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9th International Conference on computer science and engineering, UBMK 2024

作者： Kaya, M. Cagri Bouraffa, Tayssir Wnuk, Krzysztof Chalmers University of Technology University of Gothenburg Department of Computer Science and Engineering GothenburgSE-41296 Sweden Blekinge Institute of Technology Department of Software Engineering KarlskronaSE-37179 Sweden

ISBN: (纸本)9798350365887

Creating accurate and insightful personas for automotive applications requires understanding the diverse driving styles that emerge from sensor data. This paper introduces initial results of our methodology for developing personas based on the analysis of automotive sensor data, which captures various driving behaviors. We present a comprehensive process that begins with the extraction of driving events from sensor data, followed by the identification of distinct driving styles through K-Means clustering. Our approach is innovative in its use of dynamic, real-world driving data as opposed to static or direct user interaction data commonly employed in persona development. This allows for a deeper understanding of driver behaviors, which are indirectly inferred from sensor data, thus providing a foundation for creating detailed personas. © 2024 IEEE.

关键词： Automotive sensor data clustering driving styles event detection persona development

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Analytics and Data-Driven Methods and Practices in Platform Ecosystems: a systematic literature review 49

Analytics and Data-Driven Methods and Practices in Platform ...

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49th Euromicro Conference on Software engineering and Advanced Applications, SEAA 2023

作者： Hegazy, Shady Elsner, Christoph Bosch, Jan Olsson, Helena Holmstrom Siemens Technology Munich Germany Chalmers University of Technology Department of Computer Science and Engineering Göteborg Sweden Malmö University Department of Computer Science and Media Technology Malmö Sweden

ISBN: (纸本)9798350342352

The emergence of platform ecosystems has transformed the business landscape in many industries, giving rise to novel modes of interorganizational cooperation and value co-creation, as well as unconventional challenges. The vast traces of data generated by platform ecosystems makes them ripe for the use of analytics and data-driven methods aimed at improving their health, performance, business outcomes, and evolution. However, the research on the application of analytics within platform ecosystems is limited and spread across multiple disciplines. To address this gap, we conducted a systematic literature review on the application of analytics and data-driven methods and practices within platform ecosystems. A total of 56 studies were reviewed, and underwent data extraction, analysis, and synthesis processes. In addition to presenting themes and patterns in the recent and relevant literature on platform ecosystems analytics, our review offers the following outcomes: an actionable overview of the analytics toolbox currently used within platform ecosystems - spanning domains such as machine learning, deep learning, data science, modelling, simulation, among others -;a roadmap for practitioners to achieve analytics maturity;and a summary of underexplored research areas. © 2023 IEEE.

关键词： Big data

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Integration of BCH Encoder in SiGe Driver System

Integration of BCH Encoder in SiGe Driver System

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2023 Signal Processing in Photonic Communications, SPPCom 2023 in Advanced Photonics Congress, Part of Advanced Photonics Congress 2023

作者： Wang, Xu Mowlavi, Siavash Svensson, Lars Larsson-Edefors, Per Dept. of Computer Science and Engineering Chalmers University of Technology GothenburgSE-412 96 Sweden

We implement a 56-Gbps BCH encoder in a 130-nm SiGe technology suitable for VCSEL drivers for short-reach optical communication. We show that encoder-driver integration can reduce total power consumption in addition t... 详细信息

ISBN: (纸本)9781957171265

关键词： Signal encoding

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A Deep Learning Framework for Generation and Analysis of Driving Scenario Trajectories

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SN computer science 2023年第3期4卷 251页

作者： Demetriou, Andreas Alfsvåg, Henrik Rahrovani, Sadegh Haghir Chehreghani, Morteza Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Autonomous Drive Department in Volvo Cars Gothenburg Sweden

We propose a unified deep learning framework for the generation and analysis of driving scenario trajectories, and validate its effectiveness in a principled way. To model and generate scenarios of trajectories with different lengths, we develop two approaches. First, we adapt the Recurrent Conditional Generative Adversarial Networks (RC-GAN) by conditioning on the length of the trajectories. This provides us the flexibility to generate variable-length driving trajectories, a desirable feature for scenario test case generation in the verification of autonomous driving. Second, we develop an architecture based on Recurrent Autoencoder with GANs to obviate the variable length issue, wherein we train a GAN to learn/generate the latent representations of original trajectories. In this approach, we train an integrated feed-forward neural network to estimate the length of the trajectories to be able to bring them back from the latent space representation. In addition to trajectory generation, we employ the trained autoencoder as a feature extractor, for the purpose of clustering and anomaly detection, to obtain further insights into the collected scenario dataset. We experimentally investigate the performance of the proposed framework on real-world scenario trajectories obtained from in-field data collection. © 2023, The Author(s).

