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Graph-Guided Multi-view Text Classification: Advanced Solutions for Fast Inference 33rd

Graph-Guided Multi-view Text Classification: Advanced Soluti...

引用

33rd International Conference on Artificial Neural Networks and Machine Learning (ICANN)

作者： Gao, Nan Wang, Yongjian Chen, Peng Zheng, Xin Zhejiang Univ Technol Hangzhou Peoples R China

ISBN: (纸本)9783031723438;9783031723445

Recently, traditional large-scale pre-trained models such as BERT perform well in text classification tasks. However, these models have a large number of parameters and high memory requirements, making it difficult to implement in some real-time scenarios or limited resources. Therefore, researchers attempt to use lightweight Graph Neural Networks(GNN) with excellent feature expression as an alternative solution. However, current GNN-based methods solely focus on the structure information of texts, but ignore the sequence information and long-distance dependency relationships between nodes. To solve the above problem, we propose a lightweight network structure G2TX based on multi view feature fusion, which can achieve a balance between model performance and parameters. First, to address the challenge of unordered nodes in graph structure, we introduce a Multi Sequence Fusion Module (MSF) to enhance node sequence information. It integrates features from multiple views through diverse strategies for both word-level and text-level fusion. Secondly, to expand the receptive field of nodes, we propose a Remote Feature Extraction Module (RFE) to bridge the difficult interaction gap between word nodes and remote nodes. Finally, we use KL divergence to integrate the features of both MSF and RFE. The experimental results demonstrate that our model achieves state-of-the-art performance under smaller parameter settings and fast inference conditions.

关键词： Graph Neural Network Multi-Perspective Fusion Remote Feature Extraction Fast Inference

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Application of Business Intelligence to Analysis of Factors for the Prevention of Non-communicable Diseases 23rd

Application of Business Intelligence to Analysis of Factors ...

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23rd International Conference on computer Information Systems and Industrial Management (CISIM)

作者： Carlos Calabria-Sarmiento, Juan Meza-Gomez, Adonai Escorcia-Gutierrez, Jose Univ Simon Bolivar Dept Syst Engn Barranquilla 080002 Colombia Univ Costa Dept Computat Sci & Elect CUC Barranquilla 080002 Colombia

ISBN: (纸本)9783031711145;9783031711152

In Atlantico, Colombia, the Departmental Health Secretariat has been proactive in promoting healthy lifestyles to prevent non-communicable diseases (NCDs), adhering to the strategies outlined in the Health Action Plan (PAS) as recommended by the Ministry of Health's Promotion and Prevention and Epidemiology and Demography Directorates. However, the efficacy of these activities is often hampered by a reliance on external data sources or studies from regions with dissimilar health determinants. This paper highlights the role of business intelligence tools-including dimensional fact models, multidimensional databases, ETL (Extract, Transform and Load) processes, and data visualization dashboards-in enhancing local data analysis, thereby providing valuable insights for public health management and research within Atlantico. By leveraging accurate, localized data, this approach strengthens decision-making processes in NCD prevention. The study aims to ascertain the impact of a healthy diet and physical activity on preventing NCDs among Atlantico's vulnerable populations, utilizing a confirmatory research methodology that employs observation, surveys, and interviews within the constraints of the COVID-19 pandemic. The findings underscore the critical role of diet and exercise in NCD prevention and demonstrate the efficacy of employing digital analytical tools and business intelligence to inform user-centric health interventions. This research lays the groundwork for developing evidence-based, locally tailored NCD prevention strategies, marking a significant advancement in public health initiatives in Atlantico.

关键词： Non-communicable diseases prevention Business intelligence in healthcare Data analysis for public health Healthy lifestyle interventions Evidence-based health policy

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Reinforcement Learning Applied to the Dynamic Capacitated Profitable Tour Problem with Stochastic Requests 24th

Reinforcement Learning Applied to the Dynamic Capacitated Pr...

引用

24th International Conference on Computational science and Its Applications (ICCSA)

作者： Caspar, Marvin Wendt, Oliver Univ Kaiserslautern Landau Business Informat Syst & Operat Res BISOR Postfach 3049 D-67653 Kaiserslautern Germany

