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A Word Embedding Model Learned from Political Tweets 13

A Word Embedding Model Learned from Political Tweets

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13th International conference on Computer Engineering and Systems (ICCES)

作者： Alnajran, Noufa N. Crockett, Keeley A. McLean, David Latham, Annabel Manchester Metropolitan Univ Dept Comp Math & Digital Technol Manchester Lancs England

ISBN: (纸本)9781538651117

distributed word representations have recently contributed to significant improvements in many natural language processing (NLP) tasks. Distributional semantics have become amongst the important trends in machine learning (ML) applications. Word embeddings are distributed representations of words that learn semantic relationships from a large corpus of text. In the social context, the distributed representation of a word is likely to be different from general text word embeddings. This is relatively due to the unique lexical semantic features and morphological structure of social media text such as tweets, which implies different word vector representations. In this paper, we collect and present a political social dataset that consists of over four million English tweets. An artificial neural network (NN) is trained to learn word co -occurrence and generate word vectors from the political corpus of tweets. The model is 136MB and includes word representations for a vocabulary of over 86K unique words and phrases. The learned model shall contribute to the success of many ML and NLP applications in microblogging Social network Analysis (OSN), such as semantic similarity and cluster analysis tasks.

关键词： Word Embedding Language Modelling Deep Learning Social network Analysis Twitter Analysis

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Fruit Tree Disease Recognition Based on Convolutional neural networks

Fruit Tree Disease Recognition Based on Convolutional Neural...

引用

IEEE International Symposium on Parallel and distributed processing with Applications and IEEE International conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Zechen Zheng Shaowei Pan Yichi Zhang School of Computer Science Xi’an Shiyou University xi’an China China

ISBN: (纸本)9781728152103

In order to realize the rapid and accurate recognition of fruit tree diseases in orchard environment, this paper puts forward a deep learning model based on Convolution neural network to identify fruit tree diseases. In this paper, the data set is processed by the Sobel operator and image enhancement respectively. Then, the network depth, convolution kernel, feature maps, and fully connected layer in the Convolution neural network structure use different parameters and softmax classifier. Differently composition networks are used to train processed dataset. Convolution neural network models are used to predict test sets, and the results show that deeper Convolution neural networks and mean pooling for tiny features in the dataset are more accurate. It can achieve the disease recognition, which includes cab disease, black rot, rust of apple leaves and bacterial spot disease of peach tree leaves. The model has a good recognition function for disease identification of fruit trees and can help real-time monitoring of orchard diseases.

关键词： agricultural products convolutional neural nets feature extraction image enhancement image recognition learning (artificial intelligence) plant diseases image enhancement Image recognition agricultural products agricultural byproducts fruit trees Feature extraction Sobel operator Recognition (Psychology) Convolution maladjustment Dataset neural network Plant Diseases tree diseases Prunus persica

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A distributed Sensor network for Monitoring Noise Level and Noise Sources in Urban Environments 6

A Distributed Sensor Network for Monitoring Noise Level and ...

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6th IEEE International conference on Future Internet of Things and Cloud (FiCloud)

作者： Abesser, Jakob Graefe, Robert Kuehn, Christian Clauss, Tobias Lukashevich, Hanna Goetze, Marco Kuehnlenz, Stephanie Fraunhofer Inst Digital Media Technol IDMT Ilmenau Germany IMMS Inst Mikroelekt & Mechatron Syst Gemeinnutzi Ilmenau Germany Software Serv John GmbH Ilmenau Germany

ISBN: (纸本)9781538675038

Smart city applications for acoustic monitoring become essential to cope with the overall increasing noise pollution in urban environments. This paper gives an overview over a distributed sensor network for noise monitoring in the German city of Jena. Several acoustic sensor units allow for classifying among various acoustic scenes and events using Convolutional neural networks (CNN) and measuring different noise level parameters. Connected by a communication system based on MQTT (Message Queue Telemetry Transport), these sensors communicate measurement data to a central server for data post-processing and storage. Finally, a web-based application allows for various real-time visualizations of noise exposure distributed over the city.

