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International Conference on Information, Intelligence, systems and Applications (IISA)

作者： Konstantinos Alexakis Jabier Martinez Panagiotis Kokkinakos Antonio Filograna Yury Glikman Xabier Uriarte Dimitris Askounis Decision Support Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Software Technologies Tecnalia Basque Research and Technology Alliance (BRTA) Derio Spain Open Public Service Innovation Engineering Ingegneria Informatica S.p.A. Rome Italy Open Service Engineering Group Fraunhofer FOKUS Berlin Germany

Public policy-making has a strong impact on our daily lives and on most of the aspects of our society including its relation with the environment. Sustainability as a global goal cannot be achieved without effective policies supporting it. The current trend in policy-making has two main pillars, policies are a) co-created with citizens and other relevant stakeholders and b) data-based to take informed decisions. Information technologies are needed to support these pillars and facilitate evidence-based policy-making. We present the DECIDO Portal that acts as a catalyst towards putting these general principles into practice gathering the technical components to be used within the whole policy life cycle. Notably, the portal lays its foundations on a well-defined co-creation methodology that can be seamlessly followed from a unique portal and provides best-practices for participatory processes and data-driven analyses. A set of real-world experimental policies in different countries serves as case studies to showcase and discuss the usage of the portal.

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Tumbling Down the Rabbit Hole: How do Assisting Exploration Strategies Facilitate Grey-box Fuzzing?

arXiv

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arXiv 2024年

作者： Wu, Mingyuan Xiang, Jiahong Chen, Kunqiu Di, Peng Tan, Shin Hwei Cui, Heming Zhang, Yuqun Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Southern University of Science and Technology Shenzhen China Ant Group Hangzhou China Concordia University Montreal Canada The University of Hong Kong Hong Kong The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

Many assisting exploration strategies have been proposed to assist grey-box fuzzers in exploring program states guarded by tight and complex branch conditions such as equality constraints. Although they have shown promising results in their original papers, their evaluations seldom follow equivalent protocols, e.g., they are rarely evaluated on identical benchmarks. Moreover, there is a lack of sufficient investigations on the specifics of the program states explored by these strategies which can obfuscate the future application and development of such strategies. Consequently, there is a pressing need for a comprehensive study of assisting exploration strategies on their effectiveness, versatility, and limitations to enlighten their future development. To this end, we perform the first comprehensive study about the assisting exploration strategies for grey-box fuzzers. Specifically, we first collect nine recent fuzzers representing the mainstream assisting exploration strategies as our studied subjects and 21 real-world projects to form our benchmark suite. After evaluating the subjects on the benchmark suite, we then surprisingly find that the dictionary strategy is most promising since it not only achieves similar or even slightly better performance over the other studied assisting exploration strategies in terms of exploring program states but also is more practical to be enhanced. Accordingly, we propose CDFUZZ, which generates a customized dictionary for each seed upon the baseline fuzzer AFL to improve over the original dictionary strategy. The evaluation results demonstrate that CDFUZZ increases the edge coverage by 16.1% on average for all benchmark projects over the best performer in our study (i.e., AFL++ with the dictionary strategy). CDFUZZ also successfully exposed 37 previously unknown bugs, with nine confirmed and seven fixed by the corresponding developers. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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STARS-Enabled Full-Duplex Two-Way mMIMO System Under Spatially-Correlated Channels

arXiv

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arXiv 2025年

作者： Papazafeiropoulos, Anastasios Kourtessis, Pandelis Chatzinotas, Symeon Kaklamani, Dimitra I. Venieris, Iakovos S. The Communications and Intelligent Systems Research Group University of Hertfordshire HatfieldAL10 9AB United Kingdom SnT The University of Luxembourg Luxembourg The Microwave and Fiber Optics Laboratory Greece The Intelligent Communications and Broadband Networks Laboratory School of Electrical and Computer Engineering National Technical University of Athens Athens Zografou15780 Greece

