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Assessing mowing intensity:A new index incorporating frequency,type of machinery,and technique

Grassland Research 2024年第3期3卷 264-274页

作者： Margarita Hartlieb Sebastian Raubitzek Johanna L.Berger Michael Staab Juliane Vogt Manfred Ayasse Andreas Ostrowski Wolfgang Weisser Nico Blüthgen Ecological Networks Department of BiologyTechnical University of DarmstadtDarmstadtGermany SBA-Research ViennaAustria Natura 2000-Station Unstrut-Hainich/Eichsfeld Wildtierland Hainich gGmbHHörselberg-HainichGermany Institute of Evolutionary Ecology and Conservation Genomics Ulm UniversityUlmGermany Heinz Nixdorf Chair for Distributed Information Systems Faculty of Mathematics and Computer ScienceFriedrich Schiller UniversityJenaGermany Department of Life Science Systems School of Life SciencesTechnical University of MunichTerrestrial Ecology Research GroupFreisingGermany

Background:Only a few decades ago,colorful,small-scale,heterogeneous,and species-rich hay meadows or extensive pastures were common,but have often been replaced by species-poor,uniform,large-scale multicut *** advancements and improved efficiency in grassland management have come at the cost of ***:In Germany,150 grassland plots have been investigated since *** these extensive data,we propose a new compound index for estimating the site-specific mowing intensity in order to facilitate assessment of the impact of mowing intensity on biodiversity and ecosystem *** index integrates the various qualitative components of mowing machine type,mowing height and use of a conditioner,with the annual number of ***:The newly proposed index achieves a much finer gradation of mowing intensity compared to the previous quantification based on the number of cuts ***,a decrease in plant and arthropod species was observed at higher mowing ***:The proposed mowing intensity index offers enhanced precision in calculations and can easily be integrated into assessments of land-use intensity in ***,it could serve as a basis for providing subsidies to farmers,who adopt low-impact mowing practices.

关键词： applications conditioner mower mowing height number of cuts species richness

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Let me quickly share it - Time Pressure when Sharing on Social Media 24

Let me quickly share it - Time Pressure when Sharing on Soci...

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23rd International Conference on Mobile and Ubiquitous Multimedia, MUM 2024

作者： Panskus, Rebecca Tanni, Tangila Islam Ponticello, Alexander Meissner, Echo Solihin, Yan Krombholz, Katharina Marky, Karola Ruhr University Bochum Bochum Germany Department of Computer Science University of Central Florida Orlando United States CISPA Helmholtz Center for Information Security Saarland University Germany Saarbrücken Germany Institute of Distributed Systems Ulm University Ulm Germany CISPA Helmholtz Center for Information Security Saarbrücken Germany

ISBN: (纸本)9798400712838

Photo sharing has become an integral part of social media;many new platforms even prompt their users to share photos under time pressure. Yet, sharing photos can also affect the privacy of individuals in a negative way. In this paper, we investigate the factor of time pressure when sharing on social media in relation to target audiences and photo content. For this, we conducted an online study (N=195) where we simulated time-pressured sharing scenarios with photos containing different sorts of content, including private information and unfamiliar bystanders. Among our results, we show that time pressure can impact sharing decisions even though this is not consciously considered by users. Additionally, we confirm the influence of non-privacy-related influence factors like trust towards the target audience, photo aesthetics, and connection with others. Based on our results, we discuss design implications for photo-sharing prompts to better protect private information and bystanders in social media photos. © 2024 Copyright held by the owner/author(s).

关键词： Social Media

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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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Emotion Classification Through Speech Data Analysis 23

Emotion Classification Through Speech Data Analysis

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Proceedings of the Winter Simulation Conference

作者： Luzalen Marcos Kristiina Valter Mai Abdolreza Abhari Resilience Engineering Lab Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto Canada Distributed Systems and Multimedia Processing (DSMP) Lab Department of Computer Science Toronto Metropolitan University Toronto Canada

ISBN: (纸本)9798350369663

Good quality healthcare services require effective communication between the patient and the healthcare provider. This work will help improve the areas of healthcare systems automation and optimization by applying Speech Emotion Recognition (SER) in health consultations to prevent miscommunication between patients and healthcare providers. Crowd-Sourced Emotional Multimodal Actors Dataset (CREMA-D) was used to compare the performances of different machine learning models in classifying emotions. Before feeding the raw dataset to the models, exploratory data analysis was done to determine features that should be considered for future analysis. Our results showed that depending on the emotion, there are some syllables in the text that were emphasized or took time to be pronounced by the speaker. After data analysis, the dataset was fed into different models and determined that the Support Vector Machine (SVM) is a machine-learning model for SER.

