Int. J. Interact. Multim. Artif. Intell.最新文献

{"title":"The Yield Curve as a Recession Leading Indicator. An Application for Gradient Boosting and Random Forest","authors":"Pedro Cadahia Delgado, E. Congregado, A. Golpe, José Carlos Vides","doi":"10.9781/ijimai.2022.02.006","DOIUrl":"https://doi.org/10.9781/ijimai.2022.02.006","url":null,"abstract":"INCE the decade of the '80s, economic crises have been more recurrent and deeper. In this respect, researchers and practitioners have tried to understand, model, and even predict a recession differently. One popular forecasting tool suggested in the literature and followed by economists is the analysis of the slope of the yield curve or the term spread, i.e., the difference between longterm and short-term interest rates [1].","PeriodicalId":143152,"journal":{"name":"Int. J. Interact. Multim. Artif. Intell.","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132074105","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}

{"title":"Variational Learning for the Inverted Beta-Liouville Mixture Model and Its Application to Text Categorization","authors":"Yongfa Ling, Wenbo Guan, Qiang Ruan, Heping Song, Yuping Lai","doi":"10.9781/ijimai.2022.08.006","DOIUrl":"https://doi.org/10.9781/ijimai.2022.08.006","url":null,"abstract":"The finite invert Beta-Liouville mixture model (IBLMM) has recently gained some attention due to its positive data modeling capability. Under the conventional variational inference (VI) framework, the analytically tractable solution to the optimization of the variational posterior distribution cannot be obtained, since the variational object function involves evaluation of intractable moments. With the recently proposed extended variational inference (EVI) framework, a new function is proposed to replace the original variational object function in order to avoid intractable moment computation, so that the analytically tractable solution of the IBLMM can be derived in an elegant way. The good performance of the proposed approach is demonstrated by experiments with both synthesized data and a real-world application namely text categorization.","PeriodicalId":143152,"journal":{"name":"Int. J. Interact. Multim. Artif. Intell.","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130545290","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}

{"title":"Why the Future Might Actually Need Us: A Theological Critique of the 'Humanity-As-Midwife-For-Artificial-Superintelligence' Proposal","authors":"M. Dorobantu","doi":"10.9781/ijimai.2021.07.005","DOIUrl":"https://doi.org/10.9781/ijimai.2021.07.005","url":null,"abstract":"","PeriodicalId":143152,"journal":{"name":"Int. J. Interact. Multim. Artif. Intell.","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121560155","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}

{"title":"Artificial Canaries: Early Warning Signs for Anticipatory and Democratic Governance of AI","authors":"Carla Zoe Cremer, Jess Whittlestone","doi":"10.17863/CAM.65790","DOIUrl":"https://doi.org/10.17863/CAM.65790","url":null,"abstract":"We propose a method for identifying early warning signs of transformative progress in artificial intelligence (AI), and discuss how these can support the anticipatory and democratic governance of AI. We call these early warning signs ‘canaries’, based on the use of canaries to provide early warnings of unsafe air pollution in coal mines. Our method combines expert elicitation and collaborative causal graphs to identify key milestones and identify the relationships between them. We present two illustrations of how this method could be used: to identify early warnings of harmful impacts of language models; and of progress towards high-level machine intelligence. Identifying early warning signs of transformative applications can support more efficient monitoring and timely regulation of progress in AI: as AI advances, its impacts on society may be too great to be governed retrospectively. It is essential that those impacted by AI have a say in how it is governed. Early warnings can give the public time and focus to influence emerging technologies using democratic, participatory technology assessments. We discuss the challenges in identifying early warning signals and propose directions for future work.","PeriodicalId":143152,"journal":{"name":"Int. J. Interact. Multim. Artif. Intell.","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128661695","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}

{"title":"Music Boundary Detection using Convolutional Neural Networks: A comparative analysis of combined input features","authors":"Carlos Hernandez-Olivan, J. R. Beltrán, David Diaz-Guerra","doi":"10.9781/ijimai.2021.10.005","DOIUrl":"https://doi.org/10.9781/ijimai.2021.10.005","url":null,"abstract":"The analysis of the structure of musical pieces is a task that remains a challenge for Artificial Intelligence, especially in the field of Deep Learning. It requires prior identification of structural boundaries of the music pieces. This structural boundary analysis has recently been studied with unsupervised methods and textit{end-to-end} techniques such as Convolutional Neural Networks (CNN) using Mel-Scaled Log-magnitude Spectograms features (MLS), Self-Similarity Matrices (SSM) or Self-Similarity Lag Matrices (SSLM) as inputs and trained with human annotations. Several studies have been published divided into unsupervised and textit{end-to-end} methods in which pre-processing is done in different ways, using different distance metrics and audio characteristics, so a generalized pre-processing method to compute model inputs is missing. The objective of this work is to establish a general method of pre-processing these inputs by comparing the inputs calculated from different pooling strategies, distance metrics and audio characteristics, also taking into account the computing time to obtain them. We also establish the most effective combination of inputs to be delivered to the CNN in order to establish the most efficient way to extract the limits of the structure of the music pieces. With an adequate combination of input matrices and pooling strategies we obtain a measurement accuracy $F_1$ of 0.411 that outperforms the current one obtained under the same conditions.","PeriodicalId":143152,"journal":{"name":"Int. J. Interact. Multim. Artif. Intell.","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114787513","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}

