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5th International Conference on Recent Innovations in Computing , ICRIC 2022

作者： Kalsgonda, Vaishali Kulkarni, Raja Kalasgonda, Shivkumar Department of Computer Science Shivraj College Maharashtra Gadhinglaj416502 India Department of Computer Studies Chhatrapati Shahu Institute of Business Education Research University Road Maharashtra Kolhapur416004 India E-Commerce Distributed Systems Seattle United States

ISBN: (纸本)9789819906000

The insurance process is done manually in India. The insurance client has to depend on an insurance agent from buying to the claim firing process which leads to wrong entry, fraudulent claims, and cost overhead on the agent’s commission. An insurance client has to maintain documents and make them available at the time of claim firing, which is very cumbersome. Blockchain smart contracts will provide a secure, automatic, cost-cutting, paperless, and real-time insurance process. This paper aims to provide a blockchain solution for life insurance claim processing, as it is the most purchased and needy insurance. An architectural illustration of a blockchain prototype is contributed to this paper. As a result, the authors found that using Hyperledger Composer, we can develop fine-grained insurance applications. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smart contract

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A Hybrid Neural Network and Box-Jenkins Models for Time Series Forecasting

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computers, Materials & Continua 2022年第3期70卷 4829-4845页

作者： Mohammad Hadwan Basheer M.Al-Maqaleh Fuad N.Al-Badani Rehan Ullah Khan Mohammed A.Al-Hagery Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Science College of Applied SciencesTaiz UniversityTaizYemen Intelligent Analytics Group(IAG) College of ComputerQassim UniversityBuraydahSaudi Arabia Faculty of Computer Sciences and Information Systems Thamar UniversityThamarYemen Faculty of Administration Science Ibb UniversityIbbYemen Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia

Time series forecasting plays a significant role in numerous applications,including but not limited to,industrial planning,water consumption,medical domains,exchange rates and consumer price *** main problem is insufficient forecasting *** present study proposes a hybrid forecastingmethods to address this *** proposed method includes three *** first model is based on the autoregressive integrated moving average(ARIMA)statistical model;the second model is a back propagation neural network(BPNN)with adaptive slope and momentum parameters;and the thirdmodel is a hybridization between ARIMA and BPNN(ARIMA/BPNN)and artificial neural networks and ARIMA(ARIMA/ANN)to gain the benefits of linear and *** forecasting models proposed in this study are used to predict the indices of the consumer price index(CPI),and predict the expected number of cancer patients in the Ibb Province in *** standard measures used to evaluate the proposed method include(i)mean square error,(ii)mean absolute error,(iii)root mean square error,and(iv)mean absolute percentage *** on the computational results,the improvement rate of forecasting the CPI dataset was 5%,71%,and 4%for ARIMA/BPNN model,ARIMA/ANN model,and BPNN model respectively;while the result for cancer patients’dataset was 7%,200%,and 19%for ARIMA/BPNNmodel,ARIMA/ANN model,and BPNNmodel ***,it is obvious that the proposed method reduced the randomness degree,and the alterations affected the time series with data *** ARIMA/ANN model outperformed each of its components when it was applied separately in terms of increasing the accuracy of forecasting and decreasing the overall errors of forecasting.

关键词： Hybrid model forecasting non-linear data time series models cancer patients neural networks box-jenkins consumer price index

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Functional group richness increases multifunctionality in intensively managed grasslands

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Grassland Research 2023年第3期2卷 225-240页

作者： Laura Argens Caroline Brophy Wolfgang W.Weisser Sebastian Meyer Terrestrial Ecology Research Group Department for Life Science SystemsSchool of Life SciencesTechnical University of MunichFreisingGermany School of Computer Science and Statistics Trinity College DublinDublinIreland

Background:Agricultural yields have increased continuously over the last few ***,a focus solely on production can harm the *** of agriculture has been suggested to increase production and *** experiments showed positive effects on ecosystems and ***,application of these results to intensively managed grasslands has been questioned due to differences in plant species and management *** on whether diversity can benefit multifunctionality,that is,an integrated index of multiple ecosystem functions,under intensive management,is still ***:To address this,we manipulated plant species richness from one to six species spanning three functional groups(legumes,herbs,and grasses)in intensively managed multispecies grassland leys and examined seven ecosystem ***:We found that multifunctionality increased with functional group and species ***+herb mixtures showed high multifunctionality,while grass monocultures and mixtures with high proportions of grasses had low *** plant species and plant communities drove different ecosystem *** and herbs improved productivity and water availability,while grasses enhanced invasion *** results indicate that multifunctionality and individual ecosystem functions can be promoted through targeted combinations of plants with complementary ecological ***:Plant diversity can improve multifunctionality also under intensive management,potentially benefitting agroeconomics and sustainability.

