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检索条件"丛书名=Lecture notes in computer science,"

共 300228 条记录，以下是21-30 订阅

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Optimizing Airline Destinations with AIRNODE: A Graph Attention Network Approach 16th

Optimizing Airline Destinations with AIRNODE: A Graph Attent...

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16th International Conference on Social Networks Analysis and Mining

作者： Shaik, Abdul Raheem Alhamadani, Abdulaziz Sarkar, Shailik Lu, Chang-Tien Virginia Tech Bradley Dept Elect & Comp Engn Falls Church VA 22043 USA Florida Polytech Univ Dept Data Sci & Business Analyt Lakeland FL 33805 USA Virginia Tech Dept Comp Sci Falls Church VA 22043 USA

ISBN: (纸本)9783031785535;9783031785542

The airline industry faces the critical challenge of meeting increasing passenger expectations amidst rapid technological advancements and intense competition. To remain competitive, airlines must gain a deeper understanding of passenger satisfaction and use this knowledge to improve service quality. This paper addresses this challenge by leveraging online customer reviews to derive actionable insights into passenger experiences. We introduce AIRNODE (Attention-based Insights for Reviewing Node Optimized Destinations and Experiences), a comprehensive two-stage model designed to analyze these reviews. AIRNODE constructs a weighted graph to aggregate review data and utilizes a Graph Attention Network (GAT) to model complex spatial relationships between destinations, achieving 84% accuracy in classifying destinations based on aggregated user satisfaction. Through advanced keyword extraction, we identify key aspects such as customer service, delays, and staff behavior, providing deep insights into the factors that drive passenger satisfaction. Case studies highlight destinations with varying levels of satisfaction, identifying positive attributes and areas needing improvement, and offering detailed insights and justifications for enhancing customer satisfaction. These insights equip airlines with a data-driven strategy to enhance service quality, meet traveler expectations, and maintain a competitive edge in a dynamic market.

关键词： Airline Industry Large Language Models Graph Neural Networks Online Customer Reviews Analysis

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Regulatory Implications of MEV Mitigations 9th

Regulatory Implications of MEV Mitigations

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28th International Conference on Financial Cryptography and Data Security

作者： Ji, Yan Grimmelmann, James Cornell Tech New York NY 10044 USA

ISBN: (纸本)9783031692307;9783031692314

This paper examines the legal ramifications of Miner / Maximal Extractable Value (MEV), a phenomenon in which some entities (e.g., miners or validators) leverage their positional advantages to generate extra profits on blockchains. In previous work, Barczentewicz et al. argued that some MEV extraction techniques could constitute illegal market manipulation under United States securities law, depending on the publicness of the victim transactions. While their analysis applies to typical Ethereum and Flashbots implementations, we contend that the rapidly evolving blockchain ecosystem and the emergence of new MEV mitigation measures necessitate a revised test for market-manipulation liability. Our proposal focuses on the principle of respecting the initiating user's intent, rather than simply the network status of the transactions. We also identify new enforcement challenges that arise from the decentralization nature of blockchains. By offering a nuanced understanding of the MEV landscape and exploring the legal implications for manipulation liability, this paper contributes to the ongoing discussion on MEV regulation in blockchain ecosystems.

关键词： Regulation MEV DeFi Blockchain

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Evaluating LLMs Capabilities Towards Understanding Social Dynamics 16th

Evaluating LLMs Capabilities Towards Understanding Social Dy...

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16th International Conference on Social Networks Analysis and Mining

作者： Tahir, Anique Cheng, Lu Sandoval, Manuel Silva, Yasin N. Hall, Deborah L. Liu, Huan Arizona State Univ Sch Comp & AI Tempe AZ 85287 USA Univ Illinois Chicago IL USA Loyola Univ Chicago Chicago IL 60611 USA Arizona State Univ Sch Social & Behav Sci Glendale AZ USA

ISBN: (纸本)9783031785474;9783031785481

Social media discourse involves people from different backgrounds, beliefs, and motives. Thus, often such discourse can devolve into toxic interactions. Generative Models, such as Llama and ChatGPT, have recently exploded in popularity due to their capabilities in zero-shot question-answering. Because these models are increasingly being used to ask questions of social significance, a crucial research question is whether they can understand social media dynamics. This work provides a critical analysis regarding generative LLM's ability to understand language and dynamics in social contexts, particularly considering cyberbullying and anti-cyberbullying (posts aimed at reducing cyberbullying) interactions. Specifically, we compare and contrast the capabilities of different large language models (LLMs) to understand three key aspects of social dynamics: language, directionality, and the occurrence of bullying/anti-bullying messages. We found that while fine-tuned LLMs exhibit promising results in some social media understanding tasks (understanding directionality), they presented mixed results in others (proper paraphrasing and bullying/anti-bullying detection). We also found that fine-tuning and prompt engineering mechanisms can have positive effects in some tasks. We believe that a understanding of LLM's capabilities is crucial to design future models that can be effectively used in social applications.

