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Where and When: Event-Based Spatiotemporal Trajectory Prediction from the iCub’s Point-Of-View

Where and When: Event-Based Spatiotemporal Trajectory Predic...

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IEEE international conference on Robotics and Automation (ICRA)

作者： Marco Monforte Ander Arriandiaga Arren Glover Chiara Bartolozzi Istituto Italiano di Tecnologia Università degli Studi di Genova and EDPR Research Line Italy

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

Fast, non-linear trajectories have been shown to be more accurately visually measured, and hence predicted, when sampled spatially (that is when the target position changes) rather than temporally, i.e. at a fixed-rate as in traditional frame-based cameras. Event-cameras, with their asynchronous, low latency information stream, allow for spatial sampling with very high temporal resolution, improving the quality of the data and the accuracy of post-processing operations. This paper investigates the use of Long Short-Term Memory (LSTM) networks with event-cameras spatial sampling for trajectory prediction. We show the benefit of using an Encoder-Decoder architecture over parameterised models for regression on event-based human-to-robot handover trajectories. In particular, we exploit the temporal information associated to the events stream to predict not only the incoming spatial trajectory points, but also when these will occur in time. After having studied the proper LSTM input/output sequence length, the network performance are compared to other regression models. Then, prediction behavior and computational time are analysed for the proposed method. We carry out the experiment using an iCub robot equipped with event-cameras, addressing the problem from the robot perspective.

关键词： Trajectory Robots Predictive models Cameras Target tracking data models Pipelines

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A Study on Machine Learning based Conversational Agents and Designing Techniques

A Study on Machine Learning based Conversational Agents and ...

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international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Santhana Lakshmi V. PSGR Krishnammal College for Women Coimbatore India

ISBN: (数字)9781728154640

ISBN: (纸本)9781728154657

Chatbots are a computer program that was created to imitate the human during a conversation. In this technological era, humans were replaced by machines for performing most of the work. So chatbots were developed to mimic the conversation a human does with another person. The work a chatbot does ranges from answering simple queries to acting as personal assistant to the boss. There are different kinds of chatbots developed to cater to the needs of the people in different domain. The methodology of creating them also varies depending on their type. In this paper, the various types of chatbots and techniques such as Machine Learning, deep learning and natural language processing used for designing them were discussed in detail.

关键词： Deep learning Chatbot data models

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A Study of Deep Learning for Factoid Question Answering System

A Study of Deep Learning for Factoid Question Answering Syst...

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IEEE international conference on Information Reuse and Integration (IRI)

作者： Min-Yuh Day Yu-Ling Kuo Institute of Information Management National Taipei University Taipei Taiwan Department of Information Management Tamkang University New Taipei City Taiwan

ISBN: (数字)9781728110547

ISBN: (纸本)9781728110554

End-to-end question answering system has attracted considerable attention in the artificial intelligence research community in recent years. In this paper, we proposed an integrated deep learning model for factoid question answering system. This study uses the Delta Reading Comprehension dataset (DRCD) to build a model to implement a factoid question answering system and to combine the classification of question and answer to evaluate with exact match (EM) and F1 score. The study determines whether the comparison can increase the proportion of EM and whether the expected answer type can effectively increase the answer accuracy rate. To perfect the transformation, a question-and-answer system that uses the BERT pre-training model is applied to the DRCD dataset together with the expected answer type analysis and comparison. The contribution of this paper is that we proposed a system architecture of factoid question answering (QA) system using BERT with question expected answer type (Q-EAT) and answer type classification (AT) models. Findings confirm that the classification of question and answer can improve the EM ratio. When the question sentence and the answer classification are the same, the prediction accuracy EM of the question answering system will be improved.

关键词： Knowledge discovery Bit error rate Predictive models Machine learning Natural language processing data models Numerical models

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A data Generator for Cloud-Edge Vehicle Communication in Multi Domain Cellular Networks

A Data Generator for Cloud-Edge Vehicle Communication in Mul...

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international conference on Internet of Things: Systems, Management and Security

作者： Marco Pomalo Van Thanh Le Nabil El Ioini Claus Pahl Hamid R. Barzegar Free University of Bozen-Bolzano Bozen-Bolzano Italy