关键词： Smart city acoustic scene classification event detection sensor network noise level measurement TA Larm

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An Integrative Approach to Robust Hand Detection Using CPM-YOLOv3 and RGBD Camera in Real Time

An Integrative Approach to Robust Hand Detection Using CPM-Y...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Muhammad Raza Vachiraporn Ketsoi Haopeng Chen School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Innovative methods to interact with electronic devices are attracting researchers attentions, and many applications of Human Computer Interaction have been developed in recent years. Hand detection under different illuminations and backgrounds is crucial for tasks such as, actions recognition, gestures recognition and hand-based interaction with wearable devices. Many existing methods are able to detect the human hand precisely, but the task still remains challenging due to environment complexity, different invariants, processing time, and accuracy. It is required for an application to recognize the human hand precisely for proper interaction with the wearable devices. This paper proposed a robust and a compact method for hand detection by employing you only look once (YOLO) and convolutional pos machines (CPM). This paper consists of two parts, network training and testing. In the first part, Oxford hand dataset is used to train a neural network. This dataset has various hand images under different invariants, and backgrounds. Afterwards, images are captured using a digital camera in real-time and then human hands are detected using a trained neural network. In addition, detected hands, positions and orientations are calculated accordance with defined active zone parameters and a depth sensor. Our trained neural network is dubbed as HHDNet, an abbreviation for human hand detection network. The HHDNet is tested under different environment conditions, and experimental results showed that the accuracy and the frame rate of the proposed model are superior than others state of the art methods.

关键词： Image color analysis neural networks Feature extraction Cameras Robot sensing systems Estimation Skin

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Hand Motion Based Human Computer Interaction Using 3D Convolutional neural network

Hand Motion Based Human Computer Interaction Using 3D Convol...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Vachiraporn Ketsoi Muhammad Raza Haopeng Chen School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Electronic devices have achieved smart functionality due to the emergence of action recognition. More specifically, hand motions classification is one of the main research areas among computer vision research communities, which have tremendous potential in the field of human computer interaction. However, there are various challenges related to the computational cost of this approach. In this paper, a compact method is proposed to recognize hand actions by utilizing 3D convolutional neural network. Without using multiple sensors, our method is merely based on a mono camera. A network is trained using multiple videos of human hand motions, and then an image sequence is forward to the trained neural network for recognizing the corresponding action. Our trained neural network is dubbed as HMRNet, an abbreviation for hand motions recognition network. The HMRNet is trained and tested using 20BN Jester dataset, and experimental results showed that both the estimated error and the accuracy of the proposed network are superior compare to other traditional methods including sensors and features based. The computational efficiency and logical steps of our approach are straightforward that any user interface-based device equipped with a camera and proper hardware can adapt this method. This method allows users to interact with the personal computers in a very interactive and futuristic way, and has the potential to eliminate the use of input devices including mouse and touch pen.

关键词： Sensors Three-dimensional displays Feature extraction Kernel Visualization Gesture recognition Convolutional neural networks

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Improvement of Quantum Genetic Algorithms and Application of DDoS Attack Detection

Improvement of Quantum Genetic Algorithms and Application of...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Changqing Gong Tongyao Shi Ming Mu Liang Zhao Abdullah Gani Han Qi School of Computer Science Shenyang Aerospace University Shenyang China Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Aiming at the problem of insufficient searching ability of Quantum Genetic Algorithms (QGA), this paper proposes a method to improve QGA by dynamically changing the rotation angle of quantum revolving gates. Thirteen typical standard functions are used to test the improved QGA, and to further verify the improved QGA. In this paper, a DDoS attack detection model based on quantum genetic optimization BP neural network (DQGA-BP) is constructed. The model combines improved quantum genetic algorithm with BP neural network, and uses KDD-Cup 1999 data set (9-week network connection collected from the USAF LAN) to detect DDoS attacks. It effectively improves the accuracy of DDoS attack detection. The experimental results show that the improved quantum genetic algorithm has faster convergence speed and stronger optimization ability. Under DDoS attack detection, the average detection rate of DQGA-BP is 0.51491% higher than that of the original quantum genetic optimization BP neural network (QGA-BP), and the average false alarm rate is 0.37%.

关键词： Genetic algorithms Denial-of-service attack Biological cells Logic gates Convergence Optimization neural networks

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MIX: Multi-Channel Information Crossing for Text Matching 18

MIX: Multi-Channel Information Crossing for Text Matching

引用

24th ACM SIGKDD conference on Knowledge Discovery and Data Mining (KDD)

作者： Chen, Haolan Han, Fred X. Niu, Di Liu, Dong Lai, Kunfeng Wu, Chenglin Xu, Yu Tencent Mobile Internet Grp Toronto ON Canada Univ Alberta Edmonton AB Canada

