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Solar Energetic Particles (SEP) events are interesting from a scientific perspective as they are the product of a broad set of physical processes from the corona out through the extent of the heliosphere, and provide insight into processes of particle acceleration and transport that are widely applicable in astrophysics. From the operations perspective, SEP events pose a radiation hazard for aviation, electronics in space, and human space exploration, in particular for missions outside of the Earth’s protective magnetosphere including to the Moon and Mars. Thus, it is critical to imific understanding of SEP events and use this understanding to develop and improve SEP forecasting capabilities to support operations. Many SEP models exist or are in development using a wide variety of approaches and with differing goals. These include computationally intensive physics-based models, fast and light empirical models, machine learning-based models, and mixed-model approaches. The aim of this paper is to summarize all of the SEP models currently developed in the scientific community, including a description of model approach, inputs and outputs, free parameters, and any published validations or comparisons with data.

In this study we extract the deep features and investigate the compression of the Mg II k spectral line profiles observed in quiet Sun regions by NASA's IRIS satellite. The data set of line profiles used for the analysis was obtained on April 20th, 2020, at the center of the solar disc, and contains almost 300,000 individual Mg II k line profiles after data cleaning. The data are separated into train and test subsets. The train subset was used to train the autoencoder of the varying embedding layer size. The early stopping criterion was implemented on the test subset to prevent the model from overfitting. Our results indicate that it is possible to compress the spectral line profiles more than 27 times (which corresponds to the reduction of the data dimensionality from 110 to 4) while having a 4 DN average reconstruction error, which is comparable to the variations in the line continuum. The mean squared error and the reconstruction error of even statistical moments sharply decrease when the dimensionality of the embedding layer increases from 1 to 4 and almost stop decreasing for higher numbers. The observed occasional improvements in training for values higher than 4 indicate that a better compact embedding may potentially be obtained if other training strategies and longer training times are used. The features learned for the critical four-dimensional case can be interpreted. In particular, three of these four features mainly control the line width, line asymmetry, and line dip formation respectively. The presented results are the first attempt to obtain a compact embedding for spectroscopic line profiles and confirm the value of this approach, in particular for feature extraction, data compression, and denoising., 6 pages, 3 figures, submitted to CBMI 2021 (Special session: Mining and indexing multimedia data for remote sensing of the environment and our changing planet)

The project develops innovative tools to extract and analyze the available observational and modeling data in order to enable new physics-based and machine-learning approaches for understanding and...

© 2019 World Scientific Publishing Company. The K-nearest neighbor graph (K-NNG) is a data structure used by many machine-learning algorithms. Naive computation of the K-NNG has quadratic time complexity, which in many cases is not efficient enough, producing the need for fast and accurate approximation algorithms. NN-Descent is one such algorithm that is highly efficient, but has a major drawback in that K-NNG approximations are accurate only on data of low intrinsic dimensionality. This paper represents an experimental analysis of this behavior, and investigates possible solutions. Experimental results show that there is a link between the performance of NN-Descent and the phenomenon of hubness, defined as the tendency of intrinsically high-dimensional data to contain hubs-points with large in-degrees in the K-NNG. First, we explain how the presence of the hubness phenomenon causes bad NN-Descent performance. In light of that, we propose four NN-Descent variants to alleviate the observed negative inuence of hubs. By evaluating the proposed approaches on several real and synthetic data sets, we conclude that our approaches are more accurate, but often at the cost of higher scan rates.

K-nearest neighbor graphs (K-NNGs) are used in many data-mining and machine-learning algorithms. Naive construction of K-NNGs has a complexity of O(n2), which could be a problem for large-scale data sets. In order to achieve higher efficiency, many exact and approximate algorithms have been developed, including the NN-Descent algorithm of Dong, Charikar and Li. Empirical evidence suggests that the practical complexity of this algorithm is in O(n1.14), which is a significant improvement over brute force construction. However, NN-Descent has a major drawback --- it produces good results only on data of low intrinsic dimensionality. This paper presents an experimental analysis of this behavior, and investigates possible solutions. We link the quality of performance of NN-Descent with the phenomenon of hubness, defined as the tendency of intrinsically high-dimensional data to contain hubs --- points with high in-degrees in the K-NNG. We propose two approaches to alleviate the observed negative influence of hubs on NN-Descent performance.