关键词： large language models social media anti-bullying

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Robust Stance Detection: Understanding Public Perceptions in Social Media 16th

Robust Stance Detection: Understanding Public Perceptions in...

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16th International Conference on Social Networks Analysis and Mining

作者： Kim, Nayoung Mosallanezhad, David Cheng, Lu Mancenido, Michelle V. Liu, Huan Arizona State Univ Tempe AZ 85281 USA NVIDIA Santa Clara CA 95051 USA Univ Illinois Chicago IL 60607 USA Arizona State Univ West Valley Campus Glendale AZ 85306 USA

ISBN: (纸本)9783031785375;9783031785382

The abundance of social media data has presented opportunities for accurately determining public and group-specific stances around policy proposals or controversial topics. In contrast with sentiment analysis which focuses on identifying prevailing emotions, stance detection identifies precise positions (i.e., supportive, opposing, neutral) relative to a well-defined topic, such as perceptions toward specific global health interventions during the COVID-19 pandemic. Traditional stance detection models, while effective within their specific domain (e.g., attitudes towards masking protocols during COVID-19), often lag in performance when applied to new domains and topics due to changes in data distribution. This limitation is compounded by the scarcity of domain-specific, labeled datasets, which are expensive and labor-intensive to create. A solution we present in this paper combines counterfactual data augmentation with contrastive learning to enhance the robustness of stance detection across domains and topics of interest. We evaluate the performance of current state-of-the-art stance detection models, including a prompt-optimized large language model, relative to our proposed framework succinctly called STANCE-C3 (domain-adaptive Cross-target STANCE detection via Contrastive learning and Counterfactual generation). Empirical evaluations demonstrate STANCE-C3's consistent improvements over the baseline models with respect to accuracy across domains and varying focal topics. Despite the increasing prevalence of general-purpose models such as generative AI, specialized models such as STANCE-C3 provide utility in safety-critical domains wherein precision is highly valued, especially when a nuanced understanding of the concerns of different population segments could result in crafting more impactful public policies.

关键词： Stance Detection Contrastive Learning Counterfactual Generation Domain Adaptation Cross-target Stance Detection

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Dynamic Inter-organizational Communication Network in a Post-merger Integration 16th

Dynamic Inter-organizational Communication Network in a Post...

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16th International Conference on Social Networks Analysis and Mining

作者： Benzinger, Michael Ghawi, Raji Zenk, Lukas Pfeffer, Juergen Tech Univ Munich Arcisstr 21 D-80333 Munich Germany Danube Univ Krems Dr Karl Dorrek Str 30 A-3500 Krems Austria

ISBN: (纸本)9783031785405;9783031785412

During a Post-Merger Integration, the communication structure of an organization changes profoundly. Effective communication and personal interaction are crucial for the success of Mergers and Acquisitions, as many such endeavours fail to achieve their targets. Mergers and Acquisitions involve the combination of two organizations to create a new entity. Creating personal networks across organizational boundaries can build bridges between several organization entities to create a cohesive, adjustable and resilient holistic organization and leverage synergies. However, little actual network data on communication and network structure during Post-Merger Integration is available. Our results are based on two merged organizational units with approximately 3,800 employees in total and a collective sum of around 250,000 email connections from an actual Post-Merger Integration data set. We show that various communication networks stabilize over time in Post-Merger Integration and, that homophily is partially reflected in the informal social network. We also find that homophily occurs on a department level but somewhat less on an organizational level and that some critical departments communicate less than expected. Our results indicate that the formal merger is reflected in the communication and ego-network structure. We expect that our findings will add insights into Post-Merger Integration research and how networks are adapting, since the findings are based on actual organizational network data.

关键词： Dynamic Networks Network Stabilization Communication Homophily Mergers & Acquisitions Post-Merger Integration

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Medical Report Generation from Medical Images Using Vision Transformer and Bart Deep Learning Architectures 16th

Medical Report Generation from Medical Images Using Vision T...

