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Exploring Variational Auto-encoder Architectures, Configurations, and datasets for Generative Music Explainable AI

Machine Intelligence Research 2024年第1期21卷 29-45页

作者： Nick Bryan-Kinns Bingyuan Zhang Songyan Zhao Berker Banar School of Electronic Engineering and Computer Science Queen Mary University of LondonLondon E14NSUK Computer Science Department Carleton CollegeNorthfield MN 55057USA

Generative AI models for music and the arts in general are increasingly complex and hard to understand.The field of ex-plainable AI(XAI)seeks to make complex and opaque AI models such as neural networks more understandable to people.One ap-proach to making generative AI models more understandable is to impose a small number of semantically meaningful attributes on gen-erative AI models.This paper contributes a systematic examination of the impact that different combinations of variational auto-en-coder models(measureVAE and adversarialVAE),configurations of latent space in the AI model(from 4 to 256 latent dimensions),and training datasets(Irish folk,Turkish folk,classical,and pop)have on music generation performance when 2 or 4 meaningful musical at-tributes are imposed on the generative model.To date,there have been no systematic comparisons of such models at this level of com-binatorial detail.Our findings show that measureVAE has better reconstruction performance than adversarialVAE which has better musical attribute independence.Results demonstrate that measureVAE was able to generate music across music genres with inter-pretable musical dimensions of control,and performs best with low complexity music such as pop and rock.We recommend that a 32 or 64 latent dimensional space is optimal for 4 regularised dimensions when using measureVAE to generate music across genres.Our res-ults are the first detailed comparisons of configurations of state-of-the-art generative AI models for music and can be used to help select and configure AI models,musical features,and datasets for more understandable generation of music.

关键词： Variational auto-encoder explainable AI(XAI) generative music musical features datasets

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七套土地覆被数据在羌塘高原的精度评价（英文）

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Journal of Geographical Sciences 2019年第11期29卷 1841-1858页

作者：刘琼欢张镱锂刘林山李兰晖祁威 Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources ResearchCASBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China CAS Center for Excellence in Tibetan Plateau Earth Sciences Beijing 100101China

We analyzed the spatial local accuracy of land cover (LC) datasets for the Qiangtang Plateau,High Asia,incorporating 923 field sampling points and seven LC compilations including the International Geosphere Biosphere Programme Data and Information System (IGBPDIS),Global Land cover mapping at 30 m resolution (GlobeLand30),MODIS Land Cover Type product (MCD12Q1),Climate Change Initiative Land Cover (CCI-LC),Global Land Cover 2000 (GLC2000),University of Maryland (UMD),and GlobCover 2009 (Glob- Cover).We initially compared resultant similarities and differences in both area and spatial patterns and analyzed inherent relationships with data sources.We then applied a geographically weighted regression (GWR) approach to predict local accuracy variation.The results of this study reveal that distinct differences,even inverse time series trends,in LC data between CCI-LC and MCD12Q1 were present between 2001 and 2015,with the exception of category areal discordance between the seven datasets.We also show a series of evident discrepancies amongst the LC datasets sampled here in terms of spatial patterns,that is,high spatial congruence is mainly seen in the homogeneous southeastern region of the study area while a low degree of spatial congruence is widely distributed across heterogeneous northwestern and northeastern regions.The overall combined spatial accuracy of the seven LC datasets considered here is less than 70%,and the GlobeLand30 and CCI-LC datasets exhibit higher local accuracy than their counterparts,yielding maximum overall accuracy (OA) values of 77.39% and 61.43%,respectively.Finally,5.63% of this area is characterized by both high assessment and accuracy (HH) values,mainly located in central and eastern regions of the Qiangtang Plateau,while most low accuracy regions are found in northern,northeastern,and western regions.

关键词： land cover datasets spatial accuracy assessment remote sensing Qiangtang Plateau High Asia

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Intercomparison of the Extended Reconstructed Sea Surface Temperature v4 and v3b datasets

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Journal of Ocean University of China 2018年第2期17卷 209-218页

作者： WANG Jinping CHEN Xianyao Department of Oceanography College of Oceanic and Atmospheric Sciences Ocean University of China Physical Oceanography Laboratory/CIMST Ocean University of China and Qingdao National Laboratory for Marine Science and Technology

