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18th Iberian Conference on Information Systems and Technologies, CISTI 2023

作者： Bortoluzzi, Fabricio Irwin, Barry Westphall, Carla Merkle Noroff University College Department of Applied Computing Kristiansand Norway Rhodes University Grahamstown Department of Computer Science South Africa Federal University of Santa Catarina Postgraduate Programme in Computer Science Florianópolis Brazil

ISBN: (纸本)9789893347928

Among the existing methods for analysing internet traffic, one focuses on unsolicited and often harmful packets, referred to as Internet Background Radiation (IBR). IBR data can be captured by using cloud-based computing instances to listen for incoming packets and recording the received headers and contents in files using the PCAP format. No services are run on these hosts so all traffic can be regarded as non-legitimate. This work presents the research plan for capturing a year-long sample of the IBR arriving to up to 1500 geographically distributed sensors across a major service cloud provider. In combination with collection, a data analysis pipeline will be constructed to enable the querying of most relevant quantitative and qualitative aspects of the resulting dataset. The ultimate goal is to answer 'how is the IBR characterised when captured within the context of cloud computing?'. Results will include packet distribution according to the properties of the network, transport, and application layers, together with an evaluation of traffic linking to botnet activity such as Mirai and Moobot. © 2023 ITMA.

关键词： Cloud computing

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Enhancement of the RFID data management using EPCIS and e-Transaction

Enhancement of the RFID data management using EPCIS and e-Tr...

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1st International Conference on Advanced Data and Information Engineering, DaEng 2013

作者： Jakkhupan, Worapot Arch-Int, Somjit Information and Communication Technology Programme Faculty of Science Prince of Songkla University Songkla 90112 Thailand Department of Computer Science Faculty of Science Khon Kaen University Khon Kaen 40000 Thailand

ISBN: (纸本)9789814585170

One of the challenges in RFID adoption is to deal with massive RFID data. The large amount of data continuously generated from RFID devices come with noise and redundant, and the data may be missing. This study proposes the enhancement of a high level RFID data management using the data from EPCIS and e-Transaction as pre-processing data sources. The results reveal that the enhanced features improve the performance of the system by eliminating noise and redundant, therefore, the operation time is significantly reduced. The missing data can be detected at the end of the operation. © Springer science+Business Media Singapore 2014.

关键词： Supply chains

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Comparative Analysis of Eigenface and Learning Vector Quantization (LVQ) to Face Recognition 4

Comparative Analysis of Eigenface and Learning Vector Quanti...

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4th International Conference on Computing and Applied Informatics 2019, ICCAI 2019

作者： Chandra, R. An-Nissa, S. Zamzami, E.M. Master Programme in Informatics Universitas Sumatera Utara Medan Indonesia Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

Face recognition is a topic most often discussed in this era because it can be applied and developed for several needs that can be useful in daily life. Face recognition always use learning method like eigenface and learning vector quantization (LVQ). The learning process is using the face of a digital image taken from a camera in five angles for one person that will apply for dataset learning (training and learning data set), and the live image is taken from the camera for testing (testing data image). The first step image will be detected with a haar cascade and captured by a camera. It will use to processing images to get the best image and prepare to be input into the network. From the experiments with 10 testings with various parameter values, the experiment results obtained the LVQ is more accurate than eigenface to identifying faces with average accuracy 66.29% and eigenface is 56.67% with comparison 9.62%, but eigenface is faster in running time with average time 4.39 seconds and 7.38 seconds for LVQ with comparison 2.98 seconds. © Published under licence by IOP Publishing Ltd.

关键词： Face recognition

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Gestural system based on multi-functional sensors and ZigBee networks for squad communication

Gestural system based on multi-functional sensors and ZigBee...

