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Optimal learning from verified training data 20

Optimal learning from verified training data

引用

Proceedings of the 34th international conference on Neural Information processing Systems

作者： Nicholas Bishop Enrico Gerding Long Tran-Thanh University of Southampton UK University of Warwick UK

ISBN: (纸本)9781713829546

Standard machine learning algorithms typically assume that data is sampled independently from the distribution of interest. In attempts to relax this assumption, fields such as adversarial learning typically assume that data is provided by an adversary, whose sole objective is to fool a learning algorithm. However, in reality, it is often the case that data comes from self-interested agents, with less malicious goals and intentions which lie somewhere between the two settings described above. To tackle this problem, we present a Stackelberg competition model for least squares regression, in which data is provided by agents who wish to achieve specific predictions for their data. Although the resulting optimisation problem is nonconvex, we derive an algorithm which converges globally, outperforming current approaches which only guarantee convergence to local optima. We also provide empirical results on two real-world datasets, the medical personal costs dataset and the red wine dataset, showcasing the performance of our algorithm relative to algorithms which are optimal under adversarial assumptions, outperforming the state of the art.

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Interpretable Spatiotemporal Deep Learning Model for Traffic Flow Prediction Based on Potential Energy Fields

Interpretable Spatiotemporal Deep Learning Model for Traffic...

引用

IEEE international conference on data Mining (ICDM)

作者： Jiahao Ji Jingyuan Wang Zhe Jiang Jingtian Ma Hu Zhang School of Computer Science and Engineering Beihang University Beijing China State Key Laboratory of Software Development Environment Beihang University Beijing China MOE Engineering Research Center of ACAT Beihang University Beijing China Department of Computer Science The University of Alabama Tuscaloosa Alabama USA

ISBN: (数字)9781728183169

ISBN: (纸本)9781728183176

Traffic flow prediction is of great importance in traffic management and public safety, but is challenging due to the complex spatial-temporal dependencies as well as temporal dynamics. Existing work either focuses on traditional statistical models, which have limited prediction accuracy, or relies on black-box deep learning models, which have superior prediction accuracy but are hard to interpret. In contrast, we propose a novel interpretable spatiotemporal deep learning model for traffic flow prediction. Our main idea is to model the physics of traffic flow through a number of latent Spatio-Temporal Potential Energy Fields (ST-PEFs), similar to water flow driven by the gravity field. We develop a Wind field Decomposition (WD) algorithm to decompose traffic flow into poly-tree components so that ST-PEFs can be established. We then design a spatiotemporal deep learning model for the ST-PEFs, which consists of a temporal component (modeling the temporal correlation) and a spatial component (modeling the spatial dependencies). To the best of our knowledge, this is the first work that make traffic flow prediction based on ST-PEFs. Experimental results on real-world traffic datasets show the effectiveness of our model compared to the existing methods. A case study confirms our model interpretability.

关键词： Deep learning Potential energy Predictive models Prediction algorithms data models Spatiotemporal phenomena Safety

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A Novel Weighted FP-Stream Algorithm for IoT data Streams

A Novel Weighted FP-Stream Algorithm for IoT Data Streams

引用

IEEE international conference on Big data

作者： Halil Ibrahim Dede Cemile Timurkaan Metehan Guzel Suat Ozdemir Gazi University Ankara Turkey Graduate School of Natural and Applied Sciences Gazi University Ankara Turkey Hacettepe University Ankara Turkey

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

The Internet of Things (IoT) is a technology that is being widely used in daily life. This technology makes it easier for devices to connect with each other. As a result of the high connectivity between devices, enormous volumes of data are being collected. Such data is called big streaming data which can be used to curate useful information by data mining techniques. One of the most used processing methods is called Frequent Itemset (Pattern) Mining (FIM) which detects recurring and common patterns over data streams. In this paper, a new algorithm based on frequently used FP-Stream algorithm is presented. The proposed algorithm enhances conventional FP-Stream algorithm to make it more adaptive to concept drifts when retaining its applicability to data streams. Conventional FP-Stream algorithms store all detected patterns. By adding weights during the pruning process based on pattern freshness, the proposed algorithm prioritizes newer patterns thereby learns new patterns and forgets older one swiftly. Performance evaluations are performed using data acquired from an IoT testbed established in KAVEM Lab of Gazi University. Evaluation results indicate that the proposed algorithm performs better than conventional FP-Stream significantly.

