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Improved Gas Optimization of Smart Contracts 11th

Improved Gas Optimization of Smart Contracts

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11th IFIP WG 2.2 international conference on Fundamentals of Software engineering, FSEN 2025

作者： Barakbayeva, Togzhan Farokhnia, Soroush Goharshady, Amir Kafshdar Li, Pingjiang Lin, Zhaorun Hong Kong University of Science and Technology New Territories Clear Water Bay Hong Kong University of Oxford Oxford United Kingdom

ISBN: (纸本)9783031870538

Smart contracts are programs executed on top of a blockchain consensus protocol. their compiled code (bytecode) is stored on the blockchain and is immutable after deployment. they are self-enforcing in the sense that any function call to a smart contract is executed by all nodes on the network, ensuring that they all reach consensus about the final state of the contract. To prevent denial-of-service attacks, such an execution is costly by design. A "gas" cost is assigned to each bytecode operation, roughly proportional to the resources required to execute it, and any user who initiates a function call to a smart contract has to pay the total gas cost of the resulting execution. On Ethereum alone, the users pay an astounding gas cost of more than 4 billion USD/year. Smart contracts are often written in high-level programming languages such as Solidity and then compiled to bytecode before being deployed on the blockchain. thus, a natural compiler optimization problem arising in this context is to produce efficient bytecode that minimizes the total gas usage. A leading approach in this direction is superoptimization, which considers every basic block of the smart contract separately and tries to rewrite it as an equivalent block that uses as little gas as possible. the current state-of-the-art tool is syrup 2.0, which encodes gas superoptimization as Max-SMT and then relies on SMT-solvers to synthesize an equivalent contract with optimized gas usage. In this work, we make two observations: First, the performance of Max-SMT declines significantly as block sizes increase. thus, although syrup is able to find an optimal rewriting for a small block with a dozen bytecode operations, its output on blocks with hundreds or thousands of operations, when given any realistic timeout, is far from optimal. Second, optimizations that can be applied to basic blocks are often local and compositional, i.e. they rewrite several small and disjoint parts of the block. Such locality is los

关键词： Denial-of-service attack

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Virtual Reality Human-Computer Interaction System Based on artificial Intelligence Technology 14th

Virtual Reality Human-Computer Interaction System Based on A...

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14th international conference on Frontier Computing, FC 2024

作者： Li, Jiaqi Xiong, Gaojie Wu, Xing Guangdong Shenzhen518000 China School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu610000 China Zhejiang Vocational Academy of Art Zhejiang Hangzhou310053 China

ISBN: (纸本)9789819623907

Although artificial intelligence and virtual reality technology have been rapidly popularized, current human-computer interaction systems still have challenges such as network latency, data synchronization, and natural communication limitations. this article aims to explore ways to use artificial intelligence technology to improve human-computer interaction in virtual reality, evaluate its effects, and introduce the development status of artificial intelligence technology and virtual reality technology and their applications in the field of human-computer interaction. this article first analyzes the needs and challenges of virtual reality human-computer interaction systems, and then proposes an improvement method based on artificial intelligence technology, including using machine learning algorithms to analyze and predict user behavior, and using natural language processing technology to achieve natural language interaction. the research results show that the reliability of this system is up to 96%. Virtual reality human-computer interaction systems based on artificial intelligence technology have made significant improvements in improving user experience and interaction effects. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Virtual environments

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Innovative teaching methodologies: Keyword-based analysis to monitor the trends of the last decade 6

Innovative teaching methodologies: Keyword-based analysis to...

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6th international conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Claudio, Suppini Andrea, Volpi Federico, Solari Letizia, Tebaldi Natalya, Lysova Roberto, Montanari Department of Engineering for Industrial Systems and Technologies University of Parma Parco Area delle Scienze 181/A Parma43124 Italy

the techniques employed to enhance the art of teaching are several and must account for various factors, some of which are specific to the subject matter, while others are related to external conditions, such as social characteristics. this study examines the current landscape of innovative teaching methodologies by identifying: (i) the primary fields where these methodologies are applied, (ii) the most active countries in adopting these methods, and (iii) the main types of techniques utilized. Although other reviews exist on this topic, this research primarily focuses on methodologies used in the engineering field and identifies the key countries that are most responsive to these innovative approaches. through keyword-based analysis and the application of statistical methods, it was found that the leading methodologies in growth are gamification, virtual reality (VR), and project- or problem-based learning. Additionally, their use in doctoral and postgraduate programs remains underexplored, presenting opportunities for future research. © 2024 the Authors. Published by Elsevier B.V.

