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International Conference on Advances in Computing, Communication Control and Networking (ICACCCN)

作者： Aditi Gupta Neetika Raina Bharti Jha Vinay Kumar Jain Department of Computer Science & Engineering Manav Rachna University Faridabad Haryana Accendere Knowledge Management Pvt Ltd. New Delhi

ISBN: (纸本)9781538641200

With an increasing usage of social media to exchange, share and store information, cybercriminals also get attracted to it, to take advantage of the network for their illegal and unethical benefits. Fake online accounts pop up every day. Spammers are the users behind the screen who share unsolicited and irrelevant texts to a huge number of users with an intent of advertising some product or to make people to click on unsecured links and infecting user's system usually to make money (click bait). They often use Trending topics on social media as a medium to spam. Sometimes, spam and fake trending is created by Spammers and many a times spammers use Trending topics to lure victims into clicking them. Much research has been done and is going on to detect spammers in OSNs. This paper reviews the existing techniques to detect spammers in social media. Our Current study and future work provides an overview of the traditional classifiers, Naïve Bayes, Support Vector and how they are used to detect spam and classify a dataset taken from social media into trending and non-trending topics based spam.

关键词： Twitter Classification algorithms Support vector machine classification computer science Facebook

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Cloud based automated irrigation and plant leaf disease detection system using an android application

Cloud based automated irrigation and plant leaf disease dete...

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2017 International Conference on Electronics, Communication and Aerospace Technology, ICECA 2017

作者： Ranjith Anas, Saheer Badhusha, Ibrahim Ot, Zaheema Faseela, K. Shelly, Minnuja Department of Computer Science Engineering Eranad Knowledge City Technical Campus ManjeriKerala India

ISBN: (纸本)9781509056873;9781509056866

Due to drastic climatic changes and scarcity of water, the need for proper and sustainable irrigation methods is of high demand. The water demand for plants varies from place to place with the changes in soil content, texture, climatic factors and so on. The plants need to be irrigated according to their water requirements at that climatic conditions. As like as the water requirements, plant diseases are also a factor that keeps the plants not growing properly. Also, a variety of plant leaf diseases have also been detected nowadays. In this paper, a novel smart irrigation system has been proposed which can control the irrigation automatically using an android mobile application. Apart from this, the photos of plant leaves are captured and are sent to the cloud server, which is further processed and compared with the diseased plant leaf images in the cloud database. Based on the comparison a list of plant diseases suspected are given to the user via the android mobile application. © 2017 IEEE.

关键词： Embedded systems

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Conceptualising computational creativity: Towards automated historiography of the research field 9

Conceptualising computational creativity: Towards automated ...

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9th International Conference on Computational Creativity, ICCC 2018

作者： Podpean, Vid Lavra, Nada Wiggins, Geraint Pollak, Senja Department of Knowledge Technology Jožef Stefan Institute Ljubljana Slovenia Computational Creativity Laboratory AI Lab Vrije Universiteit Brussel Belgium School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom USHER Institute University of Edinburgh Edinburgh United Kingdom

ISBN: (纸本)9789895416004

This paper reports on the progress towards constructing automated historiography of the research field of Computational Creativity (CC). The analysis is based on papers published in the Proceedings of International Conferences on Computational Creativity in eight consecutive years since 2010. This paper extends our earlier work by proposing an approach to CC field analysis that facilitates the automation of CC conceptualisation. © ICCC *** rights reserved.

关键词： Automation

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Utilizing stability criteria in choosing feature selection methods yields reproducible results in microbiome data

arXiv

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arXiv 2020年

作者： Jiang, Lingjing Haiminen, Niina Carrieri, Anna-Paola Huang, Shi Vázquez-Baeza, Yoshiki Parida, Laxmi Kim, Ho-Cheol Swafford, Austin D. Knight, Rob Natarajan, Loki Division of Biostatistics University of California San Diego San diegoCA92093 United States IBM T. J. Watson Research Center Yorktown HeightsNY10598 United States IBM Research UK Hartree Center Warrington United Kingdom Center for Microbiome Innovation Jacobs School of Engineering UC San Diego San diegoCA92093 United States Department of Pediatrics University of California San Diego San diegoCA92093 United States Scalable Knowledge Intelligence IBM Research-Almaden San JoseCA95120 United States Department of Computer Science and Engineering University of California San Diego San diegoCA92093 United States Department of Bioengineering University of California San Diego San diegoCA92093 United States