关键词： Performance evaluation Itemsets conferences Memory management Big data data mining Internet of Things

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Modelling and Mining Brain Network data for Diagnosis of Neurodegenerative Diseases

Modelling and Mining Brain Network Data for Diagnosis of Neu...

引用

IEEE international conference on Electronics, Computing and Communication Technologies (CONECCT)

作者： B. A. Madhushree Nandyala D. Gangadhar K. S. Prafulla Kumari Dept. of Computer Science and Engineering M. S. Ramaiah University of Applied Sciences Bengaluru India

ISBN: (数字)9781728168289

ISBN: (纸本)9781728168296

Connectomes are brain networks represented as a graph with the vertices being the regions of the brain and weighted edges representing strength of connections between the regions inferred from brain imaging techniques such a Functional MRI (fMRI). An intense research activity is to use the connectomes to identify markers for brain disorders, especially neuro-degenerative diseases such as Autism Spectrum Disorder (ASD) by studying the differences in the connectomes of healthy subjects and patients. This paper presents a novel data model for the connectome data and analyzes its efficacy in the classification of ASD and Typically Developing (TD) (healthy) connectomes. The proposed data modelling begins by clustering the vertices (brain regions) using the Graph Spectral Clustering into fixed number of clusters, the number of clusters chosen as four based on the empirical evidence. The resulting clustering is used to map the vertices into a binary matrix which is then converted into a binary row vector to form a vector space model of the connectome data that is employed to classify the connectome data. The developed model is first validated using Human Connectome Protocol (HCP) FMRI derived connectome data of 812 healthy patents. Binary data models of the UCLA Autism dataset with fMRI and DTI scans of 42 ASD and 37 TD subjects are generated and employed for their classification. Different classification algorithms are trained, tested and their performance evaluated using the resulting dataset. Cross Validation (CV) estimates identified the best performance (83% recall and 83% precision) for DTI data and (73% recall and 89% precision) for the fMRI data achieved using Logistic Regression.

关键词： data models Diffusion tensor imaging Functional magnetic resonance imaging Autism Brain modeling Logistics

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Chinese Word Segmentation in Electronic Medical Record Text via Graph Neural Network-Bidirectional LSTM-CRF Model

Chinese Word Segmentation in Electronic Medical Record Text ...

引用

IEEE international conference on Bioinformatics and Biomedicine (BIBM)

作者： Jinlian Du Wei Mi Xiaolin Du Beijing University of Technology Beijing China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Electronic medical record (EMR) text word segmentation is the basis of natural language processing in medicine. Due to the characteristics of EMR, such as strong specialization, high cost of annotation, special writing style and sustained growth of terminology, the current Chinese word segmentation (CWS) methods cannot fully meet the requirements of the application of EMR. In order to solve this problem, an EMR word segmentation model based on Graph Neural Network (GNN), bidirectional Long Short-Term Memory network (Bi-LSTM) and conditional random field (CRF) is designed in this paper to improve the segmentation effect and reduce the dependence on data set. In the model, GNN based on the domain lexicon is used to learn the local composition features, Bi-LSTM is used to capture the long-term dependence and context sequence information, and CRF is used to obtain the optimal annotation sequence based on the sentence level label information. Through multi-feature interaction, the ambiguity resolution and new word recognition in the EMR word segmentation are effectively carried out. Compared with CWS tools such as Jieba and Pkuseg, as well as baseline models and state-of-the-art methods, the precision and recall rate of the model in this paper have been significantly improved.

