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作者： Pundir, Sumit Obaidat, Mohammad S. Wazid, Mohammad Das, Ashok Kumar Singh, Devesh Pratap Rodrigues, Joel J. P. C. Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun 248 002 India College of Computing and Informatics University of Sharjah UAE with King Abdullah II School of Information Technology The University of Jordan Amman 11942 Jordan University of Science and Technology Beijing Beijing 100083 China Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad 500 032 India Federal University of Piauí (UFPI) PI Teresina 64049-550 Brazil Instituto de Telecomunicações Covilhã 6201-001 Portugal

Internet of Things (IoT) is one of the fastest-growing technologies. With the deployment of massive and faster mobile networks, almost every daily-use item is connected to the Internet. IoT-enabled industrial multimedia environment is used for the collection and analysis of different types of multimedia data (i.e., images, videos, audios, etc.). This multimedia data is generated by various types of smart devices like drones, robots, smart controller, smart surveillance system which are deployed for the industrial monitoring and control. The multimedia data is generated in the enormous amount which can be considered as the big data. This data is further utilized in various types of business needs for example, chances of fire accidents in the industrial plant, overall machine health, etc., which can be predicted through the application of big data analytics. Therefore, IoT-enabled industrial multimedia environment is very helpful to the concerned authorities as they come to know the important information in advance. However, all the smart devices are connected and controlled through the Internet. It further causes severe threats to the communication happens in an IoT-enabled industrial multimedia environment. It is vulnerable to various types of attacks such as replay, man-in-the-middle, impersonation, secret information leakage, sensitive information modification, and malware injection (i.e., mirai). Therefore, it is important to prevent the communication of such an environment against the different types of possible attacks. These days, the attacks performed by botnets (i.e., malware attacks such as mirai and reaper) have drawn attention to the researchers. Under the influence of such attacks, the communication of IoT-enabled industrial multimedia environment is disrupted. Moreover, the attackers may also control the smart devices remotely and can change their functionalities. Hence, we need some robust mechanism to detect the presence of the malware attacks in such

关键词： Internet of things

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Detection of Railroad Anomalies using Machine Learning Approach 8

Detection of Railroad Anomalies using Machine Learning Appro...

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8th International Conference on ICT for Smart Society, ICISS 2021

作者： Nugraha, Ade Chandra Supangkat, Suhono Harso Nugraha, Igb Baskara Trimadi, Harno Purwadinata, Awan Hermawan Sumarni Sundari, Santi School of Electrical Engineering and Informatics Bandung Institute of Technology Department of Computer Engineering and Informatics Bandung State of Polytechnic Bandung Indonesia Smart City and Community Innovation Center Bandung Institute of Technology Bandung Indonesia Director of Railway Infrastructure Directorate of Railway Infrastructure Ministry of Transportation Jakarta Indonesia PT Kereta Api Indonesia Managing Director of Infrastructure Bandung Indonesia

ISBN: (纸本)9781665416979

Maintenance of assets owned by an organization or company is an activity that will never stop. From the time of implementation, maintenance will be more optimal if it is carried out before the asset is in a damaged condition or cannot operate. Pro-active repair model is proven to reduce 15-60% of operational costs. The existence of technology and computing models currently supports big data processing, both in the form of transactional data, historical data and statistical data. The asset maintenance cycle transformed into an autonomous and integrated system, will assist in the decision-making process. A machine learning approach that is supported by big data analysis is one solution that can realize the predictive maintenance process. To accurately predict the condition of critical components, it can be started with data collection, followed by detecting normal and abnormal behavior, and continued by training algorithms to make predictions. Detection of railroad anomalies is used as the initial process in the predictive maintenance of railroads. The process of detecting railroad anomalies can be done by comparing the lateral, longitudinal and vertical acceleration from the sensing results through the accelerometers on both sides of the train wheels. Differences will pay attention to the data acceleration draft rail geometry either angkatan or listringan. The results of rail anomaly detection will indicate the rail maintenance process that can be carried out immediately. © 2021 IEEE.

