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18th International Symposium on Parameterized and Exact Computation, IPEC 2023

作者： Gima, Tatsuya Kim, Eun Jung Köhler, Noleen Melissinos, Nikolaos Vasilakis, Manolis JSPS Research Fellow Nagoya University Japan Université Paris-Dauphine PSL University CNRS UMR7243 LAMSADE Paris France Department of Theoretical Computer Science Faculty of Information Technology Czech Technical University Prague Czech Republic

ISBN: (纸本)9783959773058

Given a graph G and an integer b, Bandwidth asks whether there exists a bijection π from V (G) to {1, ⋯, |V (G)|} such that max{u,v}∈E(G) |π(u) - π(v)| ≤ b. This is a classical NP-complete problem, known to remain NP-complete even on very restricted classes of graphs, such as trees of maximum degree 3 and caterpillars of hair length 3. In the realm of parameterized complexity, these results imply that the problem remains NP-hard on graphs of bounded pathwidth, while it is additionally known to be W[1]-hard when parameterized by the treedepth of the input graph. In contrast, the problem does become FPT when parameterized by the vertex cover number of the input graph. In this paper, we make progress towards the parameterized (in)tractability of Bandwidth. We first show that it is FPT when parameterized by the cluster vertex deletion number cvd plus the clique number ω of the input graph, thus generalizing the previously mentioned result for vertex cover. On the other hand, we show that Bandwidth is W[1]-hard when parameterized only by cvd. Our results generalize some of the previous results and narrow some of the complexity gaps. © 2023 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. All rights reserved.

关键词： Bandwidth

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A Novel Approach for Activity, Fall and Gait Detection Using Multiple 2D LiDARs

A Novel Approach for Activity, Fall and Gait Detection Using...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Mondher Bouazizi Kevin Feghoul Alejandro Lorite Tomoaki Ohtsuki Department of Information and Computer Science Faculty of Science and Technology Keio University Univ. Lille Inserm CHU Lille UMR-S1172 - LilNCog Lille France Graduate School of Science and Technology Keio University Yokohama Japan

A key concept in health monitoring systems for elderly people is the continuous and non-intrusive detection of their activities to identify when hazardous events such as sudden falling occur/are about to occur. The existence of obstacles in the environment largely limits the detection performance of existing approaches of activity detection relying on non-contact sensors. A simple, yet effective, approach to address this issue is the use of multiple sensors which collaborate with one another. In this paper, we propose an approach that relies on 2D Light Detection and Ranging (LiDAR) technology for activity detection. We employ multiple 2D LiDARs placed at different locations in a single room with difference obstacles (e.g., furniture) and working in coordination to construct a fuller representation of the activities being performed. Our approach transforms the concatenation of the different LiDAR data into a more comprehensible data format (i.e., images). The generated images are then processed using a Convolutional LSTM Neural Network to perform the classification. For 3 different tasks, namely activity detection, fall detection, and unsteady gate detection, our proposed approach reaches an accuracy equal to 96.10%, 99.13% and 93.13%, respectively.

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Indoor spatial queries: Modeling, indexing, and processing 24

Indoor spatial queries: Modeling, indexing, and processing

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Advances in Database technology - 24th International Conference on Extending Database technology, EDBT 2021

作者： Liu, Tiantian Li, Huan Lu, Hua Cheema, Muhammad Aamir Shou, Lidan Department of Computer Science Aalborg University Denmark Department of People and Technology Roskilde University Denmark Faculty of Information Technology Monash University Australia College of Computer Science Zhejiang University China

ISBN: (纸本)9783893180844

To support indoor spatial queries and indoor location-based services (LBS), multiple techniques including model/indexes and search algorithms have been proposed. In this work, we conduct an extensive experimental study on existing proposals for indoor spatial queries. We survey five model/indexes, compare their algorithmic characteristics, and analyze their space and time complexities. We also design an in-depth benchmark with real and synthetic datasets, evaluation tasks and performance metrics. Enabled by the benchmark, we obtain and report the performance results of all model/indexes under investigation. By analyzing the results, we summarize the pros and cons of all techniques and suggest the best choice for typical scenarios. © 2021 Copyright held by the owner/author(s).

关键词： Location based services

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Enhancing Coarse-Grained and Fine-Grained Image-Text Retrieval Models via Multi-Scale Modal Distance Learning

Enhancing Coarse-Grained and Fine-Grained Image-Text Retriev...