关键词： Feature extraction Aggregates Terminology data models Tools Deep learning Context modeling

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Representation of the OpenFlow Switch Flow Table

Representation of the OpenFlow Switch Flow Table

引用

international Science and Technology conference (Modern Networking Technologies) (MoNeTeC)

作者： Andrei Markoborodov Yuliya Skobtsova Dmitry Volkanov Lomonosov Moscow State University Moscow Russia

ISBN: (数字)9781728182681

ISBN: (纸本)9781728182698

The paper considers the OpenFlow 1.3 switch based on a programmable network processing unit (NPU). OpenFlow switch performs flow entry lookup in a flow table by the values of packet header fields to determine actions to apply to incoming packet (classification). In the considered NPU, lookup operation is based on the search trees. The trees are implemented in the NPU assembly language. But these trees cannot be directly used for OpenFlow classification because of compared operands width limitation. In this paper, we propose flow table representation designed for easy translation into NPU search trees. Another goal of our research was to create a compact program that fits in the NPU memory. Another NPU limitation requires program update after each flow table data modification. Consequently, the switch must maintain the currently installed flows of the flow tables to provide a fast NPU program update. We developed algorithms for incremental update of flow table representation (flow addition and removal). To evaluate the proposed flow table translation approach, we used patterns of flow tables operated from RUNOS OpenFlow controller. A set of flow tables based on the patterns was translated into NPU assembly language using a simple algorithm (based on related work) and an improved algorithm (our proposal). The evaluation was performed on the NPU simulation model and showed that our approach effectively reduces program size.

关键词： Switches data structures Protocols Registers Metadata Control systems Software algorithms

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Eyes speak out Mind: Deep models for Gaze-based Analysis of Bilingual And Monolingual Reading

Eyes speak out Mind: Deep models for Gaze-based Analysis of ...

引用

IEEE India conference (INDICON)

作者： Arka Prava Roy Soumen Kumar Koley Utpal Garain National Inst. of Technology Durgapur India Comp. Vis. & Patt. Recog. (CVPR) Unit Indian Statistical Institute Kolkata India CVPR Unit and Centre for AIML (CAIML) Indian Statistical Institute Kolkata India

ISBN: (数字)9781728169163

ISBN: (纸本)9781728169170

This paper presents an approach that attempts to explore effects of bilingualism in reading by analysis of eye-gaze data. As readers are more comfortable in reading first language (L1) compared to second language (L2), their eyes move differently in these two cases. Being motivated by this hypothesis, a deep learning model is developed to predict whether a reader is reading first or second language by analyzing the reading behavior given by the eye-tracking data. Exposure to different languages simultaneously may influence the proficiency in native language reading and this matter is also investigated by using deep learning model which can differentiate monolingual readers from bilingual readers analyzing their eye movement during reading in their respective native language. Experiments are conducted on GECO Corpus which provides eye-tracking data of 19 readers reading a novel in L1 and L2 along with eye-tracking data of several monolingual readers reading same text in their native language. Experimental results show that the proposed models are quite efficient in capturing the differences in eye gaze pattern for reading L1 and L2 for a reader as well as in identifying monolingual and bilingual readers by processing eye-gaze data.

关键词： Deep learning Analytical models Psychology Predictive models Feature extraction data models data mining

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FPGA-Accelerated Time Series Mining on Low-Power IoT Devices

FPGA-Accelerated Time Series Mining on Low-Power IoT Devices

引用

international conference on Application Specific Systems (ASAP), Architectures and Processors

作者： Seongyoung Kang Jinyeong Moon Sang-Woo Jun Department of Computer Science Kookmin University Department of Electrical and Computer Engineering Florida State University Department of Computer Science University of California Irvine

ISBN: (数字)9781728171470

ISBN: (纸本)9781728172798

We present a case for FPGA-accelerated edge processing for low-power Internet-of-Things (IoT) devices, using time series similarity search as a driving application. As the data collection capabilities of low-power IoT device increase, the primary constraint on their capacity is becoming the resource requirements of wirelessly transferring collected data to a central repository. This work presents a solution to this limitation by augmenting the IoT device with a inexpensive, power-efficient FPGA accelerator, which can perform fairly complex edge mining operations and drastically reduce the wireless data transfer requirements. This approach reduces the total power consumption of the device despite the added FPGA component, while also reducing the computation requirements at the central server. We use the Dynamic Time Warping (DTW) algorithm as an example workload. Using a low-cost Lattice iCE40 UltraPlus FPGA, we demonstrate that the FPGA-augmented mining algorithm can both support significantly higher data collection rate while improving the computation power efficiency of the entire deployment by an order of magnitude.