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16th International Conference on Social Networks Analysis and Mining

作者： Ucan, Murat Kaya, Buket Kaya, Mehmet Alhajj, Reda Dicle Univ Dept Comp Technol Diyarbakir Turkiye Firat Univ Dept Elect & Automat Elazig Turkiye Firat Univ Dept Comp Engn Elazig Turkiye Univ Calgary Dept Comp Sci Calgary AB Canada

ISBN: (纸本)9783031785535;9783031785542

Generating medical reports from medical images using traditional methods is a time-consuming process that is prone to human error and requires experience. Failure to generate fast reports from medical images delays the treatment of patients, and misdiagnosis can lead to adverse conditions that can cause the death of patients. The main objective of this study is to develop a high-performance deep learning model that can autonomously generate medical reports from medical images. The proposed model consists of a Vision Transformer (ViT) encoder and a Bidirectional Autoregressive Transformer (BART) decoder. Training and testing on the model was conducted using images and reports from the Indiana University Chest X-Ray dataset. The developed model is analyzed with measurable parameters and then compared with its competitors in the literature using the same dataset. The proposed Vi-Ba architecture achieved success scores of 0.150, 0.154, 0.274 in bleu-4, meteor and rouge word matching evaluation metrics, respectively. The Vi-Ba model achieved high reporting performance compared to the studies reviewed in the literature. The results show that the proposed architecture can be used by specialized doctors in hospitals to diagnose diseases faster and more accurately. In this way, misdiagnosis and treatments will be reduced and human life will be protected.

关键词： Deep Learning Vision Transformer ViT Bidirectional Autoregressive Transformer BART Medical Report Generation Chest X-rays

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Exploring Behavioral Tendencies on Social Media: A Perspective Through Claim Check-Worthiness 16th

Exploring Behavioral Tendencies on Social Media: A Perspecti...

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16th International Conference on Social Networks Analysis and Mining

作者： Zhang, Zeyu Zhu, Zhengyuan Zhang, Haiqi Li, Chengkai Univ Texas Arlington Arlington TX 76019 USA

ISBN: (纸本)9783031785405;9783031785412

This study examines how factual claims of different significance influence and reflect social media users' behavioral patterns. Leveraging "check-worthiness" as a measure of the factual significance of claims, we analyze the connection between factual claims and user behaviors on Twitter. Through a series of experiments using statistical methods such as correlation analysis and hypothesis testing, we provide insights into a few pivotal inquiries: (1) whether differences exist between users' tweeting tendencies toward check-worthiness, (2) the underlying reasons for such differences, (3) whether users tend to create, share, and endorse content with check-worthiness levels similar to their own tweets, and (4) whether users with similar tendencies toward check-worthiness exhibit heightened engagement. The experiments were conducted across three datasets, comprising over 48.5 million tweets and involving 15,000 users, spanning several domains and yielding statistically significant findings. Previous studies have primarily centered on examining the effectiveness and strategies of fact-checks rather than understanding people's behavioral tendencies toward factual claims. Our research pioneers understanding in this area, offering valuable insights for behavioral modeling and social sciences.

关键词： check-worthiness fact-checking social media analytics

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Whispers of Trauma: Leveraging Social Media for Assessing Mental Health in Victims of Childhood Sexual Abuse 16th

Whispers of Trauma: Leveraging Social Media for Assessing Me...

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16th International Conference on Social Networks Analysis and Mining

作者： Phukan, Orchid Chetia Sharma, Rajesh Buduru, Arun Balaji IIIT Delhi New Delhi India Univ Tartu Tartu Estonia

