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Detecting potentially harmful and protective suicide-related content on twitter: A machine learning approach

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arXiv 2021年

作者： Metzler, Hannah Baginski, Hubert Niederkrotenthaler, Thomas Garcia, David Section for Science of Complex Systems Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Austria Unit Suicide Research & Mental Health Promotion Department of Social and Preventive Medicine Center for Public Health Medical University of Vienna Austria Complexity Science Hub Vienna Austria Institute of Interactive Systems and Data Science Department of Computer Science and Biomedical Engineering Graz University of Technology Graz Austria Institute of Globally Distributed Open Research and Education Austria Institute of Information Systems Engineering Vienna University of Technology Vienna Austria

Background Research has repeatedly shown that exposure to suicide-related news media content is associated with suicide rates, with some content characteristics likely having harmful and others potentially protective effects. Although good evidence exists for a few selected characteristics, systematic and large-scale investigations are missing. Moreover, the growing importance of social media, particularly among young adults, calls for studies on the effects of content posted on these platforms. Objective This study applies natural language processing and machine learning methods to classify large quantities of social media data according to characteristics identified as potentially harmful or beneficial in media effects research on suicide and prevention. Methods We manually labeled 3202 English tweets using a novel annotation scheme that classifies suicide-related tweets into 12 categories. Based on these categories, we trained a benchmark of machine learning models for a multi-class and a binary classification task. As models, we included a majority classifier, an approach based on word frequency (TF-IDF with a linear SVM) and two state-of-the-art deep learning models (BERT, XLNet). The first task classified postings into six main content categories, which are particularly relevant for suicide prevention based on previous evidence. These included personal stories of either (1) suicidal ideation and attempts or (2) coping and recovery, calls for action intending to spread either (3) problem awareness or (4) prevention-related information, (5) reporting of suicide cases, and (6) other tweets irrelevant to these five categories. The second classification task was binary, and separated postings in the 11 categories that refer to actual suicide, from postings in the off-topic category, which use suicide-related terms in another meaning or context. Results In both tasks, the performance of the two deep learning models was very similar and better than the majority or th

关键词： Learning algorithms

Rank-Based Filter Pruning for Real-Time Deep Uav Trackers

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SSRN

SSRN 2022年

作者： Wang, Xucheng Zeng, Dan Zhao, Qijun Li, Shuiwang Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology Guilin541001 China Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518000 China Sichuan University Chengdu610000 China

UAV tracking is an emerging task and has wide potential applications in such as agriculture, navigation, entertainment and public security. However, the limitations of computing resources, battery capacity, and maximum load of UAV hinder the deployment of DL-based tracking algorithms on UAV. In contrast to deep learning trackers, discriminative correlation filters (DCF)-based trackers stand out in the UAV tracking community because of their high efficiency. However, their precision is usually much lower than trackers based on deep learning. Model compression is a promising way to narrow the gap (i.e., effciency, precision) between DCF- and deep learning- based trackers, which has not caught much attention in UAV tracking community. In this paper, We propose the P-SiamFC++ tracker, which is the first to use rank-based filter pruning to compress the SiamFC++ model, achieving a remarkable balance between efficiency and precision. Our method is general and may encourage further studies on UAV tracking with model compression in the future. Extensive experiments on four UAV benchmarks, including UAV123@10fps, DTB70, UAVDT and Vistrone2018, show that P-SiamFC++ tracker significantly outperforms state-of-the-art UAV tracking methods. © 2022, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

Semantically supported SLA negotiation

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Semantically supported SLA negotiation

International Workshop on Grid Computing

作者： András Micsik Henar Muñoz Frutos Ioannis Kotsiopoulos Bastian Koller Department of Distributed Systems MTA SZTA Budapest Hungary Telefónica Investigaciòn y Desarrollo Madrid Spain School of Computer Science University of Manchester Institute of Science and Technology Manchester UK High Performance Computing Center Stuttgart University of Stuttgart Stuttgart Germany

The evolution of services market raises the need for automatic support for negotiating service use criteria. In order to reach an agreement, the negotiating parties need to develop a common understanding of the Quality of Service (QoS) terms which are part of the Service Level Agreement (SLA). The use of semantic annotations together with reasoning can increase the level of flexibility and automation in SLA management. A framework is presented for SLA negotiation allowing the two parties to use their own terminologies.

