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30th Benelux Conference on Artificial Intelligence, BNAIC 2018

作者： Sironi, Chiara F. Winands, Mark H.M. Games and AI Group Department of Data Science and Knowledge Engineering Maastricht University Netherlands

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Interactive visual labelling versus active learning:an experimental comparison

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Frontiers of Information Technology & Electronic engineering 2020年第4期21卷 524-535页

作者： Mohammad CHEGINI Jurgen BERNARD Jian CUI Fatemeh CHEGINI Alexei SOURIN Keith ANDREWS Tobias SCHRECK Institute of Computer Graphics and Knowledge Visualisation Graz University of TechnologyGraz 8010Austria School of Computer Science and Engineering Nanyang Technological UniversitySingapore 639798Singapore InfoVis Group University of British ColumbiaVancouver V6T1Z4Canada Max Planck Institute for Meteorology Hamburg 20146Germany Institute of Interactive Systems and Data Science Graz University of TechnologyGraz 8010Austria

Methods from supervised machine learning allow the classification of new data automatically and are tremendously helpful for data *** quality of supervised maching learning depends not only on the type of algorithm used,but also on the quality of the labelled dataset used to train the *** instances in a training dataset is often done manually relying on selections and annotations by expert analysts,and is often a tedious and time-consuming *** learning algorithms can automatically determine a subset of data instances for which labels would provide useful input to the learning *** visual labelling techniques are a promising alternative,providing effective visual overviews from which an analyst can simultaneously explore data records and select items to a *** putting the analyst in the loop,higher accuracy can be achieved in the resulting *** initial results of interactive visual labelling techniques are promising in the sense that user labelling can improve supervised learning,many aspects of these techniques are still largely *** paper presents a study conducted using the mVis tool to compare three interactive visualisations,similarity map,scatterplot matrix(SPLOM),and parallel coordinates,with each other and with active learning for the purpose of labelling a multivariate *** results show that all three interactive visual labelling techniques surpass active learning algorithms in terms of classifier accuracy,and that users subjectively prefer the similarity map over SPLOM and parallel coordinates for *** also employ different labelling strategies depending on the visualisation used.

关键词： Interactive visual labelling Active learning Visual analytics

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A deep belief network-based method to identify proteomic risk markers for Alzheimer’s disease

arXiv

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arXiv 2020年

作者： An, Ning Jin, Liuqi Ding, Huitong Yang, Jiaoyun Yuan, Jing Key Laboratory of Knowledge Engineering Big Data of Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Neurology Peking Union Medical College Hospital Chinese Academy of Medical Sciences Beijing China

While a large body of research has formally identified apolipoprotein E (APOE) as a major genetic risk marker for Alzheimer’s disease, accumulating evidence supports the notion that other risk markers may exist. The traditional Alzheimer’s-specific signature analysis methods, however, have not been able to make full use of rich protein expression data, especially the interaction between attributes. This paper develops a novel feature selection method to identify pathogenic factors of Alzheimer’s disease using the proteomic and clinical data. This approach has taken the weights of network nodes as the importance order of signaling protein expression values. After generating and evaluating the candidate subset, the method helps to select an optimal subset of proteins that achieved an accuracy greater than 90%, which is superior to traditional machine learning methods for clinical Alzheimer’s disease diagnosis. Besides identifying a proteomic risk marker and further reinforce the link between metabolic risk factors and Alzheimer’s disease, this paper also suggests that apidonectin-linked pathways are a possible therapeutic drug target. Copyright © 2020, The Authors. All rights reserved.