关键词： digital tools engineering innovative didactics keywords analysis teaching methods

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Intent Identification Using Few-Shot and Active Learning with User Feedback 25th

Intent Identification Using Few-Shot and Active Learning wi...

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25th international conference on Web Information Systems engineering, WISE 2024

作者： Yuvaraj, Senthil Ganesan Benatallah, Boualem Motahari-Nezhad, Hamid Reza Rabhi, Fethi School of Computer Science and Engineering University of New South Wales Sydney Australia Insight SFI Research Centre on Data Analytics Dublin City University Dublin Ireland UpBrains AI Inc. RestonVA United States Macquarie University Sydney Australia

ISBN: (纸本)9789819605729

Collaboration tools contain many intents in workplace conversation, and identifying these intents is important to increase workplace productivity. However, labelling these intents for a large collection of conversations is expensive and data availability for new intents is quite limited. the pre-trained models show outstanding results in text classification tasks and large language models have recently produced accurate models with few samples. We explored few-shot learning methods and active learning strategies for this problem. Our proposed method, "SetFit with AL" is a combination of Sentence Transformer Fine-tuning (SetFit) and active learning for intent identification. this method fine-tunes a sentence-transformer model to develop accurate models. the intent classification evaluation dataset was used to evaluate this method. the results show that our proposed method outperforms state-of-the-art large language model GPT-3.5 and is comparable to GPT-4. this method also can utilize user feedback to adapt to new data and develop personalized models. thus, the contribution of this paper is that fine-grained intents are identified using minimal data and the model is adaptable based on user feedback. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Active learning

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An Efficient Method to Deal with Missing Values and Class Imbalance in Healthcare Data 5th

An Efficient Method to Deal with Missing Values and Class Im...

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5th international conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Singh, Harpreet Singh, Birmohan Kaur, Manpreet Rani, Suvita Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Sangrur Longowal India Department of Electrical and Instrumentation Engineering Sant Longowal Institute of Engineering and Technology Sangrur Longowal India

ISBN: (纸本)9789819773701

the present era belongs to the age of digital devices, where everything is going to be digitized. this makes the massive production of data at faster rates and brings Big Data to light. the arrival of big data has influenced and reformed many domains like Healthcare, Bioinformatics, Agriculture, etc. Healthcare big data has gained the attention of various researchers as it aids in the early diagnosis and prediction of diseases. Generally, real-world healthcare data contains missingness and imbalance distribution of classes which makes classification a challenging problem. therefore, an efficient method is introduced to deal with the problems of data incompleteness and class imbalance in healthcare data. A class mean and standard deviation-based imputation strategy is proposed to impute the missing values within the region of a particular class. Further, an Improved Elephant Herding Optimization (IEHO) algorithm is utilized to apply the optimal weights for improving the imbalanced learning in healthcare data. A new fitness function is proposed which considers the data missingness and class imbalance along with the feature weighting. the optimized k-Nearest Neighbor (k-NN) learning algorithm is used to perform the classification. the performance of the proposed method is evaluated on four healthcare datasets: Pima diabetes, Wisconsin Breast Cancer (Original), Hepatitis, and HCC Survival. A comparative analysis with state-of-the-art methods is performed based on four-performance metrics: accuracy, balanced accuracy, g-mean, and f-score. the proposed method obtained higher classification accuracy for Wisconsin Breast Cancer (Original), Hepatitis, and HCC Survival datasets than state-of-the-art methods. Experimental results validate the effectiveness of the proposed method in dealing with the missing values and class imbalance problems in healthcare data. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Process Tree Alignments 28th

Process Tree Alignments

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28th international conference on Enterprise Design, Operations, and Computing, EDOC 2024