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Fourth Workshop on Big (and Small) Data in science and Humanities (BigDS 2023)

Fourth Workshop on Big (and Small) Data in Science and Human...

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2023 Datenbanksysteme fur Business, Technologie und Web, BTW 2023 - 2023 Database systems for Business, Technology and Web, BTW 2023

作者： Henrich, Andreas Karam, Naouel König-Ries, Birgitta Seeger, Bernhard University of Bamberg Media Informatics Bamberg96047 Germany Fraunhofer FOKUS & InfAI e.V. Berlin Germany University of Jena Heinz Nixdorf Chair for Distributed Information Systems Jena07743 Germany University of Marburg Department of Mathematics and Computer Science Marburg35032 Germany

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GPU-Based Efficient Parallel Heuristic Algorithm for High-Utility Itemset Mining in Large Transaction Datasets (Extended Abstract)

GPU-Based Efficient Parallel Heuristic Algorithm for High-Ut...

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International Conference on Data Engineering

作者： Wei Fang Haipeng Jiang Hengyang Lu Jun Sun Xiaojun Wu Jerry Chun-Wei Lin Department of Computer Science and Technology Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi China Department of Distributed Systems and IT Devices Silesian University of Technology Poland

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Heuristic algorithms have been developed to find approximate solutions for high-utility itemset mining (HUIM) problems that compensate for the performance bottlenecks of exact algorithms. However, heuristic algorithms still face the problem of long runtime and insufficient mining quality, especially for large transaction datasets with thousands to tens of thousands of items and up to millions of transactions. To solve these problems, a novel GPU-based efficient parallel heuristic algorithm for HUIM (PHA-HUIM) is proposed in this paper. The iterative process of PHA-HUIM consists of three main steps: the search strategy, fitness evaluation, and ring topology communication. The search strategy and ring topology communication are designed to run in constant time on GPU. The parallelism of fitness evolution helps to substantially accelerate the algorithm. To improve the mining quality, a multi-start strategy with an unbalanced allocation strategy is employed in the search process. Ring topology communication is adopted to maintain population diversity. A load balancing strategy is introduced to reduce the thread divergence to improve the parallel efficiency. The experimental results on nine large datasets show that PHA-HUIM outperforms state-of-the-art HUIM algorithms in terms of speedup performance, runtime, and mining quality.

关键词： Runtime Itemsets Heuristic algorithms Sociology Approximation algorithms Search problems Topology

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A Communication-Induced Checkpointing Algorithm for Consistent-Transaction in distributed Database systems 1

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8th International Symposium on Security in Computing and Communications, SSCC 2020

作者： Mansouri, Houssem Pathan, Al-Sakib Khan Laboratory of Networks and Distributed Systems Computer Science Department Faculty of Sciences Ferhat Abbas Setif University 1 Setif Algeria Department of Computer Science and Engineering Independent University Dhaka Bangladesh

ISBN: (数字)9789811604225

ISBN: (纸本)9789811604218

For better protection of distributed systems, two well-known techniques are: checkpointing and rollback recovery. While failure protection is often considered a separate issue, it is crucial for establishing better secure distributed systems. This article is dedicated to the proposal of a new checkpointing algorithm for saving a consistent-transaction state in distributed databases ensuring that database management systems are able, after a failure, to recover the state of the database. The proposed communication-induced algorithm does not hamper the normal transaction processing and saves a global consistent-transaction state that records only the fully completed transactions. Analysis and experimental results of our proposal show that the proposed scheme saves a minimum number of forced checkpoints and has some performance gains compared to the alternative approaches. © 2021, Springer Nature Singapore Pte Ltd.

关键词： distributed database systems

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Securing ML-based Android Malware Detectors: A Defensive Feature Selection Approach against Backdoor Attacks

Securing ML-based Android Malware Detectors: A Defensive Fea...