Low-cost reconfigurable intelligent surfaces (RISs) are being considered as promising physical-layer technology for next-generation wireless networks due to their ability to re-engineer the propagation environment by tuning their elements. Although RIS-aided systems have many advantages, one of their major bottlenecks is that they can provide coverage only in front of the surface. Fortunately, simultaneous transmitting and reflecting surface (STARS)-assisted systems have emerged to fill this gap by providing 360◦ wireless coverage. In parallel, full-duplex (FD) communication offers a higher achievable rate through efficient spectrum utilization compared to the half-duplex (HD) counterpart. Moreover, two-way/bi-directional communications in an FD system can further enhance the system’s spectral efficiency. Hence, in this paper, we propose a STARS-enabled massive MIMO deployment in an FD two-way communication network for highly efficient spectrum utilization, while covering the dead zones around the STARS. This model enables simultaneous information exchange between multiple nodes, while potentially doubling the spectral efficiency (SE). By invoking the use-and-then-forget (UaTF) combining scheme, we derive a closed-form expression for an achievable SE at each user of the system considering both uplink and downlink communications based on statistical channel state information (CSI), while also accounting for imperfect CSI and correlated fading conditions. Moreover, we formulate an optimization problem to obtain an optimal passive beamforming matrix design at the STARS that maximizes the sum achievable SE. The considered problem is non-convex and we propose a provably-convergent low-complexity algorithm, termed as projected gradient ascent method (ProGrAM), to obtain a stationary solution. Extensive numerical results are provided to establish the performance superiority of the FD STARS-enabled system over the HD STARS-enabled and FD conventional RIS (cRIS)-enabled coun

关键词： Digital television

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Mechanical Vibrations on a Deployable Nanosatellite Antenna: SAR Performance Analysis

Mechanical Vibrations on a Deployable Nanosatellite Antenna:...

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European Conference on Antennas and Propagation, EuCAP

作者： Andrew C. M. Austin Timothy Pelham Simone Mencarelli Annalisa Tresoldi Mohammed Dabboor Guglielmo Aglietti Michael J. Neve Communication Systems and Networks Group University of Bristol Bristol UK Department of Electrical Computer and Software Engineering University of Auckland Auckland New Zealand Te Pūnaha Ātea—Auckland Space Institute University of Auckland Auckland New Zealand Science and Technology Branch Environment and Climate Change Canada Dorval QC Canada

The mechanical vibrations of a high-strain glass-fibre composite deployable X-band synthetic aperture radar (SAR) antenna structure are investigated using a finite element method. The antenna is approximately $\mathrm{2}\ \mathrm{m}\times \mathrm{0}.\mathrm{3}\ \mathrm{m}$ , and exhibits natural frequencies at 2.8 Hz and 7.6 Hz. The radiation patterns for a reflectarray formed from the structure are calculated using a ray-tracing approach for the nominal and mechanically deformed cases. The mechanically deformed antenna pattern is defocused, and a SAR system model shows this reduces the signal-to-noise ratio of a point scattering target by approximately 6 dB.

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Computing Safest $st$-Paths in Backbone Networks: Efficiently Solvable Cases and Fast Heuristics

Computing Safest $st$-Paths in Backbone Networks: Efficientl...

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International Workshop on Reliable Networks Design and Modeling (RNDM)

作者： Balázs Vass Péter Revisnyei Alija Pašíć Department of Telecommunications and Artificial Intelligence (TMIT) Faculty of Electrical Engineering and Informatics (VIK) High Speed Networks Laboratory (HSNLab) Budapest University of Technology and Economics (BME) Budapest Hungary Faculty of Mathematics and Computer Science Babeș-Bolyai University Cluj Napoca Romania HUN-REN-BME Information Systems Research Group Budapest Hungary

ISBN: (数字)9798331518189

ISBN: (纸本)9798331518196

For proper evaluation and optimization of the expected availability of a backbone network service, two related very fundamental modeling and algorithmic questions are the following. 1) Realistically estimate the availability of a given $st$ -path for some source and target pair of communicating nodes $s$ and $t$ , and 2) Find a safest $st$ -path. For these, traditional approaches suppose network element failures are independent. In this paper, we show that, by not considering joint failure probabilities, the traditional approaches may misguess the $st$ -path availabilities and, consequently, the total connection availability, which can lead to more frequent Service Level Agreement (SLA) violations and a financial burden on the Communication Service Provider (CSP). Due to the inconsistent estimations, the supposedly safest paths yielded by these approaches may turn out to be suboptimal. On the positive side, we propose a fast algorithmic approach, that, under some assumptions, returns with a (truly) safest $st$ -path accompanied by its exact expected availability. When the aforementioned assumptions do not hold, in our simulations, the proposed algorithmic scheme proved to be a good heuristic, performing at least as well as the traditional method.

