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Extended Pre-Processing Pipeline For Text Classification: On the Role of Meta-Features, Sparsification and Selective Sampling

Washington Cunha, Leonardo Rocha, Marcos A. Gonçalves
2021 Anais Estendidos do XXXVI Simpósio Brasileiro de Banco de Dados (SBBD Estendido 2021)   unpublished
This Master Thesis introduces three new steps into the traditional pre-processing phase: 1) Meta-Features Generation; 2) Sparsification; and 3) Selective Sampling.  ...  The pre-processing phase of these pipelines involves different ways of manipulating documents for the learning phase.  ...  Contributions The first contribution of this Master Thesis is the proposal of (distance-based) Meta-Feature (MF) generation as an explicit pre-processing step in a text classification pipeline.  ... 
doi:10.5753/sbbd_estendido.2021.18180 fatcat:szigdol42ncalk5optqyvjostu

Improving the quality of K-NN graphs through vector sparsification: application to image databases

Michael E. Houle, Xiguo Ma, Vincent Oria, Jichao Sun
2014 International Journal of Multimedia Information Retrieval  
Experimental results on several datasets are provided in this dissertation, to demonstrate the effectiveness of the proposed techniques for the local selection of features, and for the image applications  ...  ABSTRACT LOCAL SELECTION OF FEATURES AND ITS APPLICATIONS TO IMAGE SEARCH AND ANNOTATION by Jichao Sun In multimedia applications, direct representations of data objects typically involve hundreds or thousands  ...  The whole process will be repeated m times, where m is a user-specified positive number. ReliefF [Robnik-Sikonja and Kononenko 2003] extends Relief for multi-class classification scenarios.  ... 
doi:10.1007/s13735-014-0067-7 fatcat:2w3k3fcexjax5i6h42hqqcrtzi

Orchestrating the Development Lifecycle of Machine Learning-Based IoT Applications: A Taxonomy and Survey

Bin Qian, Jie Su, Zhenyu Wen, Devki Nandan Jha, Yinhao Li, Yu Guan, Deepak Puthal, Philip James, Renyu Yang, Albert Y. Zomaya, Omer Rana, Lizhe Wang (+2 others)
2020 ACM Computing Surveys  
Based on the specification and the available computing resources, the ML models are developed to meet the specified requirements while optimizing the training processes in terms of the cost of time and  ...  Consisting of multiple layers, DL is powerful for modeling complex non-linear relationships (between the input and output) and thus does not require the aforementioned heuristic (and expensive) feature  ...  Batching the received user queries optimizes throughput by fully utilizing the features of the pre-trained models, which is faster than processing one query at a time.  ... 
doi:10.1145/3398020 fatcat:zzgfcjxjxbhnhf53dmlo63rs3i

Orchestrating the Development Lifecycle of Machine Learning-Based IoT Applications: A Taxonomy and Survey [article]

Bin Qian, Jie Su, Zhenyu Wen, Devki Nandan Jha, Yinhao Li, Yu Guan, Deepak Puthal, Philip James, Renyu Yang, Albert Y. Zomaya, Omer Rana, Lizhe Wang (+2 others)
2020 arXiv   pre-print
This paper provides a comprehensive and systematic survey on the development lifecycle of ML-based IoT application.  ...  Hence, orchestrating ML pipelines that encompasses model training and implication involved in holistic development lifecycle of an IoT application often leads to complex system integration.  ...  Batching the received user queries optimizes throughput by fully utilizing the features of the pre-trained models, which is faster than processing one query at a time.  ... 
arXiv:1910.05433v5 fatcat:ffvjipmylve6feuzdbav2syxfu

Graph Neural Networks for Natural Language Processing: A Survey [article]

Lingfei Wu, Yu Chen, Kai Shen, Xiaojie Guo, Hanning Gao, Shucheng Li, Jian Pei, Bo Long
2021 arXiv   pre-print
To the best of our knowledge, this is the first comprehensive overview of Graph NeuralNetworks for Natural Language Processing.  ...  We propose a new taxonomy of GNNs for NLP, whichsystematically organizes existing research of GNNs for NLP along three axes: graph construction,graph representation learning, and graph based encoder-decoder  ...  Recall at k is often used in the next utterance selection task. 7.4 Text Classification Background and Motivation Traditional text classification methods heavily rely on feature engi- neering (e.g., BOW  ... 
arXiv:2106.06090v1 fatcat:zvkhinpcvzbmje4kjpwjs355qu

A Modular Benchmarking Infrastructure for High-Performance and Reproducible Deep Learning [article]

Tal Ben-Nun, Maciej Besta, Simon Huber, Alexandros Nikolaos Ziogas, Daniel Peter, Torsten Hoefler
2019 arXiv   pre-print
We introduce Deep500: the first customizable benchmarking infrastructure that enables fair comparison of the plethora of deep learning frameworks, algorithms, libraries, and techniques.  ...  Finally, as the first distributed and reproducible benchmarking system for deep learning, Deep500 provides software infrastructure to utilize the most powerful supercomputers for extreme-scale workloads  ...  The process of eval-uating a given operator for a given input data (referred to as a sample) is called inference.  ... 
arXiv:1901.10183v2 fatcat:egohwkvmavdehorgxtmj2rc6t4