关键词： Railroads

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Personalizing text summarization based on sentence weighting

Personalizing text summarization based on sentence weighting

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2007 IADIS European Conference on Data Mining, DM 2007, part of the 1st IADIS Multi Conference on computer Science and Information Systems, MCCSIS 2007

作者： Bouras, Christos Poulopoulos, Vassilis Tsogkas, Vassilis Research Academic Computer Technology Institute Computer Engineering and Informatics Department University of Patras N. Kazantzaki Str. Rion26500 Greece

ISBN: (纸本)9789728924409

The amount of data that exists on the Internet is enormous enough to distract users when trying to find useful information. In addition, the expansive use of small screen devices for browsing the World Wide Web generates huge problems when trying to find and read information. A solution to these problems is to personalize the web and try to reduce algorithmically the amount of text. Many text summarizers have been presented in order to reduce the valueless information that is presented to the users and many web sites, especially news portals, introduce personalization features for the users, though, still these techniques are not often used in combination in order to create more effective results. In this paper a mechanism for creating personalized summaries for the members of a news portal that reproduces articles collected from major portals is presented, together with evaluation both of the summarizer and the personalized summaries. The evaluators of the personalized summaries are members of the news portal. The personalized summary mechanism can also be utilized by users of small screen devices, for easier reading of less but inclusive information. © 2007 IADIS

关键词： Text processing

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Fast light-field 3D microscopy with out-of-distribution detection and adaptation through Conditional Normalizing Flows

arXiv

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arXiv 2023年

作者： Vizcaíno, Josué Page Symvoulidis, Panagiotis Wang, Zeguan Jelten, Jonas Favaro, Paolo Boyden, Edward S. Lasser, Tobias Computational Imaging and Inverse Problems Department of Informatics School of Computation Information and Technology Technical University of Munich Germany Munich Institute of Biomedical Engineering Technical University of Munich Germany Synthetic Neurobiology Group Massachusetts Institute of Technology United States Computer Vision Group University of Bern Switzerland

Real-time 3D fluorescence microscopy is crucial for the spatiotemporal analysis of live organisms, such as neural activity monitoring. The eXtended field-of-view light field microscope (XLFM), also known as Fourier light field microscope, is a straightforward, single snapshot solution to achieve this. The XLFM acquires spatial-angular information in a single camera exposure. In a subsequent step, a 3D volume can be algorithmically reconstructed, making it exceptionally well-suited for real-time 3D acquisition and potential analysis. Unfortunately, traditional reconstruction methods (like deconvolution) require lengthy processing times (0.0220 Hz), hampering the speed advantages of the XLFM. Neural network architectures can overcome the speed constraints at the expense of lacking certainty metrics, which renders them untrustworthy for the biomedical realm. This work proposes a novel architecture to perform fast 3D reconstructions of live immobilized zebrafish neural activity based on a conditional normalizing flow. It reconstructs volumes at 8 Hz spanning 512x512x96 voxels, and it can be trained in under two hours due to the small dataset requirements (10 image-volume pairs). Furthermore, normalizing flows allow for exact Likelihood computation, enabling distribution monitoring, followed by out-of-distribution detection and retraining of the system when a novel sample is detected. We evaluate the proposed method on a cross-validation approach involving multiple in-distribution samples (genetically identical zebrafish) and various out-of-distribution ones. © 2023, CC BY.

关键词： Neurons

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RIS-Assisted MIMO CV-QKD at THz Frequencies: Channel Estimation and SKR Analysis

arXiv

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arXiv 2024年

作者： Kumar, Sushil Dash, Soumya P. Ghose, Debasish Alexandropoulos, George C. the School of Electrical Sciences Indian Institute of Technology Bhubaneswar Khordha Argul752050 India School of Economics Innovation and Technology Kristiania University College Bergen Norway Department of Informatics and Telecommunications National and Kapodistrian University of Athens Panepistimiopolis Ilissia Athens15784 Greece Department of Electrical and Computer Engineering University of Illinois ChicagoIL60601 United States

—In this paper, a multiple-input multiple-output (MIMO) wireless system incorporating a reconfigurable intelligent surface (RIS) to efficiently operate at terahertz (THz) frequencies is considered. The transmitter, Alice, employs continuous-variable quantum key distribution (CV-QKD) to communicate secret keys to the receiver, Bob, which utilizes either homodyne or heterodyne detection. The latter node applies the least-squared approach to estimate the effective MIMO channel gain matrix prior to receiving the secret key, and this estimation is made available to Alice via an error-free feedback channel. An eavesdropper, Eve, is assumed to employ a collective Gaussian entanglement attack on the feedback channel to avail the estimated channel state information. We present a novel closed-form expression for the secret key rate (SKR) performance of the proposed RIS-assisted THz CV-QKD system. The effect of various system parameters, such as the number of RIS elements and their phase configurations, the channel estimation error, and the detector noise, on the SKR performance are studied via numerical evaluation of the derived formula. It is demonstrated that the RIS contributes to larger SKR for larger link distances, and that heterodyne detection is preferable over homodyne at lower pilot symbol powers. © 2024, CC BY.