关键词： Feature extraction

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Unsupervised vehicle re-identification with progressive adaptation

arXiv

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arXiv 2020年

作者： Peng, Jinjia Wang, Yang Wang, Huibing Zhang, Zhao Fu, Xianping Wang, Meng College of Information and Science Technology Dalian Maritime University LiaoningDalian China Pengcheng Laboratory ShenzhenGuangdong China Key Laboratory of Knowledge Engineering with Big Data Ministry of education Hefei University of Technology China School of Computer Science and Information Engineering Hefei University of Technology China

Vehicle re-identification (reID) aims at identifying vehicles across different non-overlapping cameras views. The existing methods heavily relied on well-labeled datasets for ideal performance, which inevitably causes fateful drop due to the severe domain bias between the training domain and the real-world scenes;worse still, these approaches required full annotations, which is labor-consuming. To tackle these challenges, we propose a novel progressive adaptation learning method for vehicle reID, named PAL, which infers from the abundant data without annotations. For PAL, a data adaptation module is employed for source domain, which generates the images with similar data distribution to unlabeled target domain as "pseudo target samples". These pseudo samples are combined with the unlabeled samples that are selected by a dynamic sampling strategy to make training faster. We further proposed a weighted label smoothing (WLS) loss, which considers the similarity between samples with different clusters to balance the confidence of pseudo labels. Comprehensive experimental results validate the advantages of PAL on both VehicleID and VeRi-776 dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Vehicles

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Discovering implicational knowledge in wikidata

arXiv

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arXiv 2019年

作者： Hanika, Tom Marx, Maximilian Stumme, Gerd Knowledge and Data Engineering Group University of Kassel Germany ITeG University of Kassel Germany TU Dresden Germany

knowledge graphs have recently become the state-of-The-Art tool for representing the diverse and complex knowledge of the world. Ex-amples include the proprietary knowledge graphs of companies such as Google, Facebook, IBM, or Microsoft, but also freely available ones such as YAGO, DBpedia, and Wikidata. A distinguishing feature of Wikidata is that the knowledge is collaboratively edited and curated. While this greatly enhances the scope of Wikidata, it also makes it impossible for a single individual to grasp complex connections between properties or understand the global impact of edits in the graph. We apply Formal Concept Analysis to efficiently identify comprehensible implications that are implicitly present in the data. Although the complex structure of data modelling inWikidata is not amenable to a direct approach, we overcome this limitation by extracting contextual representations of parts of Wiki-data in a systematic fashion. We demonstrate the practical feasibility of our approach through several experiments and show that the results may lead to the discovery of interesting implicational knowledge. Besides providing a method for obtaining large real-world data sets for FCA, we sketch potential applications in offering semantic assistance for editing and curating Wikidata.68T30 03G10 68T27 Copyright © 2019, The Authors. All rights reserved.

关键词： Formal concept analysis

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Molecular dynamics simulation of thermal behavior of ammonia refrigerant in the presence of copper nanoparticles

International Journal of Thermofluids

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International Journal of Thermofluids 2025年 28卷

作者： Hawzhen Fateh M. Ameen Ali B.M. Ali Ahmed Shawqi Sadeq Narinderjit Singh Sawaran Singh Soheil Salahshour Sh. Baghaei Department of Petroleum Technology Erbil Technology College Erbil Polytechnic University Erbil Kurdistan Region Iraq Department of Petroleum Engineering Knowledge University Erbil Iraq Air Conditioning Engineering Department College of Engineering University of Warith Al-Anbiyaa Karbala Iraq Chemical Engineering Department University of Basrah Basrah Iraq Faculty of Data Science and Information Technology INTI International University Persiaran Perdana BBN Putra Nilai Nilai 71800 Malaysia Faculty of Engineering and Natural Sciences Istanbul Okan University Istanbul Turkey Faculty of Engineering and Natural Sciences Bahcesehir University Istanbul Turkey Research Center of Applied Mathematics Khazar University Baku Azerbaijan Fast Computing Center Shabihsazan Ati Pars Tehran Iran

