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arXiv 2021年

作者： Rebolledo, Margarita Zeeuwe, Daan Bartz-Beielstein, Thomas Eiben, A.E. Institute for Data Science Engineering and Analytics TH Köln Gummersbach Germany Department of Computer Science Vrije Universiteit Amsterdam Netherlands

Most evolutionary robotics studies focus on evolving some targeted behavior without taking the energy usage into account. This limits the practical value of such systems because energy efficiency is an important property for real-world autonomous robots. In this paper, we mitigate this problem by extending our simulator with a battery model and taking energy consumption into account during fitness evaluations. Using this system we investigate how energy awareness affects the evolution of robots. Since our system is to evolve morphologies as well as controllers, the main research question is twofold: (i) what is the impact on the morphologies of the evolved robots, and (ii) what is the impact on the behavior of the evolved robots if energy consumption is included in the fitness evaluation? The results show that including the energy consumption in the fitness in a multi-objective fashion (by NSGA-II) reduces the average size of robot bodies while at the same time reducing their speed. However, robots generated without size reduction can achieve speeds comparable to robots from the baseline set. © 2021, CC BY-NC-SA.

关键词： Health

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Change detection for geodatabase updating

arXiv

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arXiv 2021年

作者： Qin, Rongjun Department of Civil Environmental and Geodetic Engineering Department of Electrical and Computer Engineering Translational Data Analytics Institute The Ohio State University 2036 Neil Avenue ColumbusOH United States

The geodatabase (vectorized data) nowadays becomes a rather standard digital city infrastructure;however, updating geodatabase efficiently and economically remains a fundamental and practical issue in the geospatial industry. The cost of building a geodatabase is extremely high and labor intensive, and very often the maps we use have several months and even years of latency. One solution is to develop more automated methods for (vectorized) geospatial data generation, which has been proven a difficult task in the past decades. An alternative solution is to first detect the differences between the new data and the existing geospatial data, and then only update the area identified as changes. The second approach is becoming more favored due to its high practicality and flexibility. A highly relevant technique is change detection. This article aims to provide an overview the state-of-the-art change detection methods in the field of Remote Sensing and Geomatics to support the task of updating geodatabases. data used for change detection are highly disparate, we therefore structure our review intuitively based on the dimension of the data, being 1) change detection with 2D data;2) change detection with 3D data. Conclusions will be drawn based on the reviewed efforts in the field, and we will share our outlooks of the topic of updating geodatabases. . Copyright © 2021, The Authors. All rights reserved.

关键词： Remote sensing

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A Secured and Optimized Broadcast Authentication Scheme for the Internet of Medical Things 4

A Secured and Optimized Broadcast Authentication Scheme for ...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Riaz, Syed Waleed Khan, Fazlullah Ullah, Insaf Alshawi, Bandar Alturki, Ryan Wedyan, Mohammad Abdul Wali Khan University Mardan Computer Science Department KPK Pakistan School of Computer Science University of Nottingham Ningbo China Faculty of Science and Engineering Zhejiang Ningbo315104 China Institute for Analytics and Data Science University of Essex ColchesterCO4 3SQ United Kingdom College of Computing Umm Al-Qura University Department of Computer and Network Engineering Makkah Saudi Arabia College of Computing Umm Al-Qura University Department of Software Engineering Makkah Saudi Arabia Faculty of Information Technology and Computer Sciences Department of Computer Sciences Irbid21163 Jordan

ISBN: (纸本)9798350395914

This Internet of Medical Things (IoMT), facilitates the medical stop regarding real-time monitoring of patients, medical emergency management, remote surgery, patient information management, medical equipment, drug monitoring, etc. However, IoMT devices communicate with each other in an open environment that makes them vulnerable to a wide range of malicious threats from malicious entities. To protect the devices and their associated data, we designed a broadcast authentication scheme for IoMT devices using identity-based public key cryptography that exploits the lightweight features of the Hyper-Elliptic Curve (HEC). We then performed the security analysis based on the Random Oracle Model (ROM), in which we have proved that the proposed scheme is unforgeable under the hardiness of the hyperelliptic curve discrete logarithm problem. The proposed scheme is analyzed in terms of computational and communication overhead and the experimental results justify the superiority of the proposed work in comparison to the existing schemes. © 2024 IEEE.

