bluuebunny/crossref_metadata_2025 huggingface dataset:
Towards Highly Realistic Artistic Style Transfer via Stable Diffusion with Step-aware and Layer-aware Prompt
Authors: Zhang Zhanjie , Zhang Quanwei , Lin Huaizhong , Xing Wei , Mo Juncheng , Huang Shuaicheng , Xie Jinheng , Li Guangyuan , Luan Junsheng , Zhao Lei , Zhang Dalong , Chen Lixia
Published: July 2024

Artistic style transfer aims to transfer the learned artistic style onto an arbitrary content image, generating artistic stylized images. Existing generative adversarial network-based methods fail to generate highly realistic stylized images and always introduce obvious artifacts and disharmonious patterns. Recently, large-scale pre-trained diffusion models opened up a new way for generating highly realistic artistic stylized images. However, diffusion model-based methods generally fail to preserve the content structure of input content images well, introducing some undesired content structure and style patterns. To address the above problems, we propose a novel pre-trained diffusion-based artistic style transfer method, called LSAST, which can generate highly realistic artistic stylized images while preserving the content structure of input content images well, without bringing obvious artifacts and disharmonious style patterns. Specifically, we introduce a Step-aware and Layer-aware Prompt Space, a set of learnable prompts, which can learn the style information from the collection of artworks and dynamically adjusts the input images' content structure and style pattern.

To train our prompt space, we propose a novel inversion method, called Step-ware and Layer-aware Prompt Inversion, which allows the prompt space to learn the style information of the artworks collection.

In addition, we inject a pre-trained conditional branch of ControlNet into our LSAST, which further improved our framework's ability to maintain content structure.

Extensive experiments demonstrate that our proposed method can generate more highly realistic artistic stylized images than the state-of-the-art artistic style transfer methods. Code is available at https://github.com/Jamie-Cheung/LSAST.
Human-Agent Cooperation in Games under Incomplete Information through Natural Language Communication
Authors: Chen Shenghui , Fried Daniel , Topcu Ufuk
Published: July 2024

Developing autonomous agents that can strategize and cooperate with humans under information asymmetry is challenging without effective communication in natural language. We introduce a shared-control game, where two players collectively control a token in alternating turns to achieve a common objective under incomplete information. We formulate a policy synthesis problem for an autonomous agent in this game with a human as the other player. To solve this problem, we propose a communication-based approach comprising a language module and a planning module. The language module translates natural language messages into and from a finite set of flags, a compact representation defined to capture player intents. The planning module leverages these flags to compute a policy using an asymmetric information-set Monte Carlo tree search with flag exchange algorithm we present. We evaluate the effectiveness of this approach in a testbed based on Gnomes at Night, a search-and-find maze board game. Results of human subject experiments show that communication narrows the information gap between players and enhances human-agent cooperation efficiency with fewer turns.
Data Ownership and Privacy in Personalized AI Models in Assistive Healthcare
Authors: Debic Boris , Medvidovic Luka
Published: July 2024

The use of personalized artificial intelligence (AI) models in assistive healthcare presents a number of ethical and legal challenges, they are examined in this paper. We look at a number of situations in which AI models have close interactions with persons under care. In particular we are interested in the question of model and data ownership, privacy, and ethical implications. The paper also surveys the existing regulatory environment, including the US Congress's legislative initiatives and the US Federal Trade Commission's examination of AI. Additionally, it covers AI strategies such as modular AI and discusses possible solutions to the issues described. We present an overview of the current outstanding problems and with this work offer a researched and organized contribution for a public discussion of responsible application of AI in this field of healthcare. Our selection of topics is guided by keeping in mind the key stakeholders: technology providers, healthcare or care providers, care beneficiaries and their families.
Lifted Planning: Recent Advances in Planning Using First-Order Representations
Authors: Corrêa Augusto B. , De Giacomo Giuseppe
Published: July 2024

Lifted planning is usually defined as planning directly over a first-order representation. From the mid-1990s until the late 2010s, lifted planning was sidelined, as most of the state-of-the-art planners first ground the task and then solve it using a propositional representation. Moreover, it was unclear whether lifted planners could scale. But as planning problems become harder, they also become infeasible to ground. Recently, lifted planners came back into play, aiming at problems where grounding is a bottleneck. In this work, we survey recent advances in lifted planning. The main techniques rely either on state-space search or logic satisfiability. For lifted search-based planners, we show the direct connections to other areas of computer science, such as constraint satisfaction problems and databases. For lifted planners based on satisfiability, the advances in modeling are crucial to their scalability. We briefly describe the main planners available in the literature and their techniques.
A Survey of Multimodal Sarcasm Detection
Authors: Farabi Shafkat , Ranasinghe Tharindu , Kanojia Diptesh , Kong Yu , Zampieri Marcos
Published: July 2024

Sarcasm is a rhetorical device that is used to convey the opposite of the literal meaning of an utterance. Sarcasm is widely used on social media and other forms of computer-mediated communication motivating the use of computational models to identify it automatically. While the clear majority of approaches to sarcasm detection have been carried out on text only, sarcasm detection often requires additional information present in tonality, facial expression, and contextual images. This has led to the introduction of multimodal models, opening the possibility to detect sarcasm in multiple modalities such as audio, images, text, and video. In this paper, we present the first comprehensive survey on multimodal sarcasm detection - henceforth MSD - to date. We survey papers published between 2018 and 2023 on the topic, and discuss the models and datasets used for this task. We also present future research directions in MSD.
Robust Counterfactual Explanations in Machine Learning: A Survey
Authors: Jiang Junqi , Leofante Francesco , Rago Antonio , Toni Francesca
Published: July 2024

