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A smart filtering-based adaptive optimized link state routing protocol in flying ad hoc networks for traffic monitoring

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Journal of King Saud University - Computer and Information Sciences 2024年第4期36卷

作者： Hosseinzadeh, Mehdi Ali, Saqib Rahmani, Amir Masoud Lansky, Jan Nulicek, Vladimir Yousefpoor, Mohammad Sadegh Yousefpoor, Efat Darwesh, Aso Lee, Sang-Woong Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Department of Information Systems College of Economics and Political Science Sultan Qaboos University Al Khoudh Muscat Oman Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic Center of Research and Strategic Studies Lebanese French University Kurdistan Region Iraq Department of Information Technology University of Human Development Sulaymaniyah Kurdistan region Iraq Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam 13120 South Korea

Nowadays, the use of drones as a fundamental element of smart cities has attracted the attention of many researchers to monitor and control the traffic of vehicles. Because of the high flexibility of multi-drone systems, like flying ad hoc networks (FANETs), they provide various services and improve modern life in smart cities. However, due to the unique features of FANET, especially the high speed of drones and rapid changes in network topology, communication reliability is a serious challenge in this network. Hence, traditional routing protocols, such as optimized link state routing (OLSR) scheme, cannot work well in these networks. In this paper, a smart filtering-based adaptive optimized link state routing (SFA-OLSR) scheme is proposed in FANETs. To increase adaptability to the FANET environment, SFA-OLSR provides a new solution to adjust the hello broadcast period so that each flying node specifies its broadcast period based on a new scale called cosine similarity between real and predicted positions. Furthermore, in SFA-OLSR, each flying node develops a filtering algorithm based on two parameters, namely link lifetime and remaining energy. The purpose of this algorithm is to reduce the size of the single-hop neighboring set of each flying node and minimize the search space when finding multi-point relays (MPRs). This increases the convergence speed of the algorithm. Then, SFA-OLSR exploits the sparrow search algorithm (SSA) to single out the best MPRs. This algorithm introduces a multi-objective function by focusing on three components, including energy, link lifespan, and neighbor degree. Lastly, the simulation process of SFA-OLSR is performed by the NS3 simulator. This process evaluates the performance of the proposed method and three schemes, namely Gangopadhyay et al., P-OLSR, and OLSR-ETX. These evaluations show that SFA-OLSR has a good performance in terms of three scales, namely packet delivery ratio, delay, and throughput, but its overhead is more than

关键词： Artificial intelligence (AI) Flying ad hoc networks (FANETs) Meta-heuristic algorithms Reliability Routing

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Integrated system for the efficient maintenance of urban pavements

Carreteras

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Carreteras 2021年第235期4卷 8-18页

作者： Pellicer Armiñana, Eugenio Paredes Palacios, Roberto Felipo Sanjuán, Jesús Sánchez-Robles Bello, Juan Grupo de Gestión del Proceso Proyecto-Construcción Universitat Politécnica de Valencia Spain Centro de Investigación Pattern Recognition and Human Language Technology Universitat Politécnica de Valencia Spain Pavasal Empresa Constructora S.A. Spain Proyectos y Obras. CPS infraestructuras Movilidad y Medio Ambiente S.L. Spain

The integrated system for the efficient maintenance of urban pavements is an innovation project derived from collaboration between a public university and prívate enterprises. This system automates the tasks of auscultation of urban pavements obtaining images with conventional cameras and later processing them, using deep learning techniques. In addition, it Incorporates optimization and predictive decision making that Includes, not only traditional technical criteria (such as the state of the pavement and weather conditions, for example), but also environmental (CO2emissions) and social criteria (proximity to critical infrastructure). The application is based on a friendly visual interface that allows identification and quantification of pavement deterioration in an automatic way, the prediction of the future state of the network, as well as the establishment of an efficient and proactive maintenance and rehabilitation program of urban pavement in the medium-long term. © 2021 Asociacion Espanola de la Carretera. All rights reserved.