The authors of this report met on 28-30 March 2018 at the New Jersey Institute of Technology, Newark, New Jersey, for a 3-day workshop that brought together a group of data providers, expert modelers, and computer and data scientists, in the solar discipline. Their objective was to identify challenges in the path towards building an effective framework to achieve transformative advances in the understanding and forecasting of the Sun-Earth system from the upper convection zone of the Sun to the Earth's magnetosphere. The workshop aimed to develop a research roadmap that targets the scientific challenge of coupling observations and modeling with emerging data-science research to extract knowledge from the large volumes of data (observed and simulated) while stimulating computer science with new research applications. The desire among the attendees was to promote future trans-disciplinary collaborations and identify areas of convergence across disciplines. The workshop combined a set of plenary sessions featuring invited introductory talks and workshop progress reports, interleaved with a set of breakout sessions focused on specific topics of interest. Each breakout group generated short documents, listing the challenges identified during their discussions in addition to possible ways of attacking them collectively. These documents were combined into this report-wherein a list of prioritized activities have been collated, shared and endorsed., Comment: Workshop Report

Research on audio-based music retrieval has primarily concentrated on refining audio features to improve search quality. However, much less work has been done on improving the time efficiency of music audio searches. Representing music audio documents in an indexable format provides a mechanism for achieving efficiency. To address this issue, in this work Exact Locality Sensitive Mapping (ELSM) is suggested to join the concatenated feature sets and soft hash values. On this basis we propose audio-based music indexing techniques, ELSM and Soft Locality Sensitive Hash (SoftLSH) using an optimized Feature Union (FU) set of extracted audio features. Two contributions are made here. First, the principle of similarity-invariance is applied in summarizing audio feature sequences and utilized in training semantic audio representations based on regression. Second, soft hash values are pre-calculated to help locate the searching range more accurately and improve collision probability among features similar to each other. Our algorithms are implemented in a demonstration system to show how to retrieve and evaluate multi-version audio documents. Experimental evaluation over a real "multi-version" audio dataset confirms the practicality of ELSM and SoftLSH with FU and proves that our algorithms are effective for both multi-version detection (online query, one-query vs. multi-object) and same content detection (batch queries, multi-queries vs. one-object).

In content-based image retrieval, relevance feedback (RF) is a prominent method for reducing the semantic gap between the low-level features describing the content and the usually higher-level meaning of user's target. Recent RF methods are able to identify complex target classes after relatively few feedback iterations. However, because the computational complexity of such methods is linear in the size of the database, retrieval can be quite slow on very large databases. To address this scalability issue for active learning-based RF, we put forward a method that consists in the construction of an index in the feature space associated to a kernel function and in performing approximate kNN hyperplane queries with this feature space index. The experimental evaluation performed on two image databases show that a significant speedup can be achieved at the expense of a limited increase in the number of feedback rounds.

International audience; This report summarizes the presentations and discussions of the First International Workshop on Computer Vision meets Databases, or CVDB 2004, which was held in Paris, France, on June 13, 2004. The workshop was co-located with the 2004 ACM SIGMOD/PODS conferences and was attended by forty-two participants from all over the world.

Images like other multimedia data need to be described as it is difficult to grasp their semantics from the raw data. With the emergence of standards like MPEG-7, multimedia data will be increasingly produced together with some semantic descriptors. But a description of a multimedia data is just an interpretation, a point of view on the data and different interpretations can exist for the same multimedia data. In this paper we explore the use of view techniques to define and manage different points of view on images. Views have been widely used in relational database management systems to extend modeling capabilities, and to provide logical data independence. Since our image model is defined on an object-oriented model, we will first propose a powerful object-oriented mechanism based on the distinction between class and type. The object view is used in the image view definition. The image view mechanism exploits the separation of the physical representation in an image of a real world object from the real object itself to allow different interpretations of an image region. Finally we will discuss the implementation of the image view mechanisms on the existing object models.