Version 4(v4) of the Extended Reconstructed Sea Surface Temperature(ERSST) dataset is compared with its precedent, the widely used version 3b(v3b). The essential upgrades applied to v4 lead to remarkable differences in the characteristics of the sea surface temperature(SST) anomaly(SSTa) in both the temporal and spatial domains. First, the largest discrepancy of the global mean SSTa values around the 1940 s is due to ship-observation corrections made to reconcile observations from buckets and engine intake thermometers. Second, differences in global and regional mean SSTa values between v4 and v3b exhibit a downward trend(around-0.032℃ per decade) before the 1940s, an upward trend(around 0.014℃ per decade) during the period of 1950–2015, interdecadal oscillation with one peak around the 1980s, and two troughs during the 1960s and 2000s, respectively. This does not derive from treatments of the polar or the other data-void regions, since the difference of the SSTa does not share the common features. Third, the spatial pattern of the ENSO-related variability of v4 exhibits a wider but weaker cold tongue in the tropical region of the Pacific Ocean compared with that of v3b, which could be attributed to differences in gap-filling assumptions since the latter features satellite observations whereas the former features in situ ones. This intercomparison confirms that the structural uncertainty arising from underlying assumptions on the treatment of diverse SST observations even in the same SST product family is the main source of significant SST differences in the temporal domain. Why this uncertainty introduces artificial decadal oscillations remains unknown.

关键词： ERSST datasets sea surface temperature global warming Arctic data intercomparison

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Controllable multi-domain semantic artwork synthesis

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Computational Visual Media 2024年第2期10卷 355-373页

作者： Yuantian Huang Satoshi Iizuka Edgar Simo-Serra Kazuhiro Fukui Department of Computer Science University of TsukubaTsukuba 305-8577Japan Department of Computer Science and Engineering Waseda UniversityTokyo 169-8050Japan

We present a novel framework for the multidomain synthesis of artworks from semantic layouts.One of the main limitations of this challenging task is the lack of publicly available segmentation datasets for art synthesis.To address this problem,we propose a dataset called ArtSem that contains 40,000 images of artwork from four different domains,with their corresponding semantic label maps.We first extracted semantic maps from landscape photography and used a conditional generative adversarial network(GAN)-based approach for generating high-quality artwork from semantic maps without requiring paired training data.Furthermore,we propose an artwork-synthesis model using domain-dependent variational encoders for high-quality multi-domain synthesis.Subsequently,the model was improved and complemented with a simple but effective normalization method based on jointly normalizing semantics and style,which we call spatially style-adaptive normalization(SSTAN).Compared to the previous methods,which only take semantic layout as the input,our model jointly learns style and semantic information representation,improving the generation quality of artistic images.These results indicate that our model learned to separate the domains in the latent space.Thus,we can perform fine-grained control of the synthesized artwork by identifying hyperplanes that separate the different domains.Moreover,by combining the proposed dataset and approach,we generated user-controllable artworks of higher quality than that of existing approaches,as corroborated by quantitative metrics and a user study.

关键词： semantic artwork synthesis generative adversarial network(GAN) datasets non-photorealistic rendering

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Generation of Labelled datasets to Quantify the Impact of Security Threats to Cloud Data Centers

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Journal of Information Security 2016年第3期7卷 172-184页

作者： Sai Kiran Mukkavilli Sachin Shetty Liang Hong Department of Electrical & Computer Engineering Tennessee State University Nashville TN USA

Anomaly based approaches in network intrusion detection suffer from evaluation, comparison and deployment which originate from the scarcity of adequate publicly available network trace datasets. Also, publicly available datasets are either outdated or generated in a controlled environment. Due to the ubiquity of cloud computing environments in commercial and government internet services, there is a need to assess the impacts of network attacks in cloud data centers. To the best of our knowledge, there is no publicly available dataset which captures the normal and anomalous network traces in the interactions between cloud users and cloud data centers. In this paper, we present an experimental platform designed to represent a practical interaction between cloud users and cloud services and collect network traces resulting from this interaction to conduct anomaly detection. We use Amazon web services (AWS) platform for conducting our experiments.

关键词： Amazon Web Services Anomaly Detection Cloud Computing datasets Intrusion Detection Network Traces

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Taxonomy-based differences in feeding guilds of fish

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Current Zoology 2020年第1期66卷 51-56页

作者： Javier SANCHEZ-HERNANDEZ Departamento de Zooloxla Xenetica e Antropoloxfa FlsicaFacultade de BioloxfaUniversidade de Santiago de CompostelaCampus Vida s/nSantiago de CompostelaEspana

It has been taken for granted that feeding guilds and behavior in animals are linked to the taxonomic relatedness of species,but empirical evidence supporting such relationship is virtually missing.To examine the importance of taxonomy on trophic ecology,I here present the first well-resolved dietary taxonomy analysis based on feeding guilds(predation,herbivory,and filtering)among families and genera within the fish order Perciformes.Taxonomic relatedness in feeding did not vary with ecosystem dimension(marine vs.freshwater).Although predation dominates among Perciformes fishes,this study shows that in most cases taxonomic units(family or genus)are composed by species with several feeding guilds.Related species are more similar in feeding compared with species that are taxonomically more distant demonstrating that there is a greater variation of feeding guilds within families than genera.Thus,there is no consistency in feeding guilds between family-and genus-level taxonomy.This study provides empirical support for the notion that genera are more informative than families,underlining that family-level taxonomy should be avoided to infer feeding habits of fish species at finer taxonomic resolution.Thus,the choice of taxonomic resolution(family or genus level)in ecological studies is key to avoid information loss and misleading results.I conclude that high-rank taxonomic units(i.e.,above the generic level)are not appropriate to test research hypotheses about the feeding of fish.