ISBN: (纸本)9781450355520

Short Text Matching plays an important role in many natural language processing tasks such as information retrieval, question answering, and dialogue systems. Conventional text matching methods rely on predefined templates and rules. However, for a short piece of text with a limited number of words, these rules are unable to generalize well to unobserved data. With the success of deep learning in fields like computer vision, speech recognition and recommender systems, many recent efforts have been made to apply deep neural network models to natural language processing tasks to reduce the cost of manual feature engineering. In this paper, we present the design of Multi-Channel Information Crossing (MIX), a multi-channel convolutional neural network (CNN) model for text matching in a production environment, with additional attention mechanisms on sentences and semantic features. MIX compares text snippets at varied granularities to form a series of multi-channel similarity matrices, which are then crossed with another set of carefully designed attention matrices to expose the rich structures of sentences to deep neural networks. We implemented MIX and deployed the system on Tencent's Venus distributed computation platform. Thanks to the well-engineered multi-channel information crossing, evaluation results suggest that MIX outperforms a wide range of state-of-the-art deep neural network models by at least 11.1% in terms of the normalized discounted cumulative gain (NDCG@3), on the English WikiQA dataset. Moreover, we performed online A/B tests with real users on the search service of Tencent QQ Browser. Results show that MIX raised the number of clicks on the returned results by 5.7%, due to the increased accuracy in query-document matching, which demonstrates the superior performance of MIX in a real-world production environment.

关键词： Text matching ad-hoc retrieval question answering convolutional neural networks attention mechanism

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P-Spiking Deep neural network Based on Adaptive SPCNN Temporal Coding

P-Spiking Deep Neural Network Based on Adaptive SPCNN Tempor...

引用

IEEE International Symposium on Parallel and distributed processing with Applications and IEEE International conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Yuli Chen Huiting Yao Miao Ma Zhao Pei Xingwei Li Zengguo Sun dept.Shaanxi Normal Universi School of Computer Science Xi’an China

ISBN: (纸本)9781728152103

In this paper, we propose a P-spike deep neural network (P-SDNN) for image classification based on an adaptive simplified pulse coupled neural network (SPCNN) temporal coding. The proposed P-SDNN model introduces a SPCNN tem-coding layer into a spiking deep neural network (SDNN) with parameters adjusted by unsupervised STDP learning rule. The advantage of the proposed SPCNN temporal coding is to obtain adaptive time steps in terms of different input images. Each time step corresponds to a spiking-timing map which may contain a semantic segmentation of the input image. This is guaranteed by the working principle of SPCNN that the higher the neuron intensity is, the larger its internal activity will be and the earlier it will fire. And the adjacent neurons with similar intensity will pulse synchronously in a spiking-timing map. We evaluate the proposed P-SDNN model in the tasks of image classification on the Caltech face/motorbike and MNIST datasets. The experiments show that, under the same experimental conditions, the proposed P-SDNN model performs better than the SDNN model without SPCNN tem-coding.

关键词： image classification image segmentation neural nets unsupervised learning Image classification neural network Unsupervised learning image segmentation coding operating principles Self tuning

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Microblog search method based on neural network language model

Microblog search method based on neural network language mod...

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Chinese Intelligent Systems conference, CISC 2017

作者： Lai, Jincai Du, Junping Zheng, Wenzhen Wang, Wei Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer Science Beijing University of Posts and Telecommunications Beijing100876 China SINA Corporation Beijing China

ISBN: (纸本)9789811064951

Deep neural network language model has gained significant development among natural language processing (NLP) in recent years. In this paper, we focused on using neural language model (NNLM) to enhance microblog search. This paper proposed a microblog search method based on neural network language model (NBSM). Firstly, we train neural network language model based on microblog data, so as to get the distributed representation of words which may contain internal express model of microblog. Then, we use the distributed representation of words to get the expanding words of users’ searching words. Finally, we re-rank microblog search results combining deep sematic text similarity and social signal features. The method we proposed can effectively obtain microblog express model, and its search result can reflect the social hot-topics of the topic related to users searching words. Experiment results show that the proposed method yields significant improvements over state-of-arts methods and significantly improves the user’s search experience. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Citation Count Prediction Based on Academic network Features 32

Citation Count Prediction Based on Academic Network Features

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32nd IEEE International conference on Advanced Information networking and Applications (AINA)

作者： Zhu, XinPing Ban, ZhiJie Inner Mongolia Univ Sch Comp Sci Hohhot Peoples R China

ISBN: (纸本)9781538621950

Citation count is an important factor to measure the influence of academic publications. Identifying future citation count in advance can help scientists to find references and research area. There are many academic network features which are related to citation count. However, these features have not been completely explored in the existing studies. In this paper, we propose a citation count prediction model based on academic network features. Firstly, some important features are introduced and analyzed in detail. Then, we verify the importance of each feature and use a neural network model to select a set of optimal features. Finally, we present several machine learning methods and one multiple linear regression strategy to predict a paper's future citation. Experimental results on real datasets demonstrate that our model significantly outperforms the baseline method.

关键词： citation count prediction academic social network feature selection

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