关键词： Computational modeling Heuristic algorithms Conferences Estimation Hazards Computational efficiency Optimization Service level agreements

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IRS Channel Estimation in Cell-free MIMO Network: A Coalition Formation Guided Federated Learning Approach

IRS Channel Estimation in Cell-free MIMO Network: A Coalitio...

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IEEE Conference on Wireless Communications and Networking

作者： Haoxuan Liu Nan Qi Xiaojie Li Alexandros A. A. Boulogeorgos Theodoros A. Tsiftsis Ming Xiao Juha Röning Nanjing University of Aeronautics and Astronautics Nanjing China National Mobile Communications Research Laboratory Southeast University Nanjing China Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Department of Informatics and Telecommunications University of Thessaly Lamia Greece Department of Electrical and Electronic Engineering University of Nottingham Ningbo China Ningbo China School of Electrical Engineering of KTH Royal Institute of Technology Stockholm Sweden Biomimetics and Intelligent Systems Group University of Oulu Finland

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The downlink channel estimation is currently a critical bottleneck for IRS-assisted cell-free multiple input multiple output communication. Conventionally, most studies have employed deep learning methods to estimate the high-dimensional, complex cascaded channels generated by IRS, necessitating data collection from all users for centralized model training, which results in excessively large overheads, and data privacy problems. To tackle this challenge, a federated learning (FL)-based channel estimation framework incorporates coalition formation to guide the formation of FL user groups. We propose a coalition formation-enabled federated learning framework for channel estimation, utilizing a deep reinforcement learning (DRL) approach to intelligently group users into multiple coalitions, thereby improving channel estimation accuracy. Moreover, considering that nodes with similar distances to the base station and similar received signal power have a strong likelihood that they experience similar channel fading, we designed a transfer learning method that incorporates both received reference signal power and distance similarity metrics. The transfer learning technique is designed to accelerate the convergence of DRL-federated learning process. Simulations reveal that the proposed algorithms significantly reduce communication overhead for local users and improve data privacy while maintaining commendable channel estimation accuracy.

关键词： Training Data privacy Accuracy Federated learning Transfer learning Channel estimation Deep reinforcement learning Data models Surface treatment Convergence

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Hybrid super-twisting fractional-order terminal sliding mode control for rolling spherical robot

Hybrid super-twisting fractional-order terminal sliding mode...

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作者： Salemizadeh Parizi, Mohammad Salemizadehparizi, Fatemeh Vanaei, Shohreh Karimi, Arash Komijani, Hossein School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Biomedical Engineering Binghamton University BinghamtonNY United States Laboratory of Complex Biological Systems and Bioinformatics Department of Bioinformatics Institute of Biochemistry and Biophysics University of Tehran Tehran Iran Chemobot Group School of Chemistry University of Glasgow Glasgow United Kingdom

The unmodeled parameters, uncertainties, chattering, and disturbances can be serious problems for a controller. These issues decrease the performance of the controller and make it difficult to have a satisfactory movement path as well. This paper presents a novel hybrid super-twisting fractional-order terminal sliding mode controller (HSTFOTSMC) for a rolling spherical robot (RSR) considering bounded model uncertainties for robot dynamics in presence of external disturbances and noise. The controller is based on a new adaptive fractional-order terminal sliding surface. The stability of the proposed controller is proved by the Lyapunov theory, and then, it is tuned by a novel extended grey wolf optimizing (EGWO) algorithm to adjust the controller parameters, and assure to achieve the precise robot performance. The results of the optimized controller (EGWO-HSTFOTSMC) are compared with the typical sliding mode controller (SMC), fractional-order terminal sliding mode controller (FOTSMC), and super-twisting fractional-order terminal sliding mode controller (STFOTSMC), which demonstrate the effectiveness of the controller scheme. The controller implementation can potently eliminate the impacts of disturbances, noise, and uncertainties;meanwhile, it decreases the tracking error and increases the convergence speed of robot responses. © 2021 Chinese Automatic Control Society and John Wiley & Sons Australia, Ltd

关键词： Controllers

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Innovative flexibility management framework for distributed energy resources participating in local markets

Innovative flexibility management framework for distributed ...

