Pub Date : 2022-05-18DOI: 10.1142/s1793351x22400074
Ishita Dasgupta, Susmit Shannigrahi, M. Zink
With live video streaming becoming accessible in various applications on all client platforms, it is imperative to create a seamless and efficient distribution system that is flexible enough to choose from multiple Internet architectures best suited for video streaming (live, on-demand, AR). In this paper, we highlight the benefits of such a hybrid system for live video streaming as well as present a detailed analysis with the goal to provide a high quality of experience (QoE) for the viewer. For our hybrid architecture, video streaming is supported simultaneously over TCP/IP and Named Data Networking (NDN)-based architecture via operating system and networking virtualization techniques to design a flexible system that utilizes the benefits of these varying Internet architectures. Also, to relieve users from the burden of installing a new protocol stack (in the case of NDN) on their devices, we developed a lightweight solution in the form of a container that includes the network stack as well as the streaming application. At the client, the required Internet architecture (TCP/IP versus NDN) can be selected in a transparent and adaptive manner. Based on a prototype, we have designed and implemented maintaining efficient use of network resources, we demonstrate that in the case of live streaming, NDN achieves better QoE per client than IP and can also utilize higher than allocated bandwidth through in-network caching. Even without caching, as opposed to IP-only, our hybrid setup achieves better average bitrate and better perceived visual quality (computed via VMAF metric) over live video streaming services. Furthermore, we present detailed analysis on ways adaptive video streaming with NDN can be further improved with respect to QoE.
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Pub Date : 2022-05-12DOI: 10.1142/s1793351x22400104
M. Vötter, Maximilian Mayerl, Günther Specht, Eva Zangerle
Estimating the success of a song before its release is an important music industry task. This work uses audio descriptors to predict the success (popularity) of a song, where typical measures of success are chart measures such as peak position and streaming measures such as listener-count. Currently, a wide range of datasets is used for that purpose, but most of them are not publicly available; likewise, available datasets are restricted either in size, available features, or popularity measures. This substantially impedes the evaluation of the predictive power of a wide range of models. Therefore, we present two novel datasets called HSP-S and HSP-L based on data from AcousticBrainz, Billboard Hot 100, the Million Song Dataset, and last.fm. Both datasets contain audio features, mel-spectrograms as well as streaming listener- and play-counts. The larger HSP-L dataset contains 73,482 songs, whereas the smaller HSP-S dataset contains 7736 songs and additionally features Billboard Hot 100 chart measures. In contrast to the previous publicly available datasets, our datasets contain substantially more songs and richer and more diverse features. We solely utilize data from the public domain, allowing us to evaluate and compare a wide range of models on our datasets. To demonstrate the use of the datasets, we perform regression and classification (popular/unpopular) tasks on both datasets using a wide variety of models to predict song popularity for all provided target measures of success.
在歌曲发行前评估其成功与否是音乐行业的一项重要任务。这项工作使用音频描述符来预测歌曲的成功(受欢迎程度)，其中成功的典型衡量标准是图表衡量标准，如峰值位置和流媒体衡量标准，如听众数。目前，广泛的数据集被用于这一目的，但其中大多数不是公开可用的;同样，可用的数据集在大小、可用特征或受欢迎程度方面也受到限制。这在很大程度上阻碍了对各种模型预测能力的评估。因此，我们基于来自AcousticBrainz、Billboard Hot 100、百万歌曲数据集和last.fm的数据，提出了两个新的数据集，称为HSP-S和HSP-L。这两个数据集都包含音频功能，mel谱图以及流媒体听众和播放计数。较大的HSP-L数据集包含73,482首歌曲，而较小的HSP-S数据集包含7736首歌曲，另外还包含Billboard Hot 100排行榜。与之前的公开数据集相比，我们的数据集包含了更多的歌曲和更丰富、更多样化的特征。我们完全利用来自公共领域的数据，允许我们评估和比较我们数据集上的各种模型。为了演示数据集的使用，我们使用各种模型对两个数据集执行回归和分类(流行/不流行)任务，以预测所有提供的目标成功度量的歌曲流行程度。
{"title":"HSP Datasets: Insights on Song Popularity Prediction","authors":"M. Vötter, Maximilian Mayerl, Günther Specht, Eva Zangerle","doi":"10.1142/s1793351x22400104","DOIUrl":"https://doi.org/10.1142/s1793351x22400104","url":null,"abstract":"Estimating the success of a song before its release is an important music industry task. This work uses audio descriptors to predict the success (popularity) of a song, where typical measures of success are chart measures such as peak position and streaming measures such as listener-count. Currently, a wide range of datasets is used for that purpose, but most of them are not publicly available; likewise, available datasets are restricted either in size, available features, or popularity measures. This substantially impedes the evaluation of the predictive power of a wide range of models. Therefore, we present two novel datasets called HSP-S and HSP-L based on data from AcousticBrainz, Billboard Hot 100, the Million Song Dataset, and last.fm. Both datasets contain audio features, mel-spectrograms as well as streaming listener- and