Counterfactual explanations (CEs) are advocated as being ideally suited to providing algorithmic recourse for subjects affected by the predictions of machine learning models. While CEs can be beneficial to affected individuals, recent work has exposed severe issues related to the robustness of state-of-the-art methods for obtaining CEs. Since a lack of robustness may compromise the validity of CEs, techniques to mitigate this risk are in order. In this survey, we review works in the rapidly growing area of robust CEs and perform an in-depth analysis of the forms of robustness they consider. We also discuss existing solutions and their limitations, providing a solid foundation for future developments.
Automated Essay Scoring: Recent Successes and Future Directions
Authors: Li Shengjie , Ng Vincent
Published: July 2024

Automated essay scoring (AES), the task of automatically assigning a score to an essay that summarizes its quality, is a challenging task that remains largely unsolved despite more than 50 years of research. This survey paper discusses the milestones in AES research and reflects on future directions.
Parameterized Complexity of Kidney Exchange Revisited
Authors: Hébert-Johnson Úrsula , Lokshtanov Daniel , Sonar Chinmay , Surianarayanan Vaishali
Published: July 2024

As of January 2023, there are more than 90,000 people on the national transplant waiting list in need of a kidney in the United States. These patients often have a friend or family member who is willing to donate, but whose kidney type might not be compatible. To help match these patients to suitable donors, patient-donor compatibility can be modeled as a directed graph. Specifically, in the Kidney Exchange problem, the input is a directed graph G, a subset B of vertices (altruistic donors), and two integers l_p and l_c. An altruistic donor is a donor who is not paired with a patient, and the remaining vertices are patient-donor pairs. Whenever a donor is compatible with a patient from a patient-donor pair, we place a directed edge from the donor vertex to the patient-donor pair. Here the donor vertex can be either altruistic or non-altruistic. The goal is to find a collection of vertex-disjoint cycles and paths covering the maximum number of patients such that each cycle has length at most l_c, and such that each path has length at most l_p and begins at a vertex in B. The path and cycle lengths are bounded so that the surgeries for a given path or cycle can be performed simultaneously.

Kidney Exchange has received a great deal of attention in recent years. We contribute to this line of work by closing two open problems from IJCAI '18 and IJCAI '22: "Is Kidney Exchange FPT when parameterized by (i) the treewidth (omega) of G and (ii) the number of vertex types in G?'' Two vertices have the same vertex type if they have the same in- and out-neighborhoods. We show that Kidney Exchange is FPT parameterized by the number of vertex types. On the other hand, we show W[1]-hardness with respect to omega. We also design a randomized 4^t * n^O(1)-time algorithm parameterized by t, the number of patients helped, significantly improving upon the previous state of the art, which was 161^t * n^O(1).
Safety of Multimodal Large Language Models on Images and Text
Authors: Liu Xin , Zhu Yichen , Lan Yunshi , Yang Chao , Qiao Yu
Published: July 2024

Attracted by the impressive power of Multimodal Large Language Models (MLLMs), the public is increasingly utilizing them to improve the efficiency of daily work. Nonetheless, the vulnerabilities of MLLMs to unsafe instructions bring huge safety risks when these models are deployed in real-world scenarios. In this paper, we systematically survey current efforts on the evaluation, attack, and defense of MLLMs' safety on images and text. We begin with introducing the overview of MLLMs on images and text and understanding of safety, which helps researchers know the detailed scope of our survey. Then, we review the evaluation datasets and metrics for measuring the safety of MLLMs. Next, we comprehensively present attack and defense techniques related to MLLMs' safety. Finally, we analyze several unsolved issues and discuss promising research directions. The relevant papers are collected at "https://github.com/isXinLiu/Awesome-MLLM-Safety".
Label Leakage in Vertical Federated Learning: A Survey
Authors: Liu Yige , Lou Yiwei , Liu Yang , Cao Yongzhi , Wang Hanpin
Published: July 2024

Vertical federated learning (VFL) is a distributed machine learning paradigm that collaboratively trains models using passive parties with features and an active party with additional labels. While VFL offers privacy preservation through data localization, the threat of label leakage remains a significant challenge. Label leakage occurs due to label inference attacks, where passive parties attempt to infer labels for their privacy and commercial value. Extensive research has been conducted on this specific VFL attack, but a comprehensive summary is still lacking. To bridge this gap, our paper aims to survey the existing label inference attacks and defenses. We propose two new taxonomies for both label inference attacks and defenses, respectively. Beyond summarizing the current state of research, we highlight techniques that we believe hold potential and could significantly influence future studies. Moreover, experimental benchmark datasets and evaluation metrics are summarized to provide a guideline for subsequent work.
Graph Neural Networks for Brain Graph Learning: A Survey
Authors: Luo Xuexiong , Wu Jia , Yang Jian , Xue Shan , Beheshti Amin , Sheng Quan Z. , McAlpine David , Sowman Paul , Giral Alexis , Yu Philip S.
Published: July 2024