关键词： Ontologies Web services Terminology Quality of service Measurement units computer science High performance computing Automation Semantic Web Vocabulary

Enhancing Drone Surveillance with NeRF: Real-World Applications and Simulated Environments

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Enhancing Drone Surveillance with NeRF: Real-World Applicati...

Conference on Digital Avionics systems (DASC)

作者： Joakim Lindén Giovanni Burresi Håkan Forsberg Masoud Daneshtalab Ingemar Söderquis Division of Intelligent Future Technologies School of Innovation Design and Engineering Malardalen University Vasteras Sweden Saab Aeronautics Stockholm Sweden Division of Electronics and Embedded Systems School of Electrical Engineering and Computer Science Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9798350349610

ISBN: (纸本)9798350349627

Machine Learning (ML) systems require representative and diverse datasets to accurately learn the objective task. In supervised learning data needs to be accurately annotated, which is an expensive and error-prone process. We present a method for generating synthetic data tailored to the use-case achieving excellent performance in a real-world usecase. We provide a method for producing automatically annotated synthetic visual data of multi rotor unmanned aerial vehicles (UAV) and other airborne objects in a simulated environment with a high degree of scene diversity, from collection of 3D models to generation of annotated synthetic datasets (synthsets). In our data generation framework SynRender we introduce a novel method of using Neural Radiance Field (NeRF) methods to capture photo-realistic high-fidelity 3D-models of multirotor UAVs in order to automate data generation for an object detection task in diverse environments. By producing data tailored to the real-world setting, our NeRF-derived results show an advantage over generic 3D asset collection-based methods where the domain gap between the simulated and real-world is unacceptably large. In the spirit of keeping research open and accessible to the research community we release our dataset VISER DroneDiversity used in this project, where visual images, annotated boxes, instance segmentation and depth maps are all generated for each image sample.

关键词： Training Visualization Three-dimensional displays Object detection Machine learning Data collection Neural radiance field Autonomous aerial vehicles Data models Synthetic data

Reputation systems for Supply Chains: The Challenge of Achieving Privacy Preservation

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arXiv 2023年

作者： Bader, Lennart Pennekamp, Jan Thevaraj, Emildeon Spiß, Maria Kanhere, Salil S. Wehrle, Klaus Cyber Analysis & Defense Fraunhofer FKIE Germany Communication and Distributed Systems RWTH Aachen University Germany Institute for Industrial Management RWTH Aachen University Germany School of Computer Science and Eng. University of New South Wales Australia

Consumers frequently interact with reputation systems to rate products, services, and deliveries. While past research extensively studied different conceptual approaches to realize such systems securely and privacy-preservingly, these concepts are not yet in use in business-to-business environments. In this paper, (1) we thus outline which specific challenges privacy-cautious stakeholders in volatile supply chain networks introduce, (2) give an overview of the diverse landscape of privacy-preserving reputation systems and their properties, and (3) based on well-established concepts from supply chain information systems and cryptography, we further propose an initial concept that accounts for the aforementioned challenges by utilizing fully homomorphic encryption. For future work, we identify the need of evaluating whether novel systems address the supply chain-specific privacy and confidentiality needs. Copyright © 2023, The Authors. All rights reserved.

关键词： Supply chains

Variable petri nets for mobility

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arXiv 2021年

作者： Ding, Zhijun Yang, Ru Cui, Puwen Zhou, MengChu Jiang, Changjun Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China Department of Computer Science and Technology Tongji University Shanghai201804 China Institute of Systems Engineering Macau University of Science and Technology Macau999078 China Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

Mobile computing systems, service-based systems and some other systems with mobile interacting components have recently received much attention. However, because of their characteristics such as mobility and disconnection, it is difficult to model and analyze them by using a structure-fixed model. This work proposes a new Petri net model called Variable Petri Net (VPN) for modeling and analyzing these systems. The definition, firing rule, and related analysis technology of VPN are introduced in detail. In a VPN, the possible interaction interfaces are abstracted as a new kind of places called virtual places, and the occurrences of (dis)connections are described by new functions, which makes it appropriate to describe the component collaboration in systems and realize the scalability and pluggability of systems. Moreover, to overcome the shortcoming that markings cannot reflect link capability of a system, VPNs add a constraint function along with a marking to represent a complete system configuration. Several examples are used to demonstrate the newly proposed model and method. Copyright © 2021, The Authors. All rights reserved.