Feature selection is indispensable in microbiome data analysis, but it can be particularly challenging as microbiome data sets are high-dimensional, underdetermined, sparse and compositional. Great efforts have recently been made on developing new methods for feature selection that handle the above data characteristics, but almost all methods were evaluated based on performance of model predictions. However, little attention has been paid to address a fundamental question: how appropriate are those evaluation criteria? Most feature selection methods often control the model fit, but the ability to identify meaningful subsets of features cannot be evaluated simply based on the prediction accuracy. If tiny changes to the training data would lead to large changes in the chosen feature subset, then many of the biological features that an algorithm has found are likely to be a data artifact rather than real biological signal. This crucial need of identifying relevant and reproducible features motivated the reproducibility evaluation criterion such as Stability, which quantifies how robust a method is to perturbations in the data. In our paper, we compare the performance of popular model prediction metric MSE and proposed reproducibility criterion Stability in evaluating four widely used feature selection methods in both simulations and experimental microbiome applications. We conclude that Stability is a preferred feature selection criterion over MSE because it better quantifies the reproducibility of the feature selection method. © 2020, CC BY.

关键词： Forecasting

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Deep learning models for predicting RNA degradation via dual crowdsourcing

arXiv

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arXiv 2021年

作者： Wayment-Steele, Hannah K. Kladwang, Wipapat Watkins, Andrew M. Kim, Do Soon Tunguz, Bojan Reade, Walter Demkin, Maggie Romano, Jonathan Wellington-Oguri, Roger Nicol, John J. Gao, Jiayang Onodera, Kazuki Fujikawa, Kazuki Mao, Hanfei Vandewiele, Gilles Tinti, Michele Steenwinckel, Bram Ito, Takuya Noumi, Taiga He, Shujun Ishi, Keiichiro Lee, Youhan Öztürk, Fatih Chiu, Anthony Öztürk, Emin Amer, Karim Fares, Mohamed Participants, Eterna Das, Rhiju Department of Chemistry Stanford University StanfordCA94305 United States Department of Biochemistry Stanford University CA94305 United States NVIDIA Corporation Santa ClaraCA95051 United States Kaggle San FranciscoCA94107 United States Eterna Massive Open Laboratory Department of Computer Science and Engineering State University of New York at Buffalo BuffaloNY14260 United States High-flyer AI Zhejiang Hangzhou310000 China NVIDIA Corporation Minato-ku Tokyo107-0052 Japan DeNA Shibuya-ku Tokyo150-6140 Japan Yanfu Investments Shanghai 200000 China IDLab Ghent University Technologiepark-Zwijnaarde GentB-9052 Belgium College of Life Sciences University of Dundee DundeeDD1 4HN United Kingdom Universal Knowledge Inc. Tokyo150-0013 Japan Keyence Corporation 1-3-14 Higashi-Nakajima Higashi-Yodogawa-ku Osaka533-8555 Japan Department of Chemical Engineering Texas A&M University College StationTX77843 United States Rist Inc Meguro-ku Tokyo153-0063 Japan Kakao Brain Gyeonggi-do Seongnam Korea Republic of H2O Istanbul3400 Turkey Clover Health Hong Kong999077 Hong Kong Afiniti Istanbul3400 Turkey Center for Informatics Science Nile University Sheikh Zayed Giza12588 Egypt National Research Centre Dokki Cairo12622 Egypt Department of Physics Stanford University CA94305 United States

