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15th International Conference on Business Information systems 2020: Developments, Opportunities and Challenges of Digitization, WIRTSCHAFTSINFORMATIK 2020

作者： Bitomsky, Laura Meindl, Oliver Schmidt, Marco Regal, Christian Fim Research Center University of Augsburg Germany Faculty of Computer Science Augsburg University of Applied Sciences Augsburg Germany Fraunhofer Project Group Business and Information Systems Engineering Augsburg Germany

ISBN: (纸本)9783955453350

Due to improved insulation, decreasing indoor environmental quality (IEQ) is an emerging issue in the design of sustainable and energy-efficient buildings. Poor IEQ has severe long-term implications for the occupants' health. Manual airing is a promising energy-efficient solution to combine good insulation and healthy IEQ. However, occupants' behavior is crucial for its effectiveness. Digital nudging can help to influence people's behavior, e.g., by means of giving feedback. We conduct a field experiment over four weeks, in which we provide real-time IEQ feedback to nudge occupants towards opening the windows. We find a significant improvement in IEQ for offices in the treatment group, but also see that the nudge's effectiveness reduces over time, possibly as a result of habituation. This experiment paves the way for further studies examining the design of nudges towards improving IEQ. © Proceedings of the 15th International Conference on Business Information systems 2020 "Developments, Opportunities and Challenges of Digitization", WIRTSCHAFTSINFORMATIK 2020.

关键词： Energy efficiency

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On the robustness of EEG tensor completion methods

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Science China(Technological Sciences) 2021年第9期64卷 1828-1842页

作者： DUAN Feng JIA Hao ZHANG ZhiWen FENG Fan TAN Ying DAI YangYang CICHOCKI Andrzej YANG ZhengLu CAIAFA Cesar F. SUN Zhe SOLE-CASALS Jordi College of Artificial Intelligence Nankai UniversityTianjin 300350China Skolkowo Institute of Science and Technology Moscow 121205Russia College of Computer Science Hangzhou Dianzi UniversityHangzhou 310018China Department of Informatics Nicolaus Copernicus UniversityTorun 87-100Poland Systems Research Institute Polish Academy of SciencesWarsaw 01-447Poland College of Computer Science Nankai UniversityTianjin 300350China Instituto Argentino de Radioastronom´ıa IAR-CCT La Plata CONICET/CIC-PBA/UNLPVilla Elisa 1894Argentina Computational Engineering Applications Unit Head Office for Information Systems and CybersecurityRIKENWako-Shi 351-0106Japan Department of Psychiatry University of CambridgeCambridge CB28AHUK Data and Signal Processing Research Group University of Vic-Central University of CataloniaCatalonia 08500Spain

During the acquisition of electroencephalographic(EEG) signals, data may be missing or corrupted by noise and artifacts. To reconstruct the incomplete data, EEG signals are firstly converted into a three-order tensor(multi-dimensional data) of shape time × channel × trial. Then, the missing data can be efficiently recovered by applying a tensor completion method(TCM).However, there is not a unique way to organize channels and trials in a tensor, and different numbers of channels are available depending on the EEG setting used, which may affect the quality of the tensor completion results. The main goal of this paper is to evaluate the robustness of EEG completion methods with several designed parameters such as the ordering of channels and trials, the number of channels, and the amount of missing data. In this work, the results of completing missing data by several TCMs were compared. To emulate different scenarios of missing data, three different patterns of missing data were ***, the amount of missing data on completion effects was analyzed, including the time lengths of missing data and the number of channels or trials affected by missing data. Secondly, the numerical stability of the completion methods was analyzed by shuffling the indices along channels or trials in the EEG data tensor. Finally, the way that the number of electrodes of EEG tensors influences completion effects was assessed by changing the number of channels. Among all the applied TCMs, the simultaneous tensor decomposition and completion(STDC) method achieves the best performance in providing stable results when the amount of missing data or the electrode number of EEG tensors is changed. In other words, STDC proves to be an excellent choice of TCM, since permutations of trials or channels have almost no influence on the complete results. The STDC method can efficiently complete the missing EEG signals. The designed simulations can be regarded as a procedure to validate whether or

关键词： electroencephalogram corrupted data missing data tensor completion tensor decomposition

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A hybrid method to select morphometric features using tensor completion and F-score rank for gifted children identification