{"title":"Machine Learning Based Method for Estimating Energy Losses in Large-Scale Unbalanced Distribution Systems with Photovoltaics","authors":"K. Mahmoud, M. Abdel-Nasser, H. Kashef, D. Puig, M. Lehtonen","doi":"10.9781/ijimai.2020.08.002","DOIUrl":"https://doi.org/10.9781/ijimai.2020.08.002","url":null,"abstract":"Powered by TCPDF (www.tcpdf.org) This material is protected by copyright and other intellectual property rights, and duplication or sale of all or part of any of the repository collections is not permitted, except that material may be duplicated by you for your research use or educational purposes in electronic or print form. You must obtain permission for any other use. Electronic or print copies may not be offered, whether for sale or otherwise to anyone who is not an authorised user. Mahmoud, Karar; Abdelnasser, Mohamed; Kashef, Heba; Puig, Domenec; Lehtonen, Matti","PeriodicalId":143152,"journal":{"name":"Int. J. Interact. Multim. Artif. Intell.","volume":"366 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134503381","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}

{"title":"Guidelines for performing Systematic Research Projects Reviews","authors":"A. García-Holgado, Samuel Marcos, F. García-Peñalvo","doi":"10.9781/ijimai.2020.05.005","DOIUrl":"https://doi.org/10.9781/ijimai.2020.05.005","url":null,"abstract":"T number of scientific articles published, regardless of the academic discipline, has dramatically increased in the last decades. The publication in impact journals is considered one of the KPI (key performance indicators) in research centres and one of the measures to get funds. Moreover, in the current information society, most of the published works are available in online journals, repositories, databases, so researchers have access to them. One of the first tasks before conducting a research, regardless of the field of study, is to identify related works and previous studies as a way to support the need to conduct new research on a particular topic. Likewise, the review of available research provides answers to particular research questions and a knowledge base to learn from previous experiences and identify new research opportunities. Nevertheless, although the need to synthesise research evidence has been recognised for well over two centuries, it was not until the end of the last century that researchers began to develop explicit methods for this form of research. In particular, a literature review allows for achieving this objective. According to Grant and Booth [1], it involves some process for identifying materials for potential inclusion, for selecting included materials, for synthesizing them in textual, tabular or graphical form and for making some analysis of their contributions or value. There are different review types and associated methodologies. Specifically, before 1990, narrative reviews were typically used, but they have some limitations such as the subjectivity, coupled with the lack of transparency, and the early expiration because the synthetization process becomes complicated and eventually untenable as the number of studies increases [2]. The systematic review or systematic literature review method seeks to mitigate the limitations of narrative reviews. Systematic reviews have their origin in the field of Medicine and Health. Nevertheless, the logic of systematic methods for reviewing the literature can be applied to other areas of research such as Humanities, Social Sciences or Software Engineering; therefore there can be as much variation in systematic reviews as is found in primary research [3], [4]. A systematic review is a protocol-driven comprehensive review and synthesis of data focusing on a topic or related key questions. It is typically performed by experienced methodologists with the input of domain experts [5]. The systematic review methods are a way of bringing together what is known from the research literature using explicit and accountable methods [4]. According to Kitchenham [6][8], a systematic review is a means of evaluating and interpreting all available research relevant to a particular research question, topic area, or phenomenon of interest by using a trustworthy, rigorous, and auditable methodology. The analysis of related works and previous studies is not only associated with scientific literat","PeriodicalId":143152,"journal":{"name":"Int. J. Interact. Multim. Artif. Intell.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131126496","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}