Exploring the complex structure of the human brain is crucial for understanding its functionality and diagnosing brain disorders. Thanks to advancements in neuroimaging technology, a novel approach has emerged that involves modeling the human brain as a graph-structured pattern, with different brain regions represented as nodes and the functional relationships among these regions as edges. Moreover, graph neural networks (GNNs) have demonstrated a significant advantage in mining graph-structured data. Developing GNNs to learn brain graph representations for brain disorder analysis has recently gained increasing attention. However, there is a lack of systematic survey work summarizing current research methods in this domain. In this paper, we aim to bridge this gap by reviewing brain graph learning works that utilize GNNs. We first introduce the process of brain graph modeling based on common neuroimaging data. Subsequently, we systematically categorize current works based on the type of brain graph generated and the targeted research problems. To make this research accessible to a broader range of interested researchers, we provide an overview of representative methods and commonly used datasets, along with their implementation sources. Finally, we present our insights on future research directions. The repository of this survey is available at https://github.com/XuexiongLuoMQ/Awesome-Brain-Graph-Learning-with-GNNs.
X-former Elucidator: Reviving Efficient Attention for Long Context Language Modeling
Authors: Miao Xupeng , Zhu Shenhan , Fu Fangcheng , Guo Ziyu , Yang Zhi , Tu Yaofeng , Jia Zhihao , Cui Bin
Published: July 2024

Transformer-based LLMs are becoming increasingly important in various AI applications. However, apart from the success of LLMs, the explosive demand of long context handling capabilities is a key and in-time problem for both academia and industry.

Due to the limitations from the quadratic complexity of the attention mechanism, long context scenarios require much more resources for LLM development and deployment, bringing huge challenges to the underlying AI infrastructure.

Meanwhile, we observe that there is a trend of reviving previous efficient attention mechanisms to latest LLMs.

However, it still remains an open question about how to select from these diverse approaches in practice.

In this paper, we answer this question from several aspects.

First, we revisit these latest long-context LLM innovations and discuss their relationship with prior approaches with a novel and comprehensive taxonomy.

Next, we conduct a thorough evaluation over various types of workloads considering both efficiency and effectiveness.

Finally, we provide an in-depth analysis, summarize our key findings, and offer insightful suggestions on the trade-offs of designing and deploying efficient attention mechanisms for Transformer-based LLMs.
A Comprehensive Survey of Cross-Domain Policy Transfer for Embodied Agents
Authors: Niu Haoyi , Hu Jianming , Zhou Guyue , Zhan Xianyuan
Published: July 2024

The burgeoning fields of robot learning and embodied AI have triggered an increasing demand for large quantities of data. However, collecting sufficient unbiased data from the target domain remains a challenge due to costly data collection processes and stringent safety requirements. Consequently, researchers often resort to data from easily accessible source domains, such as simulation and laboratory environments, for cost-effective data acquisition and rapid model iteration. Nevertheless, the environments and embodiments of these source domains can be quite different from their target domain counterparts, underscoring the need for effective cross-domain policy transfer approaches. In this paper, we conduct a systematic review of existing cross-domain policy transfer methods. Through a nuanced categorization of domain gaps, we encapsulate the overarching insights and design considerations of each problem setting. We also provide a high-level discussion about the key methodologies used in cross-domain policy transfer problems. Lastly, we summarize the open challenges that lie beyond the capabilities of current paradigms and discuss potential future directions in this field.
A Survey on Network Alignment: Approaches, Applications and Future Directions
Authors: Saxena Shruti , Chandra Joydeep
Published: July 2024

Network alignment, the task of mapping corresponding nodes across networks, is attracting more attention for cross-network analysis in diverse domains, including social, biological, and co-authorship networks. Although a variety of methods have been proposed, we lack a holistic understanding of the approaches and applications. Our survey aims to bridge this gap by first proposing a taxonomy of network alignment, characterizing existing approaches, and then systematically summarizing and reviewing their performance and highlighting their scopes for future development. Finally, we discuss some important applications and give directions for future research within this domain.
Inferring Ontological Categories of OWL Classes Using Foundational Rules (Extended Abstract)
Authors: Barcelos Pedro Paulo F. , Prince Sales Tiago , Romanenko Elena , Almeida João Paulo A. , Engelberg Gal , Klein Dan , Guizzardi Giancarlo
Published: July 2024

Several efforts that leverage the tools of formal ontology have demonstrated the fruitfulness of considering key metaproperties of classes in ontology engineering. Despite that, it is still a common practice to apply representation schemes and approaches--such as OWL--that do not benefit from identifying ontological categories, and simply treat all classes in the same manner. In the original study, we proposed an approach to support the automated classification of classes into the ontological categories underlying the (g)UFO foundational ontology. We proposed a set of inference rules derived from (g)UFO's axiomatization that, given an initial classification of the classes in an OWL ontology, supports the inference of the classification for the remaining classes in the ontology. We formalized these rules, implemented them in a tool, and assessed them against a catalog of ontologies.
Why Only Text: Empowering Vision-and-Language Navigation with Multi-modal Prompts
Authors: Hong Haodong , Wang Sen , Huang Zi , Wu Qi , Liu Jiajun
Published: July 2024