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Science China(Technological Sciences) 2021年第9期64卷 1863-1871页

作者： ZHANG Jin FENG Fan HAN TianYi DUAN Feng SUN Zhe CAIAFA Cesar F. SOLÉ-CASALS Jordi College of Computer Science Nankai UniversityTianjin 300350China Department of Artificial Intelligence Nankai UniversityTianjin 300350China Computational Engineering Applications Unit Head Office for Information Systems and CybersecurityRIKEN351-0198 SaitamaJapan Instituto Argentino de Radioastronomía-CCT La Plata CONICET/CIC-PBA/UNLP1894 V.ElisaArgentina Department of Psychiatry University of CambridgeCambridge CB20SZUnited Kingdom Data and Signal Processing Group University of Vic-Central University of Catalonia08500 VicCataloniaSpain

Gifted children are able to learn in a more advanced way than others, probably due to neurophysiological differences in the communication efficiency in neural pathways. Topological features contribute to understanding the correlation between the brain structure and intelligence. Despite decades of neuroscience research using MRI, methods based on brain region connectivity patterns are limited by MRI artifacts, which therefore leads to revisiting MRI morphometric features, with the aim of using them to directly identify gifted children instead of using brain connectivity. However, the small, high-dimensional morphometric feature dataset with outliers makes the task of finding good classification models challenging. To this end, a hybrid method is proposed that combines tensor completion and feature selection methods to handle outliers and then select the discriminative features. The proposed method can achieve a classification accuracy of 93.1%, higher than other existing algorithms, which is thus suitable for the small MRI datasets with outliers in supervised classification scenarios.

关键词： gifted children identification morphometric features tensor completion feature selection

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Level of Trust and Privacy Management in 6G Intent-based Networks for Vertical Scenarios

Level of Trust and Privacy Management in 6G Intent-based Net...

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6G Networking (6GNet), International Conference on

作者： Jesú s A. Alonso-Lupez Luis Alberto Martí nez Herná ndez Sandra Pé rez Arteaga Ana Lucila Sandoval Orozco Luis Javier Garcí a Villalba Antonio Pastor Diego Ló pez Garcí a Department of Software Engineering and Artificial Intelligence (DISIA) Faculty of Computer Science and Engineering Office 431 Group of Analysis Security and Systems (GASS) Universidad Complutense de Madrid (UCM) Madrid Spain CTIO Unit Telef&#x00F3 nica I+D Ronda de la Comunicacion S/N Madrid Spain

Security is one of the main concerns when designing future 6G networks. Most of the components of service chains are software-based and their security must be ensured. In this work we propose a system that will check the level of trust of 6G services following 3 complementary approaches: attestation, generation of proofs of transit and usage of smart contracts whose execution will be published via Distributed Ledger Technology in order to enable their traceability. The service orchestration function will use reinforcement learning to formulate optimised service orchestration decisions. Services will be specified in a declarative model, following a privacy-aware flavour of intent-based networking. The proposed architecture will take into account privacy of every stakeholder: final users, service providers and infrastructure providers.

关键词： 6G mobile communication Privacy Distributed ledger Network topology Smart contracts Reinforcement learning Service-oriented architecture

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A Video-based Study on Perceived Intelligence, Subjective Performance and Trust under Variation of Prior Information given to Users in Autonomous Driving

A Video-based Study on Perceived Intelligence, Subjective Pe...

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IEEE Workshop on Advanced Robotics and its Social Impacts, ARSO

作者： Kolja Kü hnlenz Ute Schmid Barbara Kü hnlenz Department of Electrical Engineering and Computer Science Robotics Research Lab Coburg University of Applied Sciences and Arts Coburg Germany Cognitive Systems Group University of Bamberg Bamberg Germany Academic Center for Sciences and Humanities Coburg University of Applied Sciences and Arts Coburg Germany

This paper presents results from a video-based study on the impact of prior information on the user experience dimensions perceived intelligence, subjective performance, and trust in autonomous driving. A simulated autonomous driving situation is presented to test participants, while they are given different prior information in terms of descriptions of the autonomous vehicle and its integration in a network of intelligent vehicles. Significant differences of the tested user experience dimensions are revealed underlining the importance of information provided to the potential users and towards the criticality of information regarding the introduction of intelligent technology in general.

