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arXiv 2025年

作者： Alhaidari, Abdulrahman Kalal, Bhavani Palanisamy, Balaji Sural, Shamik Informatics and Networked Systems University of Pittsburgh PittsburghPA United States Computer Science and Engineering Indian Institute of Technology Kharagpur India

Rug pulls in Solana have caused significant damage to users interacting with Decentralized Finance (DeFi). A rug pull occurs when developers exploit users’ trust and drain liquidity from token pools on Decentralized Exchanges (DEXs), leaving users with worthless tokens. Although rug pulls in Ethereum and Binance Smart Chain (BSC) have gained attention recently, analysis of rug pulls in Solana remains largely under-explored. In this paper, we introduce SolRPDS (Solana Rug Pull Dataset), the first public rug pull dataset derived from Solana’s transactions. We examine approximately four years of DeFi data (2021-2024) that covers suspected and confirmed tokens exhibiting rug pull patterns. The dataset, derived from 3.69 billion transactions, consists of 62,895 suspicious liquidity pools. The data is annotated for inactivity states, which is a key indicator, and includes several detailed liquidity activities such as additions, removals, and last interaction as well as other attributes such as inactivity periods and withdrawn token amounts, to help identify suspicious behavior. Our preliminary analysis reveals clear distinctions between legitimate and fraudulent liquidity pools and we found that 22,195 tokens in the dataset exhibit rug pull patterns during the examined period. SolRPDS can support a wide range of future research on rug pulls including the development of data-driven and heuristic-based solutions for real-time rug pull detection and mitigation. © 2025, CC BY.

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Privacy-Preserving Utilization of Distribution System Flexibility for Enhanced TSO-DSO Interoperability: A Novel Machine Learning-Based Optimal Power Flow Approach

arXiv

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arXiv 2025年

作者： Dindar, Burak Saner, Can Berk Çakmak, Hüseyin K. Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe76131 Germany Department of Electrical and Computer Engineering National University of Singapore Singapore117581 Singapore

Due to the transformation of the power system, the effective use of flexibility from the distribution system (DS) is becoming crucial for efficient network management. Leveraging this flexibility requires interoperability among stakeholders, including Transmission System Operators (TSOs) and Distribution System Operators (DSOs). However, data privacy concerns among stakeholders present significant challenges for utilizing this flexibility effectively. To address these challenges, we propose a machine learning (ML)-based method in which the technical constraints of the DSs are represented by ML models trained exclusively on non-sensitive data. Using these models, the TSO can solve the optimal power flow (OPF) problem and directly determine the dispatch of flexibility-providing units (FPUs)—in our case, distributed generators (DGs)-in a single round of communication. To achieve this, we introduce a novel neural network (NN) architecture specifically designed to efficiently represent the feasible region of the DSs, ensuring computational effectiveness. Furthermore, we incorporate various PQ charts rather than idealized ones, demonstrating that the proposed method is adaptable to a wide range of FPU characteristics. To assess the effectiveness of the proposed method, we benchmark it against the standard AC-OPF on multiple DSs with meshed connections and multiple points of common coupling (PCCs) with varying voltage magnitudes. The numerical results indicate that the proposed method achieves performant results while prioritizing data privacy. Additionally, since this method directly determines the dispatch of FPUs, it eliminates the need for an additional disaggregation step. By representing the DSs technical constraints through ML models trained exclusively on nonsensitive data, the transfer of sensitive information between stakeholders is prevented. Consequently, even if reverse engineering is applied to these ML models, no sensitive data can be extracted. This allows

关键词： Reverse engineering

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Prediction with Time-Series Mixer for the S&P500 Index

Prediction with Time-Series Mixer for the S&P500 Index

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International Conference on Data Engineering Workshops

作者： Junyi Ye Jingyi Gu Ankan Dash Fadi P. Deek Guiling Grace Wang Department of Computer Science New Jersey Institute of Technology Newark USA Department of Informatics New Jersey Institute of Technology Newark USA