ISBN: (数字)9780738124605

ISBN: (纸本)9781665419260

The rapid development of telecommunications and cellular network technologies gave birth to a range of services and scenarios that were considered impossible a decade ago. Various architectures, scenarios, and use-cases can be deployed on top of the different generations of cellular networks to solve different business cases. Some scenarios require a high level of reliability due to their critical usage e.g., Vehicular Edge computing, medical IoT and so on. When offering services at the edge of the network, the information exchanged needs to be current and valid for systematic performance assessment and modeling. However, in order to run experiments, access to valid and reliable telecommunication data e.g., eNodeB (Base Station) properties, and configurations is not easy, since in most cases data is either confidential or at least difficult to obtain, especially when dealing with cross organizational boundaries (e.g., data coming from multiple telecom operators). To bridge this gap and allow researchers to build, test and analyze new protocols and algorithms with telecommunication data, we designed a mobile data generator (DG) for multi-domain cellular networks. Our generator provides a range of possible configurations and handles scenarios that include multiple participants, authorities and organizations. In this paper, we present the design and implementation of our generator. We evaluated the data generator by considering different scenarios, specifically, we have tested service interruptions and mobile network migration since these scenarios require a considerable amount of data.

关键词： Cellular networks Adaptation models Computational modeling Generators data models Telecommunications Telecommunication network reliability

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Motor data analysis of Parkinson’s disease patients

Motor data analysis of Parkinson’s disease patients

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Vasiliki Fiska Nikolaos Giannakeas Nikolaos Katertsidis Alexandros T. Tzallas Konstantinos Kalafatakis Markos G. Tsipouras Univ. of Western Macedonia Kozani Greece Science and Technology Park of Epirus UNIVEYE IKE Ioannina Greece Univ. of Ioannina Arta Greece

ISBN: (数字)9781728195742

ISBN: (纸本)9781728195759

In this manuscript, a methodology for analysing motor signals from Parkinson's disease (PD) patients is presented. The signals are obtained from PD patients while wearing a glove device and sequentially performing standard motor tests. The signals are processed in order to detect the onset and offset from specific items (items 23-25) of the Unified Parkinson's Disease Rating Scale (UPDRS) and then the isolated signal parts are analysed in order to quantity the motor findings defined in UPDRS for these items, such as hesitation, movement amplitude and frequency, and rotation range. The obtained results indicate that the methodology can achieve accurate motor assessment (related to ground-truth UPDRS) for both “Off” and “On” stages.

关键词： Performance evaluation Signal processing algorithms Signal processing Sensors data mining Standards Diseases

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Enhance Content Selection for Multi-Document Summarization with Entailment Relation

Enhance Content Selection for Multi-Document Summarization w...

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international conference on Technologies and Applications of Artificial Intelligence (TAAI)

作者： Yu-Yun Wang Jhen-Yi Wu Tzu-Hsuan Chou Ying-Jia Lin Hung-Yu Kao National Cheng Kung University Tainan Taiwan R.O.C

ISBN: (数字)9781665403801

ISBN: (纸本)9781665447379

Automatic text summarization is one of the common tasks in natural language processing. The main task is to generate a shorter version based on the original text and maintain relevant information. This paper studies multi-document summarization (MDS) that applies to news articles. MDS has two significant issues which are information overlap and information difference among multiple articles. Existing models mostly deal with MDS from the perspective of single document summarization (SDS). The models do not consider the relation between sentences in multiple news articles. Our proposed method deals with the issue and consists of two models. The sentence selector model selects representative sentences based on the entailment relation in different articles. The content is related to the event of the article extracted through the algorithm. The summary generator model generates a final summary to ensure that the summary contains no redundancy and maintains vital information. Experiment results show that our proposed model has effectively improved in the evaluation results. The main contribution of our approach is to use the entailment relation to obtain key content in multiple articles. Adding semantic comprehension can identify salient information clearly and improve the accuracy of MDS.

关键词： Training Redundancy Semantics Natural language processing Generators data mining Task analysis

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Simplified Approach for Predicting Emotions of Multi-Turn Textual Utterances

Simplified Approach for Predicting Emotions of Multi-Turn Te...

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international conference on Advances in ICT for Emerging Regions

作者： Jarsigan Vickneswaran Piruntha Navanesan Vahesan Vijayaratnam Uthayasanker Thayasivam University of Moratuwa Colombo Sri Lanka

ISBN: (数字)9781728186559

ISBN: (纸本)9781728186566

Sentiment analysis is a well-known topic in Natural Language processing (NLP). After a lot of research in this sector, the Emotion Analysis of Conversations prompted out as a trending topic. The main idea behind the Emotion Analysis of Conversations is to extract and classify the emotions expressed through a conversation. The scope of this research is adopted from "EmoContext" (SemEval 2019 - Task 3), a competition conducted by Codalab - Microsoft. There are some models that achieve a significant accuracy in this section but they are too much complicated to understand. There is a need for a model that achieves significant accuracy with a simplified approach for many uses. Here, we present a model that has achieved a microF1 score of 0.742 with our simple CNN-BiLSTM model and customized FastText word embedding, which comfortably betters the 3rd Quartile value of 0.7317 and stands up into the top quarter of the leader board of EmoContext competition.