Current Vision-and-Language Navigation (VLN) tasks mainly employ textual instructions to guide agents. However, being inherently abstract, the same textual instruction can be associated with different visual signals, causing severe ambiguity and limiting the transfer of prior knowledge in the vision domain from the user to the agent. To fill this gap, we propose Vision-and-Language Navigation with Multi-modal Prompts (VLN-MP), a novel task augmenting traditional VLN by integrating both natural language and images in instructions. VLN-MP not only maintains backward compatibility by effectively handling text-only prompts but also consistently shows advantages with different quantities and relevance of visual prompts. Possible forms of visual prompts include both exact and similar object images, providing adaptability and versatility in diverse navigation scenarios. To evaluate VLN-MP under a unified framework, we implement a new benchmark that offers: (1) a training-free pipeline to transform textual instructions into multi-modal forms with landmark images; (2) diverse datasets with multi-modal instructions for different downstream tasks; (3) a novel module designed to process various image prompts for seamless integration with state-of-the-art VLN models. Extensive experiments on four VLN benchmarks (R2R, RxR, REVERIE, CVDN) show that incorporating visual prompts would significantly boost navigation performance. While maintaining efficiency with text-only prompts, VLN-MP enables agents to navigate in the pre-explore setting and outperform text-based models, showing its broader applicability. Code is available at https://github.com/honghd16/VLN-MP.
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry (Extended Abstract)
Authors: Wiese Jonas Gregor , Wimmer Lisa , Papamarkou Theodore , Bischl Bernd , Guennemann Stephan , Ruegamer David
Published: July 2024

Bayesian inference in deep neural networks is challenging due to the high-dimensional, strongly multi-modal parameter posterior density landscape. Markov chain Monte Carlo approaches asymptotically recover the true posterior but are considered prohibitively expensive for large modern architectures. We argue that the dilemma between exact-but-unaffordable and cheap-but-inexact approaches can be mitigated by exploiting symmetries in the posterior landscape. We show theoretically that the posterior predictive density in Bayesian neural networks can be restricted to a symmetry-free parameter reference set. By further deriving an upper bound on the number of Monte Carlo chains required to capture the functional diversity, we propose a straightforward approach for feasible Bayesian inference.
Negative Human Rights as a Basis for Long-term AI Safety and Regulation (Abstract Reprint)
Authors: Bajgar Ondrej , Horenovsky Jan
Published: July 2024

If autonomous AI systems are to be reliably safe in novel situations, they will need to incorporate general principles guiding them to recognize and avoid harmful behaviours. Such principles may need to be supported by a binding system of regulation, which would need the underlying principles to be widely accepted. They should also be specific enough for technical implementation. Drawing inspiration from law, this article explains how negative human rights could fulfil the role of such principles and serve as a foundation both for an international regulatory system and for building technical safety constraints for future AI systems.
On Mitigating the Utility-Loss in Differentially Private Learning: A New Perspective by a Geometrically Inspired Kernel Approach (Abstract Reprint)
Authors: Kumar Mohit , Moser Bernhard A. , Fischer Lukas
Published: July 2024

Privacy-utility tradeoff remains as one of the fundamental issues of differentially private machine learning. This paper introduces a geometrically inspired kernel-based approach to mitigate the accuracy-loss issue in classification. In this approach, a representation of the affine hull of given data points is learned in Reproducing Kernel Hilbert Spaces (RKHS). This leads to a novel distance measure that hides privacy-sensitive information about individual data points and improves the privacy-utility tradeoff via significantly reducing the risk of membership inference attacks. The effectiveness of the approach is demonstrated through experiments on MNIST dataset, Freiburg groceries dataset, and a real biomedical dataset. It is verified that the approach remains computationally practical. The application of the approach to federated learning is considered and it is observed that the accuracy-loss due to data being distributed is either marginal or not significantly high.
Mitigating robust overfitting via self-residual-calibration regularization (Abstract Reprint)
Authors: Liu Hong , Zhong Zhun , Sebe Nicu , Satoh Shin'ichi
Published: July 2024

Overfitting in adversarial training has attracted the interest of researchers in the community of artificial intelligence and machine learning in recent years. To address this issue, in this paper we begin by evaluating the defense performances of several calibration methods on various robust models. Our analysis and experiments reveal two intriguing properties: 1) a well-calibrated robust model is decreasing the confidence of robust model; 2) there is a trade-off between the confidences of natural and adversarial images. These new properties offer a straightforward insight into designing a simple but effective regularization, called Self-Residual-Calibration (SRC). The proposed SRC calculates the absolute residual between adversarial and natural logit features corresponding to the ground-truth labels. Furthermore, we utilize the pinball loss to minimize the quantile residual between them, resulting in more robust regularization. Extensive experiments indicate that our SRC can effectively mitigate the overfitting problem while improving the robustness of state-of-the-art models. Importantly, SRC is complementary to various regularization methods. When combined with them, we are capable of achieving the top-rank performance on the AutoAttack benchmark leaderboard.
Fairness and Optimization in Dynamic Multiagent Allocation Problems
Authors: Trabelsi Yohai
Published: July 2024

