Berk Canberk

Quantum Internet - Please check our paper published on IEEE Network magazine. https://lnkd.in/e7ueWYVW Uman Khalid Junaid ur Rehman, Ph.D. Saw Nang Paing Haejoon Jung We discuss the potential of the near-term quantum networks using noisy intermediate-scale quantum (NISQ) technologies. It highlights the architectural and operational principles necessary for developing these quantum networks and emphasizes their application in enhancing quantum computing, communication, and sensing. The paper provides a comparative analysis of network performance improvements and addresses technical challenges that must be overcome to realize practical and effective quantum networks. Overall, the paper identifies the advancements and ongoing efforts towards achieving a functional quantum internet, delineating a clear path from current capabilities to future possibilities.

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📢 Call for Papers: Advances in Learning on Graphs and Information Networks I am thrilled to co-edit a special issue of Electronics (MDPI) with Soumyakant Padhee and Koray Ozcan! We welcome contributions related to graph-structured data and information networks. Please share with your colleagues who have an interest in this topic. Specifically: 🔗 Graph Neural Networks (GNNs) 📊 Graph Embeddings 🌐 Applications: Healthcare, social media, finance, and more ⏳ Dynamic Graph Learning ⚖️ Explainability & Fairness 📅 Submission Deadline: July 15, 2025 More info at the link. #GraphLearning #GNN #MachineLearning #CallForPapers #Research #informationscience

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The Computer Vision Group at the University of Birmingham is proud to announce the presentation of six papers (one oral and five posters) at the European Conference on Computer Vision (ECCV) 2024. Our group has made significant contributions to ECCV, with Prof. Ales Leonardis serving as Programme Chair and Dr. Hyung Jin Chang as Demo Chair. Additionally, we are organizing three workshops. This achievement reflects our collaboration with outstanding international partners, and we look forward to seeing you in Milan from September 29th to October 4th, 2024! Workshop Organization: 1. Visual Object Tracking and Segmentation Challenge (VOTS2024) Workshop  * Date: Sunday, September 29th, 09:00 - 13:00   * Location: Amber 4   * Details: votchallenge.net/vots2024 2. BioImage Computing Workshop  * Date: Sunday, September 29th, 09:00 - 13:00   * Location: Amber 5   * Details: bioimagecomputing.com 3. Observing and Understanding Hands in Action Workshop   * Date: Monday, September 30th, 14:00 - 18:00   * Location: Suite 8   * Details: hands-workshop.org ECCV Main Paper Presentations: [Oral Presentation]  NL2Contact: Natural Language Guided 3D Hand-Object Contact Modeling with Diffusion Model  * Authors: Zhongqun Zhang, Hengfei Wang, Ziwei Yu, Yihua Cheng, Angela Yao, Hyung Jin Chang [Poster Presentations] 1. Benchmarks and Challenges in Pose Estimation for Egocentric Hand Interactions with Objects   * Authors: Zicong Fan, Takehiko Ohkawa, Linlin Yang, Nie Lin, Zhishan Zhou, Shihao Zhou, Jiajun Liang, Zhong Gao, Xuanyang Zhang, Xue Zhang, Fei Li, Liu Zheng, Feng Lu, Karim Abou Zeid, Bastian Leibe, Jeongwan On, Seungryul Baek, Aditya Prakash, Saurabh Gupta, Kun He, Yoichi Sato, Otmar Hilliges, Hyung Jin Chang, Angela Yao 2. Surface-Centric Modeling for High-Fidelity Generalizable Neural Surface Reconstruction   * Authors: Rui Peng, Shihe Shen, Kaiqiang Xiong, Huachen Gao, Jianbo Jiao, Xiaodong Gu, Ronggang Wang 3. MVPGS: Excavating Multi-view Priors for Gaussian Splatting from Sparse Input Views   * Authors: Wangze Xu, Huachen Gao, Shihe Shen, Rui Peng, Jianbo Jiao, Ronggang Wang 4. Disentangled Generation and Aggregation for Robust Radiance Fields   * Authors: Shihe Shen, Huachen Gao, Wangze Xu, Rui Peng, Luyang Tang, Kaiqiang Xiong, Jianbo Jiao, Ronggang Wang 5. Aligning Neuronal Coding of Dynamic Visual Scenes with Foundation Vision Models   * Authors: Rining Wu, Feixiang Zhou, Ziwei Yin, Jian Liu We are excited about these presentations and workshops and look forward to fruitful discussions at ECCV 2024!

