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RoboCup 2D soccer simulation league: Evaluation challenges

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arXiv 2017年

作者： Prokopenko, Mikhail Wang, Peter Marian, Sebastian Bai, Aijun Li, Xiao Chen, Xiaoping Complex Systems Research Group Faculty of Engineering It the University of Sydney NSW2006 Australia Data Mining Csiro Data61 PO Box 76 EppingNSW1710 Compa-IT Department of Electrical Engineering and Computer Sciences University of California Berkeley Multi-Agent Systems Lab School of Computer Science and Technology University of Science and Technology of China

We summarise the results of RoboCup 2D Soccer Simulation League in 2016 (Leipzig), including the main competition and the evaluation round. The evaluation round held in Leipzig confirmed the strength of RoboCup-2015 champion (WrightEagle, i.e. WE2015) in the League, with only eventual finalists of 2016 competition capable of defeating WE2015. An extended, post-Leipzig, round-robin tournament which included the top 8 teams of 2016, as well as WE2015, with over 1000 games played for each pair, placed WE2015 third be-hind the champion team (Gliders2016) and the runner-up (HELIOS2016). This establishes WE2015 as a stable benchmark for the 2D Simulation League. We then contrast two ranking methods and suggest two options for future evaluation challenges. The first one, "The Champions Simulation League", is proposed to include 6 previous champions, directly competing against each other in a round-robin tournament, with the view to systematically trace the advancements in the League. The second proposal, "The Global Challenge", is aimed to increase the realism of the environmental conditions during the simulated games, by simulat-ing specific features of different participating countries. Copyright © 2017, The Authors. All rights reserved.

关键词： Sports

Flavours of Convolution for Unsupervised Aspect Extraction and Aspect-based Sentiment Analysis 7

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Flavours of Convolution for Unsupervised Aspect Extraction a...

7th Workshop on Natural Language for Artificial Intelligence, NL4AI 2023

作者： Chatterjee, Soumick Prakash, Sowmya Nürnberger, Andreas Genomics Research Centre Human Technopole Viale Rita Levi-Montalcini 1 Milan20157 Italy Faculty of Computer Science Otto von Guericke University Magdeburg Universitätspl. 2 Magdeburg39106 Germany Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Universitätspl. 2 Magdeburg39106 Germany Centre for Behavioural Brain Sciences Magdeburg Germany

Sentiment analysis, the task of understanding the underlying sentiment from a given data, is frequently used in various fields, from market research to recommender systems. To analyse different sentiments for different aspects present in a given sentence, aspect-based sentiment analysis (ABSA) approaches have been proposed. The task of ABSA can be divided into two subtasks: aspect extraction (AE) and aspect-based polarity detection (ABPD). Most of the state-of-the-art approaches are based on some form of neural networks using convolutional layers. Recently, flavours of convolution, like extremely separated convolution layer (XSepConv) - which can reduce computational cost along with the parameter size of large kernels and depth-wise over-parameterised convolutional layer (DOConv) - which can improve the training efficiency, have been proposed. They have shown their superior capabilities when it comes to image-related tasks, but they have not been explored for textual tasks like ABSA. This paper performs unsupervised AE and weakly supervised ABSA using those specialised convolutional layers. It could be shown that using such layers instead of traditional convolutional layers can significantly improve the performance of the model. © 2023 Copyright for this paper by its authors.

关键词： Extraction

Terahertz metamaterials fabrication from narrowband to broadband

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Terahertz metamaterials fabrication from narrowband to broad...

IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Z.Y. Pan P. Zhang B. Ng M.H. Hong Data Storage Institute Agency for Science Technology and Research Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Experimental Solid State Group Physics Department Imperial College London London UK

Split ring resonator (SRR) structures based terahertz (THz) metamaterials with different gap dimensions are fabricated on polyethylene naphthalate (PEN) films by parallel laser micro-lens array (MLA) lithography and lift-off process. The narrowband resonance is achieved in single layer SRR samples. A series of single layer SRR samples are fabricated with different gap sizes to obtain successive narrowband THz resonances over a wide THz range. For broadband design, these single layer SRR samples are stacked to form a multilayer structure. The multilayer SRR is experimentally demonstrated with a much broader bandwidth of 0.221 THz and enhanced resonance up to 2.14 times as compared to the single layer SRR.