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International Conference on computer Supported Cooperative Work in Design

作者： Kaili Zhou Yushui Geng Jing Zhao Yiyun Xing Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Graduate School Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

The current image-text cross-modal retrieval faces challenges due to the heterogeneous nature of different modalities. This paper proposes an improved model based on multi-scale modal distance learning to enhance the performance of coarse-grained and fine-grained image-text matching. In the image encoding part, an improved ResNet architecture is adopted, utilizing upsampling techniques to better preserve key semantic information. For text encoding, BERT and Bi-LSTM models are combined to extract shallow and deep textual features, respectively. We design a coarse-fine granularity interaction mechanism, leveraging a bidirectional stacked Transformer to achieve local similarity interaction between image regions and text words, while also using Bi-LSTM-extracted contextual features to interact with global image features for global similarity matching. Additionally, this paper introduces a novel multi-scale modal contrastive loss, which dynamically adjusts the distance between samples of different modalities, thereby improving the model's sensitivity to semantic similarity. Experimental results on the COCO 5K and Flickr30K datasets demonstrate that the proposed method outperforms existing mainstream methods in both retrieval accuracy and efficiency, achieving complementary advantages between coarse- and fine-grained features. This provides an effective solution and new research direction for cross-modal retrieval.

关键词： computer aided instruction Image coding Accuracy Sensitivity Semantics Bidirectional control Feature extraction Transformers Encoding Cross modal retrieval

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Semantic-Enhanced Attention Network for Image-Text Matching

Semantic-Enhanced Attention Network for Image-Text Matching

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International Conference on computer Supported Cooperative Work in Design

作者： Huanxiao Zhou Yushui Geng Jing Zhao Xishan Ma Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Graduate School Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Image-text matching is an important task in cross-modal information processing, which consists of evaluating the similarity between images and text. However, the data of the two modalities have different distributions and representations, which cannot be directly compared. Therefore, the two modalities need to be processed separately. Most existing image-text matching methods extract image fragments and text fragments separately, using an attention mechanism to establish relationships between regions in the image and words in the text. Subsequently, they aggregate the similarity of these images and text fragments. Regardless, not all fragment alignment is meaningful, and irrelevant fragment alignment will bring redundancy and reduce retrieval accuracy. In addition to that, there are contextual relationships between image regions and between text words, which should also be taken into account. In this paper, we simultaneously consider these two aspects and propose a Semantic-Enhanced Attention Network (SEAN). It first focuses on each modality, mining contextual relationships between fragments within each modality and aggregating contextual information into visual and textual embeddings. Then, we compute the similarity between all image regions and text fragments, focusing all attention on the region-word pairs with the highest similarity. Finally, we summarize these to infer the similarity between the image and text. Our method achieves competitive results on two generalized image text retrieval datasets, Flickr30K and MS-COCO.

关键词： Visualization Attention mechanisms Federated learning Computational modeling Aggregates Semantics Redundancy

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Towards crowd-aware indoor path planning 47th

Towards crowd-aware indoor path planning

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47th International Conference on Very Large Data Bases, VLDB 2021

Indoor venues accommodate many people who collectively form crowds. Such crowds in turn influence people’s routing choices, e.g., people may prefer to avoid crowded rooms when walking from A to B. This paper studies two types of crowd-aware indoor path planning queries. The Indoor Crowd-Aware Fastest Path Query (FPQ) finds a path with the shortest travel time in the presence of crowds, whereas the Indoor Least Crowded Path Query (LCPQ) finds a path encountering the least objects en route. To process the queries, we design a unified framework with three major components. First, an indoor crowd model organizes indoor topology and captures object flows between rooms. Second, a time-evolving population estimator derives room populations for a future timestamp to support crowd-aware routing cost computations in query processing. Third, two exact and two approximate query processing algorithms process each type of query. All algorithms are based on graph traversal over the indoor crowd model and use the same search framework with different strategies of updating the populations during the search process. All proposals are evaluated experimentally on synthetic and real data. The experimental results demonstrate the efficiency and scalability of our framework and query processing algorithms. © 2021, VLDB Endowment. All rights reserved.

关键词： Motion planning

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Long short-term memory (LSTM) model-based reinforcement learning for nonlinear mass spring damper system control

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Procedia computer science 2023年 216卷 213-220页

作者： Santo Wijaya Yaya Heryadi Yulyani Arifin Wayan Suparta Lukas Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta 55281 Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta 12930 Indonesia

The Neural Networks (NN) model which is incorporated in the control system design has been studied, and the results show better performance than the mathematical model approach. However, some studies consider that only offline NN model learning and does not use the online NN model learning directly on the control system. As a result, the controller's performance decreases due to changes in the system environment from time to time. The Reinforcement Learning (RL) method has been investigated intensively, especially Model-based RL (Mb-RL) to predict system dynamics. It has been investigated and performs well in making the system more robust to environmental changes by enabling online learning. This paper proposes online learning of local dynamics using the Mb-RL method by utilizing Long Short-Term Memory (LSTM) model. We consider Model Predictive Control (MPC) scheme as an agent of the Mb-RL method to control the regulatory trajectory objectives with a random shooting policy to search for the minimum objective function. A nonlinear Mass Spring Damper (NMSD) system with parameter-varying linear inertia is used to demonstrate the effectiveness of the proposed method. The simulation results show that the system can effectively control high-oscillating nonlinear systems with good performance.