关键词： Autonomous drive safety verification Clustering Generative Adversarial Networks (GANs) Outlier detection Time series analysis

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Transformer-based Swedish Semantic Role Labeling through Transfer Learning 30

Transformer-based Swedish Semantic Role Labeling through Tra...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Buhr, Lucy Yang Dannélls, Dana Johansson, Richard Department of Computer Science and Engineering Sweden Språkbanken Text Department of Swedish Multilingualism Language Technology Sweden Chalmers University of Technology University of Gothenburg Sweden University of Gothenburg Sweden

ISBN: (纸本)9782493814104

Semantic Role Labeling (SRL) is a task in natural language understanding where the goal is to extract semantic roles for a given sentence. English SRL has achieved state-of-the-art performance using Transformer techniques and supervised learning. However, this technique is not a viable choice for smaller languages like Swedish due to the limited amount of training data. In this paper, we present the first effort in building a Transformer-based SRL system for Swedish by exploring multilingual and cross-lingual transfer learning methods and leveraging the Swedish FrameNet resource. We demonstrate that multilingual transfer learning outperforms two different cross-lingual transfer models. We also found some differences between frames in FrameNet that can either hinder or enhance the model's performance. The resulting end-to-end model is freely available and will be made accessible through Språkbanken Text's research infrastructure. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Semantics

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Improved Tactical Decision Making and Control Architecture for Autonomous Truck in SUMO Using Reinforcement Learning

Improved Tactical Decision Making and Control Architecture f...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Pathare, Deepthi Laine, Leo Chehreghani, Morteza Haghir Chalmers University of Technology Department of Computer Science and Engineering Göteborg Sweden Göteborg Sweden Chalmers University of Technology Department of Mechanics and Maritime Sciences Göteborg Sweden

ISBN: (纸本)9798350324457

We employ Reinforcement Learning (RL) techniques with improved state and action spaces for tactical decision making in an autonomous truck. Specifically, we implement Adaptive Cruise Control (ACC) and lane change maneuvers for the autonomous truck in a highway scenario. We show the results obtained using three reinforcement learning algorithms: Deep Q-Network (DQN), Advantage Actor-Critic (A2C) and Proximal Policy Optimization (PPO). Our results demonstrate that it is beneficial to separate high-level decision-making processes and low-level control actions between the RL agent and the low-level controllers based on physical models. Furthermore, we design a realistic reward function based on the Total Cost of Operation (TCOP) of the truck to guide the RL agent towards optimal driving strategy. © 2023 IEEE.

关键词： Autonomous Trucks Reinforcement Learning Tactical Decision making Total Cost of Operation Traffic Simulations

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On the Performance of Multidimensional Constellation Shaping for Linear and Nonlinear Optical Fiber Channel 49

On the Performance of Multidimensional Constellation Shaping...

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49th European Conference on Optical Communications, ECOC 2023

作者： Chen, Bin Liang, Zhiwei Li, Shen Lei, Yi Liga, Gabriele Alvarado, Alex School of Computer Science and Information Engineering Hefei University of Technology China Department of Electrical Engineering Chalmers University of Technology Gothenburg Sweden Department of Electrical Engineering Eindhoven University of Technology Netherlands

ISBN: (纸本)9781839539954

Multidimensional constellation shaping of up to 32 dimensions with different spectral efficiencies are compared through AWGN and fiber-optic s imulations. The results show that no constellation is universal and the balance of required and effective SNRs should be jointly considered for the specific optical transmission scenario. © The Institution of engineering & technology 2023.