关键词： Feature extraction data models Social networking (online) Blogs Analytical models Context modeling Bit error rate

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Image Colorization Using Generative Adversarial Networks and Transfer Learning

Image Colorization Using Generative Adversarial Networks and...

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Iranian Machine Vision and Image processing (MVIP)

作者： Leila Kiani Masoud Saeed Hossein Nezamabadi-pour Computer Engineering Department Shahid Bahonar University of Kerman Kerman Iran Intelligent Data Processing Laboratory (IDPL) Shahid Bahonar University of Kerman Kerman Iran

ISBN: (数字)9781728168326

ISBN: (纸本)9781728168333

Automatic colorizing is one of the most interesting problems in computer graphics. During the colorization process, the gray one-dimensional images are converted to three-dimensional images with colored components. As a typical technique, Convolutional neural networks (CNNs) have been well studied and used for automatic coloring. In these networks, the information that is generalized over in the top layers is available in intermediate layers. Although the output of the last layer of CNNs is usually used in many applications, in this paper, we use a concept called "Hypercolumn" derived from neuroscience to exploit information at all levels to develop a fully automated image colorization system. There are not always millions of data available in the real world to train complex deep learning models. Therefore, the VGG19 model trained with the big data set of ImageNet is used as a pre-trained model in the generator network and the hypercolumn idea is implemented in it with DIV2K datasets. We train our model to predict each pixel's color texture. The results obtained indicate that the proposed method is superior to competing models.

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scSNVIndel. accurate and efficient calling of SNVs and indels from single cell sequencing using integrated Bi-LSTM

scSNVIndel. accurate and efficient calling of SNVs and indel...

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IEEE international conference on Bioinformatics and Biomedicine (BIBM)

作者： Lei Cai Yufeng Wu Jingyang Gao Beijing University of Chemical Technology Beijing China University of Connecticut Storrs CT US

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Single-cell data are sparse and have coverage fluctuations, making it difficult, in comparison with data obtained from next-generation sequencing (NGS), to call single nucleotide variants (SNVs) and indels. Furthermore, most existing sequencing methods are unable to effectively call whole-genome SNVs and indels from single cell sequencing (SCS) data. In this study, we propose a new method for the efficient identification of SNVs and indels from SCS data, called scSNVIndel. scSNVIndel uses bidirectional long short-term memory (Bi-LSTM) as its base and integrates new natural language processing (NLP) technology. It automatically extracts features and accurately calls SNVs and indels when using SCS data, which is characterized by uneven and discontinuous coverage. Moreover, scSNVIndel can call variants from the sequence directly, retaining valuable information from the SCS data, as it does not convert the sequence into an image like the DeepVariant method. The results show that scSNVIndel performs better in terms of accuracy and recall for calling variants, when compared with other existing methods. scSNVIndel is currently an open-source method, available at https://***/CSuperlei/scSNVIndel, and its usage methods are published on the following website: https://***/2020/06/18/scSNVIndel/.

关键词： Sequential analysis Feature extraction Task analysis Tensors Training data models data mining

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Social Media data Analysis for Intelligent Transportation Systems

Social Media Data Analysis for Intelligent Transportation Sy...

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Emerging Trends in Information Technology and Engineering (ic-ETITE), conference on

作者： B Mounica. K Lavanya. School of Computer Science and Engineering(SCOPE) VIT University Vellore India

ISBN: (数字)9781728141428

ISBN: (纸本)9781728141435

Transportation is the major tribulations in urban cities. Traditional transportation approach are not smart for traffic management so Intelligent transport systems works with automated system and gives better solutions for smart parking, electronic toll collection, traffic monitoring and planning etc by accessing data from different sources. Among passive data, Social media data is widely sourced for Intelligent Transportation Systems (ITS), users communicate and interact online by writing blogs through face book, twitter and other social media applications. This data from social media can be used for transportation analysis like traffic prediction, accident zones analysis etc. unfortunately the data sourced is highly unstructured and massive in volume leading to complex processing for data usage. Big data helps in processing, storing and analyzing this data. In this paper we summarize main research on review topics in the field of data preprocessing techniques for noisy data removal, data concentration methods for filtering the data, feature selection methods to extract the main features, abnormal behavior of data to identify the suspicious behavior accounts and road condition prediction for detecting the road patterns and traffic behavior by supervised learning and unsupervised machine learning algorithms, this paper discusses towards the end on future research directions in social media analysis in ITS.

关键词： Intelligent Transportation Systems Social Media data Machine Learning Intelligent transportation systems Social Media transport traffic behavior Artificial Intelligence Machine Learning

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