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Research on Business Incubator Evaluation System Based on Support Vector Machine and Machine Learning

Research on Business Incubator Evaluation System Based on Su...

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Computers, Information processing and Advanced Education (CIPAE), 2020 international conference on

作者： Doudou Yao School of Management Shanghai University Shanghai China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

This paper studies the evaluation system of business incubator based on support vector machine and machine learning. Firstly, in view of the complexity and multi-dimensional characteristics of incubator evaluation, an evaluation framework was proposed that comprehensively considered the operational efficiency, service quality, innovation ability and market performance of incubators. Then, by collecting a large amount of incubator data, support vector machine is used for feature extraction and classification modeling, and the evaluation system model is built. The results show that the model can accurately evaluate the comprehensive performance of incubators and effectively identify the advantages and disadvantages of incubator operation. Finally, the feasibility and effectiveness of the evaluation system were verified by practical cases, which provided scientific decision-making basis for incubator managers and investors and promoted the healthy development of the incubator industry.

关键词： Support vector machines Industries Technological innovation Machine learning Feature extraction data models Vectors Complexity theory Reliability Business

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Topical Grouping of Thousands of Biomimetics Articles According to Their Goals, Results and Methods 12th

Topical Grouping of Thousands of Biomimetics Articles Acco...

引用

Proceedings of the 12th international conference on Biomimetic and Biohybrid Systems, Living Machines 2023

作者： Carniel, Théophile Cazenille, Leo Dalle, Jean-Michel Halloy, José Université Paris Cité CNRS LIED UMR 8236 Paris75006 France Agoranov Paris France Sorbonne Université Paris France

ISBN: (纸本)9783031395031

Here we discuss the limitations of current topic modeling methods for scientific documents, which are often too coarse-grained to identify the specific themes of an article accurately. This is particularly challenging in interdisciplinary research, such as in the field of Living Machines, where scientific methods from different fields may be used to achieve a scientific goal. To address this problem, we extend previous methods and propose a new targeted topic modeling method that goes beyond a simple thematic classification. Our data processing relies on ChatGPT to extract information from abstracts of the corpus, which are then classified into thematic clusters using topic modeling algorithms. This approach adds clustering of the articles according to the scientific goal, the type of method used, and the type of results obtained. By using this technique, each document is associated with three targeted themes and one general theme, allowing for more fine-grained clustering. We demonstrate the effectiveness of this method in biomimetics with a representative corpus of 2099 documents. Our framework provides a detailed scientometric analysis of different topics in the field. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data handling

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Query-Efficient and Scalable Black-Box Adversarial Attacks on Discrete Sequential data via Bayesian Optimization 39

Query-Efficient and Scalable Black-Box Adversarial Attacks o...

引用

39th international conference on Machine Learning (ICML)

作者： Lee, Deokjae Moon, Seungyong Lee, Junhyeok Song, Hyun Oh Seoul Natl Univ Dept Comp Sci & Engn Seoul South Korea

We focus on the problem of adversarial attacks against models on discrete sequential data in the black-box setting where the attacker aims to craft adversarial examples with limited query access to the victim model. Existing black-box attacks, mostly based on greedy algorithms, find adversarial examples using pre-computed key positions to perturb, which severely limits the search space and might result in suboptimal solutions. To this end, we propose a query-efficient black-box attack using Bayesian optimization, which dynamically computes important positions using an automatic relevance determination (ARD) categorical kernel. We introduce block decomposition and history subsampling techniques to improve the scalability of Bayesian optimization when an input sequence becomes long. Moreover, we develop a post-optimization algorithm that finds adversarial examples with smaller perturbation size. Experiments on natural language and protein classification tasks demonstrate that our method consistently achieves higher attack success rate with significant reduction in query count and modification rate compared to the previous state-of-the-art methods.

关键词： Machine learning

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Reweighting Strategy based on Synthetic data Identification for Sentence Similarity 29

Reweighting Strategy based on Synthetic Data Identification ...