关键词： Authentication

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Hybrid Variable Selection and Support Vector Regression for Gas Sensor Optimization 9th

Hybrid Variable Selection and Support Vector Regression for ...

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9th International Conference on Bioinspired Optimization Methods and Their Applications, BIOMA 2020

作者： Rebolledo, Margarita Stoean, Ruxandra Eiben, A.E. Bartz-Beielstein, Thomas Institute for Data Science Engineering and Analytics TH Köln Köln Germany Romanian Institute of Science and Technology Cluj-Napoca Romania Grupo Ingeniería de Sistemas Integrados E.T.S.I. Telecomunicación Universidad de Malaga Malaga Spain Department of Computer Science Vrije Universiteit Amsterdam Amsterdam Netherlands

ISBN: (纸本)9783030637095

The improvement of combustion processes in industry, especially in the automotive branch, is of great importance to maintain the environmental permitted limits. Carbon monoxide concentration in the exhaust gases can give an insight into the efficiency of the combustion taking place and for this reason, it is important to have sensors that can measure it accurately. First results of a long term study with one of the leading sensor manufactures showed high performance using genetic programming. However, this expensive approach is difficult to apply in real-world settings. Therefore a hybrid optimization that combines support vector regression (SVR) with variable pre-selection is proposed. Three different methods for variable selection are compared for this application, a genetic algorithm, and two methods from Bayesian statistics: statistical equivalent signatures and projection predictive variable selection. Furthermore, a multi-objective approach using the same hybrid definition is implemented for the cases in which several sensors need to be considered simultaneously. Our results show that the hybrid model is an improvement compared to the previous study, while delivering good performance when dealing with a multivariate formulation. Genetic algorithms in combination with SVR lead to enhanced variation on the groups of selected variables. © 2020, Springer Nature Switzerland AG.

关键词： Genetic algorithms

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Findings of the Sentiment Analysis of Dravidian Languages in Code-Mixed Text 13

Findings of the Sentiment Analysis of Dravidian Languages in...

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Working Notes of FIRE - 13th Forum for Information Retrieval Evaluation, FIRE-WN 2021

作者： Chakravarthi, Bharathi Raja Priyadharshini, Ruba Thavareesan, Sajeetha Chinnappa, Dhivya Thenmozhi, Durairaj Sherly, Elizabeth McCrae, John P. Hande, Adeep Ponnusamy, Rahul Banerjee, Shubhanker Vasantharajan, Charangan Insight SFI Research Centre for Data Analytics Data Science Institute National University of Ireland Galway Ireland ULTRA Arts and Science College Tamil Nadu Madurai India Eastern University Sri Lanka Thomson Reuters United States Sri Sivasubramaniya Nadar College of Engineering Tamil Nadu India Indian Institute of Information Technology and Management Kerala India Indian Institute of Information Technology Tiruchirappalli India ADAPT Centre Data Science Institute National University of Ireland Galway Ireland Department of Computer Science and Engineering University of Moratuwa Sri Lanka

We present the results of the Dravidian-CodeMix shared task1 held at FIRE 2021, a track on sentiment analysis for Dravidian Languages in Code-Mixed Text. We describe the task, its organization, and the submitted systems. This shared task is the continuation of last year’s Dravidian-CodeMix shared task2 held at FIRE 2020. This year’s tasks included code-mixing at the intra-token and inter-token levels. Additionally, apart from Tamil and Malayalam, Kannada was also introduced. We received 22 systems for Tamil-English, 15 systems for Malayalam-English, and 15 for Kannada-English. The top system for Tamil-English, Malayalam-English and Kannada-English scored weighted average F1-score of 0.711, 0.804, and 0.630, respectively. In summary, the quality and quantity of the submission show that there is great interest in Dravidian languages in code-mixed setting and state of the art in this domain still needs more improvement. © 2021 Copyright for this paper by its authors.

关键词： Sentiment analysis

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Transforming Shopping with IoT: Exploring the Intelligent Way to Design the Smart Trolley using IoT Principles

Transforming Shopping with IoT: Exploring the Intelligent Wa...