In many allocation problems, understanding individual agents' needs, wants, and tradeoffs is crucial for providing fair and efficient solutions. This paper begins with motivating applications and critical definitions. We review existing results, such as advising agents on relaxing restrictions for improved resource allocation, optimizing task allocation in online settings without rejection of a task, and more. We conclude by outlining three potential directions for future research.
Transforming Recommender Systems: Balancing Personalization, Fairness, and Human Values
Authors: Neidhardt Julia
Published: July 2024

Recent advancements in recommender systems highlight the importance of metrics beyond accuracy, including diversity, serendipity, and fairness. This paper discusses various aspects of modern recommender systems, focusing on challenges such as preference elicitation, the complexity of human decision-making, and multi-domain applicability. The integration of Generative AI and Large Language Models offers enhanced personalization capabilities but also raises concerns regarding transparency and fairness. This work examines ongoing research efforts aimed at developing transparent, fair, and contextually aware systems. Our approach seeks to prioritize user wellbeing and responsibility, contributing to a more equitable and functional digital environment through advanced technologies and interdisciplinary insights.
PyXAI: An XAI Library for Tree-Based Models
Authors: Audemard Gilles , Lagniez Jean-Marie , Marquis Pierre , Szczepanski Nicolas
Published: July 2024

PyXAI (Python eXplainable AI) is a Python library designed for providing explanations and cor- recting tree-based Machine Learning (ML) models. It is suited to decision trees, random forests, and boosted trees, when used for regression or classification tasks. In contrast to many model-agnostic approaches to XAI, PyXAI exploits the model it- self to generate explanations, ensuring them to be faithful. PyXAI includes several algorithms for the generation of explanations, which can be abductive or contrastive. PyXAI also includes algorithms for correcting tree-based models when their predictions conflict with pieces of user knowledge.
Design of a Data-driven Intervention Dashboard for SDG Localization
Authors: Bassin Pooja , G K Abraham , Srinivasa Srinath
Published: July 2024

The localization problem of the United Nations Sustainable Development Goals (SDGs) involves adopting strategies that are in tune with local conditions, to achieve a given SDG target. However, even within a given region, localized conditions may vary drastically. With increasing amounts of Open Government Data (OGD) being available, there is an opportunity to systematically address the localization problem by using predictive and prescriptive modeling techniques. This work presents a predictive and prescriptive modeling dashboard for the SDG indicator maternal deaths (MD) for the Indian state of Karnataka. The dashboard was created by examining a vast set of data points to focus on four factors that showed high correlations with that of MD. We then construct a multivariate linear regression model to showcase the differential impact that a given factor has on the indicator and identify prescribed values for different factors to achieve a given target value of the indicator. Finally, a budget allocation dashboard is also provided that helps policymakers allocate budgets to specific schemes to help operationalize these changes. This dashboard was built by combining data coming from five different OGD sources.
3D-FuM: Benchmarking 3D Molecule Learning with Functional Groups
Authors: Chen Tingwei , Chen Jianpeng , Zhou Dawei
Published: July 2024

Molecular graph representation learning plays a crucial role in various domains, such as drug discovery and chemical reaction prediction, where molecular graphs are typically depicted as 2D topological structures. However, recent insights highlight the critical role of 3D geometric information and functional groups in accurately predicting molecular properties, aspects often neglected in existing molecular graph benchmark datasets. To bridge the research gap, we introduce a comprehensive molecular learning benchmark named 3D-FUM, which incorporates both 3D geometric information and functional groups of a large number of molecules. 3D-FUM integrates 18 state-of-the-art algorithms and 19 evaluation metrics on three molecular learning tasks, including general molecule generation, conditional molecule generation, and property predictions. 3D-FUM, for the first time, take into consideration both 3D geometric information and molecular functional groups, which enables researchers and practitioners to effectively and impartially evaluate newly proposed methods in comparison to existing baselines across diverse datasets. Furthermore, we design a user interface for user-friendly interaction and development with the benchmark for evaluation metrics selection, parameter adjustment, and leaderboard comparison. To ensure accessibility and reproducibility, we opensource our benchmark 3D-FUM and experimental results at https://3dfunctiongroupmoleculedataset.github.io/3D-FuM/#/Home.
Indigenizar el Estado: visiones y prácticas de mujeres-indígenas en los albores del Estado Plurinacional de Bolivia
Authors: Gigena Andrea Ivanna
Published: July 2024

Con este artículo me propongo analizar la indigenización del Estado a partir de las visiones y las prácticas de mujeres-indígenas que ocuparon cargos de gestión en: la Unidad de Despatriarcalización del Ministerio de Culturas y el Ministerio de Desarrollo Productivo y Economía Plural del Estado Plurinacional de Bolivia.
Con indigenizar refiero a un conjunto de prácticas que buscaron trasladar al ámbito estatal rasgos singulares de los diferentes Pueblos y Naciones indígenas. Destaco, asimismo, que esta acción estuvo orientada a la politización de la plurinacionalidad en el Estado a partir de dimensiones propias de la esfera “doméstica” o “reproductiva”: idioma, comida, trabajo doméstico y relaciones familiares, entre otras.
Finalmente, reflexiono sobre el escaso tiempo que duraron estas prácticas y si esto se relaciona con la singularidad de las mismas y con la condición étnica/género de sus promotoras. También me pregunto si no pueden encontrarse aquí algunos indicios del malestar con el devenir el “proceso de cambio” en Bolivia, manifiesto entre diferentes sectores (como el plural movimiento indígena y el movimiento de mujeres y/o feministas).
El análisis está basado en información secundaria (documental) referida al “proceso de cambio” e información primaria: entrevistas en profundidad, historias de vida y observaciones de campo realizadas en Bolivia en el mes de octubre del año 2011.
Feminismos comunitarios territoriales de Abya Yala: mujeres organizadas contra las violencias y los despojos
Authors: Cruz Hernandez Delmy Tania
Published: July 2024