作者： Schwanen, Christopher T. Pakusa, Wied van der Aalst, Wil M. P. RWTH Aachen University Aachen Germany Federal University of Applied Administrative Sciences Brühl Germany

ISBN: (纸本)9783031783371

the state-of-the-art approach for computing alignments is to apply an exhaustive state-space search with a well-tailored A*-heuristic function. If the heuristic fails to provide good estimates, the alignment computation quickly becomes infeasible even for small event logs given the exponential search space. Since intractability is unavoidable for general process models, we here consider the restricted class of process trees which provides a good balance between expressiveness and algorithmic feasibility. As our main result, we prove that alignments on process trees can be expressed as solutions of Mixed Integer Linear Programs (MILP). Our novel approach does not only position the problem inside the class of NP, but also paves the way for applying a host of new optimization techniques from the field of mathematical programming to alignments on process trees. We further show that for process trees without parallel executions, our MILP formulation becomes a Linear Program (LP) which can be solved efficiently. this result gives fresh insights into the structure of the alignment problem and the role of concurrency as a key factor for intractability. Finally, we implement our new algorithmic approach in PM4Py and evaluate the performance against the standard algorithms. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Mixed-integer linear programming

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Dynamic Emotion-Adaptive Attention Mechanism (DEAAM) 5th

Dynamic Emotion-Adaptive Attention Mechanism (DEAAM)

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5th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Garugu, Suresh Kumar Madhuri, P. Shyamala Atchana, Venu Gopal Penmetsa, Gopala Krishnam Raju Bandaru, Venkata Naga Rani Sastry Eemani, V.R.J. Department of Computer Science and Engineering Vishnu Institute of Technology Bhimavaram India

ISBN: (纸本)9783031770807

the Dynamic Emotion-Adaptive Attention Mechanism (DEAAM) offers a groundbreaking framework for analyzing emotions in real-time video streams, leveraging a state-of-the-art convolution neural network (CNN) for rapid and accurate emotion detection. By integrating an adaptive attention mechanism, DEAAM dynamically adjusts video sampling rates and focuses on areas of emotional intensity, optimizing computational resources for the most expressive moments. this system intricately combines facial landmark detection with saliency mapping to pinpoint critical expressive features, enhancing the depth and precision of the analysis. Furthermore, DEAAM incorporates temporal emotional coherence tracking, utilizing advanced recurrent neural networks to capture the evolution of emotional states, adding a rich contextual layer to the emotion recognition process. this comprehensive approach not only increases the accuracy of emotion analysis but also ensures efficiency by focusing on key emotional expressions. Ideal for applications in virtual communication, mental health assessment, and any domain requiring nuanced emotion recognition, DEAAM sets a new standard in empathetic technology, offering a sophisticated, contextually aware system for extracting real-time emotional insights from video feeds. © ICST Institute for Computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Recurrent neural networks

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Beyond the Scroll: Eye-Tracking Evaluations on the National Gallery of Indonesia’s Instagram 26th

Beyond the Scroll: Eye-Tracking Evaluations on the National ...

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26th international conference on Asia-Pacific Digital Libraries, ICADL 2024

作者： Sari, Desy Novita Prameswari, Clarissa Rahmi Anshori, Isa Master’s Program in Library Science Universitas Indonesia Kota Depok Indonesia Department of Library and Information Sciences Universitas Indonesia Kota Depok Indonesia Biomedical Engineering Department School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia National Gallery of Indonesia Central Jakarta Indonesia