As an essential US economic indicator, the S&P500 Index is used to assess the current state of market performance and gauge the economy’s future course. However, stock market index prediction is challenging due to its nonlinearity and inherently volatile character. Recurrent Neural Networks (RNN) and their variants are de facto standards for sequence modeling. Recently, Convolutional Neural Networks (CNN) and attention-based networks, such as dilated casual convolutions and Transformers, have also become popular in time series forecasting. In this paper, we report on the design of a Time-Series Mixer (TS-Mixer) architecture based on MLP-Mixer, an all-MLP architecture for time series forecasting. To the best of our knowledge, this is the first implementation of MLP-Mixer-based architecture for sequence modeling. Modern deep learning models are increasingly built to handle univariate time series data. They generally pay attention to analyzing temporal dependencies while ignoring the relationship among features. The proposed architecture is specifically created for multivariate time series forecasting to capture temporal feature interactions while simultaneously learning feature correlations. To accomplish this, the proposed Time-Feature Mixer contains two types of MLP layers: feature mixer and temporal mixer. The feature mixer is applied independently to each data point to capture the correlation among features. In contrast, the temporal mixer extracts temporal dependency (trend, seasonal, cyclical, or random characteristics) of each feature across the whole input sequence. Compared to prevalent neural networks in sequence modeling, TS-Mixer exhibits competitive performance regarding S&P500 Index prediction.

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A survey on cyber threat intelligence sharing based on Blockchain

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Advances in Computational Intelligence 2023年第3期3卷 1-9页

作者： El-Kosairy, Ahmed Abdelbaki, Nashwa Aslan, Heba Nile University Cairo Egypt Centre of Informatics Science School of Information Technology and Computer Science Nile University Cairo Egypt Informatics Department Electronics Research Institute Cairo Egypt

In recent years, cyber security attacks have increased massively. This introduces the need to defend against such attacks. Cyber security threat intelligence has recently been introduced to secure systems against security attacks. Cyber security threat intelligence (CTI) should be fast, trustful, and protect the sender's identity to stop these attacks at the right time. Threat intelligence sharing is vitally important since it is considered an effective way to improve threat understanding. This leads to protecting the assets and preventing the attack vectors. However, there is a paradox between the privacy safeguard needs of threat intelligence sharing; the need to produce complete proper threat intelligence feeds to be shared with the community, and other challenges and needs that are not covered in the traditional CTI. This paper aims to study how Blockchain technology can be incorporated with the CTI to solve the current issues and challenges in the traditional CTI. We collected the latest contributions that use Blockchain to overcome the conventional CTI problems and compared them to raise the reader’s awareness about the different methods used. Also, we mentioned the uncovered areas for each paper to offer a wide range of details and information about different areas that need to be investigated. Furthermore, the prospect challenges of integrating the Blockchain and CTI are discussed.

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Improving fNIRS Signal Quality Using Smoothing Filtering

Improving fNIRS Signal Quality Using Smoothing Filtering

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2023 Progress in Applied Electrical Engineering, PAEE 2023

作者： Kawala-Sterniuk, Aleksandra Mikolajewski, Dariusz Leiva, Luis A. Ruotsalo, Tuukka Lysiak, Adam Grochowicz, Barbara Jacek Gorzenariczyk, Edward Luckiewicz, Adrian Wieczorek, Anna Pelc, Mariusz Automatic Control and Informatics Opole University of Technology Faculty of Electrical Engineering Opole Poland Institute of Computer Science Kazimierz Wielki University Bydgoszcz Poland University of Luxembourg Luxembourg Department of Computer Science Luxembourg Luxembourg University of Helsinki Denmark University of Copenhagen Finland Institute of Philosophy Kazimiez Wielki University Bydgoszcz Poland

ISBN: (纸本)9798350316254

Biomedical signals are extremely difficult to analyze, mainly due to the non-stationary nature of these signals. Filtering does not always bring the desired results, because often the desired information is filtered out. In the case of EEG signals, smoothing filters gave very good results. In this paper, various types of smoothing filters for the analysis of infrared spectroscopy signals were compared. © 2023 IEEE.