关键词： Metamaterials Magnetic materials Broadband communication Nonhomogeneous media Narrowband Periodic structures Broadband antennas

Tracking temporal evolution of network activity for botnet detection

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arXiv 2019年

作者： Sinha, Kapil Viswanathan, Arun Bunn, Julian Department of Computer Science California Institute of Technology PasadenaCA15213 United States Cyber Defense Engineering and Research Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States Center for Data Driven Discovery California Institute of Technology PasadenaCA15213 United States

Botnets are becoming increasingly prevalent as the primary enabling technology in a variety of malicious campaigns such as email spam, click fraud, distributed denial-of-service (DDoS) attacks, and cryptocurrency mining. Botnet technology has continued to evolve rapidly making detection a very challenging problem. There is a fundamental need for robust detection methods that are insensitive to characteristics of a specific botnet and are generalizable across different botnet types. We propose a novel supervised approach to detect malicious botnet hosts by tracking a host's network activity over time using a Long Short-Term Memory (LSTM) based neural network architecture. We build a prototype to demonstrate the feasibility of our approach, evaluate it on the CTU-13 dataset, and compare our performance against existing detection methods. We show that our approach results in a more generalizable, botnet-agnostic detection methodology, is amenable to real-time implementation, and performs well compared to existing approaches, with an overall accuracy score of 96.2%. Copyright © 2019, The Authors. All rights reserved.

关键词： Long short-term memory

An Efficient Automated Technique for Classification of Breast Cancer Using Deep Ensemble Model

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computer Systems Science & engineering 2023年第7期46卷 897-911页

作者： Muhammad Zia Ur Rehman Jawad Ahmad Emad Sami Jaha Abdullah Marish Ali Mohammed A.Alzain Faisal Saeed Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City University15 Bartholomew RowBirminghamB55JUUK

Breast cancer is one of the leading cancers among *** has the second-highest mortality rate in women after lung *** detection,especially in the early stages,can help increase survival ***,manual diagnosis of breast cancer is a tedious and time-consuming process,and the accuracy of detection is reliant on the quality of the images and the radiologist’s ***,computer-aided medical diagnosis has recently shown promising results,leading to the need to develop an efficient system that can aid radiologists in diagnosing breast cancer in its early *** research presented in this paper is focused on the multi-class classification of breast *** deep transfer learning approach has been utilized to train the deep learning models,and a pre-processing technique has been used to improve the quality of the ultrasound *** proposed technique utilizes two deep learning models,Mobile-NetV2 and DenseNet201,for the composition of the deep ensemble *** learning models are fine-tuned along with hyperparameter tuning to achieve better ***,entropy-based feature selection is *** cancer identification using the proposed classification approach was found to attain an accuracy of 97.04%,while the sensitivity and F1 score were 96.87%and 96.76%,*** performance of the proposed model is very effective and outperforms other state-of-the-art techniques presented in the literature.

关键词： Breast cancer image enhancement ensemble model transfer learning feature selection

Special issue on the best papers of the Conference on Intelligent data Understanding (CIDU 2010)

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Statistical Analysis and data Mining 2011年第4期4卷 355-357页

作者： Srivastava, Ashok N. Chawla, Nitesh V. Intelligent Systems Division Intelligent Data Understanding Group NASA Ames Research Center Moffett Field CA United States Department of Computer Science and Engineering Interdisciplinary Center for Network Science and Applications University of Notre Dame South Bend IN United States

Dual encoding for zero-example video retrieval

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arXiv 2018年

作者： Dong, Jianfeng Li, Xirong Xu, Chaoxi Ji, Shouling He, Yuan Yang, Gang Wang, Xun College of Computer and Information Engineering Zhejiang Gongshang University Key Lab of Data Engineering and Knowledge Engineering Renmin University of China AI & Media Computing Lab School of Information Renmin University of China College of Computer Science Zhejiang University Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies Alibaba Group

