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共 585 条记录，以下是131-140 订阅

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dataset Bias in Deception Detection 26

Dataset Bias in Deception Detection

引用

26th international Conference on pattern recognition / 8th international workshop on Image mining - Theory and Applications (IMTA)

作者： Mambreyan, Ara Punskaya, Elena Gunes, Hatice Univ Cambridge Dept Engn Cambridge England Univ Cambridge Dept Comp Sci & Technol Cambridge England

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

With the advances in machine learning, lie detection technology gained significant attention. In recent years, several multi-modal techniques achieved as high as 99% accuracy results using the Real-life Trial dataset with only 121 data points. This led to considerable media hype and research interest in lie detection with machine learning. In this paper, we analyze the effect of dataset bias in deception detection. More specifically, we train a classifier to predict the sex of the identity appearing in the video. On a test data point, we use the sex predictor to predict sex which we use as a proxy for predicting deception, predicting lie for females and truth for males. This lie predictor simulates a classifier that uses nothing but dataset bias. Nevertheless, we find that the performance of this biased classifier is comparable to those of state-of-the-art papers. More specifically, when using IDT features, our biased classifier achieves 64.6% and 59.3% AUC while a classifier trained normally on truth/lie labels achieves 57.4% accuracy and 69.3% AUC. We perform similar experiments on the Bag-of-Lies dataset and show that it too is biased with respect to sex. In addition, we apply the state-of-the-art techniques on an unbiased dataset and show that their performance is no better than chance. Our experiments strongly suggest that the results of recent deception detection techniques can be explained by the bias inherent in the datasets.

关键词： Measurement Industries Ethics machine learning algorithms Government machine learning Detectors

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Unexpected challenges in large scale machine learning

Unexpected challenges in large scale machine learning

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1st international workshop on Big data, streams and Heterogeneous Source mining: Algorithms, Systems, Programming Models and Applications, BigMine-12 - Held in Conjunction with SIGKDD Conference

作者： Parker, Charles BigML Inc. 2851 NW 9th St. Corvallis OR 97330 United States

ISBN: (纸本)9781450315470

In machine learning, scale adds complexity. The most obvious consequence of scale is that data takes longer to process. At certain points, however, scale makes trivial operations costly, thus forcing us to re-evaluate algorithms in light of the complexity of those operations. Here, we will discuss one important way a general large scale machine learning setting may differ from the standard supervised classification setting and show some the results of some preliminary experiments highlighting this difference. The results suggest that there is potential for significant improvement beyond obvious solutions. Copyright © 2012 ACM.

关键词： Supervised learning

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Character based pattern mining for neology detection 1

Character based pattern mining for neology detection

引用

EMNLP 2017 1st workshop on Subword and Character Level Models in NLP, SCLeM 2017

作者： Lejeune, Gael Cartier, Emmanuel Lipn Paris Xiii University 99 avenue Jean-Baptiste Clement Villetaneuse93430 France

ISBN: (纸本)9781945626913

Detecting neologisms is essential in real-time natural language processing applications. Not only can it enable to follow the lexical evolution of languages, but it is also essential for updating linguistic resources and parsers. In this paper, neology detection is considered as a classification task where a system has to assess whether a given lexical item is an actual neologism or not. We propose a combination of an unsupervised data mining technique and a supervised machine learning approach. It is inspired by current researches in stylometry and on tokenlevel and character-level patterns. We train and evaluate our system on a manually designed reference dataset in French and Russian. We show that this approach is able to outperform stateof- the-art neology detection systems. Furthermore, character-level patterns exhibit good properties for multilingual extensions of the system. © EMNLP *** right reserved.

关键词： Supervised learning

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Defect Detection of Forming Mesh Based on YOLOV8N Model 24

Defect Detection of Forming Mesh Based on YOLOV8N Model

引用

1st international Conference on Image Processing machine learning and pattern recognition

作者： Li, Jing Wang, Lijuan Zhang, Peng Guangdong Mech & Elect Polytech Guangzhou Guangdong Peoples R China Macao Polytech Univ Macau Peoples R China

ISBN: (纸本)9798400707032

A method for detecting defects in forming mesh based on the YOLOV8N model is proposed. Firstly, an illumination system is designed, and then image data with defects is collected to construct an image dataset. The constructed image dataset is annotated and augmented to build a corresponding image sample set. The YOLOV8N model is trained using the sample set to obtain a defect recognition and localization model. The original image to be recognized is input into the defect recognition and localization model, which then outputs the corresponding defect recognition and localization results. Experimental results show that this method provides accurate localization and fast speed.