关键词： Heterodyne detection

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A Bionic Data-driven Approach for Long-distance Underwater Navigation with Anomaly Resistance

arXiv

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arXiv 2024年

作者： Yang, Songnan Zhang, Xiaohui Zhang, Shiliang Ma, Xuehui Bai, Wenqi Li, Yushuai Huang, Tingwen The School of Automation and Information Engineering Xi’an University of Technology Shaanxi 710048 China The Institute of Advanced Navigation and Electromagnetics Xi’an University of Technology Shaanxi 710048 China The Department of Informatics University of Oslo Oslo0316 Norway The Department of Computer Science Aalborg University Aalborg9220 Denmark Texas A&M University at Qatar Doha23874 Qatar

Various animals exhibit accurate navigation using environment cues. The Earth’s magnetic field has been proved a reliable information source in long-distance fauna migration. Inspired by animal navigation, this work proposes a bionic and data-driven approach for long-distance underwater navigation. The proposed approach uses measured geomagnetic data for the navigation, and requires no GPS systems or geographical maps. Particularly, we construct and train a Temporal Attention-based Long Short-Term Memory (TA-LSTM) network to predict the heading angle during the navigation. To mitigate the impact of geomagnetic anomalies, we develop the mechanism to detect and quantify the anomalies based on Maximum Likelihood Estimation. We integrate the developed mechanism with the TA-LSTM, and calibrate the predicted heading angles to gain resistance against geomagnetic anomalies. Using the retrieved data from the WMM model, we conduct numerical simulations with diversified navigation conditions to test our approach. The simulation results demonstrate a resilience navigation against geomagnetic anomalies by our approach, along with precision and stability of the underwater navigation in single and multiple destination missions. © 2024, CC BY-NC-ND.

关键词： Animals

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Analysis of the effectiveness of the optimization processes of trans active power system management with distributed multi-purpose energy resources

Analysis of the effectiveness of the optimization processes ...

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Artificial Intelligence, computer, Data Sciences and Applications (ACDSA), International Conference on

作者： Volodymyr Khilenko Halyna Bielokha Serhii Denysiuk Oleksii Stepanov Ivan Kotuliak Institute of Computer Engineering and Applied Informatics Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Bratislava Slovakia Power Supply Department National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Kyiv Ukraine Department of Information Technologies National University of Life and Environmental Sciences of Ukr Article ACDSA Khilenko ID 414 09-02-2024aine Kyiv Ukraine

The article the efficiency of the Microgrid network when transitioning to a transactive power system that uses control algorithms called to optimize the distribution of power between sources of distributed generation and to minimize the time of decision-making about mode switching and at the corresponding change of the power system architecture. Tables and graphs of the conducted modeling are presented and the efficiency of this control-optimized system is calculated in comparison with the power system where uniform distribution of load power is used.

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User-experience oriented POI recommendation with pseudo rating

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Multimedia Tools and Applications 2025年第16期84卷 16583-16603页

作者： Yi, Kun Yamagishi, Ryu Li, Jiyi Li, Taishan Bai, Zhengyang Ma, Qiang Kyoto Institute of Economic Research Kyoto University Yoshida-honmachi Sakyo-ku Kyoto Kyoto606-8501 Japan Graduate School of Informatics Kyoto University Yoshida-honmachi Sakyo-ku Kyoto Kyoto606-8501 Japan Department of Computer Science and Engineering University of Yamanashi Takeda 4-3-11 Kofu 400- 8511 Yamanashi Japan Research Institute of Non-Timber Forestry Chinese Academy of Forestry Key Laboratory of Non-Timber Forest Germplasm Enhancement & Utilization of State Administration of Forestry and Grassland No. 3 Weiwu Road Jinshui District Zhengzhou450003 China RIKEN Center for Computational Science RIKEN Nihonbashi 1-chome Mitsubishi Bld. 15th floor 1-4-1 Nihonbashi Chuo-ku Tokyo103-0027 Japan Department of Information Science Kyoto Institute of Technology Matsugasaki Sakyo-ku Kyoto Kyoto606-8585 Japan