El artículo muestra la ruta epistémica que trazo para nombrar la existencia de los feminismos comunitarios territoriales de Abya Yala. El camino lo esbozo a partir de la compartición de experiencias de mujeres organizadas que luchan contra las violencias y los despojos de sus cuerpos-territorios en la frontera de Chiapas.
En un primer momento, sitúo la voz desde donde hablo y las experiencias  de las que he sido parte. Después dibujo algunas corrientes epistémicas de los feminismos de Abya Yala con las que he dialogado desde la práctica teórico-política. Finalmente formulo el significado de los feminismos comunitarios territoriales.
Editorial v28n48
Authors: Ceballos Molano Raquel
Published: July 2024

Estimados autores, árbitros y lectores. Iniciamos con este número, el año 36 de publicación de la Revista y queremos empezar haciendo un reconocimiento a nuestros editores, quienes desde el primer número en 1976, hicieron posible su  continuidad. Ellos son los profesores de la Facultad de Ciencias de la Administración, Milton Mora, Andrés Sevilla, Leonel Monroy, León Blank, Bernardo Barona, Jorge Burbano, Arcadio Guzmán, Edgar Varela Barrios, Javier Medina Vásquez, Diego Delgadillo, Augusto Rodríguez Orejuela, Rafael Carvajal Baeza, Jorge Eduardo Lemus, Enrique Jorge Ágreda, Mónica García Solarte, Karem Sánchez de Roldán, Benjamín Betancourt, Carlos Hernán González Campo y Raquel Ceballos Molano. Durante estos años de edición, expertos académicos aceptaron nuestra invitación para escribir sobre diversos aspectos de la administración de las organizaciones, abarcando temas de interés regional, nacional e internacional. Es así que, en los primeros números de la Revista los manuscritos indagaban por la estructura social y la administración, el papel social del administrador, las habilidades de comunicación, de prospectiva, manejo de personal, lo ambiental como exigencia de una mayor conciencia de la responsabilidad social de la empresa, nuevas metodologías de la enseñanza de la administración, vista desde la escuela tradicional crítica, la historia del desarrollo empresarial como campo de investigaciones de las Facultades de Administración. En ediciones posteriores, se desarrollaron temas sobre la descentralización municipal, reformas municipales, reflexiones sobre la calidad total, las políticas de apertura económica, modernización industrial y tecnológica, la deuda externa y el comercio exterior. En los números más recientes, los temas responden a desarrollos sobre prospectiva, planeación estratégica, educación en administración para América Latina, reforma del Estado, crisis de la educación en administración, humanismo y gestión, cultura empresarial, la formación del administrador para el siglo XXI, pasando en la actualidad a temas como el marketing, gestión del conocimiento, emprendimiento empresarial, propiedad intelectual, derecho comercial, nuevas tecnologías, propios de los desarrollos actuales de la ciencia de la administración.
Obtaining coffee mucilage microcapsules by spray drying using chemically modified banana starch
Authors: Ríos-Hernández Jenny , Chávez-Salazar Andrés , Restrepo-Montoya Erika M. , Castellanos-Galeano Francisco J. , Ospina-López Diana Y.
Published: July 2024

The growing global consumption of coffee has positioned Colombia as the third largest producer worldwide. However, the processing of coffee beans generates significant by-products, such as mucilage, which is rich in phenolic compounds with antioxidant activity. Inadequate disposal of this waste creates environmental problems and economic losses. This research aimed to develop high-quality coffee mucilage capsules using spray drying technology. Chemically modified banana starch with octenyl succinic anhydride (OSA) and commercial maltodextrin were used as coating materials to enhance the properties of the capsules. Physicochemical and functional characterization was performed on samples of fresh and filtered coffee mucilage (MF), concentrated mucilage (MC), concentrated mucilage with the addition of maltodextrin and OSA-modified starch (MCMA), and finally, spray-dried coffee mucilage powder (MP). The antioxidant capacity (AC) of the mucilage was evaluated throughout the processing stages. MF showed an AC of 179.2 µmol of trolox/100 g, while the final coffee mucilage powder (MP) showed a significantly higher AC of 5444.35 µmol of trolox/100 g. Additionally, the MP showed a stability index of 0.48, water activity (aw) of 0.19, solubility of 14.64%, and a high water retention capacity of 90.53%. Microscopic analysis revealed amorphous structures with an average size of 29.16 ± 2.12 µm in the MP. These findings highlight the potential of spray drying with food-grade matrices such as OSA-modified banana starch to encapsulate coffee mucilage, preserving its antioxidant capacity and creating a new functional food ingredient with water retention properties.
Editorial
Authors: Arboleda Franco Santiago Adolfo
Published: July 2024

