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Action2Activity: Recognizing Complex Activities from Sensor data 24

Action2Activity: Recognizing Complex Activities from Sensor ...

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1st international workshop on Social Influence Analysis / 24th international Joint Conference on Artificial Intelligence (IJCAI)

作者： Liu, Ye Nie, Liqiang Han, Lei Zhang, Luming Rosenblum, David S. Natl Univ Singapore Sch Comp Singapore Singapore Hong Kong Baptist Univ Dept Comp Sci Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781577357384

As compared to simple actions, activities are much more complex, but semantically consistent with a human's real life. Techniques for action recognition from sensor generated data are mature. However, there has been relatively little work on bridging the gap between actions and activities. To this end, this paper presents a novel approach for complex activity recognition comprising of two components. The first component is temporal pattern mining, which provides a mid-level feature representation for activities, encodes temporal relatedness among actions, and captures the intrinsic properties of activities. The second component is adaptive Multi-Task learning, which captures relatedness among activities and selects discriminant features. Extensive experiments on a real-world dataset demonstrate the effectiveness of our work.

关键词： Artificial intelligence

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Facing Digital Agriculture Challenges with Knowledge Engineering 1

Facing Digital Agriculture Challenges with Knowledge Enginee...

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1st IEEE international Conference on Artificial Intelligence for Industries (AI4I)

作者： Nery, Marcelo Santos, Rodrigo Santos, Wallas Lourenco, Vitor Moreno, Marcio IBM Res Rio De Janeiro Brazil

ISBN: (纸本)9781538692097

Knowledge Engineering is key to enable knowledge extraction, representation and reasoning, leading to better business insights and decisions. Current advances in machine learning and new trends in AI are bringing a plethora of algorithms capable of performing advanced pattern recognition and data classification. The ability to link, to organize and to query the outputs of these algorithms as well as the ability to handle huge amounts of data and its multiple sources is crucial to maximize the potential of such advances, specially over large datasets. This paper presents challenges in the context of digital agriculture and our position in moving forward with these capabilities whilst using knowledge engineering techniques.

关键词： Knowledge Engineering machine learning Hybrid knowledge representation Digital agriculture

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OPTIMIZING FEATURE INTEGRATION USING A machine learning ADAPTIVE SYNTHESIS LAYER

OPTIMIZING FEATURE INTEGRATION USING A MACHINE LEARNING ADAP...

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1st CONF ON SCIENCE OF ARTIFICIAL NEURAL NETWORKS

作者： BIELAK, D Westinghouse Electric Corp. (United States)

ISBN: (纸本)0819408751

The classification process for pattern recognition uses sensors to read measurements from input examples. A feature function next reduces and quantizes these measurements into feature vectors (combinations of feature data). Finally, using the feature vectors, a decision function classifies the current example by comparison to a statistical model of feature vector data. Which particular feature vectors are made available to the decision function has usually been determined during the design phase by the person constructing the system depending on the hardware available for the sensors. With an adaptive synthesis layer, however, a collective learning automata learns which feature vectors are contributing to correct classification and dynamically adjusts the decision function accordingly. A weighted average scheme is used to combine multiple subhypotheses of the example's class (known as rank hypotheses) into a single output hypothesis (known as the super hypothesis). Updating the weights depends on two factors: an evaluation score and a feature vector compensation. The score is a collective measure of the weighted average combination of rank hypotheses. The feature vector compensation is an individual measure of each feature vector's contribution to the overall decision based on a history of detected patterns. This two-layer approach is one of the most efficient methods in multi-objective programming, yet the application of this approach to machine learning as proposed in this dissertation is unique. In particular, a collective learning automation is used to enhance the combination of a number of candidate class subhypotheses into as single, unique classification. This process is refereed to as adaptive synthesis. This approach has been applied to black and white character recognition and grey scale block classification using the Adaptive learning Image Analysis System (ALIAS) at the George Washington University and the Research Institute for Applied Knowledge Process

关键词： Sensors Image classification pattern recognition machine learning Artificial neural networks Image processing Classification systems Neural networks data modeling Image analysis

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On Using Pretext Tasks to Learn Representations from Network Logs 1

On Using Pretext Tasks to Learn Representations from Network...

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1st ACM international workshop on Native Network Intelligence (NativeNI) Part of 18th international Conference on Emerging Networking Experiments and Technologies (CoNEXT)

作者： Boffa, Matteo Vassio, Luca Mellia, Marco Drago, Idilio Milan, Giulia Ben Houidi, Zied Rossi, Dario Politecn Torino Italy Turin Italy Univ Torino Turin Italy Huawei Technol Co Ltd Paris France

ISBN: (纸本)9781450398879

learning meaningful representations from network data is critical to ease the adoption of AI as a cornerstone to process network logs. Since a large portion of such data is textual, Natural Language Processing (NLP) appears as an obvious candidate to learn their representations. Indeed, the literature proposes impressive applications of NLP applied to textual network data. However, in the absence of labels, objectively evaluating the goodness of the learned representations is still an open problem. We call for a systematic adoption of domain-specific pretext tasks to select the best representation from network data. Relying on such tasks enables us to evaluate different representations on side machine learning problems and, ultimately, unveiling the best candidate representations for the more interesting downstream tasks for which labels are scarce or unavailable. We apply pretext tasks in the analysis of logs collected from SSH honeypots. Here, a cumbersome downstream task is to cluster events that exhibit a similar attack pattern. We propose the following pipeline: first, we represent the input data using a classic NLP-based approach. Then, we design pretext tasks to objectively evaluate the representation goodness and to select the best one. Finally, we use the best representation to solve the unsupervised task, which uncovers interesting behaviours and attack patterns. All in all, our proposal can be generalized to other text-based network logs beyond honeypots.