{"title":"Time-Dependent Performance Prediction System for Early Insight in Learning Trends","authors":"Carlos Villagrá-Arnedo, F. J. Gallego-Durán, F. Llorens-Largo, Rosana Satorre-Cuerda, Patricia Compañ-Rosique, R. Molina-Carmona","doi":"10.9781/ijimai.2020.05.006","DOIUrl":"https://doi.org/10.9781/ijimai.2020.05.006","url":null,"abstract":"K students' learning trends is relevant to diagnose learning performance and early detect situations where teachers' intervention would be most effective. Prediction systems represent one of the bests tools for this purpose. Predicting performance is the basis for student diagnostics, learning trends projection and early detection. Most performance prediction systems output numerical grades or performance class memberships. Research tends to focus on prediction accuracy. Accuracy is relevant, because it helps improving diagnostics, but it should not be confused with the main goal: improving learning. To help teachers improve student performance many other aspects can be considered: more accessible prediction data, better graphical representations, methods for detecting learning trends and most suitable moments for intervention, etc. Most of these improvements rely on the ability to consider learning data evolution over time. This is particularly relevant due to cumulative nature of learning and so it is one of the main characteristics considered in this work. This work is an empirical research in the search for practical systems to help teachers in their guidance duties. It relays on teachers receiving in-depth information on student learning trends during semester. This information is elaborated from an automatic system which yields predictions on expected student performance. Main contribution of this work is a custom-designed practical prediction system. Main innovations of the proposed system are its time-dependent nature and the use of probabilistic predictions. The proposed system delivers by-weekly probabilistic performance predictions and analytical timedependent graphs that help gaining insight in students’ learning trends. The proposed system is tested during a complete semester in the subject Mathematics I at the University of Alicante. Data gathered is used as initial evidence to empirically test the system and results are shown and discussed. Usefulness, convenience and advantages of the time-dependent nature of learning data are also tested and discussed. As an additional consequence derived from these tests, some initial methods for selecting the best moments for teacher intervention are proposed and discussed. Performance predictions are shown as point graphs over time, along with calculated trends. This information is summarized and organized to help teachers explore and analyse student learning performance efficiently. Some case examples are presented and analysed using these graphs, showing their potential to help teachers understand beyond raw data. Teachers can use this information to diagnose students, understand learning trends, early detect intervention situations and act accordingly to help students improve their learning results. This research considers only learning trend diagnosis and detection of most suitable moments for teacher intervention. Intervention strategies and their results are out of scope. This paper is s","PeriodicalId":143152,"journal":{"name":"Int. J. Interact. Multim. Artif. Intell.","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129884453","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}

{"title":"Learning Models for Semantic Classification of Insufficient Plantar Pressure Images","authors":"Yao Wu, Qun Wu, N. Dey, R. Sherratt","doi":"10.9781/IJIMAI.2020.02.005","DOIUrl":"https://doi.org/10.9781/IJIMAI.2020.02.005","url":null,"abstract":"Establishing a reliable and stable model to predict a target by using insufficient labeled samples is feasible and \u0000effective, particularly, for a sensor-generated data-set. This paper has been inspired with insufficient data-set \u0000learning algorithms, such as metric-based, prototype networks and meta-learning, and therefore we propose \u0000an insufficient data-set transfer model learning method. Firstly, two basic models for transfer learning are \u0000introduced. A classification system and calculation criteria are then subsequently introduced. Secondly, a dataset \u0000of plantar pressure for comfort shoe design is acquired and preprocessed through foot scan system; and by \u0000using a pre-trained convolution neural network employing AlexNet and convolution neural network (CNN)- \u0000based transfer modeling, the classification accuracy of the plantar pressure images is over 93.5%. Finally, \u0000the proposed method has been compared to the current classifiers VGG, ResNet, AlexNet and pre-trained \u0000CNN. Also, our work is compared with known-scaling and shifting (SS) and unknown-plain slot (PS) partition \u0000methods on the public test databases: SUN, CUB, AWA1, AWA2, and aPY with indices of precision (tr, ts, H) \u0000and time (training and evaluation). The proposed method for the plantar pressure classification task shows high \u0000performance in most indices when comparing with other methods. The transfer learning-based method can be \u0000applied to other insufficient data-sets of sensor imaging fields.","PeriodicalId":143152,"journal":{"name":"Int. J. Interact. Multim. Artif. Intell.","volume":"70 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123548626","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}

{"title":"Comparative Study on Ant Colony Optimization (ACO) and K-Means Clustering Approaches for Jobs Scheduling and Energy Optimization Model in Internet of Things (IoT)","authors":"Sumit Kumar, Vijender Kumar Solanki, S. Choudhary, A. Selamat, R. G. Crespo","doi":"10.9781/ijimai.2020.01.003","DOIUrl":"https://doi.org/10.9781/ijimai.2020.01.003","url":null,"abstract":"T latest IoT applications depend on promotion of wireless sensor networks (WSNs) with expert of engineering. These IoT applications contain a large number of devices, connected with different requirements and technologies. Such kinds of IoT applications do the sensing and collection of data with transmission of data to the administrator nodes for other possible operations and even a cloud at the backdrop for data analytics. These processes require routing protocols for their completion. Routing protocols have two major challenges. The first challenge is to improve data transmission and scalability whereas the second challenge is to minimize energy consumption. In an IoT application, network nodes under different network topology collect different kind of data so that an IoT application produces an enormous amount of data. The heterogeneity in network topology restricts the TCP/IP to become the best policy for proper resource allocation to computing and routing [1]-[3], [27]-[29]. Owing to the above-mentioned challenges, different persons view IoT in different ways, based on their perception and requirements. A routing protocol includes the multiple job scheduling methodologies. These job scheduling methodologies are reported as either heuristic or metaheuristic-based approaches. Heuristic-based methodologies are comparatively more helpful when we look for a local optimum whereas metaheuristic methodologies further try to explore the solution DOI: 10.9781/ijimai.2020.01.003","PeriodicalId":143152,"journal":{"name":"Int. J. Interact. Multim. Artif. Intell.","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129888394","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}