Esta edición de la revista Praxis, Educación y Pedagogía ofrece a sus lectores una serie de artículos cuyo eje temático transversal, en consideración del Comité Editorial, está referido a los diversos entornos y estrategias de aprendizaje en el campo de la educación y la pedagogía. Las contribuciones de nuestros autores nos permiten presentar a los lectores, cuatro artículos de investigación y dos de reflexión, todos referidos, al contexto escolar y universitario, y al rol del maestro sobre un escenario conceptual. Con esta producción la revista aporta diversas perspectivas a la discusión actual sobre la transformación de la educación.
Filantropí­a católica y sociedad civil: los voluntarios mexicanos de San Vicente de Paúl, 1845-1910
Authors: Arrom Silvia Mariana
Published: July 2024

Las conferencias laicas de San Vicente de Paúl florecieron en México en la segunda mitad del siglo XIX como parte de un movimiento más amplio de renovación católica. Sus dos organizaciones, una de hombres y otra de mujeres, se dedicaron a asistir a los pobres al mismo tiempo que fortalecían la fe. Los voluntarios hicieron importantes contribuciones al desarrollo del sistema de educación, salubridad y asistencia mexicano. El número y extensión nacional de estos grupos revela una fuerte tradición de filantropía y voluntarismo entre las clases medias y altas. El éxito fenomenal de las Señoras de la Caridad en particular demuestra la feminización del trabajo de la caridad en la época, que ayudó a expandir los límites tradicionales de la esfera femenina en México.
¿Voluntariado, compasión o autorrealización?
Authors: Béjar Helena
Published: July 2024

Aunque el acto cotidiano de ayudar al otro aparezca como lo más natural, es de hecho un gesto social, diverso según las sociedades e históricamente fechado en su especificidad. Aquí se le interroga en rigurosos términos sociológicos, los que incluyen de manera novedosa la realización de entrevistas y consulta de fuentes diversas, inscribiéndose el resultado en una compleja reflexión sobre los sentimientos morales, el individualismo y la modernidad.
Demanda laboral industrial en el área metropolitana de Cali: un análisis entre 1995 y 2001
Authors: Castillo Caicedo Maribel
Published: July 2024

En el presente documento se estudia el comportamiento de la demanda laboral para el Área Metropolitana de Cali en el período 1995-2001, se utiliza la información de la Encuesta Anual Manufacturera del Dane para 17 sectores (3 dígitos CIIU), y se trabaja con un Modelo de Efectos Fijos en Datos de Panel para determinar la estructura de la demanda laboral en términos de la elasticidad empleo-salario y empleo producto. La estimación del modelo muestra que la estructura de la demanda laboral está muy acorde con la teoría con una elasticidad empleo-salario negativa y una elasticidad empleo-producto positiva.
La etnografía reflexiva en el campo de la migración del diario de una emigrante: la partida
Authors: Guerra Arias Faiselly
Published: July 2024

En este documento se presentan los movimientos migratorios internacionales como una de las dinámicas culturales más relevantes de la actualidad. De manera sintáctica, se alude a los contextos sociales de las migraciones. El ejercicio etnográfico sobre la migración colombiana a España muestra que las razones más relevantes para salir del país son la precariedad económica y la inestabilidad laboral.
Determinantes de la supervivencia de empresas industriales en el área metropolitana de Cali 1994-2003
Authors: Felipe Martínez Andrés
Published: July 2024

En este trabajo se analiza el horizonte vital de las empresas de la cohorte de 1994 hasta el año 2003. A partir de la estimación de un modelo de duración, se calculó la probabilidad de supervivencia y el nivel de riesgo de los establecimientos industriales. Se encontró que las empresas enfrentan mayores barreras a la supervivencia cuando el sector donde deciden operar se encuentra en una etapa madura del ciclo de vida. De igual modo, las empresas pertenecientes a sectores industriales no concentrados presentan bajos niveles de supervivencia.
Hebert A. Simon y la economía organizacional
Authors: Estrada Gallego Fernando
Published: July 2024

Este artículo desarrolla una evaluación de los aportes de Herbert A. Simon a la teoría de las organizaciones dando especial énfasis al criterio de racionalidad limitada (bounded rationality). Se interpreta la crítica de Simon a la versión ortodoxa de la burocracia organizacional y se extiende su análisis hacia la economía institucional. Uno de los principales logros de Simon en teoría organizacional consiste en haber valorado analíticamente la psicología del comportamiento individual y colectivo abriendo de este modo el terreno a las investigaciones posteriores de D. Kahneman, y T. Schelling.
Room for variation? The experience of Colombian gay asylum seekers and asylees in Miami
Authors: Avivi Yamil
Published: July 2024

Este artículo ofrece una narración de las lesbianas, homosexuales, bisexuales y transvestis colombianos que llegan a Miami (Florida) en busca de asilo debido a la persecución por orientación sexual. Se examina cómo estos migrantes negocian la identidad nacional, étnica y sexual. Estos migrantes particulares hacen parte del gran éxodo colombiano que comenzó en los noventa. ¿Cuáles evidencias sugieren que estos migrantes son marginados en el contexto de este fenómeno migratorio? ¿Con qué recursos y con cual apoyo institucional se cuenta para que estos migrantes puedan enfrentar las barreras migratorias?
Aprendizaje manufacturero, dependencia tecnológica y crecimiento económico: el caso colombiano
Authors: Ortiz Quevedo Carlos Humberto , Vásquez Castro Lina Marcela
Published: July 2024