National audience; This paper presents an ongoing work that aims at assisting video-protection agents in the search for particular video scenes of interest in transit network. The video-protection agent inputs a query in the form of date, time, location and a visual description of the scene. The query processing starts by selecting a set of cameras likely to have filmed the scene followed by an analysis of the video content obtained from these cameras. The main contribution of this paper is the innovative framework that is composed of: 1) a spatiotemporal filtering method based on a spatio-temporal modelling of the transit network and associated cameras, and 2) a content-based retrieval based method on visual features. The presented filtering framework is to be tested on real data acquired within a French National project in partnership with the French Interior Ministry and the French National Police. The project aims at setting up public demonstrators that will be used by researchers and commercials from the video-protection community.

International audience; With the proliferation of wireless communication devices integrating GPS technology,trajectory datasets are becomingmore and more available. The problems concerning the transmissionand the storage of such data have become prominent with the continuous increase in volume of thesedata. A few works in the field of moving object databases deal with spatio-temporal compression.However, these works only consider the case of objects moving freely in the space. In this paper, wetackle the problem of compressing trajectory data in road networks with deterministic error bounds.We analyze the limitations of the existing methods and data models for road network trajectorycompression. Then, we propose an extended data model and a network partitioning algorithm intolong paths to increase the compression rates for the same error bound.We integrate these proposalswith the state-of-the-art Douglas-Peucker compression algorithm to obtain a new technique tocompress road network trajectory data with deterministic error bounds. The extensive experimentalresults confirm the appropriateness of the proposed approach that exhibits compression rates close tothe ideal ones with respect to the employed Douglas-Peucker compression algorithm.

Solar flares are complicated physical phenomena that are observable in a broad range of the electromagnetic spectrum, from radiowaves to $\gamma$-rays. For a more comprehensive understanding of flares, it is necessary to perform a combined multi-wavelength analysis using observations from many satellites and ground-based observatories. For efficient data search, integration of different flare lists and representation of observational data, we have developed an Interactive Multi-Instrument Database of Solar Flares (https://solarflare.njit.edu/). The web accessible database is fully functional and allows the user to search for uniquely-identified flare events based on their physical descriptors and availability of observations by a particular set of instruments. Currently, the data from three primary flare lists (GOES, RHESSI and HEK) and a variety of other event catalogs (Hinode, Fermi GBM, Konus-Wind, OVSA flare catalogs, CACTus CME catalog, Filament eruption catalog) and observing logs (IRIS and Nobeyama coverage) are integrated, and an additional set of physical descriptors (temperature and emission measure) is provided along with an observing summary, data links, and multi-wavelength light curves for each flare event since January, 2002. We envision that this new tool will allow researchers to significantly speed up the search of events of interest for statistical and case studies., Comment: 28 pages, 3 figures, 2 tables

Currently, more than 80 % of the information exchanged on the Web carry personal data and are primarily of multimedia nature (video, audio, images, etc.). Two main reasons are behind this phenomenon: 1) each of the major players on the Internet (individual users, companies, local and/ or regional authorities, etc.) is both data producer and data consumer at the same time, and 2) the use of various tools ofWeb 2.0 (blogs, social networks, etc.) allows to ease sharing, accessing, and publishing information on the Web. While such tools provide users many easy-to-use functionalities, several issues, however, remain unaddressed. For instance, how to automate the processes of annotation and description of some photos using the annotations/descriptions provided by the user friends on his/her blog/wiki/social network? How to provide the user with more effective and expressive means to multimedia information retrieval? How to protect a multimedia data repository (e.g., photo album) while several related information about the same content is already published (by some user friends) on the same or other blog/wiki/social network? This special issue seeks to assess the current approaches and technologies, as well as to outline the major challenges and future perspectives, related to the use ofWeb 2.0 in providing automatic annotation and easing retrieval and access control of multimedia data. It aims to provide an overview of the state of the art and future directions in this field, by including interdisciplinary contributions from various research groups. All the papers we received for this special issue have been submitted to an independent peer reviewing and reviewed by at least three experts.