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Cluster, Cloud and Internet Computing Workshops (CCGridW), IEEE/ACM International Symposium on

作者： Bartłomiej Marek Kacper Pieniążek Filip Ratajczak Wojciech Adamczyk Bartosz Bok Mateusz Krzysztoń Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wrocław Poland Department of Distributed Systems Research and Academic Computer Network (NASK-PIB) Warsaw Poland

ISBN: (数字)9798350377514

ISBN: (纸本)9798350377521

This work investigates the vulnerability of ML-based Android malware detectors to backdoor attacks, and proposes a novel feature selection method to reduce system vulnerability. We present a realistic attack scenario and enhance the state-of-the-art genetic algorithm for trigger generation. The algorithm is used to design a feature selection method that prioritises attributes less prone to exploitation by attackers. The solution was evaluated using a dataset from the Koodous platform. The results show that the proposed improvements to the trigger selection algorithm were effective, resulting in a 10 percentage point increase. Furthermore, the proposed feature selection method significantly reduced the vulnerability of the ML system by 30%, without affecting the quality of the malicious application detection task execution.

关键词： Training Measurement Operating systems Computational modeling Neural networks Machine learning Predictive models Feature extraction Malware Genetic algorithms

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A New Approach for the Analysis of Resistance to Change in the Digital Transformation Context 11th

A New Approach for the Analysis of Resistance to Change in t...

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10th International Conference on Innovation and New Trends in Information Technology, INTIS 2022 and 11th International Conference on Innovation and New Trends in Information Technology, INTIS 2023

作者： El Karam, Bachira Abou Marghoubi, Rabia Khanboubi, Fadoua Allaki, Driss InnovatiVE REsearch on Software Systems and data INPT Rabat Morocco LIM Lab. IOS team Department of Computer Science FSTM Casablanca Morocco Smart Embedded Enterprise and Distributed Systems INPT Rabat Morocco

ISBN: (纸本)9783031473654

In a context marked by the rapid evolution of technologies and the omnipresence of IT at the heart of all activities, the digital transformation of companies is no longer a luxury but an absolute necessity. This technological transformation can lead to resistance from the employees affected by the change. In order to properly conduct the change management process, it is necessary to ensure the involvement of employees from the beginning of this process, which is composed of three phases: the change management strategy, the diagnosis, and the actions implementation. This paper focuses on the diagnostic phase and more specifically on the evaluation stage. This evaluation measures the employees’ hesitation to express their degree of resistance toward the digital initiatives launched in the company. However, this assessment is usually done subjectively, and employees express their hesitation in non-uniform manners. In this work, we propose a weighted generalized hesitating fuzzy approach based on the fusion of theoretical concepts of generalized hesitant fuzzy sets and formal concept analysis (FCA), to formalize the measurement of degree hesitation in change resistance. This approach is flexible and allows the top management of each organizational unit to correctly measure the resistance to change of their teams and then evaluate that measure at the global organizational level. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

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Exploiting Open Data for Co2 Estimation Via Artificial Intelligence and Explainable Ai

SSRN

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SSRN 2024年

作者： Bilotta, Stefano Palesi, Luciano Alessandro Ipsaro Nesi, Paolo University of Florence Department of Mathematics and Computer Science "U. Dini" Firenze50134 Italy University of Florence Distributed Systems and Internet Technology DISIT Lab Firenze50139 Italy

In present paper, a general CO2 emission estimation approach is developed by means of a large range of open data as predictions. Such open data are related to human activity regarding services and (socio-economic) conditions arising in a given (urban) area. The proposed model focuses on fine-scale prediction to better understand the dynamics of emissions via machine (or deep) learning approaches and taking into account an innovative study based on open data sources including city services. The goal is to understand how specific predictors can contribute to or mitigate CO2 emissions in the observed area. To this end, the impact of several features has been analyzed in order to identify the key factors influencing emissions. Thus, a formal study has been conducted to perform feature relevance analysis by using eXplainable AI (XAI) approach. The proposed model is useful to define targeted policies reducing the pollutant impact of cities, promote a more ecologically sustainable urban lifestyle and improve sustainable urban planning. Both solution and model have been improved to be more flexible by using transfer learning techniques. This study and its related results have been produced and validated by exploiting the Snap4City framework for smart city, mobility and transport and data analytics. In particular, such results deal with the metropolitan cities of Florence and Bologna. © 2024, The Authors. All rights reserved.

关键词： Open Data

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