Messenger RNA-based medicines hold immense potential, as evidenced by their rapid deployment as COVID-19 vaccines. However, worldwide distribution of mRNA molecules has been limited by their thermostability, which is fundamentally limited by the intrinsic instability of RNA molecules to a chemical degradation reaction called in-line hydrolysis. Predicting the degradation of an RNA molecule is a key task in designing more stable RNA-based therapeutics. Here, we describe a crowdsourced machine learning competition ("Stanford OpenVaccine") on Kaggle, involving single-nucleotide resolution measurements on 6043 102-130-nucleotide diverse RNA constructs that were themselves solicited through crowdsourcing on the RNA design platform Eterna. The entire experiment was completed in less than 6 months, and 41% of nucleotide-level predictions from the winning model were within experimental error of the ground truth measurement. Furthermore, these models generalized to blindly predicting orthogonal degradation data on much longer mRNA molecules (504-1588 nucleotides) with improved accuracy compared to previously published models. Top teams integrated natural language processing architectures and data augmentation techniques with predictions from previous dynamic programming models for RNA secondary structure. These results indicate that such models are capable of representing in-line hydrolysis with excellent accuracy, supporting their use for designing stabilized messenger RNAs. The integration of two crowdsourcing platforms, one for data set creation and another for machine learning, may be fruitful for other urgent problems that demand scientific discovery on rapid timescales. © 2021, CC BY.

关键词： RNA

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Corrigendum to: Multi-Swarm Cuckoo Search Algorithm withQ-Learning Model

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The computer Journal 2022年第7期65卷 1938-1938页

作者： Li, Juan Xiao, Dan-dan Zhang, Ting Liu, Chun Li, Yuan-xiang Wang, Gai-ge School of Artificial Intelligence Wuhan Technology and Business University Wuhan 430065 China School of Artificial Intelligence Wuchang University of Technology Wuhan 430223 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University No. 2699 Qianjin Street Gaoxin District Changchun Jilin China 130012 School of Computer Wuhan University No. 299 Bayi Street Wuchang District Wuhan Hubei China 430072 Department of Computer Science and Technology Ocean University of China No. 238 Songling Street Laoshan District Qingdao Shandong China 266100 Institute of Algorithm and Big Data Analysis Northeast Normal University No. 5268 Renmin Street Nanguan District Changchun Jilin China 130117

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SPFLOW: An easy and extensible library for deep probabilistic learning using sum-product networks

arXiv

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arXiv 2019年

作者： Molina, Alejandro Vergari, Antonio Stelzner, Karl Peharz, Robert Subramani, Pranav Di Mauro, Nicola Poupart, Pascal Kersting, Kristian Machine Learning Group Computer Science Department TU Darmstadt Germany Probabilistic Learning Group Empirical Inference Department Max-Planck-Institute Germany Machine Learning Group Engineering Dept. University of Cambridge United Kingdom Knowledge Acquisition & Machine Learning Group University of Bari "Aldo Moro" Italy Waterloo AI Institute Vector Institute University of Waterloo CA Canada Centre for Cognitive Science TU Darmstadt Germany

We introduce SPFlow, an open-source Python library providing a simple interface to inference, learning and manipulation routines for deep and tractable probabilistic models called Sum-Product Networks (SPNs). The library allows one to quickly create SPNs both from data and through a domain specific language (DSL). It efficiently implements several probabilistic inference routines like computing marginals, conditionals and (approximate) most probable explanations (MPEs) along with sampling as well as utilities for serializing, plotting and structure statistics on an SPN. Moreover, many of the algorithms proposed in the literature to learn the structure and parameters of SPNs are readily available in SPFlow. Furthermore, SPFlow is extremely extensible and customizable, allowing users to promptly distill new inference and learning routines by injecting custom code into a lightweight functional-oriented API framework. This is achieved in SPFlow by keeping an internal Python representation of the graph structure that also enables practical compilation of an SPN into a TensorFlow graph, C, CUDA or FPGA custom code, significantly speeding-up computations. Copyright © 2019, The Authors. All rights reserved.

关键词： Python

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Linked USDL Privacy: Describing Privacy Policies for Services

Linked USDL Privacy: Describing Privacy Policies for Service...