关键词： Information filtering

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A Hybrid Computational Framework Using NLP and ML for Emotion Analysis in Sinhala Songs

A Hybrid Computational Framework Using NLP and ML for Emotio...

引用

Advanced Research in Computing (ICARC), International Conference on

作者： Malinda Peiris Deshan Sumanathilaka School of Computing Informatics Institute of Technology Colombo Sri Lanka School of Mathematics and Computer Science Swansea University Wales United Kingdom

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

This study Rasathura explores the emotional dimensions of Sri Lankan music, specifically Sinhala songs, by developing an emotional classification model using both audio features and lyrical analysis. The research addresses the limitations in emotional classification using advanced Natural Language Processing (NLP) techniques for Sinhala music. For audio analysis, JAudio was used to extract musical data, and machine learning (ML) techniques, including Random Forest, Sequential Minimal Optimization (SMO), Naive Bayes, Decision Tree, and Logistic Regression, were evaluated for classification. Relief-based and correlation-based attribute selection were utilized to enhance classification accuracy. The SMO algorithm achieved an accuracy of 75.92% using JAudio features, which increased to 79.01% with Relief-based attribute selection. The BERT model (built on XLM-RoBERTa) was used for lyrical analysis, achieving a maximum accuracy of 61.22%. TFIDF, Word2Vec with n-grams, and BERT embeddings were evaluated for feature extraction in lyrical data. The findings could benefit the future music industry by creating personalized music recommendation systems, enhancing music therapy applications, and contributing to preserving cultural heritage through digital music archiving.

关键词： Analytical models Accuracy Machine learning algorithms Music Medical treatment Feature extraction Natural language processing Recommender systems Random forests Optimization

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Effect of Interest Variation and Prefix Length on Pending Interest Table (PIT) Performance in Named Data Networking (NDN) 9

Effect of Interest Variation and Prefix Length on Pending In...

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9th International Conference on Wireless and Telematics, ICWT 2023

作者： Khaderani, Helmi Ahmad Muhammadi, Ishlah Muhammad, Lutfi Razan Sucipto, Adi Kusumastuti, Luthfi Restu Nurhayati, Ade Ahdan, Syaiful Syambas, Nana Rachmana Bandung Institute of Technology Dept. of Telecommunication Engineering Bandung40132 Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung40132 Indonesia Universitas Teknokrat Indonesia Faculty of Engineering & Computer Science Lampung35132 Indonesia Telkom University Faculty of Engineering Jakarta Barat11710 Indonesia

ISBN: (纸本)9798350305029

Pending interest table (PIT) is one of the data structures on each named data network router. The speed of delivery of interest as well as the interest served at PIT are several important parameters for measuring PIT performance. This research was conducted to analyze the performance of PIT by measuring the round trip time (RTT) of sending data and sending interests from consumers to producers on the Palapa Ring Sumatra topology. Testing is carried out using miniNDN by varying the length of the prefix and the number of interests sent. From the simulation and results of the analysis that has been carried out, it is known that the correlation between the length of the prefix and RTT is directly proportional. The longer the prefix of an interest packet, the higher the RTT value. Apart from the simulation results, the number of interest packets sent is the same as those received at each node. © 2023 IEEE.

关键词： Topology

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Cost Optimization for Serverless Edge Computing with Budget Constraints using Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Chen, Chen Guan, Peiyuan Chen, Ziru Taherkordi, Amir Hou, Fen Cai, Lin X. Department of Computer Science and Technology University of Cambridge United Kingdom Department of Informatics University of Oslo Norway Department of Electrical and Computer Engineering Illinois Institute of Technology United States Department of Electrical and Computer Engineering University of Macau China