A review of Federated Learning in Intrusion Detection Systems for IoT [article]

Aitor Belenguer, Javier Navaridas, Jose A. Pascual
2022 arXiv   pre-print
This paper focuses on the application of Federated Learning approaches in the field of Intrusion Detection.  ...  Finally, the paper highlights the limitations present in recent works and presents some future directions for this technology.  ...  ACKNOWLEDGMENTS This work is supported by the Basque Government (projects ELKARTEK21/89 and IT1244-19) and by the Spanish Ministry of Economy and Competitiveness MINECO (PID2019-104966GB-I00). Dr.  ... 
arXiv:2204.12443v2 fatcat:eodordo7b5hwpim4qpvr3dxhm4

Mid-level Representation for Visual Recognition [article]

Moin Nabi
2015 arXiv   pre-print
We investigate on discovering and learning a set of mid-level patches to be used for representing the images of an object category.  ...  Visual Recognition is one of the fundamental challenges in AI, where the goal is to understand the semantics of visual data.  ...  Object bank: A high-level image represen- tation for scene classification and semantic feature sparsification. NIPS 2010, 24. [95] D. G. Lowe.  ... 
arXiv:1512.07314v1 fatcat:knmhkwxqk5aczis7ce6g2sv2wm

Pretrained Transformers for Text Ranking: BERT and Beyond [article]

Jimmy Lin, Rodrigo Nogueira, Andrew Yates
2021 arXiv   pre-print
Although the most common formulation of text ranking is search, instances of the task can also be found in many natural language processing applications.  ...  The combination of transformers and self-supervised pretraining has been responsible for a paradigm shift in natural language processing (NLP), information retrieval (IR), and beyond.  ...  We'd like to thank the following people for comments on earlier drafts of this work: Chris Buckley, Danqi Chen, Maura Grossman, Sebastian Hofstätter, Kenton Lee, Sheng-Chieh Lin, Xueguang Ma, Bhaskar  ... 
arXiv:2010.06467v3 fatcat:obla6reejzemvlqhvgvj77fgoy

Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19

Hanan Farhat, George E. Sakr, Rima Kilany
2020 Machine Vision and Applications  
Yet, coronavirus can be the real trigger to open the route for fast integration of DL in hospitals and medical centers.  ...  It summarizes and discusses the current state-of-the-art approaches in this research domain, highlighting the challenges, especially with COVID-19 pandemic current situation.  ...  [159] delineated a pipeline of pre-processing techniques highlighting lung regions and extracting features using U-net and ResNet models.  ... 
doi:10.1007/s00138-020-01101-5 pmid:32834523 pmcid:PMC7386599 fatcat:tkkylrptc5hkpoj52hjs3kuttu

Deep Learning in Mobile and Wireless Networking: A Survey

Chaoyun Zhang, Paul Patras, Hamed Haddadi
2019 IEEE Communications Surveys and Tutorials  
The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure.  ...  We complete this survey by pinpointing current challenges and open future directions for research.  ...  After pre-processing and load-balancing (e.g.  ... 
doi:10.1109/comst.2019.2904897 fatcat:xmmrndjbsfdetpa5ef5e3v4xda

Deep Learning in Mobile and Wireless Networking: A Survey [article]

Chaoyun Zhang, Paul Patras, Hamed Haddadi
2019 arXiv   pre-print
The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure.  ...  We complete this survey by pinpointing current challenges and open future directions for research.  ...  This scheme learns temporal features and fuses representations extracted by all models for the final prediction.  ... 
arXiv:1803.04311v3 fatcat:awuvyviarvbr5kd5ilqndpfsde

Semi-supervised learning for scalable and robust visual search

Jun Wang
2011 ACM SIGMultimedia Records  
for the bivariate optimization procedure; c) novel applications of the proposed techniques, such as interactive image retrieval, automatic re-ranking for text based image search, and a brain computer  ...  However, most of the existing graphbased semi-supervised learning methods are sensitive to the graph construction process and the initial labels.  ...  After pre-processing, features are extracted from each sample. TAG does not dictate usage of specific features.  ... 
doi:10.1145/2069210.2069213 fatcat:hblb5ncrprcrlgi6ugph6naucy

Edge Intelligence: Architectures, Challenges, and Applications [article]

Dianlei Xu, Tong Li, Yong Li, Xiang Su, Sasu Tarkoma, Tao Jiang, Jon Crowcroft, Pan Hui
2020 arXiv   pre-print
We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems  ...  Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis in locations close to where data is captured based on artificial intelligence.  ...  The authors use edge server to pre-process raw data and extract key features.  ... 
arXiv:2003.12172v2 fatcat:xbrylsvb7bey5idirunacux6pe

Deep Neural Mobile Networking [article]

Chaoyun Zhang
2020 arXiv   pre-print
This makes monitoring and managing the multitude of network elements intractable with existing tools and impractical for traditional machine learning algorithms that rely on hand-crafted feature engineering  ...  performance requirements in terms of throughput, latency, and reliability.  ...  Specifically, for each input traffic flow, we gather the decisions of all NID models individually, and make the classification using a voting process.  ... 
arXiv:2011.05267v1 fatcat:yz2zp5hplzfy7h5kptmho7mbhe
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