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2011 Defense science Research Conference and Expo, DSR 2011

作者： Tongrod, Nattapong Lokavee, Shongpun Kerdcharoen, Teerakiat Watthanawisuth, Natthapol Tuantranont, Adisorn Materials Science and Engineering Programme Faculty of Science Mahidol University Bangkok 10400 Thailand Nanoelectronic and MEMS Lab National Electronic and Computer Technology Center Pathumthani 12120 Thailand

ISBN: (纸本)9781424492763

In this paper, we have developed a communication system for military based on hand gestures which will be useful for squad communication. In particular, we present an electronic soldier glove that employs wireless networks based on low-cost ZigBee technology, multi-functional sensors such as accelerometer and flex sensor based input device for collecting and classifying hand gestures. This system delivers data output through computer display. Experimental results have proved the success of the proposed hand gesture recognition system that can adapt to communicate with multimodal signals to enhance visual signal in difficult and stressful environments where visual communication may not be possible. © 2011 IEEE.

关键词： Visual communication

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Detection of Fake Reviews on Products Using Machine Learning 7th

Detection of Fake Reviews on Products Using Machine Learning

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7th International Conference on Information and Communication Technology for Competitive Strategies, ICTCS 2022

作者： Narayana Royal, M. Reddy, Rajula Pavan Kalyan Sangathya, Gokina Sri Sai Madesh Pretam, B. Kaliappan, Jayakumar Suganthan, C. School of Computer Science and Engineering Vellore Institute of Technology Vellore India Institute of Industry and International Programme Vellore Institute of Technology Vellore India

ISBN: (纸本)9789811993039

It is hard to exaggerate how crucial user reviews are in figuring out an organization’s e-commerce income. Before purchasing any goods or service, online customers rely on product and service reviews. As a result, firms must consider how reliable Internet reviews are because they might directly affect their reputation and bottom line. Because of this, some businesses pay spammers to post false reviews. These false reviews profit from consumer buying choices. As a result, during the past twelve years, there has been substantial study into techniques for spotting false reviews. However, there is still a need for a survey that can analyze and summarize the diverse techniques. In this paper, we are going to put the SVM and Naive Bayesian machine learning model system into place that can spot false reviews with an accuracy of 0.801 and 0.687. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electronic commerce

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Learning from Data: Cleft Lip and Palate Patients in the West Coast of Sabah 12

Learning from Data: Cleft Lip and Palate Patients in the Wes...

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12th Seminar on science and Technology 2018

作者： Omar, Z.A. Chin, S.N. Hamzah, Norhafiza Yassin, Fouziah Mathematics with Computer Graphics Programme Faculty of Science and Natural Resources Universiti Malaysia Sabah Kota Kinabalu Sabah88400 Malaysia Mathematics with Economy Programme Faculty of Science and Natural Resources Universiti Malaysia Sabah Kota Kinabalu Sabah88400 Malaysia Physic with Electronics Programme Faculty of Science and Natural Resources Universiti Malaysia Sabah Kota Kinabalu Sabah88400 Malaysia

Analysing data can be quite a challenge sometimes due to the nature of the data and the vast options of methods and techniques that can be used on the data. In this study, for example, a six years Cleft Lip and Palate dataset were gathered on these patients' conditions in the quest to identify the contributing factors for a successful pre-graft orthodontic treatment. The challenges faced was in the small number of datasets and imbalance sample class. Therefore, this study had taken a step back and tried to approach the dataset with a combination of unsupervised and supervised learning methods to tackle the challenges by incorporating clustering - for testing records creation and;resampling - for balancing sample class. We also observed if the auto-created testing records are replaceable with the manually selected testing records by looking at the performances of the classification models. Based on the feature that was selected, k-Means and PAM were implemented as the clustering algorithm using the Euclidean formula as the distance measure. Resampling was done using SMOTE and Random Forest as the classification model. When the comparison was done on the models, the ones that were fed by resampled training records showed an increase in the AUC values and decrease in the OOB error. Comparable results were also achieved between the training records produced by PAM and by manual selection as both models, based on the AUC values, was classified as excellent classification models. © Published under licence by IOP Publishing Ltd.

