scholarly journals A Learning Framework for Intelligent Selection of Software Verification Algorithms

2020 ◽  
Vol 2 (4) ◽  
pp. 177-187
Author(s):  
Weipeng Cao ◽  
Zhongwu Xie ◽  
Xiaofei Zhou ◽  
Zhiwu Xu ◽  
Cong Zhou ◽  
...  
Keyword(s):  
Software Verification ◽  
Learning Framework ◽  
Verification Algorithms ◽  
Selection Of

scholarly journals An Introduction to Intertask Transfer for Reinforcement Learning

AI Magazine ◽  
2011 ◽  
Vol 32 (1) ◽  
pp. 15 ◽  
Author(s):  
Matthew E. Taylor ◽  
Peter Stone
Keyword(s):  
Reinforcement Learning ◽  
Transfer Learning ◽  
Learning Problem ◽  
Open Problems ◽  
Learning Framework ◽  
Learning Domains ◽  
Multiple Tasks ◽  
Exciting Area ◽  
Generalize Information ◽  
Selection Of

Transfer learning has recently gained popularity due to the development of algorithms that can successfully generalize information across multiple tasks. This article focuses on transfer in the context of reinforcement learning domains, a general learning framework where an agent acts in an environment to maximize a reward signal. The goals of this article are to (1) familiarize readers with the transfer learning problem in reinforcement learning domains, (2) explain why the problem is both interesting and difficult, (3) present a selection of existing techniques that demonstrate different solutions, and (4) provide representative open problems in the hope of encouraging additional research in this exciting area.

scholarly journals Improving Remote Sensing Multiple Classification by Data and Ensemble Selection

2021 ◽  
Vol 87 (11) ◽  
pp. 841-852
Author(s):  
S. Boukir ◽  
L. Guo ◽  
N. Chehata
Keyword(s):  
Remote Sensing ◽  
Ensemble Learning ◽  
Remote Sensing Data ◽  
Training Data ◽  
Dramatic Improvement ◽  
Ensemble Classifiers ◽  
Learning Framework ◽  
Ensemble Selection ◽  
Multiple Classification ◽  
Selection Of

In this article, margin theory is exploited to design better ensemble classifiers for remote sensing data. A semi-supervised version of the ensemble margin is at the core of this work. Some major challenges in ensemble learning are investigated using this paradigm in the difficult context of land cover classification: selecting the most informative instances to form an appropriate training set, and selecting the best ensemble members. The main contribution of this work lies in the explicit use of the ensemble margin as a decision method to select training data and base classifiers in an ensemble learning framework. The selection of training data is achieved through an innovative iterative guided bagging algorithm exploiting low-margin instances. The overall classification accuracy is improved by up to 3%, with more dramatic improvement in per-class accuracy (up to 12%). The selection of ensemble base classifiers is achieved by an ordering-based ensemble-selection algorithm relying on an original margin-based criterion that also targets low-margin instances. This method reduces the complexity (ensemble size under 30) but maintains performance.

scholarly journals An Algorithm of Reinforcement Learning for Maneuvering Parameter Self-Tuning Applying in Satellite Cluster

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Xiao Wang ◽  
Peng Shi ◽  
Changxuan Wen ◽  
Yushan Zhao
Keyword(s):  
Reinforcement Learning ◽  
Traditional Method ◽  
Parameter Method ◽  
Empirical Parameter ◽  
Learning Framework ◽  
Self Tuning ◽  
Flight Parameters ◽  
Cluster A ◽  
Error Ellipsoid ◽  
Selection Of

Satellite cluster is a type of artificial cluster, which is attracting wide attention at present. Although the traditional empirical parameter method (TEPM) has the potential to deal with the mission of satellite flocking, it is difficult to select the proper parameters. In order to improve the flight effect in the problem of satellite cluster, as well as to make the selection of flight parameters more reasonable, the traditional sensing zones are improved. A 3σ position error ellipsoid and an induction ellipsoid are applied for substituting the traditional repulsing zone and attracting zone, respectively. Besides, we propose an algorithm of reinforcement learning for parameter self-tuning (RLPST), which is based on the actor-critic framework, to automatically learn the suitable flight parameters. To obtain the parameters in the repulsing zone, orientating zone, and attracting zone of each member in the cluster, a three-channel learning framework is designed. The learning process makes the framework finally find the suitable parameters. Numerical experimental results have shown the superiorities compared to the traditional method, which include trajectory deviation and sensing rate or terminal matching rate, as well as the improvement of the flight paths under the learning framework.