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2024 IEEE International Conference on Big data and Machine Learning, ICBDML 2024

作者： Jayaprakash, M. Hency Juliet, A. Jaya Mabel Rani, A. Velusudha, N.T. Lakshmisridevi, S. Samuthira Pandi, V. R.M.K. Engineering College Department of Information Technology Tamil Nadu Kavaraipettai India Saveetha University Saveetha Institute of Medical and Technical Sciences Department of Data Analytics Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai India BS Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Integrated Technology Chennai Institute of Technology Centre for Advanced Wireless Tamil Nadu Chennai India

ISBN: (纸本)9798350374100

The interconnection of commonplace items is revolutionizing people's daily lives, thanks to the Internet of Things (IoT). A smart shopping system, consisting of interconnected products, might one day be a reality in supermarkets. In this Internet of Things system, products may be individually identified using low-cost RFID tags, which, when added to an RFID-enabled smart shopping cart, can be read automatically. This means shoppers won't have to endure the checkout line at all - billing can be handled right in the cart. The integration of RFID-equipped smart shelving into this system allows for the monitoring of inventory and, perhaps, the update of a central server. The suggested shopping system, which is dubbed IoT Assisted Smart Trolley (IoTAST), is cross-validated with the standard Smart Trolley (ST) model to clearly evaluate its performance. Since an RFID reader can automatically read all objects instead of a labourer physically scanning them, inventory management becomes considerably easier with this sort of technology. We construct a working system to test its functioning, determine the design criteria of a sophisticated purchasing system, and then create a reliable communication mechanism that renders the system feasible. This study validates the viability of the system. This is the first smart shopping system that takes security into account that we are aware of. © 2024 IEEE.

关键词： Internet of things

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eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction data

arXiv

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arXiv 2024年

作者： Peng, Bo Ling, Xinyi Chen, Ziru Sun, Huan Ning, Xia Department of Computer Science and Engineering The Ohio State University United States Translational Data Analytics Institute The Ohio State University United States Department of Biomedical Informatics The Ohio State University United States

With tremendous efforts in developing effective e-commerce models, conventional e-commerce models show limited success in generalist e-commerce modeling, and suffer from unsatisfactory performance on new users and new products – a typical out-of-domain generalization challenge. Meanwhile, large language models (LLMs) demonstrate outstanding performance in generalist modeling and out-of-domain generalizability in many fields. Toward fully unleashing their power for e-commerce, in this paper, we construct ECInstruct, the first open-sourced, large-scale, and high-quality benchmark instruction dataset for e-commerce. Leveraging ECInstruct, we develop eCeLLM, a series of e-commerce LLMs, by instruction-tuning general-purpose LLMs. Our comprehensive experiments and evaluation demonstrate that eCeLLM models substantially outperform baseline models, including the most advanced GPT-4, and the state-of-the-art task-specific models in in-domain evaluation. Moreover, eCeLLM exhibits excellent generalizability to out-of-domain settings, including unseen products and unseen instructions, highlighting its superiority as a generalist e-commerce model. Both the ECInstruct dataset and the eCeLLM models show great potential in empowering versatile and effective LLMs for e-commerce. ECInstruct and eCeLLM models are publicly accessible through https://***/eCeLLM/. © 2024, CC BY.

关键词： Marketplaces

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Geo-teaser: Geo-temporal sequential embedding rank for point-of-interest recommendation 26

Geo-teaser: Geo-temporal sequential embedding rank for point...

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26th International World Wide Web Conference, WWW 2017 Companion

作者： Zhao, Shenglin Zhao, Tong King, Irwin Lyu, Michael R. Shenzhen Key Laboratory of Rich Media Big Data Analytics Application Shenzhen Research Institute Chinese University of Hong Kong Nanshan District Shenzhen China Department of Computer Science and Engineering Chinese University of Hong Kong Shatin N.T. Hong Kong