关键词： Statistical tests

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Social networks: Applications and challenges

Social networks: Applications and challenges

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IET International Conference on Communication Technology and Application, ICCTA 2011

作者： Tse, Rita Tan Sim Liu, Dawei Hou, Fen Pau, Giovanni Computing/Computer Studies Programme Macao Polytechnic Institute China Joint Research Center in Ubiquitous Computing Macao Polytechnic Institute China Computer Science Department UCLA CA United States

ISBN: (纸本)9781849194709

More and more serious global warming boosters the research on environmental protection from both the industries and academia. The collection of environmental data is a basic block for the environmental protection. Due to the mobility and the release of stringent power constraints, vehicular sensor networks (VSN) provides an efficient way to collect environmental data. However, many existing work on the application of VSN in the environmental protection focuses on using the collected data to generate the environmental report. The increasing popularity of social networks motivates our interests in providing more innovative applications by integrating the VSN with social networks. In this paper, we intro-duce the structure of the integrated system, discuss the applications and the challenging issues.

关键词： Sensor networks

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Trusted-Maximizers Entropy Search for Efficient Bayesian Optimization 37

Trusted-Maximizers Entropy Search for Efficient Bayesian Opt...

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37th Conference on Uncertainty in Artificial Intelligence, UAI 2021

作者： Nguyen, Quoc Phong Wu, Zhaoxuan Low, Bryan Kian Hsiang Jaillet, Patrick Department of Computer Science National University of Singapore Singapore Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States Institute of Data Science National University of Singapore Singapore NUSGS Integrative Sciences and Engineering Programme National University of Singapore Singapore

Information-based Bayesian optimization (BO) algorithms have achieved state-of-the-art performance in optimizing a black-box objective function. However, they usually require several approximations or simplifying assumptions (without clearly understanding their effects on the BO performance) and/or their generalization to batch BO is computationally unwieldy, especially with an increasing batch size. To alleviate these issues, this paper presents a novel trusted-maximizers entropy search (TES) acquisition function: It measures how much an input query contributes to the information gain on the maximizer over a finite set of trusted maximizers, i.e., inputs optimizing functions that are sampled from the Gaussian process posterior belief of the objective function. Evaluating TES requires either only a stochastic approximation with sampling or a deterministic approximation with expectation propagation, both of which are investigated and empirically evaluated using synthetic benchmark objective functions and real-world optimization problems, e.g., hyperparameter tuning of a convolutional neural network and synthesizing 'physically realizable' faces to fool a black-box face recognition system. Though TES can naturally be generalized to a batch variant with either approximation, the latter is amenable to be scaled to a much larger batch size in our experiments. © 2021 37th Conference on Uncertainty in Artificial Intelligence, UAI 2021. All Rights Reserved.

关键词： Entropy

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Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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A Routing Scheme for Data Aggregation Based on Ant-like Agent in Wireless Sensor Networks

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电子学报(英文版) 2007年第3期16卷 449-453页

作者： YE Ning WANG Ruchuan SUN Lijuan Institute of Computer Science Nanjing University of Posts and Telecommunications Nanjing 210003 China Department of Information Science Nanjing College for Population Programme Management Nanjing 210043 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210093 China Nanjing University Nanjing 210093 China

Recent developments in processor, memory and radio technology have enabled wireless sensor networks, which are deployed to collect useful information from an area of interest. Since various sensor nodes often detect common phenomena, there is likely to be redundancy in the data. Data aggregation is an essential paradigm by combining the data coming from different sources reroutes - eliminating redundancy, minimizing the number of transmissions and thus saving energy. Inspired by the social behaviors of ant colony and the technology of intelligent entity agent, in this paper, we proposed a data aggregation routing optimization method based on ant colony algorithm using agent collaboration and migration. A number of ant-like mobile agents traverse the Wireless sensor network (WSNs) in search of data aggregation paths between given source-destination node pairs while seeking to optimum migrate probability based on updating pheromone. Also, we presented an evaluation Ant colony optimation (ACO) algorithm with data aggregation gain. Our simulation results show that our algorithms perform well in energy efficient compared with LEACH and PEGASIS.

关键词：无线传感器网络数据聚集路由选择蚁群算法

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