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100+ Citations for One of Our Published Papers The paper titled “Semiconductor technologies for 5G implementation at millimeter wave frequencies – Design challenges and current state of work,” which we published in 2021, has crossed 100 citations on Google Scholar (83 on Scopus). In this paper, we discussed in detail how evolving semiconductor technologies are contributing toward employing underutilized millimeter wave (mmW) frequencies for 5G cellular network implementation. Anybody who is working in the area of hardware design for future cellular network implementation will find this paper useful. The paper has been co-authored by Dr. RAJNISH SHARMA and Dr. Rajendra Pratap, Ph.D., under whom I completed my PhD at Chitkara University. It was published by Elsevier in the journal entitled ‘Engineering Science and Technology, an International Journal’ - https://lnkd.in/gkMcNbF9 #ChitkaraU #Elsevier #Research #semiconductor #5G Chitkara University Publications Chitkara University

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The following link provides full-text access to a view-only version of our recently published paper: https://rdcu.be/dWkex School of Civil Engineering, College of Engineering, University of Tehran

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A long wait but this paper is finally accepted to IEEE Transactions on Neural Networks and Learning Systems #TNNLS From Bi-Level to One-Level: A Framework for Structural Attacks to Graph Anomaly Detection https://lnkd.in/ghAxZdMp

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Want to know about basics of metasurface. Check out our book chapter titled "Metasurface: An Insight Into Its Applications" published at Wiley. Special thanks to Fahad Ahmed for excellent contribution. See full chapter at: https://lnkd.in/dX_6jRWp

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📢 Special Issue on Integrating Evolutionary Algorithms and Large Language Models 📢 of ACM Transactions on Evolutionary Learning and Optimization (https://lnkd.in/ea47HuBV) Call for Papers: https://lnkd.in/eejsPgHi Guest Editors: • Erik Hemberg, Massachusetts Institute of Technology, USA • Una-May O'Reilly, Massachusetts Institute of Technology, USA • Dennis G Wilson, ISAE-SUPAERO, University of Toulouse, France Topics include, but are not restricted to, the following list: • LLM-Assisted Evolution: How can Evolutionary Algorithms (EAs) and other #metaheuristics effectively utilize #LLMs for diverse representations (code, strings, images, #multimodal)? How can LLMs enhance the algorithmic aspects of exploration of cooperation, competition, and information reuse in EAs? • LLMs for Evolutionary Operators: Can LLMs be creatively used as a part of or to enhance evolutionary operators such as recombination and variation? • Novel EA Hybrids: What new variants hybridizing Genetic Algorithms, Evolution Strategies, Genetic Programming and/or another search #heuristics with LLMs are possible? In what respects are they advantageous? • #MultiObjective and Open-ended Optimization: Can EAs that integrate LLMs leverage multi-objective and open-ended optimization in compelling ways, and if so, how? • Scalability and Search Space Analysis: How do LLM-based search heuristics scale with population size and problem complexity? How can search spaces in these LLM-based heuristics be effectively characterized and analyzed in relation to problem difficulty? • Robustness and #Benchmarking: How can vulnerabilities and biases in LLMs be mitigated within evolutionary approaches? What makes a good benchmark for evaluating the performance of LLMs in an EA? Which types of problem benefit from the combination of EAs and LLMs, and which benchmarks demonstrate this?

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Excited to announce the acceptance of our paper in the IEEE Transactions on Control of Network Systems: "Quantized Privacy-Preserving Algorithms for Homogeneous and Heterogeneous Networks with Finite Transmission Guarantees". We introduce two novel algorithms designed to protect privacy while operating in distributed networked systems.  These algorithms ensure efficient communication, finite time convergence, and operation termination post-convergence, making them suitable for resource-constrained environments.  Additionally, we establish topological conditions for privacy preservation and apply our algorithms to a smart grid system. Thanks to my co-authors Christoforos Hadjicostis, and Karl Henrik Johansson for their invaluable contributions to this research. Paper available at:  https://lnkd.in/dyv5vXSg #PrivacyPreservation #NetworkSystems #Distributed #SmartGrids #IEEETransactions #ResearchPublication

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New in the AFP: The Category of Sets An axiomatization of category SET on top of HOL, following the presentation given by Halvorson, including Cantor's diagonalization theorem and mathematical induction. https://lnkd.in/g6WTQ-MX

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Our recent paper titled "FertilizeSmart: Exploiting IoT and Differential Evolution for Optimizing Crop Fertilization", has been accepted at the IEEE/IFIP Wireless On-Demand Network Systems and Services Conference (WONS), 2024. Congratulations to Xu Tao, Christian Cumini, Alessio Sacco! In this work, we introduce FertilizeSmart, an innovative framework that optimizes crop fertilization by leveraging IoT technologies. The goal is to determine the optimal fertilization strategy throughout the season. To this purpose, at the core of FertilizeSmart, is an optimization problem that maximizes crop yield while minimizing the amount of fertilizer used. The crop yield in response to different timings and rates of applied fertilizer is estimated using a process-based crop simulation model, namely the Decision Support System for Agrotechnology Transfer (DSSAT). The optimization problem is then solved periodically, by an improved DE algorithm that trades off exploration and exploitation of available solutions, throughout the crop growth cycle, following a MPC approach. This adaptive approach allows FertilizeSmart to respond to dynamic weather conditions and adjust fertilizer application to meet varying nutrient demands across growth stages.