关键词： biodiversity ecosystem functions intensively managed grassland Legacy Network(LegacyNet) multifunctionality

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Multi-channel CNN to classify Nepali COVID-19 related tweets using hybrid features

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Journal of Ambient Intelligence and Humanized Computing 2024年第3期15卷 2047-2056页

作者： Sitaula, Chiranjibi Shahi, Tej Bahadur Department of Electrical and Computer Systems Engineering Monash University Wellington Rd ClaytonVIC3800 Australia Earth Observation and AI Research Group Department of Infrastructure Engineering The University of Melbourne ParkvilleVIC3010 Australia School of Engineering and Technology Central Queensland University Rockhampton Australia Central Department of Computer Science and Information Technology Tribhuvan University TU Rd Kathmandu Kirtipur44618 Nepal

Because of the current COVID-19 pandemic’s increasing fears among people, it has triggered several health complications such as depression and anxiety. Such complications have not only affected developed countries but also developing countries such as Nepal. These complications can be understood from peoples’ tweets and comments posted online after their felicitous analysis and sentiment classification. Nevertheless, owing to the limited number of tokens and words in each tweet, it is crucial to capture multiple pieces of information associated with them for better understanding. In this study, we, first, represent each tweet by combining both syntactic and semantic information, called hybrid features. The syntactic information is generated from the bag-of-words method, whereas the semantic information is generated from the combination of the fastText-based (ft) and domain-specific (ds) methods. Second, we design a novel multi-channel CNN (MCNN) model, which ensembles the multiple CNNs, to capture multi-scale information for better classification. Last, we evaluate the efficacy of both the proposed feature extraction method and the MCNN model classifying tweets into three sentiment classes (positive, neutral, and negative) on the NepCOV19Tweets dataset, which is the only public COVID-19 tweets dataset in the Nepali language. The evaluation results show that the proposed hybrid features outperform individual feature extraction methods with the highest classification accuracy of 69.7% and the MCNN model outperforms the existing methods with the highest classification accuracy of 71.3% during classification. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： COVID-19

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Beyond Silos: Next Steps in Research Data Management Report from the 2024 Spring Symposium of FG Datenbanken

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Datenbank-Spektrum 2024年第2期24卷 171-173页

作者： König-Ries, Birgitta Leser, Ulf Seeger, Bernhard Schindler, Sirko Heinz Nixdorf Chair for Distributed Information Systems Friedrich Schiller University Jena Leutragraben 1 Jena07743 Germany Department for Computer Science Humboldt-Universität zu Berlin Unter den Linden 6 Berlin10099 Germany Department of Mathematics and Computer Science University of Marburg Hans-Meerwein-Str. 6 Marburg35032 Germany Mälzerstraße 3-5 Jena07745 Germany

On March 11 and 12, 2024, the Spring Symposium of the GI Fachgruppe Datenbanken took place in Jena. The overarching theme of the meeting was research data management beyond isolated repositories, and it explicitly aimed to foster connections between the FG Datenbanken and the NFDI community. Talks and posters focused on incentives and hurdles for FAIR data management, interoperability and reproducibility, automated Data Engineering, and AI-based user support. © Gesellschaft für Informatik e.V. and Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Knowledge management

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Software-defined event-based camera and vision algorithms for digitizing highly dynamic manufacturing tasks

Software-defined event-based camera and vision algorithms fo...

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2024 Low-Cost Digital Solutions for Industrial Automation, LoDiSA 2024

作者： Walker, Emily Turner, Christopher J. Oyekan, John Department of Automatic Control & Systems Engineering The University of Sheffield SheffieldS1 3JD United Kingdom Surrey Business School The University of Surrey Surrey GuildfordGU2 7XH United Kingdom Real Time & Distributed Systems Group Department of Computer Science University of York Heslington YorkYO10 5GH United Kingdom

ISBN: (纸本)9781837242672

Event-based cameras are inspired by the way the retina in the eye process information. Rather than capturing image frames at a constant rate, event cameras generate data asynchronously, based on changes in pixel value between frames. As a result, these cameras have a high spatiotemporal accuracy, low power constraints and reduced artefacts from motion blur. Hence, they represent favourable properties for digitizing highly dynamic manufacturing tasks such as machine vibrations and rapid human assembly actions. However, the costs of event-based cameras are prohibitively high for small to medium sized enterprises (SMEs). In this research, we present the development of a rig that combines low-cost visual sensors with low-cost algorithms to perform action recognition for highly dynamic manual manufacturing tasks. We applied our algorithms to achieve an action classification system via a novel threshold-based approach and in parallel use a neural network to perform classification of objects in the video feed. The results achieved demonstrated that our approach was able to successfully identify various rapid manual tasks being performed on identified objects. In addition to SMEs, our approach would be suited for providing low-cost digitization on legacy machines. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/).