Con base en una argumentación histórica sobre el desarrollo económico, en este trabajo se sustenta que el aprendizaje en la práctica del sector industrial manufacturero, el cambio estructural y la autonomía tecnológica se refuerzan mutuamente e inciden positiva y significativamente en el crecimiento económico. Se utilizan series de tiempo de las cuentas nacionales de Colombia para contrastar esta hipótesis
Causalidad temporal entre producto y exportaciones para Colombia: análisis sectorial
Authors: Rendón Vallejo Lina Viviana
Published: July 2024

Este trabajo investiga la relación existente entre el producto interno bruto y lasexportaciones en Colombia. Los resultados obtenidos indican que tanto las exportacionesmanufactureras como las primarias, descontando de las últimas las agroindustriales y cafeteras, causan al producto a la Granger, dando soporte a la hipótesis de crecimiento a travésdel incremento en este tipo de exportaciones.
Sobre el impacto de la infraestructura pública en el producto y el crecimiento económico: Colombia 1950-2000
Authors: Tenorio Jorge Andrés
Published: July 2024

Teniendo en cuenta el cambio estructural de la economía colombiana a partir de 1980, losefectos de escala de la educación sobre el capital humano, y el acervo de capital físico, se estimael impacto de la infraestructura pública en el PIB y en el crecimiento económico de Colombia.El análisis arroja evidencia significativa de complementariedades tecnológicas entre losfactores privados y públicos. La tendencia decreciente de la PTF que se manifiesta desde ladécada de los sesenta, es una razón fundamental de la tendencia decreciente del crecimientoeconómico de largo plazo, que se manifiesta desde la década de los setenta y se profundizadesde 1990.
Acción colectiva y región: Valle y Cauca (1995-1997)
Authors: Guzmán Barney Álvaro
Published: July 2024

A partir de una base de datos sobre hechos de Acción colectiva conflictiva regional para el período 1995-1997, se proponen algunas características de la sociedad que se sustentan en un primer análisis cuantitativo de los datos. Entre estas: el carácter fuertemente focalizado y violento del conflicto, en torno a pocos temas. El predominio del conflicto urbano, marcado por una criminalidad diversa, pero especialmente organizada, y la debilidad de las acciones colectivas no institucionales articuladas a movimientos sociales.
Acciones colectivas de las mujeres en contra de la guerra y por la paz en Colombia
Authors: Ibarra Melo María Eugenia
Published: July 2024

Este artículo describe y analiza las acciones colectivas de las mujeres en contra de la guerra y por la paz en Colombia. De igual modo, se estudia con las categorías de género y acción colectiva cómo esta participación política apoya otros procesos emancipatorios que implican interacciones estratégicas.
Guerra e identidad política: el campo de marte estadounidense postmoderno
Authors: Cairo Carou Heriberto
Published: July 2024

El presente artículo aborda el estudio de la faz interna de la ―guerra contra el terrorismo‖, tal como la ha definido la administración Bush. Haciendo uso de las nociones de Campus Martius y de herotopías (de Michel Foucault), el autor esboza una visión crítica de la política de seguridad interna en los Estados Unidos, luego de los atentados del 11 de septiembre.
Dinero, circuito, relaciones íntimas
Authors: Zelizer Viviana A.
Published: July 2024

La dicotomía entre las relaciones íntimas y las relaciones interpersonales se inscribe en una tradición de vieja data. Como alternativa a esta dicotomía, este artículo propone una aproximación basada en el reconocimiento de circuitos  interpersonales. Para ilustrar el alcance de este concepto, se ponen en consideración cuatro áreas: 1) los circuitos empresariales; 2) las monedas locales; 3) el cuidado personal pagado; y 4) la participación de los hijos en la economía familiar. La autora concluye que tanto las transacciones íntimas como las impersonales a menudo funcionan a través de circuitos que los participantes delimitan a partir de prácticas, comprensiones y representaciones claramente establecidas.
Inmigración y música latina en Barcelona: el papel de la música y el baile en procesos de reafirmación e hibridación cultural
Authors: Llano Camacho Isabel
Published: July 2024

La llegada de población procedente de Latinoamérica y la internacionalización de la salsa han propiciado la transformación del espacio sonoro, cultural y social de Barcelona en la última década, así como el desarrollo de ciertos procesos de reafirmación e hibridación cultural, que se evidencian al observar determinadas prácticas de consumo, producción y recepción musical, entre las que figuran ir a bailar en discotecas, oír la radio y participar en congresos de baile; cuestiones que aquí son analizadas
Músicas populares y músicas de estado: sobre una versión rock del himno nacional argentino
Authors: Buch Esteban
Published: July 2024

El destacado músico argentino Charly García produjo, para escándalo de muchos y satisfacción de algunos, una versión “rock” del himno nacional de su país. El presente texto analiza tal hecho, en el marco de las relaciones entre música, política y sociedad, en una argentina que salía de la época de las dictaduras militares de final del siglo XX.