In this paper we study the problem of detecting and grouping multi-variant audio tracks in large audio datasets. To address this issue, a fast and reliable retrieval method is necessary. But reliability requires elaborate representations of audio content, which challenges fast retrieval by similarity from a large audio database. To find a better tradeoff between retrieval quality and efficiency, we put forward an approach relying on local summarization and multi-level Locality-Sensitive Hashing (LSH). More precisely, each audio track is divided into multiple Continuously Correlated Periods (CCP) of variable length according to spectral similarity. The description for each CCP is calculated based on its Weighted Mean Chroma (WMC). A track is thus represented as a sequence of WMCs. Then, an adapted two-level LSH is employed for efficiently delineating a narrow relevant search region. The "coarse" hashing level restricts search to items having a non-negligible similarity to the query. The subsequent, "refined" level only returns items showing a much higher similarity. Experimental evaluations performed on a real multi-variant audio dataset confirm that our approach supports fast and reliable retrieval of audio track variants.

Complex multimedia queries, aiming to retrieve from large databases those objects that best match the query specification, are usually processed by splitting them into a set of m simpler sub-queries, each dealing with only some of the query features. To determine which are the overall best-matching objects, a rule is then needed to integrate the results of such sub-queries, i.e., how to globally rank the m-dimensional vectors of matching degrees, or partial scores, that objects obtain on the m sub-queries. It is a fact that state-of-the-art approaches all adopt as integration rule a scoring function, such as weighted average, that aggregates the m partial scores into an overall (numerical) similarity score, so that objects can be linearly ordered and only the highest scored ones returned to the user. This choice however forces the system to compromise between the different sub-queries and can easily lead to miss relevant results. In this paper we explore the potentialities of a more general approach, based on the use of qualitative preferences, able to define arbitrary partial (rather than only linear) orders on database objects, so that a larger flexibility is gained in shaping what the user is looking for. For the purpose of efficient evaluation, we propose two integration algorithms able to work with any (monotone) partial order (thus also with scoring functions): MPO, which delivers objects one layer of the partial order at a time, and iMPO, which can incrementally return one object at a time, thus also suitable for processing top k queries. Our analysis demonstrates that using qualitative preferences pays off. In particular, using Skyline and Region-prioritized Skyline preferences for queries on a real image database, we show that the results we get have a precision comparable to that obtainable using scoring functions, yet they are obtained much faster, saving up to about 70% database accesses.

The recent proliferation of XML-based standards and technologies for managing data on the Web demonstrates the need for effective and efficient management of tree-structured data. Querying tree-structured data is a challenging issue due to the diversity of the structural aspect in the same or in different trees. In this paper, we show how to evaluate queries on tree-structured data, called value trees. The formulation of these queries does not depend on the structure of a particular value tree. Our approach exploits semantic information provided by dimension graphs. Dimension graphs are semantically rich constructs that abstract the structural information of the value trees. We show how dimension graphs can be used to query efficiently value trees in the presence of structural differences and irregularities. Value trees and their dimension graphs are represented as XML documents. We present a method for transforming queries to XPath expressions to be evaluated on the XML documents. We also provide conditions for identifying strongly and weakly unsatisfiable queries. Finally, we conducted various experiments to compare our method for evaluating queries with one that does not exploit dimension graphs. Our results demonstrate the superiority of our approach.

Many analytical or computational applications especially legacy systems create documents and display screens in response to user queries "dynamically" or in "real time". These "virtual documents" do not exist in advance and thus hypermedia features must be generated "just in time" - automatically and dynamically. Additionally the hypermedia features may have to cause target documents to be generated or re-generated. This paper focuses on the specific challenges faced in hypermedia support for virtual documents of dynamic hypermedia functionality dynamic regeneration and dynamic anchor re-identification and re-location. It presents a prototype called JHE (Just-in-time Hypermedia Engine) to support just-in-time hypermedia across third party applications with dynamic content and discusses issues prompted by this research.