关键词： Interface states

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Improving unsupervised word-by-word translation with language model and denoising autoencoder

arXiv

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arXiv 2019年

作者： Kim, Yunsu Geng, Jiahui Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

Unsupervised learning of cross-lingual word embedding offers elegant matching of words across languages, but has fundamental limitations in translating sentences. In this paper, we propose simple yet effective methods to improve word-by-word translation of cross-lingual embeddings, using only monolingual corpora but without any back-translation. We integrate a language model for context-aware search, and use a novel denoising autoencoder to handle reordering. Our system surpasses state-of-the-art unsupervised neural translation systems without costly iterative training. We also analyze the effect of vocabulary size and denoising type on the translation performance, which provides better understanding of learning the cross-lingual word embedding and its usage in translation. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

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Generalizing back-translation in neural machine translation

arXiv

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arXiv 2019年

作者： Graca, Miguel Kim, Yunsu Schamper, Julian Khadivi, Shahram Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

Back-translation - data augmentation by translating target monolingual data - is a crucial component in modern neural machine translation (NMT). In this work, we reformulate back-translation in the scope of crossentropy optimization of an NMT model, clarifying its underlying mathematical assumptions and approximations beyond its heuristic usage. Our formulation covers broader synthetic data generation schemes, including sampling from a target-to-source NMT model. With this formulation, we point out fundamental problems of the sampling-based approaches and propose to remedy them by (i) disabling label smoothing for the target-to-source model and (ii) sampling from a restricted search space. Our statements are investigated on the WMT 2018 German → English news translation task. Copyright © 2019, The Authors. All rights reserved.

关键词： Neural machine translation

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When and why is document-level context useful in neural machine translation?

arXiv

引用

arXiv 2019年

作者： Duc, Yunsu Kim Tran, Thanh Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

Document-level context has received lots of attention for compensating neural machine translation (NMT) of isolated sentences. However, recent advances in document-level NMT focus on sophisticated integration of the context, explaining its improvement with only a few selected examples or targeted test sets. We extensively quantify the causes of improvements by a document-level model in general test sets, clarifying the limit of the usefulness of document-level context in NMT. We show that most of the improvements are not interpretable as utilizing the context. We also show that a minimal encoding is sufficient for the context modeling and very Copyright © 2019, The Authors. All rights reserved.

关键词： Neural machine translation

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Packed Levitated Marker for Entity and Relation Extraction

arXiv

引用

arXiv 2021年

作者： Ye, Deming Lin, Yankai Li, Peng Sun, Maosong Dept. of Comp. Sci. & Tech. Institute for AI Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology China International Innovation Center of Tsinghua University Shanghai China Jiangsu Collaborative Innovation Center for Language Ability Xuzhou China Institute Guo Qiang Tsinghua University China Pattern Recognition Center WeChat AI China Tsinghua University China

Recent entity and relation extraction works focus on investigating how to obtain a better span representation from the pre-trained encoder. However, a major limitation of existing works is that they ignore the interrelation between spans (pairs). In this work, we propose a novel span representation approach, named Packed Levitated Markers (PL-Marker), to consider the interrelation between the spans (pairs) by strategically packing the markers in the encoder. In particular, we propose a neighborhood-oriented packing strategy, which considers the neighbor spans integrally to better model the entity boundary information. Furthermore, for those more complicated span pair classification tasks, we design a subject-oriented packing strategy, which packs each subject and all its objects to model the interrelation between the same-subject span pairs. The experimental results show that, with the enhanced marker feature, our model advances baselines on six NER benchmarks, and obtains a 4.1%-4.3% strict relation F1 improvement with higher speed over previous state-of-the-art models on ACE04 and ACE05. Our code and models are publicly available at https://***/thunlp/PL-Marker. Copyright © 2021, The Authors. All rights reserved.