关键词： aquatic systems clades FishBase global datasets taxonomic sufficiency trophic ecology

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Survey of Pedestrian Action Recognition Techniques for Autonomous Driving

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Tsinghua Science and Technology 2020年第4期25卷 458-470页

作者： Li Chen Nan Ma Patrick Wang Jiahong Li Pengfei Wang Guilin Pang Xiaojun Shi Beijing Key Laboratory of Information Service Engineering College of RoboticsBeijing Union UniversityBeijing 100101China Northeastern University BostonMA 02115USA the Communication and Information Center of Ministry of Emergency Management of the People’s Republic of China Beijing 100013China the College of Robotics Beijing Union UniversityBeijing 100101China

The development of autonomous driving has brought with it requirements for intelligence,safety,and stability.One example of this is the need to construct effective forms of interactive cognition between pedestrians and vehicles in dynamic,complex,and uncertain environments.Pedestrian action detection is a form of interactive cognition that is fundamental to the success of autonomous driving technologies.Specifically,vehicles need to detect pedestrians,recognize their limb movements,and understand the meaning of their actions before making appropriate decisions in response.In this survey,we present a detailed description of the architecture for pedestrian action recognition in autonomous driving,and compare the existing mainstream pedestrian action recognition techniques.We also introduce several commonly used datasets used in pedestrian motion recognition.Finally,we present several suggestions for future research directions.

关键词： autonomous driving pedestrian action recognition action datasets two-stream network

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An Improved Memory Cache Management Study Based on Spark

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Computers, Materials & Continua 2018年第9期56卷 415-431页

作者： Suzhen Wang Yanpiao Zhang Lu Zhang Ning Cao Chaoyi Pang Hebei University of Economics and Business ShijiazhuangHebei050061China University College Dublin BelfieldDublin 4Ireland The Australian e-Health Research Centre ICT CentreCSIROAustralia

Spark is a fast unified analysis engine for big data and machine learning,in which the memory is a crucial resource.Resilient Distribution datasets(RDDs)are parallel data structures that allow users explicitly persist intermediate results in memory or on disk,and each one can be divided into several partitions.During task execution,Spark automatically monitors cache usage on each node.And when there is a RDD that needs to be stored in the cache where the space is insufficient,the system would drop out old data partitions in a least recently used(LRU)fashion to release more space.However,there is no mechanism specifically for caching RDD in Spark,and the dependency of RDDs and the need for future stages are not been taken into consideration with LRU.In this paper,we propose the optimization approach for RDDs cache and LRU based on the features of partitions,which includes three parts:the prediction mechanism for persistence,the weight model by using the entropy method,and the update mechanism of weight and memory based on RDDs partition feature.Finally,through the verification on the spark platform,the experiment results show that our strategy can effectively reduce the time in performing and improve the memory usage.

关键词： Resilient distribution datasets update mechanism weight mode

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Looking both ways： new research on old theories

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Asian Journal of Andrology 2015年第5期17卷 764-766页

作者： Trevor G Cooper Tuen Mun NT Hong Kong SAR China

In the 22 years since its inception this meetingI has become the focus of a still small but growing and devoted group of scientists, meeting purposefully at a leisurely interval of four years, so that only substantial advances are reported. As these meetings approach a quarter of a century of epididymal research, it is time to reflect on the focus of former, and direction of future, epididymal research.

关键词： cyto-chemical datasets leisurely

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A Review of Object Detection Based on Convolutional Neural Network

A Review of Object Detection Based on Convolutional Neural N...

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第36届中国控制会议

作者： Wang Zhiqiang Liu Jun Fundamental Science on Communication Information Transmission and Fusion Technology Laboratory

ISBN: (纸本)9781538629185

With the development of intelligent device and social media,the data bulk on Internet has grown with high speed.As an important aspect of image processing,object detection has become one of the international popular research fields.In recent years,the powerful ability with feature learning and transfer learning of Convolutional Neural Network（CNN） has received growing interest within the computer vision community,thus making a series of important breakthroughs in object detection.So it is a significant survey that how to apply CNN to object detection for better performance.First the paper introduced the basic concept and architecture of CNN.Secondly the methods that how to solve the existing problems of conventional object detection are surveyed,mainly analyzing the detection algorithm based on region proposal and based on regression.Thirdly it mentioned some means which improve the performance of object detection.Then the paper introduced some public datasets of object detection and the concept of evaluation criterion.Finally,it combed the current research achievements and thoughts of object detection,summarizing the important progress and discussing the future directions.

关键词： Convolutional Neural Network object detection region proposal regression datasets

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