ISBN: (纸本)9783031646041;9783031646058

This paper investigates the Dynamic Capacitated Profitable Tour Problem with Stochastic Requests (DCPTPSR), a variant of the Traveling Salesman Problem (TSP) with profits. In the DCPTPSR, online decisions must be made for accepting and scheduling requests over a finite number of periods. Requests follow a discrete-time stochastic process, and each request is characterized by a location, demand, and prize. Accepted requests must be served on a TSP tour such that the collected prize minus the transportation costs becomes maximal. The DCPTPSR has practical applications in food delivery and less-thantruckload transportation, where requests arrive in an online fashion and immediate decisions about acceptance and scheduling must be made. We model the DCPTPSR by a Markov Decision Process (MDP) and propose a Stochastic Dynamic Programming (SDP) algorithm for solving the problem to optimality. Addressing the computational challenges involved in SDP, we present a framework that integrates Reinforcement Learning (RL) as an alternative solution method. We perform an extensive numerical study where instances with up to incoming 25 requests can be solved by SDP while our RL approach can be used to adequately solve instances with even up to 100 incoming requests. Particularly, the performance of the RL approach is very close to the optimal policy by SDP and outperforms both the first come first serve heuristic and the first accept traveling salesman algorithm. The latter algorithm accepts requests if the available capacity enables it and fulfills these demands in an optimal TSP tour afterward. Especially instances with scarce capacity show considerable potential for savings in request acceptance and transportation scheduling decisions if both decisions are made simultaneously.

关键词： Online Optimization Profitable Tour Problem Temporal Difference Learning Stochastic Dynamic Programming

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A Multitask Deep Learning Model for Voxel-Level Brain Age Estimation 14th

A Multitask Deep Learning Model for Voxel-Level Brain Age Es...

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14th International Workshop on Machine Learning in Medical Imaging (MLMI)

作者： Gianchandani, Neha Ospel, Johanna MacDonald, Ethan Souza, Roberto Univ Calgary Dept Biomed Engn Calgary AB Canada Univ Calgary Dept Elect & Software Engn Calgary AB Canada Univ Calgary Hotchkiss Brain Inst Calgary AB Canada Univ Calgary Dept Radiol Calgary AB Canada Univ Calgary Dept Clin Neurosci Calgary AB Canada

ISBN: (纸本)9783031456756;9783031456763

Global brain age estimation has been used as an effective biomarker to study the correlation between brain aging and neurological disorders. However, it fails to provide spatial information on the brain aging process. Voxel-level brain age estimation can give insights into how different regions of the brain age in a diseased versus healthy brain. We propose a multitask deep-learning-based model that predicts voxel-level brain age with a Mean Absolute Error (MAE) of 5.30 years on our test set (n=50) and 6.92 years on an independent test set (n = 359). The results of our model outperformed a recently proposed voxel-level age prediction model. The source code and pre-trained models will be made publicly available to make our research reproducible.

关键词： Brain Age Deep Learning Voxel-level predictions

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The Search Futures Workshop 46th

The Search Futures Workshop

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46th European Conference on Information Retrieval (ECIR)

作者： Azzopardi, Leif Clarke, Charles L. A. Kantor, Paul B. Mitra, Bhaskar Trippas, Johanne R. Ren, Zhaochun Univ Strathclyde Glasgow Lanark Scotland Univ Waterloo Waterloo ON Canada Rutgers State Univ New Brunswick NJ USA Microsoft Res Montreal PQ Canada RMIT Univ Melbourne Vic Australia Leiden Univ Leiden Netherlands

ISBN: (纸本)9783031560682;9783031560699

The field and community of Information Retrieval (IR) are changing and evolving in response to the latest developments and advances in Artificial Intelligence (AI) and research culture. As the field and community re-oriented and re-consider its positioning within computing and information sciences more generally - it is timely to gather and discuss more seriously our field's vision for the future - the challenges and threats that the community and field faces - along with the bold new research questions and problems that are arising and emerging as we re-imagine search. This workshop aims to provide a forum for the IR community to voice and discuss their concerns and pitch proposals for building and strengthening the field and community.

关键词： Search Information retrieval Artificial intelligence

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Obtaining (ε, δ)-Differential Privacy Guarantees When Using a Poisson Mechanism to Synthesize Contingency Tables

Obtaining (ε, δ)-Differential Privacy Guarantees When Usin...

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International Conference on Privacy in Statistical Databases (PSD)

作者： Jackson, James Mitra, Robin Francis, Brian Dove, Iain Univ Lancaster Lancaster England UCL Dept Stat Sci London England Off Natl Stat Titchfield England

ISBN: (纸本)9783031696503;9783031696510

We show that differential privacy type guarantees can be obtained when using a Poisson synthesis mechanism to protect counts in contingency tables. Specifically, we show how to obtain (epsilon, delta)-probabilistic differential privacy guarantees via the Poisson distribution's cumulative distribution function. We demonstrate this empirically with the synthesis of an administrative-type confidential database.

关键词： contingency tables differential privacy Poisson mechanism synthetic data

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Impact and Performance of Randomized Test-Generation Using Prolog 34th

Impact and Performance of Randomized Test-Generation Using P...