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Deep Learning Model with GA-based Visual Feature Selection and Context Integration

Deep Learning Model with GA-based Visual Feature Selection a...

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Congress on Evolutionary Computation

作者： Ranju Mandal Basim Azam Brijesh Verma Mengjie Zhang Centre for Intelligent Systems School of Engineering and Technology Central Queensland University Brisbane Australia Evolutionary Computation Research Group School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand

Deep learning models have been very successful in computer vision and image processing applications. Since its inception, Convolutional Neural Network (CNN)-based deep learning models have consistently outperformed other machine learning methods on many significant image processing benchmarks. Many top-performing methods for image segmentation are also based on deep CNN models. However, deep CNN models fail to integrate global and local context alongside visual features despite having complex multi-layer architectures. We propose a novel three-layered deep learning model that learns independently global and local contextual information alongside visual features, and visual feature selection based on a genetic algorithm. The novelty of the proposed model is that One-vs-All binary class-based learners are introduced to learn Genetic Algorithm (GA) optimized features in the visual layer, followed by the contextual layer that learns global and local contexts of an image, and finally the third layer integrates all the information optimally to obtain the final class label. Stanford Background and CamVid benchmark image parsing datasets were used for our model evaluation, and our model shows promising results. The empirical analysis reveals that optimized visual features with global and local contextual information play a significant role to improve accuracy and produce stable predictions comparable to state-of-the-art deep CNN models.

关键词： Deep learning Visualization Image segmentation Computational modeling Benchmark testing Feature extraction Convolutional neural networks

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On Constraints in First-Order Optimization: A View from Non-Smooth Dynamical systems

arXiv

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arXiv 2021年

作者： Muehlebach, Michael Jordan, Michael I. Learning and Dynamical Systems Group Max Planck Institute for Intelligent Systems Tübingen72076 Germany Department of Electrical Engineering and Computer Sciences Department of Statistics University of California BerkeleyCA94720 United States

We introduce a class of first-order methods for smooth constrained optimization that are based on an analogy to non-smooth dynamical systems. Two distinctive features of our approach are that (i) projections or optimizations over the entire feasible set are avoided, in stark contrast to projected gradient methods or the Frank-Wolfe method, and (ii) iterates are allowed to become infeasible, which differs from active set or feasible direction methods, where the descent motion stops as soon as a new constraint is encountered. The resulting algorithmic procedure is simple to implement even when constraints are nonlinear, and is suitable for large-scale constrained optimization problems in which the feasible set fails to have a simple structure. The key underlying idea is that constraints are expressed in terms of velocities instead of positions, which has the algorithmic consequence that optimizations over feasible sets at each iteration are replaced with optimizations over local, sparse convex approximations. In particular, this means that at each iteration only constraints that are violated are taken into account. The result is a simplified suite of algorithms and an expanded range of possible applications in machine learning. © 2021, CC BY.

关键词： Convex optimization

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Symbiotic system of systems design for safe and resilient autonomous robotics in offshore wind farms

arXiv

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arXiv 2021年

作者： Mitchell, Daniel Blanche, Jamie Zaki, Osama Roe, Joshua Kong, Leo Harper, Samuel Robu, Valentin Lim, Theodore Flynn, David Smart Systems Group Institute of Sensors Signals and Systems School of Engineering and Physical Sciences Heriot-Watt University EdinburghEH14 4AS United Kingdom CWI Centre for Mathematics and Computer Science Intelligent and Autonomous Systems Group Amsterdam1098 XG Netherlands Delft2628 XE Netherlands