Serverless computing adopts a pay-as-you-go billing model where applications are executed in stateless and short-lived containers triggered by events, resulting in a reduction of monetary costs and resource utilization. However, existing platforms do not provide an upper bound for the billing model which makes the overall cost unpredictable, precluding many organizations from managing their budgets. Due to the diverse ranges of serverless functions and the heterogeneous capacity of edge devices, it is challenging to receive near-optimal solutions for deployment cost in a polynomial time. In this paper, we investigated the function scheduling problem with a budget constraint for serverless computing in wireless networks. Users and IoT devices are sending requests to edge nodes, improving the latency perceived by users. We propose two online scheduling algorithms based on reinforcement learning, incorporating several important characteristics of serverless functions. Via extensive simulations, we justify the superiority of the proposed algorithm by comparing with an ILP solver (Midaco). Our results indicate that the proposed algorithms efficiently approximate the results of Midaco within a factor of 1.03 while our decision-making time is 5 orders of magnitude less than that of Midaco. © 2025, CC BY.

关键词： Budget control

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Optimizing resource allocation in cloud-native applications through proactive autoscaling with the InformerAutoScale model

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The Journal of Supercomputing 2025年第9期81卷 1-39页

作者： Kumar, Bablu Verma, Anshul Verma, Pradeepika Bennour, Akram Department of Computer Science Banaras Hindu University Varanasi India Technology Innovation Hub Indian Institute of Technology Patna India Informatics and Mathematics Laboratory (LAMIS) Echahid Cheikh Larbi Tebessi University Tebessa Algeria

Cloud-native applications are designed to utilize cloud computing resources efficiently. These applications automatically scale resources by managing containerized copies of files and creating containers, which are handled through pods in Kubernetes. However, they face challenges due to the dynamic workload associated with automatic scaling and de-scaling in cloud environments. This makes it difficult to obtain accurate monitoring information, particularly with reactive autoscaling. This research presents a proactive autoscaling approach through the proposed InformerAutoScale model, which predicts resource requirements for long sequences in cloud-native applications to enable accurate pod scaling and descaling. Experimental results demonstrate that the InformerAutoScale approach effectively reduces resource waste and manages issues such as under and over-provisioning. The real-world implementation was carried out using Docker Desktop and Kubernetes, with scale or scaled pods allocated based on application requests. Proactive autoscaling achieved a 90.66% improvement in scaling efficiency compared to reactive methods.

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Fuzzy System of Wind Turbine Defect Image Processing 14

Fuzzy System of Wind Turbine Defect Image Processing

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14th International Conference on Advanced computer Information Technologies, ACIT 2024

作者： Dubchak, Lesia Sachenko, Anatoliy Wolff, Carsten Vasylkiv, Nadia West Ukrainian National University Department of Computer Engineerin Ternopil Ukraine West Ukrainian National University Research Institute for Intelligent Computer Systems Ternopil Ukraine Kazimierz Pulaski University of Technology and Humanities in Radom Department of Tele-Informatics Radom Poland Dortmund University of Applied Science and Arts Dortmund Germany West Ukrainian National University Department of Information Computer Systems and Control Ternopil Ukraine

ISBN: (纸本)9798350350036

Wind turbines are an important component of the global strategy for the transition to renewable energy sources and the fight against climate change. Their implementation contributes to sustainable development and to improving the ecological situation of the planet. Detecting and elimination of defects helps reduce the wear of the components and prevent premature failure. This can significantly extend the life of wind turbines, which is economically beneficial for owners and operators. The use of UAVs allows for minimizing the risks for personnel, as inspection and diagnosis of defects can be carried out without the need to lift people to great heights or stay in dangerous areas. This is especially important for turbines located in places of difficult access. The images taken by the UAV contain many extraneous objects and fuzzy data. Fuzzy logic allows you to create models for pattern recognition and object classification in images, taking into account uncertainty and variation in the data. This increases the accuracy and reliability of pattern recognition systems. This article proposes an image processing system to detect wind turbine defects, with a fuzzy system module that selects images from a video stream for further processing. © 2024 IEEE.

关键词： Windmill

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