Nanofluids are mixtures of a base fluid and nanoparticles (also known as nano-scaled particles), and they were used within advanced heat transfer applications with known aggregation issues as well as unreliability in performance. Molecular dynamics simulations can effectively look at nanofluid behavior with no disruptions, especially when considering the complications and limitations involved with performing experiments at the nano-scale. We conducted molecular dynamics simulations that investigate the thermal and atomic behaviors of a nanofluid, which involved ammonia nanofluids with copper nanoparticles in aluminum nanochannels. Our results focused on evaluating the outflow of the nanofluid and on determining the primary factors including maximum velocity, temperature heat flux and nanoparticle aggregation time while modifying the initial conditions of temperature (300-350 K), and pressure (1-5 bar). Furthermore, we found the thermophysical properties of the nanofluids were heavily dependent on the initial temperature and pressure. By improving the initial temperature and pressure, thermal systems can support the promotion of efficiency and sustainability. We also measured the kinetic and potential energies, with the potential energies measuring -8399.15 eV and 80.69 eV after 5 ns with no indications of structural instabilities. The results indicated that as the initial temperature was increased, maximum velocity increased from 0.00086 to 0.00099 Å/ps and maximum temperature increased from 240 to 258 K. Furthermore, heat flux decreased from 1411 to 1397 W/m² and aggregation time decreased from 3.96 to 3.93 ns. On the other hand, maximum velocity decreased to 0.00078 Å/ps and maximum temperature decreased to 234 K, as well as heat flux increased to 1436 W/m² and aggregation time increasing time was increased to 4.07 ns, with the increasing initial pressure. These results provided some insight into the optimization of nanofluids for energy conserving thermal control

关键词： Initial temperature Initial pressure Heat flux Nanofluids Thermal properties Aggregation time Maximum velocity

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MDPL-net: Multi-layer Dictionary Learning Network with Added Skip Dense Connections

MDPL-net: Multi-layer Dictionary Learning Network with Added...

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IEEE International Conference on data Mining (ICDM)

作者： Zhao Zhang Yulin Sun Zheng Zhang Yang Wang Lin Wu Meng Wang School of Computer Science and Technology Soochow University Suzhou China Ministry of Education Key Laboratory of Knowledge Engineering with Big Data School of Computer Science and Information Engineering Hefei University of Technology Hefei China Bio-Computing Research Center Harbin Institute of Technology (Shenzhen) Shenzhen China

ISBN: (数字)9781728183169

ISBN: (纸本)9781728183176

Dictionary learning (DL) is powerful for representation learning, while it fails to capture the deep hierarchical information hidden in data. In this paper, we propose a new generalized end-to-end mulita-layer representation learning architecture referred to as Multi-layer Dictionary Pair Learning Network (MDPL-net) for the deep sparse and hierarchical representation of images. To enable MDPL-net to conduct accurate classification, MDPL-net clearly integrates the skip connection end-to-end network and multi-layer deep sparse dictionary learning into a unified architecture. The representation learning module has several hidden DL blocks, where each hidden DL block has a dictionary pair learning (DPL) layer, a batch-norm layer and an activation function layer, and the DL blocks are connected in a feed-forward manner. To further improve the information flow and maintain the privileged features between different DL blocks, a novel skip dense connectivity pattern is deployed between hidden DL blocks, which can obtain more stable and discriminative features. The DPL layer jointly formulates the discriminative synthesis dictionary and analysis dictionary by minimizing reconstruction error within each batch over the feature maps from front layers. Extensive results on benchmark databases demonstrate the effectiveness of MDPL-net for discriminative representation and robust image classification.

关键词： Dictionaries databases Conferences Machine learning data mining Image reconstruction Image classification

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ReconResNet: Regularised residual learning for MR image reconstruction of undersampled cartesian and radial data

arXiv

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arXiv 2021年

作者： Chatterjee, Soumick Breitkopf, Mario Sarasaen, Chompunuch Yassin, Hadya Rose, Georg Nürnberger, Andreas Speck, Oliver Faculty of Computer Science Otto von Guericke University Magdeburg Germany Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Otto von Guericke University Magdeburg Biomedical Magnetic Resonance Germany Research Campus Stimulate Otto von Guericke University Magdeburg Germany Institute for Medical Engineering Otto von Guericke University Magdeburg Germany German Center for Neurodegenerative Disease Magdeburg Germany Center for Behavioral Brain Sciences Magdeburg Germany Leibniz Institute for Neurobiology Magdeburg Germany

