7,536 research outputs found
Discrete Subspace Multiwindow Gabor Frames and Their Duals
This paper addresses discrete subspace multiwindow Gabor analysis. Such a scenario can model many practical signals and has potential applications in signal processing. In this paper, using a suitable Zak transform matrix we characterize discrete subspace mixed multi-window Gabor frames (Riesz bases and orthonormal bases) and their duals with Gabor structure. From this characterization, we can easily obtain frames by designing Zak transform matrices. In particular, for usual multi-window Gabor frames (i.e., all windows have the same time-frequency shifts), we characterize the uniqueness of Gabor dual of type I (type II) and also give a class of examples of Gabor frames and an explicit expression of their Gabor duals of type I (type II)
Healthy or Not: A Way to Predict Ecosystem Health in GitHub
With the development of open source community, through the interaction of developers, the collaborative development of software, and the sharing of software tools, the formation of open source software ecosystem has matured. Natural ecosystems provide ecological services on which human beings depend. Maintaining a healthy natural ecosystem is a necessity for the sustainable development of mankind. Similarly, maintaining a healthy ecosystem of open source software is also a prerequisite for the sustainable development of open source communities, such as GitHub. This paper takes GitHub as an example to analyze the health condition of open source ecosystem and, also, it is a research area in Symmetry. Firstly, the paper presents the healthy definition of GitHub open source ecosystem health and, then, according to the main components of natural ecosystem health, the paper proposes the health indicators and health indicators evaluation method. Based on the above, the GitHub ecosystem health prediction method is proposed. By analyzing the projects and data collected in GitHub, it is found that, using the proposed evaluation indicators and method, we can analyze the healthy development trend of the GitHub ecosystem and contribute to the stability of ecosystem development
Empirical research on the evaluation model and method of sustainability of the open source ecosystem
The development of open source brings new thinking and production modes to software engineering and computer science, and establishes a software development method and ecological environment in which groups participate. Regardless of investors, developers, participants, and managers, they are most concerned about whether the Open Source Ecosystem can be sustainable to ensure that the ecosystem they choose will serve users for a long time. Moreover, the most important quality of the software ecosystem is sustainability, and it is also a research area in Symmetry. Therefore, it is significant to assess the sustainability of the Open Source Ecosystem. However, the current measurement of the sustainability of the Open Source Ecosystem lacks universal measurement indicators, as well as a method and a model. Therefore, this paper constructs an Evaluation Indicators System, which consists of three levels: The target level, the guideline level and the evaluation level, and takes openness, stability, activity, and extensibility as measurement indicators. On this basis, a weight calculation method, based on information contribution values and a Sustainability Assessment Model, is proposed. The models and methods are used to analyze the factors affecting the sustainability of Stack Overflow (SO) ecosystem. Through the analysis, we find that every indicator in the SO ecosystem is partaking in different development trends. The development trend of a single indicator does not represent the sustainable development trend of the whole ecosystem. It is necessary to consider all of the indicators to judge that ecosystem’s sustainability. The research on the sustainability of the Open Source Ecosystem is helpful for judging software health, measuring development efficiency and adjusting organizational structure. It also provides a reference for researchers who study the sustainability of software engineering
LSCD: A Large-Scale Screen Content Dataset for Video Compression
Multimedia compression allows us to watch videos, see pictures and hear
sounds within a limited bandwidth, which helps the flourish of the internet.
During the past decades, multimedia compression has achieved great success
using hand-craft features and systems. With the development of artificial
intelligence and video compression, there emerges a lot of research work
related to using the neural network on the video compression task to get rid of
the complicated system. Not only producing the advanced algorithms, but
researchers also spread the compression to different content, such as User
Generated Content(UGC). With the rapid development of mobile devices, screen
content videos become an important part of multimedia data. In contrast, we
find community lacks a large-scale dataset for screen content video
compression, which impedes the fast development of the corresponding
learning-based algorithms. In order to fulfill this blank and accelerate the
research of this special type of videos, we propose the Large-scale Screen
Content Dataset(LSCD), which contains 714 source sequences. Meanwhile, we
provide the analysis of the proposed dataset to show some features of screen
content videos, which will help researchers have a better understanding of how
to explore new algorithms. Besides collecting and post-processing the data to
organize the dataset, we also provide a benchmark containing the performance of
both traditional codec and learning-based methods
Loop Restricted Existential Rules and First-order Rewritability for Query Answering
In ontology-based data access (OBDA), the classical database is enhanced with
an ontology in the form of logical assertions generating new intensional
knowledge. A powerful form of such logical assertions is the tuple-generating
dependencies (TGDs), also called existential rules, where Horn rules are
extended by allowing existential quantifiers to appear in the rule heads. In
this paper we introduce a new language called loop restricted (LR) TGDs
(existential rules), which are TGDs with certain restrictions on the loops
embedded in the underlying rule set. We study the complexity of this new
language. We show that the conjunctive query answering (CQA) under the LR TGDs
is decid- able. In particular, we prove that this language satisfies the
so-called bounded derivation-depth prop- erty (BDDP), which implies that the
CQA is first-order rewritable, and its data complexity is in AC0 . We also
prove that the combined complexity of the CQA is EXPTIME complete, while the
language membership is PSPACE complete. Then we extend the LR TGDs language to
the generalised loop restricted (GLR) TGDs language, and prove that this class
of TGDs still remains to be first-order rewritable and properly contains most
of other first-order rewritable TGDs classes discovered in the literature so
far.Comment: Full paper version of extended abstrac
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