关键词： Machine vibrations

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Enabling Sustainable and Unmanned Facial Detection and Recognition Services With Adaptive Edge Resource

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IEEE Transactions on Consumer Electronics 2024年 1-1页

作者： Xiang, Zhengzhe Xue, Xizi Zheng, Zengwei Gao, Honghao Chen, Yuanyi Dustdar, Schahram School of Computer and Computing Science Hangzhou City University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China School of Computer Engineering and Science Shanghai University Shanghai China Distributed Systems Group TU Wien Vienna Austria

Facial recognition techniques are used extensively in areas like online payments, education, and social media. Traditionally, these applications relied on powerful cloud-based systems, but advancements in edge computing have changed this, enabling fast and reliable local processing in complex and extreme environments. However, new challenges arise in availability and durability insurance to make the system run 24/7 with acceptable performance. This paper proposes a novel solution to these challenging settings. First, we use edge devices for local data processing, reducing the need for cloud communication and enhancing user privacy. Second, we implement an adaptive control strategy to improve energy management in these devices. Lastly, we establish a solar-powered energy system to facilitate long-term device operation. The experiments show our approach strikes a balance between performance, quality, and durability, enabling facial recognition systems to work energy-efficiently in complex environments. Meanwhile, considering the limited resources of devices in extreme cases, we also proposed a learning-based approach to accelerate the solution generation. IEEE

关键词： Edge Computing Edge computing Energy consumption Energy efficiency Energy Harvesting Face recognition Fifth Industrial Revolution Performance evaluation Real-time systems Resource Allocation Service Management

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Unveiling the Black Box: Neural Cryptanalysis with XAI

Unveiling the Black Box: Neural Cryptanalysis with XAI

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IEEE International Conference on systems, Man and Cybernetics

作者： Yue-Tian Goi Shu-Min Leong Raphaël C.-W. Phan Shangqi Lai Ana Sălăgean School of Information Technology Monash University Distributed Systems Security Group CSIRO's Data61 Australia Department of Computer Science Loughborough University UK

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

At CRYPTO'19, Gohr[1] presented ResNet-based neural distinguishers (ND) for the round-reduced SPECK32/64 cipher. However, due to the black-box use of such deep learning models, it is hard for humans to understand why these distinguishers work, impeding advancements in cryptanalytic knowledge. In this work, we aim to effectively adapt eXplainable Artificial Intelligence (XAI) techniques, notably Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP), to gain a detailed understanding of the important features useful in Gohr's neural distinguishers.

关键词： Deep learning Adaptation models Ciphers Correlation Additives Explainable AI Closed box Manuals Encryption Cybernetics

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Data science in the Era of Heterogeneity 23

Data Science in the Era of Heterogeneity

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4th ACM Europe Summer School on science: Towards building the Data science Stack

作者： Gustavo Alonso Systems Group Department of Computer Science Zurich ETH Zurich Switzerland

Computing platforms are evolving rapidly along many dimensions: processors, specialization, disaggregation, acceleration, smart memory and storage, etc. Many of these developments are being driven by data science but also arise from the need to make cloud computing more efficient. From a practical perspective, the result we see today is a deluge of possible configurations and deployment options, most of them too new to have a precise idea of their performance implications and lacking proper support in the form of tools and platforms that can manage the underlying diversity. The growing heterogeneity is opening up many opportunities but also raising significant challenges. In the talk I will describe the trend towards specialization at all layers of the architecture, the possibilities it opens up, and demonstrate with real examples how to take advantage of heterogeneous computing platforms. I will also discuss opportunities for systems research in the context of data science both on the software as well as on the hardware side.

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Towards New Simulation Models for DL-Based Video Streaming in Edge Networks 24

Towards New Simulation Models for DL-Based Video Streaming i...

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Proceedings of the Winter Simulation Conference

作者： Abdolreza Abhari Distributed Systems & Multimedia Processing Laboratory (DSMP Lab) Department of Computer Science Toronto Metropolitan University Toronto ON CANADA

ISBN: (纸本)9798331534202

To evaluate novel solutions for edge computing systems, suitable distribution models for simulation are essential. The extensive use of deep learning (DL) in video analytics has altered traffic patterns on edge and cloud servers, necessitating innovative models. Queuing models are used to simulate the performance and stability of edge-enabled systems, particularly video streaming applications. This paper demonstrates that traditional Markovian M/M/s and general distribution G/G/s queuing models must be revamped for accurate simulation. We examined these queuing models by characterizing the real data with discrete and continuous distributions for arrival rates to homogenous servers in AI-based video analytics edge systems. Based on achieved results, traditional methods for finding general distributions are inadequate, and an automation method for finding empirical distribution is needed. Therefore, we introduce a novel approach using a generative adversarial network (WGAN) to generate artificial data to automate the process of estimating empirical distribution for modeling these applications.

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