MRI is an inherently slow process, which leads to long scan time for high-resolution imaging. The speed of acquisition can be increased by ignoring parts of the data (undersampling). Consequently, this leads to the degradation of image quality, such as loss of resolution or introduction of image artefacts. This work aims to reconstruct highly undersampled Cartesian or radial MR acquisitions, with better resolution and with less to no artefact compared to conventional techniques like compressed sensing. In recent times, deep learning has emerged as a very important area of research and has shown immense potential in solving inverse problems, e.g. MR image reconstruction. In this paper, a deep learning based MR image reconstruction framework is proposed, which includes a modified regularised version of ResNet as the network backbone to remove artefacts from the undersampled image, followed by data consistency steps that fusions the network output with the data already available from undersampled k-space in order to further improve reconstruction quality. The performance of this framework for various undersampling patterns has also been tested, and it has been observed that the framework is robust to deal with various sampling patterns, even when mixed together while training, and results in very high quality reconstruction, in terms of high SSIM (highest being 0.990±0.006 for acceleration factor of 3.5), while being compared with the fully sampled reconstruction. It has been shown that the proposed framework can successfully reconstruct even for an acceleration factor of 20 for Cartesian (0.968±0.005) and 17 for radially (0.962±0.012) sampled data. Furthermore, it has been shown that the framework preserves brain pathology during reconstruction while being trained on healthy subjects. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Unsupervised Cross-Lingual Word Embeddings Learning with Adversarial Training

Unsupervised Cross-Lingual Word Embeddings Learning with Adv...

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IEEE International Conference on Big knowledge (ICBK)

作者： Yuling Li Yuhong Zhang Peipei Li Xuegang Hu Key Laboratory of Knowledge Engineering with Big Data(Hefei University of Technology) Hefei University of Technology Hefei China

Recent works have managed to learn cross-lingual word embeddings (CLWEs) in an unsupervised manner. As a prominent unsupervised model, generative adversarial networks (GANs) have been heavily studied for unsupervised CLWEs learning by aligning the embedding spaces of different languages. Due to disturbing the embedding distribution, the embeddings of low-frequency words (LFEs) are usually treated as noises in the alignment process. To alleviate the impact of LFEs, existing GANs based models utilized a heuristic rule to aggressively sample the embeddings of high-frequency words (HFEs). However, such sampling rule lacks of theoretical support. In this paper, we propose a novel GANs based model to learn cross-lingual word embeddings without any parallel resource. To address the noise problem caused by the LFEs, some perturbations are injected into the LFEs for offsetting the distribution disturbance. In addition, a modified framework based on Cramér GAN is designed to train the perturbed LFEs and the HFEs jointly. Empirical evaluation on bilingual lexicon induction demonstrates that the proposed model outperforms the state-of-the-art GANs based model in several language pairs.

关键词： Gallium nitride Hafnium Generators Training Perturbation methods knowledge engineering Loss measurement

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Identifying Political Influencers on YouTube during the 2016 Moroccan General Election

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Procedia Computer Science 2020年 170卷 1102-1109页

作者： Zakia Acharoui Altaf Alaoui Badia Ettaki Jamal Zerouaoui Mohamed Dakkon Laboratory of Engineering Sciences and Modeling Faculty of Sciences- Ibn Tofail University Campus Universitaire BP 133 Kenitra Morocco Laboratory of Research in Computer Science Data Sciences and Knowledge Engineering School of Information Sciences Rabat Morocco Département de Statistique et Informatique de gestion F.S.E.J.S. Universite Abdelmalek Essaadi Tetouan Morocco

The use of social media as a tool for political and electoral communication is not fully explored by Moroccan researchers. This study aims to identify the influencers of the political conversation, on YouTube, during the period of the Moroccan election campaign of 2016 by using centrality measures, and discuss the nature of information shared by those influencers and their profile. The finding shows that digital media and vloggers tend to be central instead of politician elites and official media who were absent from the top influencer list.

关键词： Centrality measures Social Network Analysis Electoral campaign Gatekeepers Modularity

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