关键词： embedded systems

Exploiting Rank-Based Filter Pruning for Real-Time Uav Tracking

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SSRN

SSRN 2022年

作者： Wang, Xucheng Zeng, Dan Zhao, Qijun Li, Shuiwang Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology Guilin541001 China Research Institute of Trustworthy Autonomous Systems Department of computer science and engineering Southern University of Science and Technology Shenzhen518000 China Sichuan University Chengdu610000 China

关键词： Unmanned aerial vehicles (UAV)

Automating the Mean-Field Method for Large Dynamic Gossip Networks

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Automating the Mean-Field Method for Large Dynamic Gossip Ne...

International Conference on the Quantitative Evaluation of systems (QEST)

作者： Rena Bakhshi Jorg Endrullis Stefan Endrullis Wan Fokkink Boudewijn Haverkort Department of Computer Science Vrije Universiteit of Amsterdam Amsterdam Netherlands Institut für Informatik Universität Leipzig Leipzig Germany Centre of Telematics & Information Technology University of Twente Enschede Netherlands Embedded Systems Institute Eindhoven Netherlands

We investigate an abstraction method, called mean-field method, for the performance evaluation of dynamic networks with pairwise communication between nodes. It allows us to evaluate systems with very large numbers of nodes, that is, systems of a size where traditional performance evaluation methods fall short. While the mean-field analysis is well-established in epidemics and for chemical reaction systems, it is rarely used for communication networks because a mean-field model tends to abstract away the underlying topology. To represent topological information, however, we extend the mean-field analysis with the concept of classes of states. At the abstraction level of classes we define the network topology by means of connectivity between nodes. This enables us to encode physical node positions and model dynamic networks by allowing nodes to change their class membership whenever they make a local state transition. Based on these extensions, we derive and implement algorithms for automating a mean-field based performance evaluation.

关键词： Peer to peer computing Mobile communication Synchronization Manganese Network topology Protocols Base stations

AdaPT: Adaptable particle tracking for spherical microparticles in lab on chip systems

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arXiv 2021年

作者： Dingel, Kristina Huhnstock, Rico Knie, André Ehresmann, Arno Sick, Bernhard Intelligent Embedded Systems Department of Electrical Engineering and Computer Science University of Kassel Wilhelmshöher Allee 73 Kassel34121 Germany Institute of Physics Center for Interdisciplinary Nanostructure Science and Technology University of Kassel Heinrich-Plett-Straße 40 Kassel34132 Germany

Due to its rising importance in science and technology in recent years, particle tracking in videos presents itself as a tool for successfully acquiring new knowledge in the field of life sciences and physics. Accordingly, different particle tracking methods for various scenarios have been developed. In this article, we present a particle tracking application implemented in Python for, in particular, spherical magnetic particles, including superparamagnetic beads and Janus particles. In the following, we distinguish between two sub-steps in particle tracking, namely the localization of particles in single images and the linking of the extracted particle positions of the subsequent frames into trajectories. We provide an intensity-based localization technique to detect particles and two linking algorithms, which apply either frame-by-frame linking or linear assignment problem solving. Beyond that, we offer helpful tools to preprocess images automatically as well as estimate parameters required for the localization algorithm by utilizing machine learning. As an extra, we have implemented a technique to estimate the current spatial orientation of Janus particles within the x-y-plane. Our framework is readily extendable and easy-to-use as we offer a graphical user interface and a command-line tool. Various output options, such as data frames and videos, ensure further analysis that can be automated. © 2021, CC BY-NC-ND.

关键词： High level languages