading with fake-quality content. For increasing the confidence in the integrity of audio files, the detection of fake-quality content is an important task. This paper proposes a method for detecting Advanced Audio Coding (AAC) compression on suspicious WAV files, in which the variance of the Modified Discrete Cosine Transform (MDCT) characterises four compression bitrates: uncompressed, 64 kbps, 128 kbps, and 256 kbps. This scheme takes advantage of the reduction of the variance of the high-frequency MDCT coefficients in compressed signals. Data obtained from MDCT coefficients generate a high-dimensional feature space. Hence, Principal Component Analysis, followed by Linear Discriminant Analysis, is used for projecting the high-dimensional data onto a lower-dimensional space. Besides, six supervised learning algorithms are compared for classifying four compression bitrates. The experiments show that using audio samples with 20 seconds and 1024 MDCT coefficients, an accuracy of 93% is reached with a Bayesian classifier. Collaterally, the detection between uncompressed and compressed signals attains an accuracy of 97% with Multinomial Logistic Regression. In conclusion, the proposed approach can detect previous AAC compression and can be potentially used when it is unfeasible to recover the suspicious signal completely.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"22 1","pages":"451 - 468"},"PeriodicalIF":2.2,"publicationDate":"2021-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79059821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Brazilian Forest Dataset: A new dataset to model local biodiversity 巴西森林数据集:一个模拟当地生物多样性的新数据集

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2021-01-31 DOI: 10.1080/0952813X.2021.1871972

Ricardo Rios, T. N. Rios, Gabriel R. Palma, R. F. de Mello

{"title":"Brazilian Forest Dataset: A new dataset to model local biodiversity","authors":"Ricardo Rios, T. N. Rios, Gabriel R. Palma, R. F. de Mello","doi":"10.1080/0952813X.2021.1871972","DOIUrl":"https://doi.org/10.1080/0952813X.2021.1871972","url":null,"abstract":"ABSTRACT The Intergovernmental Panel on Climate Change and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services have emphasised unequivocal evidences about the impact of human actions on climate and biodiversity at alarming rates. In Brazilian terms, 2019 has been marked by controversial discussions among politicians and environmentalists, leading to misinformation and misinterpretations that clearly motivate the continuous collection and scientific analysis of data to support sustainable solutions. Aiming at dealing with this issue, this manuscript brings two contributions: (i) the creation of the Brazilian Forest Dataset, including Brazilian seed plants, Fraction of Absorbed Photosynthetically Active Radiation, meteorological and geographical data composing 8,482 attributes to model and predict 20 vegetation types; and (ii) the feasibility analysis on modelling this dataset in light of supervised machine learning algorithms, so we devise confident results on the Brazilian biodiversity. Experimental results confirm Random Forest and Support Vector Machines successfully adjust models, enabling researchers to predict the occurrence of specific types of vegetation in different regions of Brazil as well as analyse how the prediction accuracy changes along time after the collection of new data. Our contributions bring important tools to support the study on the evolution of the Brazilian biodiversity.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"26 1","pages":"327 - 354"},"PeriodicalIF":2.2,"publicationDate":"2021-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77246816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A hybrid optimizer based on backtracking search and differential evolution for continuous optimization 基于回溯搜索和差分进化的连续优化混合优化器

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2021-01-22 DOI: 10.1080/0952813X.2021.1872109

Yiğit Çağatay Kuyu, E. Onieva, P. López-García

{"title":"A hybrid optimizer based on backtracking search and differential evolution for continuous optimization","authors":"Yiğit Çağatay Kuyu, E. Onieva, P. López-García","doi":"10.1080/0952813X.2021.1872109","DOIUrl":"https://doi.org/10.1080/0952813X.2021.1872109","url":null,"abstract":"ABSTRACT This paper introduces a novel hybridisation technique combining the Backtracking Search (BS) and Differential Evolution (DE) algorithms. The proposed hybridisation executes diversity loss and stagnation detection mechanisms to maintain the diversity of the populations, in addition, modifications are done over the mutation operators of the component algorithms in order to improve the search capability of the proposal. These modifications are self-adapted and implemented simultaneously. Extensive experiments to establish the optimal configuration of the parameters are also presented through the introduced technique. The proposed hybridisation approach has been applied to five classical versions and two state-of-the-art variants of DE and tested against 28 well-known benchmark functions with different dimensions, each type of which highlights a different set of characteristics and provides a baseline measurement to validate the performance of the algorithms. In order to further test the proposal, the four outstanding algorithms in the state of the art have also been included in the comparisons. Experimental results show the effectiveness of the proposed hybrid framework over the compared algorithms.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"13 1","pages":"355 - 385"},"PeriodicalIF":2.2,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74545263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Fast copula variational inference 快速耦合变分推理

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2021-01-15 DOI: 10.1080/0952813X.2021.1871970

Jinjin Chi, Jihong Ouyang, Ang Zhang, Xinhua Wang, Ximing Li

{"title":"Fast copula variational inference","authors":"Jinjin Chi, Jihong Ouyang, Ang Zhang, Xinhua Wang, Ximing Li","doi":"10.1080/0952813X.2021.1871970","DOIUrl":"https://doi.org/10.1080/0952813X.2021.1871970","url":null,"abstract":"ABSTRACT Mean-field variational inference, built on fully factorisations, can be efficiently solved; however, it ignores the dependencies between latent variables, resulting in lower performance. To address this, the copula variational inference (CVI) method is proposed by using the well-established copulas to effectively capture posterior dependencies, leading to better approximations. However, it suffers from a computational issue, where the optimisation for big models with massive latent variables is quite time-consuming. This is mainly caused by the expensive sampling when forming noisy Monte Carlo gradients in CVI. For CVI speedup, in this paper we propose a novel fast CVI (abbr. FCVI). In FCVI, we derive the gradient of CVI objective by an expectation of the mean-field factorisation. Therefore, we can achieve a much efficient sampling from the -dimensional mean-field factorisation, enabling to reduce the sampling complexity from to . To evaluate FCVI, we compare it against baseline methods on modelling performance and runtime. Experimental results demonstrate that FCVI is on a par with CVI, but runs much faster.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":"54 1","pages":"295 - 310"},"PeriodicalIF":2.2,"publicationDate":"2021-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80717923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A systematic mapping study on agent mining 智能体挖掘的系统映射研究

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2021-01-11 DOI: 10.1080/0952813X.2020.1864784

E. L. Strugeon, K. Oliveira, Marie Thilliez, Dorian Petit

引用次数: 3