scholarly journals TOUCAN: a framework for fungal biosynthetic gene cluster discovery

2020 ◽  
Vol 2 (4) ◽  
Author(s):  
Hayda Almeida ◽  
Sylvester Palys ◽  
Adrian Tsang ◽  
Abdoulaye Baniré Diallo
Keyword(s):  
Gene Clusters ◽  
Biosynthetic Gene Cluster ◽  
Amino Acid Sequences ◽  
Genomic Diversity ◽  
Complex Task ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters ◽  
Learning Framework ◽  
Limited Scope ◽  
Selection Of

Abstract Fungal secondary metabolites (SMs) are an important source of numerous bioactive compounds largely applied in the pharmaceutical industry, as in the production of antibiotics and anticancer medications. The discovery of novel fungal SMs can potentially benefit human health. Identifying biosynthetic gene clusters (BGCs) involved in the biosynthesis of SMs can be a costly and complex task, especially due to the genomic diversity of fungal BGCs. Previous studies on fungal BGC discovery present limited scope and can restrict the discovery of new BGCs. In this work, we introduce TOUCAN, a supervised learning framework for fungal BGC discovery. Unlike previous methods, TOUCAN is capable of predicting BGCs on amino acid sequences, facilitating its use on newly sequenced and not yet curated data. It relies on three main pillars: rigorous selection of datasets by BGC experts; combination of functional, evolutionary and compositional features coupled with outperforming classifiers; and robust post-processing methods. TOUCAN best-performing model yields 0.982 F-measure on BGC regions in the Aspergillus niger genome. Overall results show that TOUCAN outperforms previous approaches. TOUCAN focuses on fungal BGCs but can be easily adapted to expand its scope to process other species or include new features.

scholarly journals Abstracting Strings for Model Checking of C Programs

2020 ◽  
Vol 10 (21) ◽  
pp. 7853
Author(s):  
Henrich Lauko ◽  
Martina Olliaro ◽  
Agostino Cortesi ◽  
Petr Roc̆kai
Keyword(s):  
Programming Language ◽  
Software Verification ◽  
Model Checker ◽  
Shape Information ◽  
C Programs ◽  
C Programming Language ◽  
C Programming ◽  
Array Structure ◽  
Two Parameters ◽  
Selection Of

Data type abstraction plays a crucial role in software verification. In this paper, we introduce a domain for abstracting strings in the C programming language, where strings are managed as null-terminated arrays of characters. The new domain M-String is parametrized on an index (bound) domain and a character domain. By means of these different constituent domains, M-Strings captures shape information on the array structure as well as value information on the characters occurring in the string. By tuning these two parameters, M-String can be easily tailored for specific verification tasks, balancing precision against complexity. The concrete and the abstract semantics of basic operations on strings are carefully formalized, and soundness proofs are fully detailed. Moreover, for a selection of functions contained in the standard C library, we provide the semantics for character access and update, enabling an automatic lifting of arbitrary string-manipulating code into our new domain. An implementation of abstract operations is provided within a tool that automatically lifts existing programs into the M-String domain along with an explicit-state model checker. The accuracy of the proposed domain is experimentally evaluated on real-case test programs, showing that M-String can efficiently detect real-world bugs as well as to prove that program does not contain them after they are fixed.