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34th International Symposium on Logic-Based Program Synthesis and Transformation (LOPSTR)

作者： Gelderie, Marcus Luff, Maximilian Peltzer, Maximilian Aalen Univ Appl Sci Beethovenstr 1 D-73430 Aalen Germany

ISBN: (纸本)9783031712937;9783031712944

We study randomized generation of sequences of test-inputs to a system using Prolog. Prolog is a natural fit to generate test-sequences that have complex logical inter-dependent structure. To counter the problems posed by a large (or infinite) set of possible tests, randomization is a natural choice. We study the impact that randomization in conjunction with SLD resolution have on the test performance. To this end, this paper proposes two strategies to add randomization to a test-generating program. One strategy works on top of standard Prolog semantics, whereas the other alters the SLD selection function. We analyze the mean time to reach a test-case, and the mean number of generated test-cases in the framework of Markov chains. Finally, we provide an additional empirical evaluation and comparison between both approaches.

关键词： software testing randomization prolog

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Graph-Based Data Representation and Prediction in Medical Domain Tasks Using Graph Neural Networks 24th

Graph-Based Data Representation and Prediction in Medical Do...

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24th International Conference on Computational science (ICCS)

作者： Sofiia, Vdovkina Ilya, Derevitskii Levon, Abramyan Aleksandra, Vatian ITMO Univ St Petersburg Russia Almazov Natl Med Res Ctr World Class Res Ctr Personalized Med St Petersburg 197341 Russia

ISBN: (纸本)9783031637711;9783031637728

Medical data often presents as a time series, reflecting the disease's progression. This can be captured through longitudinal health records or hospital treatment notes, encompassing diagnoses, health states, medications, and procedures. Understanding disease evolution is critical for effective treatment. Graph embedding of such data is advantageous, as it inherently captures entity relationships, offering significant utility in medicine. Hence, this study aims to develop a graph representation of Electronic Health Records (EHRs) and combine it with a method for predictive analysis of COVID-19 using network-based embedding. Evaluation of Graph Neural Networks (GNNs) against Recurrent Neural Networks (RNNs) reveals superior performance of GNNs, underscoring their potential in medical data analysis and forecasting.

关键词： Graph neural networks data representation electronic health records

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Execution at RISC: Stealth JOP Attacks on RISC-V Applications

Execution at RISC: Stealth JOP Attacks on RISC-V Application...

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28th European Symposium on Research in computer Security (ESORICS)

作者： Buckwell, Loic Gilles, Olivier Perez, Daniel Gracia Kosmatov, Nikolai Thales Res & Technol Palaiseau France

ISBN: (纸本)9783031541285;9783031541292

RISC-V is a recently developed open instruction set architecture gaining a lot of attention. To improve the security of these systems and design efficient countermeasures, a better understanding of vulnerabilities to novel and future attacks is mandatory. This paper demonstrates that RISC-V is sensible to Jump-Oriented Programming, a class of complex code-reuse attacks. We provide an analysis of new dispatcher gadgets we discovered, and show how they can be used together to build a stealth attack, bypassing existing protections. We implemented a proof-of-concept attack on an embedded web server compiled for RISC-V, in which we introduced a vulnerability allowing an attacker to read an arbitrary file from the remote host machine.

关键词： Control-Flow Integrity Code-Reuse Attacks Embedded Systems RISC-V

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The Edifying Impact of Blending Game-Based Learning with Educational Robotics: A Systematic Review of Empirical Evidence 16th

The Edifying Impact of Blending Game-Based Learning with Edu...

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16th International Conference on Social Computing and Social Media (SCSM) Held as Part of the 26th International Conference on Human-computer Interaction (HCII)

作者： Kasenides, Nicos Piki, Andriani Paspallis, Nearchos Univ Cent Lancashire Cyprus Larnax Cyprus

ISBN: (纸本)9783031613043;9783031613050

Game-based learning is an effective pedagogical approach with a demonstrated capacity to activate learner engagement, inspire motivation, and enhance the overall learning experience. The application of educational robotics has also attracted a lot of attention in recent years across educational levels and domains. Despite their appeal and the positive learning outcomes associated with such innovative pedagogies, the synergistic edifying impact of blending them remains largely unexplored. The aim of this study is to present a synthesis of empirical evidence on game-based learning and educational robotics. A systematic literature review is conducted focusing on empirical research published between 2019 and 2023. The analysis reveals prevalent methodological approaches and pedagogical theories framing learning and instruction, as well as the most widely employed robotics and gaming platforms. The study sheds light not only on the benefits of embracing game-based learning and educational robotics, but also on the barriers and challenges associated with adopting such innovative pedagogies. Ultimately, the study attempts to portray the impact of these approaches on learning and transferable skills development.

关键词： Educational Robotics Game-Based Learning Pedagogical Frameworks Skills Competencies Empirical evidence

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