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29th international conference on Computational Linguistics, COLING 2022

作者： Kim, Taehee Park, ChaeHun Hong, Jimin Dua, Radhika Choi, Edward Choo, Jaegul KAIST AI Letsur Korea Republic of

Semantically meaningful sentence embeddings are important for numerous tasks in natural language processing. To obtain such embeddings, recent studies explored the idea of utilizing synthetically generated data from pretrained language models (PLMs) as a training corpus. However, PLMs often generate sentences much different from the ones written by human. We hypothesize that treating all these synthetic examples equally for training deep neural networks can have an adverse effect on learning semantically meaningful embeddings. To analyze this, we first train a classifier that identifies machine-written sentences, and observe that the linguistic features of the sentences identified as written by a machine are significantly different from those of human-written sentences. Based on this, we propose a novel approach that first trains the classifier to measure the importance of each sentence. The distilled information from the classifier is then used to train a reliable sentence embedding model. Through extensive evaluation on four real-world datasets, we demonstrate that our model trained on synthetic data generalizes well and outperforms the existing baselines. © 2022 Proceedings - international conference on Computational Linguistics, COLING. All rights reserved.

关键词： Embeddings

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Explainable machine learning for sleep apnea prediction 26

Explainable machine learning for sleep apnea prediction

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26th international conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2022

作者： Troncoso-García, A.R. Martínez-Ballesteros, M. Martínez-Álvarez, F. Troncoso, A. Data Science and Big Data Lab Pablo de Olavide University Seville41013 Spain Department of Computer Science University of Seville Seville41012 ES Spain

Machine and deep learning has become one of the most useful tools in the last years as a diagnosis-decision-support tool in the health area. However, it is widely known that artificial intelligence models are considered a black box and most experts experience difficulties explaining and interpreting the models and their results. In this context, explainable artificial intelligence is emerging with the aim of providing black-box models with sufficient interpretability so that models can be easily understood and further applied. Obstructive sleep apnea is a common chronic respiratory disease related to sleep. Its diagnosis nowadays is done by processing different data signals, such as electrocardiogram or respiratory rate. The waveform of the respiratory signal is of importance too. Machine learning models could be applied to the signal's analysis. data from a polysomnography study for automatic sleep apnea detection have been used to evaluate the use of the Local Interpretable Model-Agnostic (LIME) library for explaining the health data models. Results obtained help to understand how several features have been used in the model and their influence in the quality of sleep. © 2022 The Authors. Published by Elsevier B.V.

关键词： Lime

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Age estimation from face images based on deep learning

Age estimation from face images based on deep learning

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2020 international conference on Computing and data Science, CDS 2020

作者： Han, Siyu International School Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781728171067

Age estimation via face images is widely used in different fields, including intelligent security, financial transactions, and public transportation. Even after many years' research, evaluating the specific age from facial images is still a challenging problem. Recently, deep learning-based methods are playing an important role in image processing, which provides a possible way to improve estimation models. We estimate accurate age and age range by testing several deep learning models, which include InceptionResNetV2, Xception, DenseNetl21, ResNetl52V2, VGG16, and InceptionV3. By comparing their performance, we discover that Xception has the highest accuracy of age estimation. To optimize the performance of these convolutional neural network, we apply the soft labelling technique and analyze the experiment results. © 2020 IEEE

关键词： Convolutional neural networks

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Challenges and Trends in Federated Learning for Well-being and Healthcare 26

Challenges and Trends in Federated Learning for Well-being a...

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26th international conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2022

作者： Campanile, Lelio Marrone, Stefano Marulli, Fiammetta Verde, Laura Department of Maths and Physics Università Degli Studi Della Campania L. Vanvitelli Caserta Italy

Currently, research in Artificial Intelligence, both in Machine Learning and Deep Learning, paves the way for promising innovations in several areas. In healthcare, especially, where large amounts of quantitative and qualitative data are transferred to support studies and early diagnosis and monitoring of any diseases, potential security and privacy issues cannot be underestimated. Federated learning is an approach where privacy issues related to sensitive data management can be significantly reduced, due to the possibility to train algorithms without exchanging data. The main idea behind this approach is that learning models can be trained in a distributed way, where multiple devices or servers with decentralized data samples can provide their contributions without having to exchange their local data. Recent studies provided evidence that prototypes trained by adopting Federated Learning strategies are able to achieve reliable performance, thus by generating robust models without sharing data and, consequently, limiting the impact on security and privacy. This work propose a literature overview of Federated Learning approaches and systems, focusing on its application for healthcare. The main challenges, implications, issues and potentials of this approach in the healthcare are outlined. © 2022 The Authors. Published by Elsevier B.V.

关键词： Sensitive data

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Consciousness-Domain Index: a data-driven clustering-based consciousness labeling 44

Consciousness-Domain Index: a data-driven clustering-based c...