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IEEE International Conference on Web Services (ICWS)

作者： Georgia Kapitsaki Joseph Ioannou Jorge Cardoso Carlos Pedrinaci Department of Computer Science University of Cyprus Nicosia Cyprus Department of Informatics Engineering University of. Coimbra Coimbra Portugal Knowledge Media Institute The Open University United Kingdom

As the provision of services and the use of personal data expands, the need for services to explicitly detail what personal data a service handles and in which manner becomes paramount in order to achieve a fully transparent, ethical and personalized user experience. Services usually require access to sensitive information and may distribute this information to third parties. Service consumers need to be informed about the ways their data are used and about the actors involved in this process. Universal service descriptions that can be used to cover any business service are required to provide interoperability. In this paper, we describe our work on the privacy module for the Linked Unified Service Description Language (USDL). We expand the language by introducing a new module that allows the specification of privacy properties for business services. We have considered recent advances in data protection for its creation and provide a method, accompanied by a software tool, to examine the validity of privacy policy descriptions with Linked USDL Privacy module.

关键词： Privacy Data privacy Ontologies Medical services Organizations Cloud computing

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An Optimal Checkpointing Model with Online OCI Adjustment for Stream Processing Applications

An Optimal Checkpointing Model with Online OCI Adjustment fo...

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International Conference on computer Communications and Networks (ICCCN)

作者： Yuan Zhuang Xiaohui Wei Hongliang Li Yongfang Wang Xubin He College of Computer Science and Technology Jilin University Changchun China Key Lahoratory of Svmholic Computation and Knowledge Engineering of the Ministry of Education Changchun China Department of Computer and Information Sciences Temple University

Checkpoint-based fault tolerant method has been widely used to enhance the reliability of Distributed Stream Processing Engines (DSPEs), but a checkpointing process usually introduces considerable overhead. It is a critical issue to choose the Optimal Checkpoint Interval (OCI) that maximizes the processing efficiency. Traditional OCI models consider the recovery time only related to the execution time from the last checkpoint to the moment of the failure. They are not suitable for stream processing jobs because the recovery time is related to the reprocessing workload, which depends on the realtime input data before a failure. A new model is needed to choose the OCI for stream processing applications. Moreover, the input data rate of an stream processing job fluctuates over time. The OCI of an application should also be adjusted dynamically according to the input workload. To solve these problems, we present a novel DSPS Optimal Checkpoint Interval (DOCI) model in this paper. We prove that it maximizes the processing efficiency for a given time period. We propose an approach to dynamically adjust the OCI for an application to accommodate the realtime workload fluctuations. We conduct simulation experiments to verify the effectiveness of DOCI model and the efficiency of the online OCI adjustment algorithm. Experimental results with a real-world dataset show DOCI achieves an improvement on system efficiency by up to 40%, comparing with existing fault-tolerant approaches.

关键词： Task analysis Runtime Checkpointing Fault tolerance Fault tolerant systems Heuristic algorithms Optimization

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Case representation and similarity assessment in a recommender system to support dementia caregivers in geriatric and palliative care

Case representation and similarity assessment in a recommend...

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2017 ICCBR Workshops on Computational Analogy and Case-Based Reasoning, CAW 2017, Case-Based Reasoning and Deep Learning, CBRDL 2017 and Process-Oriented Case-Based Reasoning, POCBR 2017, Doctoral Consortium, and Competitions, ICCBR-WS 2017

作者： Nasiri, Sara Fathi, Madjid Department of Electrical Engineering and Computer Science University of Siegen Institute of Knowledge Based Systems and Knowledge Management Hölderlinstr. 3 Siegen57076 Germany

In this paper, a case-based reasoner uses International Classification of Functioning, Disability, and Health framework of WHO (ICF codes) and medical expressions to create keyword association profiles. Dementia Case-based Learning Assistant System (DePicT Dementia CLASS) finds significant references and learning materials by utilizing the profile of word association strengths according to the problem description. The purpose of this research is to develop a CBR system for recommending the related references by using the information retrieved from dementia books based on the ICF framework of WHO. Case-based learning assistant system helps users to find their answers in dealing with their problems. It also used their feedback to update cases and for improvement of references. This research proposes a combination of references with the highest value keyword association strengths and collaborative recommendation based on the ranked references by the user's feedback. Copyright © 2017 for this paper by its authors.

关键词： Case based reasoning

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