This paper attacks the challenging problem of zero-example video retrieval. In such a retrieval paradigm, an end user searches for unlabeled videos by ad-hoc queries described in natural language text with no visual example provided. Given videos as sequences of frames and queries as sequences of words, an effective sequence-to-sequence cross-modal matching is required. The majority of existing methods are concept based, extracting relevant concepts from queries and videos and accordingly establishing associations between the two modalities. In contrast, this paper takes a concept-free approach, proposing a dual deep encoding network that encodes videos and queries into powerful dense representations of their own. Dual encoding is conceptually simple, practically effective and end-to-end. As experiments on three benchmarks, i.e. MSR-VTT, TRECVID 2016 and 2017 Ad-hoc Video Search show, the proposed solution establishes a new state-of-the-art for zero-example video retrieval. Copyright © 2018, The Authors. All rights reserved.

关键词： Signal encoding

Mitigating Translationese in Low-resource Languages: The Storyboard Approach 30

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Mitigating Translationese in Low-resource Languages: The Sto...

Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Kuwanto, Garry Urua, Eno-Abasi Amuok, Priscilla Muhammad, Shamsuddeen Hassan Aremu, Anuoluwapo Otiende, Verrah Nanyanga, Loice Nyoike, Teresiah Akpan, Aniefon Udouboh, Nsima Archibong, Idongesit Moses, Idara Ige, Ifeoluwatayo Ajibade, Benjamin Awokoya, Olumide Abdulmumin, Idris Aliyu, Saminu Mohammad Iro, Ruqayya Ahmad, Ibrahim Said Smith, Deontae Michaels, Praise E.L. Adelani, David Ifeoluwa Wijaya, Derry Tanti Andy, Anietie Department of Computer Science Boston University United States University of Uyo Nigeria Imperial College London United Kingdom Rochester Institute of Technology United States Data Science for Social Impact Research Group University of Pretoria South Africa Bayero University Kano Nigeria Institute For Experiential AI Northeastern University United States Department of Electrical Engineering and Computer Science Howard University United States University College London United Kingdom Monash University Indonesia Indonesia Masakhane

ISBN: (纸本)9782493814104

Low-resource languages often face challenges in acquiring high-quality language data due to the reliance on translation-based methods, which can introduce the translationese effect. This phenomenon results in translated sentences that lack fluency and naturalness in the target language. In this paper, we propose a novel approach for data collection by leveraging storyboards to elicit more fluent and natural sentences. Our method involves presenting native speakers with visual stimuli in the form of storyboards and collecting their descriptions without direct exposure to the source text. We conducted a comprehensive evaluation comparing our storyboard-based approach with traditional text translation-based methods in terms of accuracy and fluency. Human annotators and quantitative metrics were used to assess translation quality. The results indicate a preference for text translation in terms of accuracy, while our method demonstrates worse accuracy but better fluency in the language focused. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Translation (languages)

PURSUhInT: In Search of Informative Hint Points Based on Layer Clustering for Knowledge Distillation

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arXiv 2021年

作者： Keser, Reyhan Kevser Ayanzadeh, Aydin Aghdam, Omid Abdollahi Kilcioglu, Caglar Toreyin, Behcet Ugur Ure, Nazim Kemal Research Group Informatics Institute Istanbul Technical University Turkey Department of Computer Science and Electrical Engineering University of Maryland Baltimore County United States Arcelik Research and Development Turkey Artificial Intelligence and Data Science Application and Research Center Istanbul Technical University Turkey

One of the most efficient methods for model compression is hint distillation, where the student model is injected with information (hints) from several different layers of the teacher model. Although the selection of hint points can drastically alter the compression performance, conventional distillation approaches overlook this fact and use the same hint points as in the early studies. Therefore, we propose a clustering based hint selection methodology, where the layers of teacher model are clustered with respect to several metrics and the cluster centers are used as the hint points. Our method is applicable for any student network, once it is applied on a chosen teacher network. The proposed approach is validated in CIFAR-100 and ImageNet datasets, using various teacher-student pairs and numerous hint distillation methods. Our results show that hint points selected by our algorithm results in superior compression performance compared to state-of-the-art knowledge distillation algorithms on the same student models and datasets. © 2021, CC BY-NC-ND.

关键词： Distillation