关键词： Field programmable gate arrays Time series analysis data mining Power demand Wireless communication Sensors Heuristic algorithms

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Chinese Sentence Pattern Feature Extraction Based on Massive data Analysis

Chinese Sentence Pattern Feature Extraction Based on Massive...

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IEEE international conference on Artificial Intelligence and Computer Applications (ICAICA)

作者： Zhengchen Cao Jingchang Pan Jiahao Li School of Mechanical Electrical & Information Engineering Shandong University Weihai China

ISBN: (数字)9781728170053

ISBN: (纸本)9781728170060

In the era of data Technology, the data is characterized by huge scale, modal diversity, and rapid growth. The worth of corpus related to Chinese is also increased by multiplication correspondingly. Based on one of the Chinese language processing systems called the Language Technology Platform (LTP), using the data Mining and the Machine Learning to extract and apply Chinese sentence features is a new perspective and entry point in the field of Chinese information processing. In this paper, the dependency grammar is selected for sentence pattern analysis, and the text representation model consisting of sequences and vectors is established. A specialized “Chinese Sentence Pattern Retrieve Library” including 1,032,480 sentences and 92,451 kinds of sentence patterns is built to provide a sentence pattern database service for more special sentence patterns studies. On the basis of this database, relevant statistics and preliminary analysis are made on the sentence patterns of different genres articles. It is found that there are about 2,000 core sentence patterns in Chinese and commonly used sentence patterns are relatively concentrated, with the frequency of the 10 sentence patterns with a higher frequency accounting for 50%. The proportion of some sentence patterns used in certain articles is much higher or lower than that in other articles. These researches achievements provide the basis for the establishment of the feature vectors of the sentence pattern in the article and offers a basis for feature extraction and application of articles in the later period.

关键词： Feature extraction databases Syntactics Grammar data models data mining

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Intelligent Computation to build a Novel Recommender of Products through (PageRank-Clustering and DgSpan- FBR)

Intelligent Computation to build a Novel Recommender of Prod...

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Decision Aid Sciences and Application (DASA), international conference on

作者： Samaher Al-Janabi Noor K. Faculty of Science for Women (SCIW) University of Babylon Babylon Iraq

ISBN: (数字)9781728196770

ISBN: (纸本)9781728196787

data is considered as one of the worthiest existent resources in the world. The fast development of technology provides us with huge/big data that needs deep analysis to understand and extraction useful information\knowledge from it. Thus, analysing such kind of data resembles a great challenge for researchers in the field of intelligent computation. The proposed model consists of three stages: the first stage is the collecting and preparing data (i.e., includes handle missing values and data convert into graphs). The second stage generates communities from graphs by developing one of the graph mining algorithms called (PageRank Clustering & Verification). Third stage includes building association patterns to the optimal clusters (subgraphs) and validating from it through developing one of the association pattern algorithms called Develop gSpan-Forward\ Backward Rules (DgSpan-FBR). The design model called Novel Recommender of products based on Graph Mining (NRGM) is distinguished by compressing the lost time of searching the best products that users need as well as increasing the accuracy of selecting of products. NRGM model is applied using on one of huge/big database have six parts different in number of samples, NRGM processed database to generated a set of communities through PageRank-Clustering. Then verification from these communities by apply two measures called (i.e., Modularity and Silhouette) to find optimal community. We test different number of communities (i.e., 2, 4, 6, 8,10 and 12) Then found two communities are the optimal where the Modularity of it is 0.679 while, silhouette is 0.699. Finally, a set of association patterns is found from the optimal community through both traditional association graph mining method called (gSpan) and developed method (DgSpan-FBR), where, the traditional method generates 3000 pattens for each community. While DgSpan-FBR generates 262 patterns. As a result, NRGM is consider as pragmatic model to deal with hug/big datas

关键词： databases data mining Clustering algorithms Buildings Social networking (online) data models Training

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