关键词： Signal encoding

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ELoPE: Fine-Grained Visual Classification with Efficient Localization, Pooling and Embedding

arXiv

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arXiv 2019年

作者： Hanselmann, Harald Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

The task of fine-grained visual classification (FGVC) deals with classification problems that display a small inter-class variance such as distinguishing between different bird species or car models. State-of-the-art approaches typically tackle this problem by integrating an elaborate attention mechanism or (part-) localization method into a standard convolutional neural network (CNN). Also in this work the aim is to enhance the performance of a backbone CNN such as ResNet by including three efficient and lightweight components specifically designed for FGVC. This is achieved by using global k-max pooling, a discriminative embedding layer trained by optimizing class means and an efficient bounding box estimator that only needs class labels for training. The resulting model achieves new best state-of-the-art recognition accuracies on the Stanford cars and FGVC-Aircraft datasets. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

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MoEfication: Transformer Feed-forward Layers are Mixtures of Experts

arXiv

引用

arXiv 2021年

作者： Zhang, Zhengyan Lin, Yankai Liu, Zhiyuan Li, Peng Sun, Maosong Zhou, Jie Dept. of Comp. Sci. & Tech. Institute for AI Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology China Pattern Recognition Center WeChat AI Tencent Inc China International Innovation Center Tsinghua University Shanghai China Beijing Academy of Artificial Intelligence China Tsinghua University China Jiangsu Collaborative Innovation Center for Language Ability Xuzhou China

Recent work has shown that feed-forward networks (FFNs) in pre-trained Transformers are a key component, storing various linguistic and factual knowledge. However, the computational patterns of FFNs are still unclear. In this work, we study the computational patterns of FFNs and observe that most inputs only activate a tiny ratio of neurons of FFNs. This phenomenon is similar to the sparsity of the human brain, which drives research on functional partitions of the human brain. To verify whether functional partitions also emerge in FFNs, we propose to convert a model into its MoE version with the same parameters, namely MoEfication. Specifically, MoEfication consists of two phases: (1) splitting the parameters of FFNs into multiple functional partitions as experts, and (2) building expert routers to decide which experts will be used for each input. Experimental results show that MoEfication can conditionally use 10% to 30% of FFN parameters while maintaining over 95% original performance for different models on various downstream tasks. Besides, MoEfication brings two advantages: (1) it significantly reduces the FLOPS of inference, i.e., 2x speedup with 25% of FFN parameters, and (2) it provides a fine-grained perspective to study the inner mechanism of FFNs. The source code of this paper can be obtained from https://***/thunlp/MoEfication. Copyright © 2021, The Authors. All rights reserved.

关键词： Brain

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Effective cross-lingual transfer of neural machine translation models without shared vocabularies

arXiv

引用

arXiv 2019年

作者： Kim, Yunsu Gao, Yingbo Ney, Hermann Human Language Technology and Pattern Recognition Group Rwth Aachen University Aachen Germany

Transfer learning or multilingual model is essential for low-resource neural machine translation (NMT), but the applicability is limited to cognate languages by sharing their vocabularies. This paper shows effective techniques to transfer a pre-trained NMT model to a new, unrelated language without shared vocabularies. We relieve the vocabulary mismatch by using cross-lingual word embedding, train a more language-agnostic encoder by injecting artificial noises, and generate synthetic data easily from the pre-training data without back-translation. Our methods do not require restructuring the vocabulary or retraining the model. We improve plain NMT transfer by up to +5.1% BLEU in five low-resource translation tasks, outperforming multilingual joint training by a large margin. We also provide extensive ablation studies on pre-trained embedding, synthetic data, vocabulary size, and parameter freezing for a better understanding of NMT transfer. Copyright © 2019, The Authors. All rights reserved.

关键词： Neural machine translation

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A neural, interactive-predictive system for multimodal sequence to sequence tasks

arXiv

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arXiv 2019年

作者： Peris, Álvaro Casacuberta, Francisco Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València València Spain

We present a demonstration of a neural interactive-predictive system for tackling multimodal sequence to sequence tasks. The system generates text predictions to different sequence to sequence tasks: machine translation, image and video captioning. These predictions are revised by a human agent, who introduces corrections in the form of characters. The system reacts to each correction, providing alternative hypotheses, compelling with the feedback provided by the user. The final objective is to reduce the human effort required during this correction process. This system is implemented following a client–server architecture. For accessing the system, we developed a website, which communicates with the neural model, hosted in a local server. From this website, the different tasks can be tackled following the interactive-predictive framework. We open-source all the code developed for building this system. The demonstration in hosted in http://***/ interactive-seq2seq. Copyright © 2019, The Authors. All rights reserved.

关键词： Websites

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