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44th Annual international conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Liuzzi, Piergiuseppe De Bellis, Francesco Magliacano, Alfonso Estraneo, Anna Mannini, Andrea IRCSS Fdn Don Carlo Gnocchi Florence FI Italy Scuola Super Sant Anna Ist BioRobot Pontedera PI Italy

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

Assessing consciousness results in one of the most complex neurological diagnosis. Even more complex and uncertain is prognosticating on consciousness recovery. Currently, consciousness is assessed by using a six-items scale, the Coma Recovery Scale-Revised. Namely, scores on the sub-items can individually assign or not a specific level of consciousness to a patient. In our work, by using solely the six sub-items of the CRS-R, we implemented a clustering algorithm labeling patients with the Consciousness-Domains Index (CDI) starting from a dataset of 190 patients with a Disorder of Consciousness (DoC). Then, the CDI is compared with the clinical state at admission and at six months via univariate analysis. The number of clusters best dividing the groups resulted equal to two and the most influencing sub-items resulted the visual and motor one. The CDI closely resembles the clinical state at admission (CSA) (Cohen's k = 0.85). On the other hand, when comparing CDI and CSA, a net improvement was found in the prognostic power of the neurological outcome at six months, targeted as presence/absence of a DoC (p < 0.001). data-driven techniques pave the way for automated and model-based search of prognostic factors, together with the use of such prognostic factors in multivariate prognostic models. Future works will address the external validation of the CDI, together with the inclusion of the CDI in a multivariate supervised model, in order to assess the true potential of such novel index.

关键词： Visualization Biological system modeling Clustering algorithms Indexes Labeling

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引用

international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Jun-You Wang Sheng Li Li-An Lu Sydney Chia-Chun Kao Jyh-Shing Roger Jang Academia Sinica Taipei Taiwan National Institute of Information and Communications Technology Kyoto Japan Taipei First Girls High School Taipei Taiwan National Taiwan University Taipei Taiwan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The primary challenge in accent recognition lies in data scarcity due to the high diversity of accents, which make the collection of large-scale training data for each accent almost impossible in practice. To overcome this challenge, we propose a simple solution that leverages both continuous and discrete feature representations from pretrained speech self-supervised learning (SSL) models. Our model is simplified to a linear projection layer and a set of trainable accent class embeddings. Cosine similarity between the accent embeddings and the latent features of an audio sample is used to predict its accent class. This approach enables the model to access features that contain rich accent-related information while reducing the risk of model overfitting. Our method provides a practical and efficient way to tackle accent recognition, especially in low-resource scenarios. Experimental results on English accent recognition show that our best model achieves an accuracy of 84.0% on the AESRC 2020 dataset and an Unweighted Average Recall (UAR) of 50.0% on the VCTK corpus, setting new state-of-the-art results on both datasets.

关键词： Accuracy Text recognition Training data Speech recognition Self-supervised learning Signal processing Acoustics Speech processing Overfitting

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Handling Disagreement in Hate Speech Modelling 19th

Handling Disagreement in Hate Speech Modelling

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19th international conference on Information processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2022

作者： Kralj Novak, Petra Scantamburlo, Teresa Pelicon, Andraž Cinelli, Matteo Mozetič, Igor Zollo, Fabiana Central European University Vienna Austria Jožef Stefan Institute Ljubljana Slovenia Ca’ Foscari University Venice Italy Jožef Stefan International Postgraduate School Ljubljana Slovenia Sapienza University Rome Italy

ISBN: (纸本)9783031089732

Hate speech annotation for training machine learning models is an inherently ambiguous and subjective task. In this paper, we adopt a perspectivist approach to data annotation, model training and evaluation for hate speech classification. We first focus on the annotation process and argue that it drastically influences the final data quality. We then present three large hate speech datasets that incorporate annotator disagreement and use them to train and evaluate machine learning models. As the main point, we propose to evaluate machine learning models through the lens of disagreement by applying proper performance measures to evaluate both annotators’ agreement and models’ quality. We further argue that annotator agreement poses intrinsic limits to the performance achievable by models. When comparing models and annotators, we observed that they achieve consistent levels of agreement across datasets. We reflect upon our results and propose some methodological and ethical considerations that can stimulate the ongoing discussion on hate speech modelling and classification with disagreement. © 2022, The Author(s).

关键词： Speech

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