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13th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion (MEDPOWER 2022)

作者： E. Papoutsis K. Tsatsakis A. Karamanidis A.L.A. Syrri A. Dimeas N. Hatziargyriou School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Suite5 Data Intelligence Solutions Limassol Cyprus Sunlight Group Energy Storage Systems Kifissia Greece

Demand side flexibility management is currently a key facilitator of various EU directions: it enables the high penetration of renewable energy resources; it allows the formation of an aggregated considerable capacity which could be utilised for ancillary services towards a secure electricity network operation and it provides a means to smaller scale flexible resources to participate in energy markets and thus gain financial benefits. This paper presents a flexibility management framework that has been created within H2020 X-FLEX project (GA. 863927), with the aim to assist the optimal coordination of local flexible resources and in parallel, facilitate their participation to local energy markets. Flexibility shall stem from multi-source systems, energy storage, electric vehicles, renewables and flexible demand. In this work particularly, the flexibility management framework is deployed on a multi-source microgrid, which has power to gas components and battery, located in a mid-scale demo site in Xanthi, Greece.

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Price Prediction of Digital Currencies using Machine Learning

Price Prediction of Digital Currencies using Machine Learnin...

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International Conference on Data Science and Advanced Analytics (DSAA)

作者： Ashutosh Dhar Dwivedi Subhayu Dutta Subhrangshu Adhikary Jens Myrup Pedersen Department of Electronic Systems Cyber Security Group Aalborg University Copenhagen Denmark Department of Computer Science & Engineering Dr. B.C. Roy Engineering College Durgapur West Bengal India Department of Research & Development Spiraldevs Automation Industries Pvt. Ltd. Raiganj West Bengal India

Cryptocurrencies have gained immense significance and popularity in recent times. With thousands of digital currencies available, selecting the right one can be challenging for users. In the financial sector, accurately predicting future prices is crucial for profitable investments in digital currencies. However, price prediction in this realm poses unique challenges, as it lacks physical goods or services as the basis, unlike stock prices. Machine learning emerges as a pivotal tool for addressing this challenge and plays a vital role in price prediction. This research analyzes five prominent currencies - Monero, Bitcoin, Ethereum, IOTA, and Zcash - employing five models: SVR, LRG, Huber, RANSAC, MLP, and AdaBoost. The experimental results demonstrate promising outcomes, showcasing the ability to predict digital currency prices with an impressive R2 score of 1.0 for specific machine learning algorithms. This advancement opens new avenues for informed decision-making and profitable ventures in the dynamic world of digital currencies

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Using GPT-4 to guide causal machine learning

arXiv

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arXiv 2024年

作者： Constantinou, Anthony C. Kitson, Neville K. Zanga, Alessio research group School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Informatics Systems and Communication University of Milano - Bicocca Milan Italy Data Science and Advanced Analytics F. Hoffmann - La Roche Ltd Basel Switzerland

Since its introduction to the public, ChatGPT has had an unprecedented impact. While some experts praised AI advancements and highlighted their potential risks, others have been critical about the accuracy and usefulness of Large Language Models (LLMs). In this paper, we are interested in the ability of LLMs to identify causal relationships. We focus on the well-established GPT-4 (Turbo) and evaluate its performance under the most restrictive conditions, by isolating its ability to infer causal relationships based solely on the variable labels without being given any other context by humans, demonstrating the minimum level of effectiveness one can expect when it is provided with label-only information. We show that questionnaire participants judge the GPT-4 graphs as the most accurate in the evaluated categories, closely followed by knowledge graphs constructed by domain experts, with causal Machine Learning (ML) far behind. We use these results to highlight the important limitation of causal ML, which often produces causal graphs that violate common sense, affecting trust in them. However, we show that pairing GPT-4 with causal ML overcomes this limitation, resulting in graphical structures learnt from real data that align more closely with those identified by domain experts, compared to structures learnt by causal ML alone. Overall, our findings suggest that despite GPT-4 not being explicitly designed to reason causally, it can still be a valuable tool for causal representation, as it improves the causal discovery process of causal ML algorithms that are designed to do just that. Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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