关键词： Optical fibers

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Ultrawideband RF-IQ Modulator Using Segmented Nonlinearly Scaled RF-DACs and Nonoverlapping LO Signals

Ultrawideband RF-IQ Modulator Using Segmented Nonlinearly Sc...

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作者： Aberg, Victor Fager, Christian Hou, Rui Svensson, Lars Chalmers University of Technology Department of Computer Science and Engineering Gothenburg41296 Sweden Chalmers University of Technology Department of Microtechnology and Nanoscience Gothenburg41296 Sweden Ericsson Stockholm16480 Sweden

A wideband Cartesian in-phase and quadrature (I/Q) modulator based on dual 10 b RF-digital-to-analog converters (DACs) is presented. Nonoverlapping local oscillator (LO) signals and a segmented nonlinear scaling with scaled unit cells contribute to a high linearity and allow for a low-complexity digital predistortion (DPD). Unit-cell flip-flops (FFs) and a balanced clock distribution enable a high sample rate with little skew. Out-of-band emissions are reduced through drive-slope control of the data-switch inputs. The modulator, implemented in the 22-nm fully-depleted silicon-on-insulator (FDSOI) CMOS, operates between 20 and 26 GHz with a peak sample rate of 11 GS/s. It has been used to demonstrate the transmission of a 64-quadrature amplitude modulation (QAM) single-carrier (SC) signal at 13.2 Gb/s, a 256-QAM SC signal at 7.33 Gb/s, and an orthogonal frequency division multiplexing (OFDM) signal comprising four aggregated 400-MHz 64-QAM channels with an error vector magnitude (EVM) of 6.43%. These results demonstrate the potential of the proposed modulator for the realization of ultrawideband transmitters in high-performance millimeter-wave (mmW) systems. © 1963-2012 IEEE.

关键词： Orthogonal frequency division multiplexing

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Self-stabilizing Byzantine-Tolerant Recycling 25th

Self-stabilizing Byzantine-Tolerant Recycling

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25th International Symposium on Stabilization, Safety, and Security of Distributed Systems, SSS 2023

作者： Georgiou, Chryssis Raynal, Michel Schiller, Elad M. Computer Science University of Cyprus Nicosia Cyprus IRISA Univ. Rennes 1 Rennes France Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden

ISBN: (纸本)9783031442735

Numerous distributed applications, such as cloud computing and distributed ledgers, necessitate the system to invoke asynchronous consensus objects for an unbounded number of times, where the completion of one consensus instance is followed by the invocation of another. With only a constant number of objects available, object reuse becomes vital. We investigate the challenge of object recycling in the presence of Byzantine processes, which can deviate from the algorithm code in any manner. Our solution must also be self-stabilizing, as it is a powerful notion of fault tolerance. Self-stabilizing systems can recover automatically after the occurrence of arbitrary transient-faults, in addition to tolerating communication and (Byzantine or crash) process failures, provided the algorithm code remains intact. We provide a recycling mechanism for asynchronous objects that enables their reuse once their task has ended, and all non-faulty processes have retrieved the decided values. This mechanism relies on synchrony assumptions and builds on a new self-stabilizing Byzantine-tolerant synchronous multivalued consensus algorithm, along with a novel composition of existing techniques. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Fault tolerance

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Metainformation Extraction from Encrypted Streaming Video Packet Traces

Metainformation Extraction from Encrypted Streaming Video Pa...

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2022 International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2022

作者： Duvignau, Romaric Chalmers University of Technology Department of Computer Science and Engineering Gothenburg Sweden

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

In this study, we present a metainformation extraction pipeline and a Proof of Concept (PoC) implementation of a recognizer capable of classifying video titles, series titles as well as video genres from encrypted video streams. We show in a promising evaluation, using the Netflix and SVT Play catalogues as examples, that our PoC is capable of learning abstract data from the packet bursts visible in DASH encrypted streams (such as if a video fingerprint is rather a Drama or a Romance movie). This is, to the best of our knowledge, the first demonstration of successful extraction of video metainformation from coarse-grain encrypted network packet traces. While advocating updates in the DASH protocol in order to preserve viewers' privacy, our results also pave the way to future computer forensics systems capable of successful content classification over encrypted channels. © 2022 IEEE.

关键词： Extraction

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