ISBN: (纸本)9783031785535;9783031785542

Warning: Reader discretion is recommended as our study tackles topics such as child sexual abuse, molestation, etc. Online Social Media (OSM) is increasingly being used by individuals with different mental health problems for finding support groups. As such extensive research has been carried out for understanding the mental health of individuals by observing their activities on OSM. However, previous studies haven't put much focus on studying mental health in victims of Childhood Sexual Abuse (CSA). CSA is a menace to society and has long-lasting effects on the mental health of the survivors. Proper care and attention towards CSA survivors facing mental health problems can drastically improve their mental health. Our work fills this gap by studying Reddit posts related to CSA. To this end, we collect and create a dataset of around 8192 CSA-related posts. For further understanding the characteristics of CSA-related posts, we performed a comparative analysis with 9198 non-CSA mental health-related posts using various natural language processing (NLP) techniques such as word-shift, word cloud, topic analysis, and emotion analysis. We found that observable differences exist between them in terms of topics being discussed and emotions. Additionally, we propose a modeling framework, Mental Feature-interaction Transformer (MentalFiT) for identifying mental health problems in posts associated with CSA. It follows the notion that mental health problems and emotions are related to each other. The modeling approach involves the use of a transformer encoder to facilitate interaction among features at the intra-level, while the feature interaction block is employed to facilitate interaction among features at the inter-level. Thorough and extensive experimentation conveys the efficacy of the proposed framework on our novel curated dataset. The proposed method demonstrates impressive performance compared to models that do not incorporate emotional features. Our study opens up a n

关键词： Childhood Sexual Abuse Mental Health Social Media Natural Language Processing Transformer

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Scam Token Detection Based on Static Analysis Before Contract Deployment 9th

Scam Token Detection Based on Static Analysis Before Contrac...

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28th International Conference on Financial Cryptography and Data Security

作者： Igarashi, Taichi Matsuura, Kanta Fast Retailing Tokyo Japan Univ Tokyo Tokyo Japan

ISBN: (纸本)9783031692307;9783031692314

In recent years, the number of crimes using smart contracts has increased. In particular, fraud using tokens, such as rug-pull, has become an ignorable issue in the field of decentralized finance because a lot of users have been scammed. Therefore, constructing a detection system for scam tokens is an urgent need. Existing methods are based on machine learning, and they use transaction and liquidity data as features. However, they cannot completely remove the risk of being scammed because these features can be extracted after scam tokens are deployed to blockchain. In this paper, we propose a scam token detection system based on static analysis. In order to detect scam tokens before deployment, we utilize code-based data, such as bytecodes and opcodes, because they can be obtained before contract deployment. Since N-gram includes information regarding the order of code sequences and scam tokens have the specific order of code-based data, we adopt N-gram of them as features. Furthermore, for the purpose of achieving a high detection performance, each feature is categorized into a scam-oriented feature or benign-oriented one to make differences in the values of feature vectors between scam and benign token. Our results show the effectiveness of code-based data for detection by achieving a higher F1-score compared to the methods in another field of fraud detection in Ethereum based on code-based data. In addition, we also confirmed that the position of effective code for detection is near the start position of runtime code in our experiments.

关键词： Ethereum Smart contract Token Fraud Scam token

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Don't Do That! Reverse Role Prompting Helps Large Language Models Stay in Personality Traits 17th

Don't Do That! Reverse Role Prompting Helps Large Language M...

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17th International Conference on Interactive Digital Storytelling

作者： Chen, Siyuan Taveekitworachai, Pittawat Xia, Yi Li, Xiaoxu Gursesli, Mustafa Can Lanata, Antonio Guazzini, Andrea Thawonmas, Ruck Ritsumeikan Univ Grad Sch Informat Sci & Engn Ibaraki Japan Univ Firenze Dept Informat Engn Florence Italy Univ Firenze Dept Educ Literatures Intercultural Studies Langu Florence Italy Ritsumeikan Univ Coll Informat Sci & Engn Ibaraki Japan

ISBN: (纸本)9783031784521;9783031784538

This paper investigates effectiveness of role prompting, an approach used to condition a large language model (LLM) with a role or personality trait. Conditioning an LLM with a specific role or personality trait is crucial for various applications, such as using an LLM as a non-playable character. Role-playing is one of the crucial techniques in doing so. However, existing studies have only observed changes when introducing a role or personality trait to LLMs, but not how effectively they conform to the given personality trait. In this study, we investigate how well LLMs adhere to given instructions using a well-established personality test. Additionally, we conduct an experiment to determine how a given personality influences biases in generating a game story ending. Through our investigations, we found that traditional role prompting is ineffective for assessing a certain personality trait. Therefore, we propose a novel variant of role prompting called reverse role prompting (RRP) to reduce the significance of personality traits other than the assigned personality. We observed that when using RRP to assign personalities to LLMs, the LLMs are better able to act the given personality. We also found that LLMs inherently have high agreeableness, affecting story ending biases when instructed to generate a game story. RRP is also effective for reducing the effects of this inherent agreeableness. Future studies should further investigate the effectiveness of the proposed RRP for a wider range of narrative elements and applications in storytelling.

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