关键词： Representation learning Cybersecurity NLP Honeypots

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FAUst - A VISION-BASED NEURAL NETWORK MULTIMAP pattern-recognition ARCHITECTURE

FAUST - A VISION-BASED NEURAL NETWORK MULTIMAP PATTERN-RECOG...

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1st CONF ON SCIENCE OF ARTIFICIAL NEURAL NETWORKS

作者： WILSON, CL National Institute of Standards and Technology (United States)

ISBN: (纸本)0819408751

A new architecture is presented for multi-map, self-organizing pattern recognition which allows concurrent massively parallel learning of features using different maps for each feature type. The method used is similar to the multi-map structures known to exist in the vertebrate sensory cortex. The learning used to update memory locations uses a feed-forward mechanism and is self-organizing. The architecture is described by the acronym FAUst (Feed-forward Association Using Symmetrical Triggering). As a demonstration of the effectiveness of FAUst, a character recognition, fingerprint classification, and forms recognition programs have been constructed on a massively parallel compute. The character recognition program can perform 99% accurate character recognition on medium-quality machine printed digits at a speed of 2.4 ms/digit, and 88% recognition on multiple-writer handprint with a 2.3% substitutional error rate. The form recognition program can achieve 94% accuracy on complex forms. The fingerprint classification program classifies 93% of fingerprints correctly with 10% rejection rate. All of the calculations were performed on an Active Memory Technology DAP 510.

关键词： Binary data Optical character recognition Classification systems Fingerprint recognition Image classification pattern recognition Content addressable memory Neural networks Feature extraction Receptors

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Combining independent component analysis with support vector machines

Combining independent component analysis with support vector...

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1st international Symposium on Systems and Control in Aerospace and Astronautics

作者： Genting, Yan Guangfu, Ma Jianting, Lv Bin, Song Department of Control Science and Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)0780393953

Recently, support vector machine (SVM) has become a popular tool in pattern recognition. In developing a successful SVM classifier, the first step is feature extraction. This paper proposes the application of independent component analysis (ICA) to SVM for feature extraction. In ICA, the original inputs are linearly transformed into features which are mutually statistically independent. By examining the statlog heart disease data and satimage data, the experimental shows that SVM by feature extraction using ICA can perform better than that without feature extraction.

关键词： learning systems

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A learning procedure for detection of process anomalies in the production of metal long products and a new industrial case study 1

A learning procedure for detection of process anomalies in t...

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1st IFAC workshop on Control of Complex Systems (COSY)

作者： Weber, Andre Denker, Joachim Jelali, Mohieddine Mannstaedt GmbH Mendener Str 51 D-53840 Troisdorf Germany ASINCO GmbH Adv Solut Ind Control Kiekenbusch 11a D-47269 Duisburg Germany TH Koln Cologne Lab Artificial Intelligence & Smart Autom Betzdorfer Str 2 D-50679 Cologne Germany

The highly individualized production processes for long products in the steel industry is subject to a variety of influencing variables with mutual interactions in a complex manner. To handle this complexity, modern data mining methods can be used for a highly efficient analysis of process data, to detect process anomalies in the process data, e.g. from rolling mills by statistical pattern recognition. This paper proposes a data-based strategy for detecting process anomalies within a hot rolling mill for long products. Suitable data are identified and selected from existing sensors and processed within a new database. This central database is used to train classification algorithms. The reliability of two prominent classifiers based on Principal Component Analysis (PCA) and One-Class Support Vector machines (OC-SVM) has been evaluated. From the comparison in this respective use case, it has been concluded that satisfying results can be obtained, but PCA is highly dependent on the data distribution. The OC-SVM has also been implemented and tested and offers advantages when the data sets have a more complex distribution. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： industry4.0 data mining machine learning anomaly detection steel production PCA SVM OC-SVM

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A Deep learning Approach for Face Detection using YOLO 1

A Deep Learning Approach for Face Detection using YOLO

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1st international Conference on data Science and Analytics, PuneCon 2018

作者： Garg, Dweepna Goel, Parth Pandya, Sharnil Ganatra, Amit Kotecha, Ketan Computer Engineering Department Devang Patel Institute of Advance Technology and Research CHARUSAT Changa Anand India Computer Science Engineering Department Navrachana University Vadodara India Devang Patel Institute of Advance Technology and Research Charotar University of Science and Technology Changa Anand India Symbiosis Institute of Technology Symbiosis International University Pune India

