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...
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.
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...
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.
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...
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.
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....
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.
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-o...
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.
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 di...
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.
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 pose...
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.
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, medi...
详细信息
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.
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 ...
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.
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 rob...
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.
暂无评论