To reduce Operation and Maintenance (O&M) costs on offshore wind farms, wherein 80% of the O&M cost relates to deploying personnel, the offshore wind sector looks to Robotics and Artificial Intelligence (RAI) for solutions. Barriers to Beyond Visual Line of Sight (BVLOS) robotics include operational safety compliance and resilience, inhibiting the commercialization of autonomous services offshore. To address safety and resilience challenges we propose a Symbiotic System Of systems Approach (SSOSA), reflecting the lifecycle learning and co-evolution with knowledge sharing for mutual gain of robotic platforms and remote human operators. Our novel methodology enables the run-time verification of safety, reliability and resilience during autonomous missions. To achieve this, a Symbiotic Digital Architecture (SDA) was developed to synchronize digital models of the robot, environment, infrastructure, and integrate front-end analytics and bidirectional communication for autonomous adaptive mission planning and situation reporting to a remote operator. A reliability ontology for the deployed robot, based on our holistic hierarchical-relational model, supports computationally efficient platform data analysis. We demonstrate an asset inspection mission within a confined space through Cooperative, Collaborative and Corroborative (C3) governance (internal and external symbiosis) via decision-making processes and the associated structures. We create a hyper enabled human interaction capability to analyze the mission status, diagnostics of critical sub-systems within the robot to provide automatic updates to our AI-driven run-time reliability ontology. This enables faults to be translated into failure modes for decision-making during the mission. Our results demonstrate that the symbiotic system of systems methodology provides enhanced run-time operational resilience and safety compliance to BVLOS autonomous missions. The SSOSA is highly transferable to other mission scenarios an

关键词： Nondestructive examination

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Compensation Mechanisms for Double Auctions in Peer-to-Peer Local Energy Markets

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Current Sustainable/Renewable Energy Reports 2020年第4期7卷 165-175页

作者： Gerwin, Christoph Mieth, Robert Dvorkin, Yury Workgroup for Infrastructure Policy Technische Universität Berlin Straße des 17. Juni 135 Berlin10623 Germany Tandon School of Engineering Department of Electrical and Computer Engineering NYU BrooklynNY11201 United States Control Systems Group Technische Universität Berlin Einsteinufer 17 Berlin10587 Germany

Purpose of Review: This paper compares different double-auction pricing mechanisms in the context of peer-to-peer trading in local electricity markets. Benchmarking against the traditional system marginal price (SMP) approach, we analyze the outcomes of the Vickrey-Clarke-Groves (VCG) and Pay-as-bid (PAB) approaches, which lead to a revenue imbalance in the market. To mitigate this imbalance, we propose a set of mechanisms that impose trading fees or subsidies with different fairness policies and discuss their impact on the behavior of market participants. Recent Findings: Proliferation of small-scale and distributed energy resources in low-voltage distribution systems has spurred the development of platforms for peer-to-peer local electricity trading. While recent studies prove that local electricity trading can be beneficial to both the peers and system, implementation of local energy markets is still in the early stages and there is no consensus on an optimal market design. Summary: This paper provides a detailed description of market properties for a competitive LEM that is cleared using the SMP, VCG, and PAB approaches. We show that the proposed compensation mechanisms effectively enforce predefined fairness policies and derive exact payments for each peer depending on market results and individual peer characteristics. The case study uses real-world data to illustrate that different compensation mechanisms can be used to incentivize desirable peer behavior, e.g., installation of new distributed energy resources, investment in grid supportive generation, or flexible energy usage. © 2020, Springer Nature Switzerland AG.

关键词： Power markets

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Simulating Cyber Attacks and Designing Malware using Python

Simulating Cyber Attacks and Designing Malware using Python

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International Conference on Signal Processing and Integrated Networks (SPIN)

作者： Akash Ankit Shubham Inder Anuj Sharma Rahul Johari Deo Prakash Vidyarthi SWINGER: Security Wireless IoT Network Group of Engineering and Research University School of Information Communication and Technology Guru Gobind Singh Indraprastha University Dwarka New Delhi India Royal Melbourne Institute of Technology (RMIT University) Melboume Victoria Australia Parallel and Distributed System Lab School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi India

In this Internet era, we have become heavily dependent on the World Wide Web and it has become a basic amenity for humans. A normal user can be trapped easily in this vicious web if not careful enough. It is a field that requires users to be on their toes every time they try to connect to this enormous interconnection of networks. Reports and Surveys have said that in the coming years, Cybercrime could become the biggest threat. In this proposed work, the authors have deployed applications and designed malware for the purpose of simulating various kinds of cyber-attacks so that they could study their effects & affects and ultimately devise solutions to resolve the vulnerabilities in a system. These applications included networking concepts & protocols and a software based keylogger which was developed with no malicious intent, but to analyse the architectural view or design of a malware and to study the stages of a malware attack like entry, distribution, exploitation, infection and execution. Efforts have been made to demonstrate spooting attacks with an idea of identifying vulnerabilities and security loopholes in a particular system that can be exploited by an attacker if not resolved within time.

关键词： Surveys Protocols Malware Internet Web sites Security Signal resolution

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