Digital Commons Network^™

Class Name Guided Out-Of-Scope Intent Classification, Chandan Gautam, Sethupathy Parameswaran, Aditya Kane, Yuan Fang, Savitha Ramasamy, Suresh Sundaram, Sunil Kumar Sahu, Xiaoli Li

Research Collection School Of Computing and Information Systems

The paper introduces Semantics of Class Labelbased Unsupervised Out of Scope Intent Detection (SCOOS), a novel method aimed at enhancing out-of-scope (OOS) intent classification in task-oriented dialogue systems. Unlike prior approaches that rely solely on indomain (ID) data features, SCOOS leverages semantic cues embedded in class labels to improve classification accuracy. The method entails forming a compact feature space centered around the semantics of class labels by minimizing losses between ID features and class names. SCOOS achieves this by creating a compact feature space centered around class label semantics, achieved through minimizing losses between in-domain (ID) features and class names. …

Ultra-High Resolution Image Segmentation Via Locality-Aware Context Fusion And Alternating Local Enhancement, Wenxi Liu, Qi Li, Xindai Lin, Weixiang Yang, Shengfeng He, Yuanlong Yu

Research Collection School Of Computing and Information Systems

Ultra-high resolution image segmentation has raised increasing interests in recent years due to its realistic applications. In this paper, we innovate the widely used high-resolution image segmentation pipeline, in which an ultra-high resolution image is partitioned into regular patches for local segmentation and then the local results are merged into a high-resolution semantic mask. In particular, we introduce a novel locality-aware context fusion based segmentation model to process local patches, where the relevance between local patch and its various contexts are jointly and complementarily utilized to handle the semantic regions with large variations. Additionally, we present the alternating local enhancement …

Irl For Restless Multi-Armed Bandits With Applications In Maternal And Child Health, Gauri Jain, Pradeep Varakantham, Haifeng Xu, Aparna Taneja, Prashant Doshi, Milind Tambe

Research Collection School Of Computing and Information Systems

Public health practitioners often have the goal of monitoring patients and maximizing patients’ time spent in “favorable” or healthy states while being constrained to using limited resources. Restless multi-armed bandits (RMAB) are an effective model to solve this problem as they are helpful to allocate limited resources among many agents under resource constraints, where patients behave differently depending on whether they are intervened on or not. However, RMABs assume the reward function is known. This is unrealistic in many public health settings because patients face unique challenges and it is impossible for a human to know who is most deserving …

Lr-Auth: Towards Practical Implementation Of Implicit User Authentication On Earbuds, Changshuo Hu, Xiao Ma, Xinger Huang, Yiran Shen, Dong Ma

Research Collection School Of Computing and Information Systems

The increasing use of earbuds in applications like immersive entertainment and health monitoring necessitates effective implicit user authentication systems to preserve the privacy of sensitive data and provide personalized experiences. Existing approaches, which leverage physiological cues (e.g., jawbone structure) and behavioral cues (e.g., gait), face challenges such as limited usability, high delay and energy overhead, and significant computational demands, rendering them impractical for resource-constrained earbuds. To address these issues, we present LR-Auth, a lightweight, user-friendly implicit authentication system designed for various earbud usage scenarios. LR-Auth utilizes the modulation of sound frequencies by the user's unique occluded ear canal, generating user-specific …

Defending Large Language Models Against Jailbreak Attacks Via Layer-Specific Editing, Wei Zhao, Zhe Li, Yige Li, Jun Sun, Jun Sun

Research Collection School Of Computing and Information Systems

Large language models (LLMs) are increasingly being adopted in a wide range of realworld applications. Despite their impressive performance, recent studies have shown that LLMs are vulnerable to deliberately crafted adversarial prompts even when aligned via Reinforcement Learning from Human Feedback or supervised fine-tuning. While existing defense methods focus on either detecting harmful prompts or reducing the likelihood of harmful responses through various means, defending LLMs against jailbreak attacks based on the inner mechanisms of LLMs remains largely unexplored. In this work, we investigate how LLMs respond to harmful prompts and propose a novel defense method termed Layer-specific Editing (LED) …

Eyegraph : Modularity-Aware Spatio Temporal Graph Clustering For Continuous Event-Based Eye Tracking, Panahetipola Mudiyanselage Nuwan Bandara, Thivya Kandappu, Archan Misra, Ila Gokarn, Archan Misra

Research Collection School Of Computing and Information Systems

Continuous tracking of eye movement dynamics plays a significant role in developing a broad spectrum of human-centered applications, such as cognitive skills (visual attention and working memory) modeling, human-machine interaction, biometric user authentication, and foveated rendering. Recently neuromorphic cameras have garnered significant interest in the eye-tracking research community, owing to their sub-microsecond latency in capturing intensity changes resulting from eye movements. Nevertheless, the existing approaches for event-based eye tracking suffer from several limitations: dependence on RGB frames, label sparsity, and training on datasets collected in controlled lab environments that do not adequately reflect real-world scenarios. To address these limitations, in …