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I am happy to share that our paper on secure, adaptive control of networked linear systems using Pailiar encryption has been accepted for publication in IEEE Transactions on Circuits and Systems I: Regular Papers: https://lnkd.in/gUxG8_-N #adaptivecontrol #homomorphicencyption

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Sensors MDPI Special Issue "Big Data Analytics, the Internet of Things (IoTs), and Robotics" is open for submission! 🎊 🎊 🎊 Guest Editors: Prof.Dr. Dan Feldman and Dr. Alaa Maalouf Special Issue link: https://lnkd.in/dJbeUqwv Welcome to your submissions! 🙌 🙌 🙌 #bigdata #IoT #robotic #streaming #sensor #SLAM

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I am pleased to announce the publication of our latest research article, "Distributed Deep Learning-Based Model for Large Image Data Classification", in the Proceedings of the 7th International Conference on Future Networks and Distributed Systems (ICFNDS '23). Abstract: Artificial intelligence has shown great potential in a variety of applications, from natural language models to audio visual recognition, classification, and manipulation. AI Researchers have to work with massive amount of collected data for use in machine learning, raising some challenges in effectively managing and utilizing the collected data in the training phase to develop and iterate on more accurate, and more generalized models. In this paper we conducted a review on parallel and distributed machine learning methods and challenges. We also propose a distributed and scalable deep learning model architecture which can span across multiple processing nodes. We tested the model on the MIT Indoor dataset, to evaluate the performance and scalability of the model using multiple hardware nodes, and showed the scaling characteristics of the different model using different model sizes. We find that distributed training is 80% faster using 2 GPUs than 1 GPU. We also find that the model keeps the benefits of distributed training such as speed and accuracy regardless of its size or training batch size.

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I would like to share the paper entitled "Sample-by-sample Power Quality Disturbance Classification Based on Sliding Window Recursive Discrete Fourier Transform" published in Electric Power System Research. This paper is the result of work carried out by Luiz Fernando, myself, Danton, Bruno, Carlos Rufino and Carlos Duque. The article's main contribution lies in Power Quality Disturbances Classification on a sample-by-sample. The literature contains several methods for classifying disturbances using batch processing. Generally, these methods use two stages, detection of the start and end of the disturbance, and classification. Our method, on the other hand, detects and classifies the disturbance in each sample. Below is the link for anyone interested in our work. I would like to take this opportunity to thank the authors of the paper: Luiz Fernando Rodrigues, Danton Ferreira, Bruno Barbosa, Carlos Antônio Rufino Júnior and Carlos Augusto Duque. #smartgrids #powerquality #electricsystems #machinelearning #signalprocessing https://lnkd.in/dWk6y6wV

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Procedural programming in Mathematica: Applications of "IF" function to construct Dirac delta and Pauli operators.

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Our IEEE Computer Society Computing in Science & Engineering (CiSE) article on Enhancing Scientific Research with FAIR Digital Objects in the National Science Data Fabric has been published. It is available for open access to all. Download the article here: https://lnkd.in/g_4CrGgu Learn more about our vision of combining findable, accessible, interoperable, and reusable (FAIR) Digital Objects with the National Science Data Fabric (NSDF) to enhance data accessibility, scientific discovery, and education. Integrating FAIR Digital Objects into the NSDF overcomes data access barriers and facilitates the extraction of machine-actionable metadata in alignment with FAIR principles. The article discusses examples of climate simulations and materials science workflows. It establishes the groundwork for a dataflow design that prioritizes inclusivity, web-centricity, and a network-first approach to democratize data access and create opportunities for research and collaboration in the scientific community. Contact us if you want to collaborate or share your thoughts about data democratization. Michela Taufer Valerio Pascucci Christine Kirkpatrick Global Computing Laboratory San Diego Supercomputer Center Scientific Computing and Imaging Institute at the University of Utah University of Tennessee, Knoxville National Science Foundation (NSF)

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I'm thrilled to announce the publication of my Research Scholar Anughna N published a paper titled "Multiplicative Basis Function Based Adaptive DOA Estimation in Optimal MIMO Sparse Antenna Reconfiguration Model" in the IETE Journal of Research! 🔍 Summary: This paper presents a novel approach for Direction of Arrival (DOA) estimation in optimal Multiple-Input Multiple-Output (MIMO) systems using Multiplicative Basis Functions. The proposed method offers enhanced accuracy and efficiency in sparse antenna reconfiguration scenarios. 🔬 Methodology: We introduce a sophisticated methodology that leverages Multiplicative Basis Functions to adaptively estimate DOA in MIMO setups, particularly in scenarios where antenna configurations are sparse. 💡 Key Findings: Our research unveils promising results, showcasing the effectiveness of our approach in accurately estimating DOA even in challenging environments. These findings hold significant implications for optimizing MIMO systems for various applications. 🔗 Read the full paper here: [https://lnkd.in/gzdFvCsS] I'm excited to share this work with my network and welcome any discussions or feedback on the topic. Let's continue pushing the boundaries of knowledge together!

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