关键词： Forming mesh 1 YOLOV8N model 2 Illumination system 3 Sample set 4

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mining Users Playbacks History for Music Recommendations

Mining Users Playbacks History for Music Recommendations

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10th international Conference on machine learning and data mining (MLDM)

作者： Dzuba, Alexandr Bugaychenko, Dmitry St Petersburg State Univ Odnoklassniki LTD St Petersburg 199034 Russia

ISBN: (纸本)9783319089799;9783319089782

This paper presents a set of methods for the analysis of user activity and data preparation for the music recommender by the example of "Odnoklassniki"(1) social network. The history of actions is being analyzed in multiple dimensions in order to find a number of collaborative and temporal correlations as well as to make the overall rankings. The results of the analysis are being exported in a form of a taste graph which is then used to generate on-line music recommendations. The taste graph displays relations between different entities connected with music (users, tracks, artists, etc.) and consists of the following main parts: user preferences, track similarities, artists' similarities, artists' works and demography profiles.

关键词： music recommendations taste graph item similarity

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Proc. of the 1st international workshop on Novel data stream pattern mining Techniques, streamKDD'10, Held in Conjunction with the 16th ACM SIGKDD Conference on Knowledge Discovery and data mining

Proc. of the 1st International Workshop on Novel Data Stream...

引用

1st international workshop on Novel data stream pattern mining Techniques, streamKDD'10, Held in Conjunction with the 16th ACM SIGKDD Conference on Knowledge Discovery and data mining

ISBN: (纸本)9781450302265

The proceedings contain 8 papers. The topics discussed include: fully decentralized computation of aggregates over data streams;detecting outliers on arbitrary data streams using anytime approaches;CALDS: context-aware learning from data streams;evolutionary clustering using frequent itemsets;towards subspace clustering on dynamic data: an incremental version of PreDeCon;visual analysis of news streams with article threads;conformal prediction for distribution-independent anomaly detection in streaming vessel data;and research issues in mining multiple data.

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Optimization of Intelligent Substation Location Scheme Based on data Fusion Technology and Artificial Intelligence 24

Optimization of Intelligent Substation Location Scheme Based...

引用

1st international Conference on Image Processing machine learning and pattern recognition

作者： Wang, Zhipeng Xie, Dan Feng, Cai He, Chunhui Lan, Feng State Grid Shandong Elect Power Co Econ &Technol Res Inst Beijing Peoples R China

ISBN: (纸本)9798400707032

At present, the world is experiencing the second information wave with data as the core and the Internet as the means, and the society is shifting from IT (information) era to DT (data) era. The planning of substation location is mainly based on the existing satellite vector data through the software simulation platform, and the optimal scheme and path planning are often based on the experience of designers in actual design and erection. However, with the continuous expansion of the project scale and increasing complexity, it has brought great challenges to the design work. Therefore, substation location needs to be combined with current data fusion technology and artificial intelligence technology to effectively improve the intelligent level of substation location.

关键词： Substation location data fusion technology Artificial intelligence technology Weighted Voronoi diagram

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Automatically Identifying a Software Product's Quality Attributes through Sentiment Analysis of Tweets

Automatically Identifying a Software Product's Quality Attri...

引用

1st international workshop on Natural Language Analysis in Software Engineering (NaturaLiSE)

作者： Dehkharghani, Rahim Yilmaz, Cemal Sabanci Univ Dept Comp Sci & Engn Istanbul Turkey

ISBN: (纸本)9781467362719

Software quality attributes can be identified based on software features such as security, reliability and user-friendliness. This process can be done either manually or automatically. Sentiment analysis refers to the sentiment extraction task from resources such as natural language texts. We study the application of sentiment analysis on extracting the quality attributes of a software product based on the opinions of end-users that have been stated in microblogs such as Twitter. Our findings obtain advantageous techniques such as document frequency of words in a large number of tweets. The extracted results can help software developers know the advantages and disadvantages of their products.