ISBN: (纸本)9781538672785

Deep learning is nowadays a buzzword and is considered a new era of machine learning which trains the computers in finding the pattern from a massive amount of data. It mainly describes the learning at multiple levels of representation which helps to make sense on the data consisting of text, sound and images. Many organizations are using a type of deep learning known as a convolutional neural network to deal with the objects in a video sequence. Deep Convolution Neural Networks (CNNs) have proved to be impressive in terms of performance for detecting the objects, classification of images and semantic segmentation. Object detection is defined as a combination of classification and localization. Face detection is one of the most challenging problems of pattern recognition. Various face related applications like face verification, facial recognition, clustering of face etc. are a part of face detection. Effective training needs to be carried out for detection and recognition. The accuracy in face detection using the traditional approach did not yield a good result. This paper focuses on improving the accuracy of detecting the face using the model of deep learning. YOLO (You only look once), a popular deep learning library is used to implement the proposed work. The paper compares the accuracy of detecting the face in an efficient manner with respect to the traditional approach. The proposed model uses the convolutional neural network as an approach of deep learning for detecting faces from videos. The FDDB dataset is used for training and testing of our model. A model is fine-Tuned on various performance parameters and the best suitable values are taken into consideration. It is also compared the execution of training time and the performance of the model on two different GPUs. © 2018 IEEE.

关键词： Face recognition

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Small-Scale CNN-N model for Covid-19 Anomaly Detection and Localization From Chest X-Rays 1

Small-Scale CNN-N model for Covid-19 Anomaly Detection and L...

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1st international Conference on Artificial Intelligence Trends and pattern recognition, ICAITPR 2022

作者： Marusani, Jagadeesh Sudha, B.G. Darapaneni, Narayana Pes University Data Science and Machine Learning Bengaluru India Data Science and Machine Learning Great Learning Bengaluru India Artificial Intelligence and Machine Learning Great Learning Bengaluru India

ISBN: (数字)9781665425216

ISBN: (纸本)9781665425216

Covid-19 has been posing a serious challenge to scientists and health organizations around the world in terms of detection and its treatment. Common methods are CT-Scans and X-rays to analyze the images of lungs for COVID-19. These days diagnosing covid-19 by manually looking at the reports has become difficult and challenging in the pandemic. Pneumonia and pulmonary infections along with covid-19 cause inflammation and fluids in the lungs. Covid-19 X-rays are very similar to viral and bacterial Pneumonia X-rays. So it becomes very difficult to differentiate between covid-19 and Pneumonia. In this paper we propose a computer vision model to detect the presence of covid19 infection along with the localization of the infection in the lungs. 6337 images consisting of Negative for pneumonia, Typical Appearance, Intermediate Appearance and Atypical Appearance is considered. Although there are pre-trained CNN models which perform well on the data, this paper aims at reducing the size of the model and validate its performance on other datasets. Different image sizes are also considered. A small scale CNN model is built from scratch to detect and localize covid-19 abnormalities on chest radiographs using object detection algorithms like Yolov5 with different weights. There is a significant reduction in model size and parameters compared to many state of the art pre-trained models thereby ensuring efficient detection of covid-19 anomalies and show the region of infection to ensure timely treatment before it causes severe infection. © 2022 IEEE.

关键词： Object detection

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Predicting Song Popularity Through machine learning and Sentiment Analysis on Social Networks 13th

Predicting Song Popularity Through Machine Learning and Sen...

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13th mining Humanistic data workshop, MHDW 2024, 9th workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2024 and 1st workshop on AI in Applications for Achieving the Green Deal Targets, AI4GD 2024 held as parallel events of the IFIP WG 12.5 international workshops on Artificial Intelligence Applications and Innovations, AIAI 2024

作者： Rompolas, Gerasimos Smpoukis, Athanasios Kafeza, Eleanna Makris, Christos Computer Engineering and Informatics Department University of Patras Patras26504 Greece College of Interdisciplinary Studies Zayed University Dubai144534 United Arab Emirates

ISBN: (纸本)9783031632266

In this paper, we delve into the intricate relationship between technology, music, and success. Our research focuses on leveraging the capabilities of machine learning algorithms to forecast the success and popularity of songs. By combining audio features, social media data, and emotion analysis, our machine learning-based model demonstrates its capability as a reliable tool for artists, amateurs, and music industry stakeholders to make informed and targeted decisions. Our analysis encompassed not only the inherent audio characteristics of each track but also the extent of audience engagement and emotional response of the fans. Through the integration of textual emotion analysis, we quantified the reactions of the audience and performed an emotion labelling, leveraging our social information to uncover the emotions surrounding each track’s release. The experimental results show that the feature data from which the classification algorithms were trained, unfolded to be qualitative and precise, while they also show that the use of the social media features improves the classifiers’ performance in predicting the popularity of the tracks. © IFIP international Federation for Information Processing 2024.

关键词： Emotion recognition

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