Point-of-interest (POI) recommendation faces challenges in managing data sparsity and cold-start problems, particularly in terms of personalized recommendation. Firstly, most existing users only visit a small number of POIs, which results in severe data sparsity and makes most conventional methods unsuitable because they require adequate historical data from each user to learn the model. In addition, new users have little or no historical record. Existing ask-to-rate methods for cold start user scenarios treat user answers as historical data, which is inappropriate and causes difficulty in capturing user preferences. Therefore, in this paper, to tackle the data sparsity problem, we propose a user-experience model (UEM) to uncover user behaviors from several aspects. The proposed UEM involves four factors: Who (does it), What (the user does), Where (the user does it), and When (the user does it). In contrast to other methods that model user behavior from individual records, our model introduces a latent variable of user experience to extract similarity among users. This feature makes our model suitable for mitigating the severe sparse-data issue in POI recommendations. Furthermore, to handle the cold-start problem for new users, we propose a pseudo-rating mechanism (PRM) to capture new users’ preferences and recommend POIs. For new users, photos are provided for rating, and these ratings are then used to capture their preferences and estimate the probability of their visits. Extensive experimental results demonstrate that the proposed method achieves better recommendation performance and fairness than the state-of-the-art methods. Moreover, user study results validates that the users’ preferences captured via PRM are satisfactory, and UEM provides satisfactory POI recommendations. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Behavioral research

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Innovative Urban Solutions with IoT-Driven Traffic and Pollution Control

Innovative Urban Solutions with IoT-Driven Traffic and Pollu...

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Automation and Computation (AUTOCOM), International Conference on

作者： Kottala Sri Yogi Avinash Sharma V Dankan Gowda Rini Saxena Tarkeshwar Barua Khalid Mohiuddin Dept of Operations Symbiosis institute of Business Management Hyderabad Symbiosis International University Pune Hyderabad Telangana Department of Computer Science and Engineering Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Mohali Punjab India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka India Department of Business Informatics College of Business King Khalid University PO Box-3247 Abha Saudi Arabia

The Internet of Things (IoT) is rethinking the model for building the smart cities, and this paper makes just such a step in that direction. It then examines various ways of cutting urban pollution. Under increasing pressure from rising urbanization, smart cities represent a huge leap forward in regard to efficiency, scalability and livability. The key is IoTtechnology, which collects and analyzes data in real time and allows for more quick-witted and intelligent urban governance. This paper combines literature review, case study analysis and data-driven methods to focus on the opportunities offered by IoT applications for restructuring urban traffic systems and pollution control. This study explains what is missing in modern smart city initiatives, and offers a road map for bringing Internet of things solutions to municipal systems. The results can also serve as some answers to cope with the dilemma and bring more intelligence, cleanness, efficiency in making a city into a smart metropolis. The article ends by highlighting the significance of IoT for future urban development, and ideas for further research into this complex yet rapidly evolving area.

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Improving Equine Intramedullary Nail Osteosynthesis via Fracture Adjacent Polymer Reinforcement

Improving Equine Intramedullary Nail Osteosynthesis via Frac...

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作者： Lang, Jan J. Baylacher, Veronika Micheler, Carina M. Wilhelm, Nikolas J. Hinterwimmer, Florian Schwaiger, Benedikt Barnewitz, Dirk von Eisenhart-Rothe, Rüdiger Grosse, Christian U. Burgkart, Rainer Munich Germany Department of Orthopedics and Sports Orthopedics School of Medicine TUM Munich Germany Institute for Machine Tools and Industrial Management School of Engineering and Design TUM Munich Germany Munich Institute of Robotics and Machine Intelligence Department of Electrical and Computer Engineering TUM Munich Germany School of Medicine Institute for AI and Informatics in Medicine TUM Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine TUM Munich Germany Research Centre of Medical Technology and Biotechnology Bad Langensalza Germany Non-destructive Testing Department of Mechanical Engineering TUM Munich Germany

Introduction: Osteosynthesis of the equine femur is still a challenge for veterinary medicine. Even though intramedullary fracture fixation is possible nowadays, the varying geometry of the medullary cavity along the bone axis is a critical factor. Limited contact area between implant and bone can cause insufficient primary stability. In this study, it was investigated whether the osteosynthesis stability can be improved with a form-adaptive reinforcement for the diaphyseal part of the proximal fragment. Material and Methods: Eight equine femora were fitted with intramedullary nail osteosynthesis and analyzed by 4-point bending. Virtual position planning of the ex-vivo implantation using CT-data increased comparability. For five femora the proximal fragment was reinforced with a flexible polymer mixture. Longterm stability was tested via cyclic loading. Bending stiffness and its development due to cyclic loading was evaluated before and after reinforcement procedure. Finally, load-to-failure was tested in the same setup. Results and Discussion: The application of the polymer reinforcement increased the maximum torque in the load-to-failure measurement by 26%. Bending stiffness was not affected in the measured loading range by the reinforcement. Cyclic loading increased bending stiffness for a conditioned state but showed to be reversible for the most part. Conclusion: The fracture adjacent reinforcement showed to be beneficial to the osteosynthesis stability, but further investigation is necessary for surgical application. © 2022 The Author(s), published by De Gruyter.

关键词： Reinforcement

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