ISBN: (纸本)9781450349147

Point-of-interest (POI) recommendation is an important application for location-based social networks (LBSNs), which learns the user preference and mobility pattern from check-in sequences to recommend POIs. Previous studies show that modeling the sequential pattern of user check-ins is necessary for POI recommendation. Markov chain model, recurrent neural network, and the word2vec framework are used to model check-in sequences in previous work. However, all previous sequential models ignore the fact that check-in sequences on different days naturally exhibit the various temporal characteristics, for instance, "work" on weekday and "entertainment" on weekend. In this paper, we take this challenge and propose a Geo-Temporal sequential embedding rank (Geo-Teaser) model for POI recommendation. Inspired by the success of the word2vec framework to model the sequential contexts, we propose a temporal POI embedding model to learn POI representations under some particular temporal state. The temporal POI embedding model captures the contextual check-in information in sequences and the various temporal characteristics on different days as well. Furthermore, We propose a new way to incorporate the geographical influence into the pairwise preference ranking method through discriminating the unvisited POIs according to geographical information. Then we develop a geographically hierarchical pairwise preference ranking model. Finally, we propose a unified framework to recommend POIs combining these two models. To verify the effectiveness of our proposed method, we conduct experiments on two real-life datasets. Experimental results show that the Geo-Teaser model outperforms state-of-the-art models. Compared with the best baseline competitor, the Geo-Teaser model improves at least 20% on both datasets for all metrics. © 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License.

关键词： Location based services

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Captions Speak Louder than Images (CASLIE): Generalizing Foundation Models for E-commerce from High-quality Multimodal Instruction data

arXiv

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arXiv 2024年

作者： Ling, Xinyi Peng, Bo Du, Hanwen Zhu, Zhihui Ning, Xia Department of Computer Science and Engineering The Ohio State University United States Translational Data Analytics Institute The Ohio State University United States Department of Biomedical Informatics The Ohio State University United States

Leveraging multimodal data to drive breakthroughs in e-commerce applications through Multimodal Foundation Models (MFMs) is gaining increasing attention from the research community. However, there are significant challenges that hinder the optimal use of multimodal e-commerce data by foundation models: (1) the scarcity of large-scale, high-quality multimodal benchmark datasets;and (2) the lack of effective multimodal information integration methods. To address these challenges, in this paper, we introduce MMECInstruct, the first-ever, large-scale, and high-quality multimodal instruction dataset for e-commerce. We also develop CASLIE, a simple, lightweight, yet effective framework for integrating multimodal information for e-commerce. Leveraging MMECInstruct, we fine-tune a series of e-commerce MFMs within CASLIE, denoted as CASLIE models. Our comprehensive evaluation demonstrates that CASLIE models substantially outperform 5 categories of advanced baseline models in the in-domain evaluation. Moreover, CASLIE models show strong generalizability to out-of-domain settings. MMECInstruct and CASLIE models are publicly accessible through https://***/CASLIE/. © 2024, CC BY.

关键词： Large datasets

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Virtual machines of high availability using hardware-assisted failure detection

Virtual machines of high availability using hardware-assiste...

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International Carnahan Conference on Security Technology

作者： Wei-Jen Wang Hung-Lin Huang Shan-Hao Chuang Shao-Jui Chen Chia Hung Kao Deron Liang Department of Computer Science and Information Engineering National Central University Taiwan Data Analytics Technology and Applications Institute Institute for Information Industry Taiwan

The virtualization technology has been widely used in today's doud computing datacenters. With the virtualization technology, each physical machine in a datacenter can be logically divided into several virtual machines, on which different types of software services can host. However, many reasons may decrease the availability of the whole system. For example, a failed physical machine automatically fails all virtual machines on the physical machine, and consequently fails every software service on the virtual machines. It is difficult to detect failures efficiently in a general-purpose computer architecture because the hardware cannot provide enough information for fast failure detection. On the contrary, the ATCA (Advanced Telecommunications Computing Architecture) physical machines provide high hardware availability, and support IPMI (Intelligent Platform Management Interface) that can quickly detect the hardware status. In this paper, we developed a novel failure model and designed a symmetric fault-tolerant mechanism using ATCA physical machines and KVM to provide a solution for high system availability. The proposed fault-tolerant mechanism divides ATCA physical machines into pairs, such that each machine of a pair supports fault tolerance for each other. Once a failure is detected in the physical machine layer or the virtualization layer, the failed virtual machines are then recovered on the other physical machine. We have compared the proposed fault-tolerance mechanism with another prior VM-based fault-tolerance tool. The results show that the proposed mechanism significantly reduces the service downtime. That is, it provides better system availability for software services running on the virtual machines.

关键词： Servers Blades Virtual machining Hardware Monitoring Software Temperature sensors

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