关键词： Sentiment analysis data mining software quality attributes machine learning twitter

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The 1st international workshop on Federated learning with Graph data (FedGraph) 22

The 1st International Workshop on Federated Learning with Gr...

引用

31st ACM international Conference on Information and Knowledge Management (CIKM)

作者： Yang, Carl Li, Xiaoxiao Baracaldo, Nathalie Shah, Neil He, Chaoyang Lyu, Lingjuan Sun, Lichao Avestimehr, Salman Emory Univ Atlanta GA 30322 USA Univ British Columbia Vancouver BC Canada IBM Res San Jose CA USA Snap Res Seattle WA USA FedML Inc Los Angeles CA USA Sony AI Tokyo Japan Lehigh Univ Bethlehem PA 18015 USA Univ Southern Calif Los Angeles CA 90007 USA

ISBN: (纸本)9781450392365

The field of graph data mining, one of the most important AI research areas, has been revolutionized by graph neural networks (GNNs), which benefit from training on real-world graph data with millions to billions of nodes and links. Unfortunately, the training data and process of GNNs involving graphs beyond millions of nodes are extremely costly on a centralized server, if not impossible. Moreover, due to the increasing concerns about data privacy, emerging data from realistic applications are naturally fragmented, forming distributed private graphs of multiple "data silos", among which direct transferring of data is forbidden. The nascent field of federated learning (FL), which aims to enable individual clients to jointly train their models while keeping their local data decentralized and completely private, is a promising paradigm for large-scale distributed and private training of GNNs. FedGraph2022 aims to bring together researchers from different backgrounds with a common interest in how to extend current FL algorithms to operate with graph data models such as GNNs. FL is an extremely hot topic of large commercial interest and has been intensively explored for machine learning with visual and textual data. The exploration from graph mining researchers and industrial practitioners is timely catching up just recently. There are many unexplored challenges and opportunities, which urges the establishment of an organized and open community to collaboratively advance the science behind it. The prospective participants of this workshop will include researchers and practitioners from both graph mining and federated learning communities, whose interests include, but are not limited to: graph analysis and mining, heterogeneous network modeling, complex data mining, large-scale machine learning, distributed systems, optimization, meta-learning, reinforcement learning, privacy, robustness, explainability, fairness, ethics, and trustworthiness.

关键词： graph mining distributed learning data privacy federated learning graph neural networks

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Predictive modeling toward identification of sex from lip prints - machine learning in cheiloscopy 4

Predictive modeling toward identification of sex from lip pr...

引用

Joint of the 4th international Conference on Applied Informatics workshops, ICAIW 2021, 2nd international workshop on Applied Artificial Intelligence, WAAI 2021, 3rd international workshop on Applied Informatics for Economy, Society, and Development, AIESD 2021, 4th international workshop on data Engineering and Analytics, WDEA 2021, 1st international workshop on Knowledge Management and Information Technologies, WKMIT 2021 and 4th international workshop on Smart and Sustainable Cities, WSSC 2021

作者： Sabelli, Agustín F. Chatterjee, Parag Pollo-Cattaneo, Ma Florencia Universidad Tecnológica Nacional Facultad Regional Buenos Aires Buenos Aires Argentina

Cheiloscopy is a forensic investigation technique that deals with identification of humans based on lips traces. Lip traces hold multifarious features and could be analyzed in different ways to identify the links with personal identifying features. machine learning holds strong application in this domain, especially for pattern recognition and further interpretation. This work is focused on a brief survey of existing machine learning approaches in Cheiloscopy. Also, a comparative study of predictive models has been presented based on an original dataset of lip prints where supervised models have been used to predict the biological sex of the persons using their lip traces. machine learning on one hand automatizes the identification process and poses a significant potential in analyzing huge number of lip traces with considerable accuracy. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0). CEUR workshop Proceedings (***)

关键词： Image processing

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