play-counts. The larger HSP-L dataset contains 73,482 songs, whereas the smaller HSP-S dataset contains 7736 songs and additionally features Billboard Hot 100 chart measures. In contrast to the previous publicly available datasets, our datasets contain substantially more songs and richer and more diverse features. We solely utilize data from the public domain, allowing us to evaluate and compare a wide range of models on our datasets. To demonstrate the use of the datasets, we perform regression and classification (popular/unpopular) tasks on both datasets using a wide variety of models to predict song popularity for all provided target measures of success.","PeriodicalId":217956,"journal":{"name":"Int. J. Semantic Comput.","volume":" 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120834258","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}
Pub Date : 2022-05-08DOI: 10.1142/s1793351x22420028
Kaoru Shimada, T. Arahira, Shogo Matsuno
{"title":"ItemSB: Itemsets with Statistically Distinctive Backgrounds Discovered by Evolutionary Method","authors":"Kaoru Shimada, T. Arahira, Shogo Matsuno","doi":"10.1142/s1793351x22420028","DOIUrl":"https://doi.org/10.1142/s1793351x22420028","url":null,"abstract":"","PeriodicalId":217956,"journal":{"name":"Int. J. Semantic Comput.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130871480","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}
Pub Date : 2022-05-08DOI: 10.1142/s1793351x22430036
D. Pavlichenko, Sven Behnke
{"title":"Flexible-Joint Manipulator Trajectory Tracking with Two-Stage Learned Model Utilizing a Hardwired Forward Dynamics Prediction","authors":"D. Pavlichenko, Sven Behnke","doi":"10.1142/s1793351x22430036","DOIUrl":"https://doi.org/10.1142/s1793351x22430036","url":null,"abstract":"","PeriodicalId":217956,"journal":{"name":"Int. J. Semantic Comput.","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121740524","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}
Pub Date : 2022-05-08DOI: 10.1142/s1793351x22500039
Narges Manouchehri, N. Bouguila
{"title":"A Non-parametric Bayesian Learning Model Using Accelerated Variational Inference on Multivariate Beta Mixture Models for Medical Applications","authors":"Narges Manouchehri, N. Bouguila","doi":"10.1142/s1793351x22500039","DOIUrl":"https://doi.org/10.1142/s1793351x22500039","url":null,"abstract":"","PeriodicalId":217956,"journal":{"name":"Int. J. Semantic Comput.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129939083","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}
Pub Date : 2022-05-08DOI: 10.1142/s1793351x2242003x
James Smith, Chris Henderson, A. Bansal
{"title":"A Generalized Search Construct for Imperative Languages to Facilitate Declarative Programming","authors":"James Smith, Chris Henderson, A. Bansal","doi":"10.1142/s1793351x2242003x","DOIUrl":"https://doi.org/10.1142/s1793351x2242003x","url":null,"abstract":"","PeriodicalId":217956,"journal":{"name":"Int. J. Semantic Comput.","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133414215","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}
Pub Date : 2022-05-08DOI: 10.1142/s1793351x22430012
Hao-Yi Wang, Jhih-Yuan Huang, Wei-Po Lee
{"title":"Integrating Scene Image and Conversational Text to Develop Human-Machine Dialogue","authors":"Hao-Yi Wang, Jhih-Yuan Huang, Wei-Po Lee","doi":"10.1142/s1793351x22430012","DOIUrl":"https://doi.org/10.1142/s1793351x22430012","url":null,"abstract":"","PeriodicalId":217956,"journal":{"name":"Int. J. Semantic Comput.","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121597494","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}
Pub Date : 2022-04-09DOI: 10.1142/s1793351x22400013
M. Basereh, A. Caputo, R. Brennan
This paper proposes a new accountability and transparency evaluation framework (AccTEF) for ontology-based systems (OSysts). AccTEF is based on an analysis of the relation between a set of widely accepted data governance principles, i.e. findable, accessible, interoperable, reusable (FAIR) and accountability and transparency concepts. The evaluation of accountability and transparency of input ontologies and vocabularies of OSysts are addressed by analyzing the relation between vocabulary and ontology quality evaluation metrics, FAIR and accountability and transparency concepts. An ontology-based knowledge extraction pipeline is used as a use case in this study. Discovering the relation between FAIR and accountability and transparency helps in identifying and mitigating risks associated with deploying OSysts. This also allows providing design guidelines that help accountability and transparency to be embedded in OSysts. We found that FAIR can be used as a transparency indicator. We also found that the studied vocabulary and ontology quality evaluation metrics do not cover FAIR, accountability and transparency. Accordingly, we suggest these concepts should be considered as vocabulary and ontology quality evaluation aspects. To the best of our knowledge, it is the first time that the relation between FAIR and accountability and transparency concepts has been found and used for evaluation.