Multimedia data are now available to a variety of users ranging from naive to sophisticated. To make querying easy, visual query languages have been proposed. Most of these languages have a low expressive power and have their own query processors. Efforts have been made to design query languages with proper semantics to facilitate query optimization and processing in existing database systems. The majority of multimedia database systems are built on top of object or object-relational database systems with the underlying query facilities inherited. The DISIMA system is being built on top of a commercial OODBMS and we have chosen to extend the standard object oriented query language OQL with some multimedia functionalities. The resulting language is called MOQL. This paper presents VisualMOQL, a visual query language implementing the image component of MOQL.

Spatial joins are join operations that involve spatial data types and operators. Spatial access methods are often used to speed up the computation of spatial joins. This paper addresses the issue of benchmarking spatial join operations. For this purpose, we first present a WWW-based tool to produce sets of rectangles. Experimentators can use a standard Web browser to specify the number of rectangles, as well as the statistical distributions of their sizes, shapes, and locations. Second, using the rectangle generator and a well-defined set of statistical models we defined several test suites to compare the performance of three spatial join algorithms: nested loop, scan-and-index, and synchronized tree traversal. We also added a real-life data set from the Sequoia 2000 storage benchmark. Our results confirm that the use of spatial indices leads to performance gains of several orders of magnitude. The tests also show that highly selective join predicates enjoy greater performance gains (and vice versa). All of the statistical models and algorithms are available on the Web, which allows for easy verification and modification of our experiments.

In the last few years, we have witnessed an exponentially growing amount of social media data from social networks, mainly driven by the huge popularity of Web and the rapid advances of multimedia and Internet technologies. Different from conventional data types, social media data are multi-model in nature, including content such as images, audio, and videos, concept such as discussion topic, tag, and annotation, and context such as links, profile, timestamp, and click-through. All these content, concept and context are essential data sources for mining semantics, user intents, trends, knowledge, etc., from social media data, as well as enabling intelligent applications on social media services. This rich media type has raised many new research challenges, ranging from large-scale social media content analysis and mining, concept discovery and monitoring, context-based services, to many other exciting new opportunities. The aim of this special issue is to bring out state-of-the-art research in this area and discover directions for future research. The special issue will serve as a forum for recent advances in the field, with an emphasis on mining the “3 C” (Content, Concept and Context). It received an enthusiastic response. Among 19 submissions, 5 papers were selected after several rounds of rigorous review by the guest editors and the invited reviewers. World Wide Web (2012) 15:115–116 DOI 10.1007/s11280-011-0142-4

A view mechanism can help handle the complex semantics in emerging application areas such as image databases. This paper presents the view mechanism we defined for the DISIMA image database system. Since DISIMA is being developed on top of an object-oriented database system, we first propose a powerful object-oriented view mechanism based on the separation between types (interface functions) and classes that manage objects of the same type. The image view mechanism uses our object-oriented view mechanism to allow us to give different semantics to the same image. The solution is based on the distinction between physical salient objects which are interesting objects in an image and logical salient objects which are the meanings of these objects.

Since most multimedia database systems are built on top of object or object-relational database systems, they inherit the underlying query facilities. The approach we present in this paper is in two steps. The first step is to design a multimedia query language that will be used as an internal language. The second step is to define an equivalent visual query language and a translator to translate a visual query into a query in the internal query language.

We believe that the most important architectural features for the next generation web publishing systems will certainly be a semantic classification and a selective access to information. As the web is becoming the leading publication channel, the publishing systems should allow publishers to more effectively control the access to the information to be presented to the readers. The current 'share with all or share with friends only' model is limited, even when the access is restricted with a password, and is far from the way people share information in real life: what is said in private is not always what is said in public and what is referred to as public can further be categorised. The webograph prototype presented in the paper explores a restrictive publishing model, called the 'iceberg visibility model', based on dynamic views defined on RSS feed classes and reader groups.