ISBN: (纸本)9789819608676

Eye-tracking studies focused on art galleries and museums within the realm of Instagram, especially in the Indonesian context, are notably scant. this investigation elucidates the user interaction and experience of the art gallery’s Instagram account, @galerinasional. Employing a quantitative approach, we implemented eye-tracking experiments using the well-known online eye-tracking software RealEye. the study participants were followers of the National Gallery of Indonesia’s (Galnas) Instagram profile, seven women and three men, in two age brackets: 18–24 years (n = 5) and 25–34 years (n = 5). the experimental protocol required participants to execute five tasks centered on locating exhibition details and artwork on the Galnas Instagram platform, with a duration of 100 s allocated to each task. the Participant Quality Grade (PQG), examination duration, optimal browser dimensionality, K-factor mean, gaze and fixation frequencies, and heat maps were determined for the participants. the key findings are as follows. (1) the participants’ PQG scores were predominantly between 4 and 6. (2) Investigative browsing typically exceeded the 100-s limit. (3) the consensus on the ideal browser dimensions hovered around 1,368.6 × 814.8 px. (4) the K-factor mean across participants tended toward K > 0. (5) the average gaze occurrence was 27 items per second for most participants. (6) A noticeable disparity in fixation counts was observed between the two age groups, with individuals aged 25–34 demonstrating prolonged engagement with Galnas’ content. (7) Heat maps accentuated a pronounced concentration on visual content, encompassing photographs, videos, and their associated captions. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Economic and social effects

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Integrating Deep Learning Frameworks for Automated Medical Image Diagnosis 8th

Integrating Deep Learning Frameworks for Automated Medical I...

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8th IFIP TC 12 international conference on Computer, Communication and Signal Processing, ICCCSP 2024

作者： Gayatri, K. Kumar, K. M. Anand Padmavathi, B. Shankar Department of Computer Science and Engineering Easwari Engineering College Ramapuram Chennai600089 India

ISBN: (纸本)9783031736162

In today’s healthcare system, medical image analysis is essential for diagnosis, treatment planning, and condition monitoring. In this study, the four well-known tools in the field are 3D Slicer, MONAI, SAMM, and YOLOv8 are examined and contrasted. the study’s scope includes its ability to process a variety of 2D and 4D medical imaging datasets, which contain images, segmentation, annotations, and transformations. the process entails a comprehensive prehensive examination of every tool’s feature, with particular emphasis on their capacity to read and write DICOM images, accommodate multiple file formats, offer interactive 3D Slicer. Key findings show that 3D Slicer performs exceptionally well when using deep learning techniques for segmentation and interactive visualization. While SAM Model exhibits adaptability in managing diverse segmentation prompts, MONAI provides a comprehensive end-to-end medical data processing solution. YOLOv8 shows potential for effective object detection. the study’s implications include the possibility of using these instruments in clinical practices and medical research contexts. the research results provide insight into the current discussion about how to strategically integrate state-of-the-art methods to improve overall healthcare outcomes, diagnostic accuracy, and treatment planning efficiency as medical image analysis continues to advance. © IFIP international Federation for Information Processing 2025.

关键词： Clinical research

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On Mining Dynamic Graphs for k Shortest Paths 16th

On Mining Dynamic Graphs for k Shortest Paths

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16th international conference on Social Networks Analysis and Mining

作者： D'Ascenzo, Andrea D'Emidio, Mattia Luiss Univ Rome Italy Univ Aquila Laquila Italy

ISBN: (纸本)9783031785405;9783031785412

Mining graphs, upon query, for k shortest paths between vertex pairs is a prominent primitive to support several analytics tasks on complex networked datasets. the state-of-the-art method to implement this primitive is KPLL, a framework that provides very fast query answering, even for large inputs and volumes of queries, by pre-computing and exploiting an appropriate index of the graph. However, if the graph's topology undergoes changes over time, such index might become obsolete and thus yield incorrect query results. Re-building the index from scratch, upon every modification, induces unsustainable time overheads, incompatible with applications using k shortest paths for analytics purposes. Motivated by this limitation, in this paper, we introduce DECKPLL, the first dynamic algorithm to maintain a KPLL index under decremental modifications. We assess the effectiveness and scalability of our algorithm through extensive experimentation and show it updates KPLL indices orders of magnitude faster than the re-computation from scratch, while preserving its compactness and query performance. We also combine DECKPLL with INCKPLL, the only known dynamic algorithm to maintain a KPLL index under incremental modifications, and hence showcase, on real-world datasets, the first method to support fast extraction of k shortest paths from graphs that evolve by arbitrary topological changes.

关键词： Graph Algorithms Dynamic Networks Algorithm engineering Experimental Algorithmics

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