{"title":"AccTEF: A Transparency and Accountability Evaluation Framework for Ontology-Based Systems","authors":"M. Basereh, A. Caputo, R. Brennan","doi":"10.1142/s1793351x22400013","DOIUrl":"https://doi.org/10.1142/s1793351x22400013","url":null,"abstract":"This paper proposes a new accountability and transparency evaluation framework (AccTEF) for ontology-based systems (OSysts). AccTEF is based on an analysis of the relation between a set of widely accepted data governance principles, i.e. findable, accessible, interoperable, reusable (FAIR) and accountability and transparency concepts. The evaluation of accountability and transparency of input ontologies and vocabularies of OSysts are addressed by analyzing the relation between vocabulary and ontology quality evaluation metrics, FAIR and accountability and transparency concepts. An ontology-based knowledge extraction pipeline is used as a use case in this study. Discovering the relation between FAIR and accountability and transparency helps in identifying and mitigating risks associated with deploying OSysts. This also allows providing design guidelines that help accountability and transparency to be embedded in OSysts. We found that FAIR can be used as a transparency indicator. We also found that the studied vocabulary and ontology quality evaluation metrics do not cover FAIR, accountability and transparency. Accordingly, we suggest these concepts should be considered as vocabulary and ontology quality evaluation aspects. To the best of our knowledge, it is the first time that the relation between FAIR and accountability and transparency concepts has been found and used for evaluation.","PeriodicalId":217956,"journal":{"name":"Int. J. Semantic Comput.","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127170865","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}
Pub Date : 2022-04-08DOI: 10.1142/s1793351x22400062
Anas Al-Tirawi, R. Reynolds
One of the major challenges facing Artificial Intelligence in the future is the design of trustworthy algorithms. The development of trustworthy algorithms will be a key challenge in Artificial Intelligence for years to come. Cultural Algorithms (CAs) are viewed as one framework that can be employed to produce a trustable evolutionary algorithm. They contain features to support both sustainable and explainable computation that satisfy requirements for trustworthy algorithms proposed by Cox [Nine experts on the single biggest obstacle facing AI and algorithms in the next five years, Emerging Tech Brew, January 22, 2021]. Here, two different configurations of CAs are described and compared in terms of their ability to support sustainable solutions over the complete range of dynamic environments, from static to linear to nonlinear and finally chaotic. The Wisdom of the Crowds method was selected for the one configuration since it has been observed to work in both simple and complex environments and requires little long-term memory. The Common Value Auction (CVA) configuration was selected to represent those mechanisms that were more data centric and required more long-term memory content. Both approaches were found to provide sustainable performance across all the dynamic environments tested from static to chaotic. Based upon the information collected in the Belief Space, they produced this behavior in different ways. First, the topologies that they employed differed in terms of the “in degree” for different complexities. The CVA approach tended to favor reduced “indegree/outdegree”, while the WM exhibited a higher indegree/outdegree in the best topology for a given environment. These differences reflected the fact the CVA had more information available for the agents about the network in the Belief Space, whereas the agents in the WM had access to less available knowledge. It therefore needed to spread the knowledge that it currently had more widely throughout the population.
未来人工智能面临的主要挑战之一是可信赖算法的设计。可信算法的开发将是未来几年人工智能领域的一个关键挑战。文化算法(ca)被视为一个框架，可以用来产生一个可信的进化算法。它们包含支持可持续和可解释计算的功能，满足Cox提出的可信赖算法的要求[九位专家关于未来五年人工智能和算法面临的最大障碍，Emerging Tech Brew, 2021年1月22日]。本文描述并比较了两种不同配置的ca在各种动态环境(从静态到线性到非线性，最后是混沌)中支持可持续解决方案的能力。选择群体智慧方法作为一种配置，因为它被观察到在简单和复杂的环境中都有效，并且需要很少的长期记忆。选择Common Value Auction (CVA)配置来表示那些更以数据为中心、需要更多长期内存内容的机制。两种方法都可以在从静态到混沌的所有动态环境中提供可持续的性能。基于在信念空间中收集的信息，他们以不同的方式产生了这种行为。首先，他们采用的拓扑在不同复杂性的“in degree”方面有所不同。CVA方法倾向于降低“度外度”，而WM方法在给定环境下的最佳拓扑结构中表现出更高的度外度。这些差异反映了CVA在信念空间中可以获得更多关于网络的信息，而在WM中可以获得较少的知识。因此，它需要在人口中更广泛地传播它目前掌握的知识。
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