Semi-supervised learning with summary statistics

2019 ◽  
Vol 17 (05) ◽  
pp. 837-851
Author(s):  
Huihui Qin ◽  
Xin Guo
Keyword(s):  
Summary Statistics ◽  
Privacy Concerns ◽  
Optimal Rate Of Convergence ◽  
Learning Framework ◽  
Regression Learning ◽  
Feature Based ◽  
Extensive Collection ◽  
Single Data ◽  
Different Sources ◽  
Selection Of

Nowadays, the extensive collection and analyzing of data is stimulating widespread privacy concerns, and therefore is increasing tensions between the potential sources of data and researchers. A privacy-friendly learning framework can help to ease the tensions, and to free up more data for research. We propose a new algorithm, LESS (Learning with Empirical feature-based Summary statistics from Semi-supervised data), which uses only summary statistics instead of raw data for regression learning. The selection of empirical features serves as a trade-off between prediction precision and the protection of privacy. We show that LESS achieves the minimax optimal rate of convergence in terms of the size of the labeled sample. LESS extends naturally to the applications where data are separately held by different sources. Compared with the existing literature on distributed learning, LESS removes the restriction of minimum sample size on single data sources.

Proposing a Framework for Mobile Applications in Disaster Health Learning

2017 ◽  
Vol 11 (4) ◽  
pp. 487-495
Author(s):  
Alexander G. Liu ◽  
Brian A. Altman ◽  
Kenneth Schor ◽  
Kandra Strauss-Riggs ◽  
Tracy N. Thomas ◽  
...  
Keyword(s):  
Mobile Applications ◽  
Mobile Apps ◽  
Learning Context ◽  
Self Monitoring ◽  
Learning Framework ◽  
Current State ◽  
Medical Student Learning ◽  
Health Field ◽  
Final Selection ◽  
Selection Of

AbstractMobile applications, or apps, have gained widespread use with the advent of modern smartphone technologies. Previous research has been conducted in the use of mobile devices for learning. However, there is decidedly less research into the use of mobile apps for health learning (eg, patient self-monitoring, medical student learning). This deficiency in research on using apps in a learning context is especially severe in the disaster health field. The objectives of this article were to provide an overview of the current state of disaster health apps being used for learning, to situate the use of apps in a health learning context, and to adapt a learning framework for the use of mobile apps in the disaster health field. A systematic literature review was conducted by using the PRISMA checklist, and peer-reviewed articles found through the PubMed and CINAHL databases were examined. This resulted in 107 nonduplicative articles, which underwent a 3-phase review, culminating in a final selection of 17 articles. While several learning models were identified, none were sufficient as an app learning framework for the field. Therefore, we propose a learning framework to inform the use of mobile apps in disaster health learning. (Disaster Med Public Health Preparedness. 2017;11:487–495)

scholarly journals DeepHINT: Understanding HIV-1 integration via deep learning with attention

2018 ◽  
Author(s):  
Hailin Hu ◽  
An Xiao ◽  
Sai Zhang ◽  
Yangyang Li ◽  
Xuanling Shi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Dna Sequences ◽  
Mechanistic Study ◽  
Sequence Information ◽  
Genomic Context ◽  
Learning Framework ◽  
Integration Sites ◽  
Hiv Integration ◽  
Hiv 1 ◽  
Selection Of

AbstractMotivationHuman immunodeficiency virus type 1 (HIV-1) genome integration is closely related to clinical latency and viral rebound. In addition to human DNA sequences that directly interact with the integration machinery, the selection of HIV integration sites has also been shown to depend on the heterogeneous genomic context around a large region, which greatly hinders the prediction and mechanistic studies of HIV integration.ResultsWe have developed an attention-based deep learning framework, named DeepHINT, to simultaneously provide accurate prediction of HIV integration sites and mechanistic explanations of the detected sites. Extensive tests on a high-density HIV integration site dataset showed that DeepHINT can outperform conventional modeling strategies by automatically learning the genomic context of HIV integration solely from primary DNA sequence information. Systematic analyses on diverse known factors of HIV integration further validated the biological relevance of the prediction result. More importantly, in-depth analyses of the attention values output by DeepHINT revealed intriguing mechanistic implications in the selection of HIV integration sites, including potential roles of several basic helix-loop-helix (bHLH) transcription factors and zinc-finger proteins. These results established DeepHINT as an effective and explainable deep learning framework for the prediction and mechanistic study of HIV integration.AvailabilityDeepHINT is available as an open-source software and can be downloaded fromhttps://github.com/nonnerdling/[email protected]@tsinghua.edu.cn

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