关键词： Model-based Reinforcement Learning Random-Shooting Policy LSTM MPC Nonlinear System

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Integrating Sustainability in Engineering with a Double Degree program Case Study 65

Integrating Sustainability in Engineering with a Double Degr...

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65th IEEE Annual International Scientific Conference on Power and Electrical Engineering of Riga Technical University, RTUCON 2024

作者： Arhun, Shchasiana Hnatov, Andrii Kunicina, Nadezhda Trunova, Iryna Ulianets, Olha Daleka, Vasyl Kharkiv National Automobile and Highway University Automobile Faculty Vehicle Electronics Department Kharkiv Ukraine Institute of Industrial Electronics and Electrical Engineering Riga Technical University Faculty of Computer Science Information Technology and Energy Riga Latvia O. M. Beketov National University of Urban Economy in Kharkiv Department of electric transport Kharkiv Ukraine

ISBN: (纸本)9798350365771

This article presents an innovative analysis of the development and implementation of double degree programs in higher education, focusing on the emerging fields of electric vehicles and automotive electronics. The study examines the 'Electric Vehicles and Automotive Electronics' educational program, highlighting the critical skills and competencies that students acquire to meet the evolving demands of the modern job market. An algorithm for establishing educational partnerships has been developed, leading to the proposal of a pioneering double degree program between Kharkiv National Automobile and Highway University (KhNAHU) and Riga Technical University (RTU), supported by the Erasmus+ DIGITRANS project. The results show that such collaborative educational frameworks significantly enrich engineering education, giving students a competitive edge in the global workforce. This research provides valuable insights for higher education and international educational initiatives, emphasizing a scalable model for developing interdisciplinary and international programs. © 2024 IEEE.

关键词： Students

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Visualization of Front-End Data Logger Internet of Things technology using *** Framework

Visualization of Front-End Data Logger Internet of Things Te...

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International Conference on information technology, information Systems and Electrical Engineering (ICITISEE)

作者： Trisna Maulida Agnis Nur Afa Zumaroh Hasri Akbar Awal Rozaq Alif Yahya Syafa'at Rona Sepri Ananda Imam Tahyudin Bambang Pilu Hartato Berlilana Department of Informatics Faculty of Computer Science Universitas Amikom Purwokerto Purwokerto Indonesia Department of Computer Science Graduate School of Informatics Gazi Üniversitesi Ankara Türkiye Department of Information Systems Faculty of Computer Science Universitas Amikom Purwokerto Purwokertos Indonesia Department of Information Technology Faculty of Computer Science Universitas Amikom Purwokerto Purwokertos Indonesia

The danger of spreading the new variant of Coronavirus Disease (COVID-19) still exists. It takes a new habit to prevent it. The World Health Organization (WHO)urges to minimize the spread of the COVID-19 variant, including by getting used to washing hands regularly. To support this call, the researchers carried out various innovations. One of them is through the application of Internet of Things (IoT) technology for routine hand washing. This research innovates by using an IoT tool that we named Amikom Purwokerto Hand Sanitizer (AMPUH). In addition to functioning as an automatic hand-washing device, this tool is also capable of recording temperature data, and the number of users, controlling hand sanitizer liquid and measuring saturation automatically. The recordings are stored through a data logger and processed through an IoT platform called ThingSpeak. ThingSpeak has the disadvantage of data visualization which is less attractive and informative. A website dashboard is needed to visualize the data contained on the ThingSpeak platform so that the display is more attractive and easier to understand. Therefore, this study aims to create a website dashboard that can provide information from the data stored on the ThingSpeak platform. The author focuses on the front-end by using the *** framework. This framework has advantages such as being flexible (can be used in large-and small-scale websites), reducing repetitive code, and making the page display more dynamic. In building this front end, we apply the Extreme programming development method. Based on the results of the study, showed that the implementation of the *** framework on the AMPUH data logger front-end resulted in performance in accordance with expectations. Websites that are built to produce attractive and informative visualizations.

关键词： COVID-19 Temperature measurement Temperature distribution Technological innovation Buildings Data visualization Temperature control

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Relational database reconstruction from SQL to Entity Relational Diagrams 4

Relational database reconstruction from SQL to Entity Relati...

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4th International Conference on Computing and Applied Informatics 2019, ICCAI 2019

作者： Hakim Lubis, Juanda Muisa Zamzami, Elviawaty Faculty of Computer Science and Information Technology Doctoral Program of Computer Science Studies Universitas Sumatera Utara Indonesia Faculty of Computer Science and Information Technology Universitas Sumatera Utara Indonesia

The absence of requirement documents or incompatibility of the requirement documents with existing software can be a problem in developing or maintaining software. Without knowing things that become requirements can cause software as a product that fails because it does not meet customer desires. One of the main problems in developing or maintaining software is that developers do not know the entity relational schema of the software. So developers will prefer to rebuild from scratch. This paper discusses database reconstruction from SQL to entity relational diagrams by identifying the primary and foreign keys of SQL sampling. © Published under licence by IOP Publishing Ltd.

关键词： Condensed matter physics

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