Cirp: Cross‑Item Relational Pre‑Training For Multimodal Product Bundling, Yunshan Ma, Yingzhi He, Wenjun Zhong, Xiang Wang, Roger Zimmermann, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Product bundling has been a prevailing marketing strategy that is beneficial in the online shopping scenario. Effective product bundling methods depend on high-quality item representations capturing both the individual items' semantics and cross-item relations. However, previous item representation learning methods, either feature fusion or graph learning, suffer from inadequate cross-modal alignment and struggle to capture the cross-item relations for cold-start items. Multimodal pre-train models could be the potential solutions given their promising performance on various multimodal downstream tasks. However, the cross-item relations have been under-explored in the current multimodal pre-train models.To bridge this gap, we propose a novel and simple …

Mm‑Forecast: A Multimodal Approach To Temporal Event Forecasting With Large Language Models, Haoxuan Li, Zhengmao Yang, Yunshan Ma, Yi Bin, Yang Yang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

We study an emerging and intriguing problem of multimodal temporal event forecasting with large language models. Compared to using text or graph modalities, the investigation of utilizing images for temporal event forecasting has not been fully explored, especially in the era of large language models (LLMs). To bridge this gap, we are particularly interested in two key questions of: 1) why images will help in temporal event forecasting, and 2) how to integrate images into the LLM-based forecasting framework. To answer these research questions, we propose to identify two essential functions that images play in the scenario of temporal event …

Adversarial Learning For Coordinate Regression Through K-Layer Penetrating Representation, Mengxi Jiang, Yulei Sui, Yunqi Lei, Xiaofei Xie, Cuihua Li, Yang Liu, Ivor W. Tsang

Research Collection School Of Computing and Information Systems

Adversarial attack is a crucial step when evaluating the reliability and robustness of deep neural networks (DNNs) models. Most existing attack approaches apply an end-to-end gradient update strategy to generate adversarial examples for a classification or regression problem. However, few of them consider the non-differentiable DNN models (e.g., coordinate regression model) that prevent end-to-end backpropagation resulting in the failure of gradient calculation. In this paper, we present a new adversarial example generation approach for both untargeted and targeted attacks on coordinate regression models with non-differentiable operations. The novelty of our approach lies in a k-layer penetrating representation, on which we …

Efficient Multiplicative-To-Additive Function From Joye-Libert Cryptosystem And Its Application To Threshold Ecdsa, Haiyang Xue, Ho Man Au, Mengling Liu, Yin Kwan Chan, Handong Cui, Xiang Xie, Hon Tsz Yuen, Chengru Zhang

Research Collection School Of Computing and Information Systems

Threshold ECDSA receives interest lately due to its widespread adoption in blockchain applications. A common building block of all leading constructions involves a secure conversion of multiplicative shares into additive ones, which is called the multiplicative-to-additive (MtA) function. MtA dominates the overall complexity of all existing threshold ECDSA constructions. Specifically, O(n2) invocations of MtA are required in the case of n active signers. Hence, improvement of MtA leads directly to significant improvements for all state-of-the-art threshold ECDSA schemes.In this paper, we design a novel MtA by revisiting the Joye-Libert (JL) cryptosystem. Specifically, we revisit JL encryption and propose a JL-based …

Developing Policies For Digital Twin Data Quality And Security Controls, Ahmad Abdelbaset Hassan

Theses

This thesis is concerned with the data quality and security of the digital twin and how it is going to impact its adoption, trustworthiness, and potential for real-world applications. By addressing the potential vulnerabilities and ensuring the integrity of data, this research aims to contribute to the development of robust and trustworthy digital twin policies that to complement the existing international standards across different domains. Moreover, it underscores the important need to establish robust policies to ensure the successful and secure deployment of digital twins across industries. Previous research, while valuable, may not have fully addressed the critical interplay between …

Substitution Tilings With Transcendental Inflation Factor, Dirk Frettlöh, Alexey Garber, Neil Mañibo

School of Mathematical & Statistical Sciences Faculty Publications

For any λ>2, we construct a substitution on an infinite alphabet which gives rise to a substitution tiling with inflation factor λ. In particular, we obtain the first class of examples of substitutive systems with transcendental inflation factors. We also show that both the associated subshift and tiling dynamical systems are strictly ergodic, which is related to the quasicompactness of the underlying substitution operator.

Advancing Crispr-Based Solutions For Covid-19 Diagnosis And Therapeutics, Roaa Hadi, Abhishek Poddar, Shivakuma Sonnaila, Venkata Suryanarayana Murthy Bhavaraju, Shilpi Agrawal

Chemistry & Biochemistry Faculty Publications and Presentations

Since the onset of the COVID-19 pandemic, a variety of diagnostic approaches, including RT-qPCR, RAPID, and LFA, have been adopted, with RT-qPCR emerging as the gold standard. However, a significant challenge in COVID-19 diagnostics is the wide range of symptoms presented by patients, necessitating early and accurate diagnosis for effective management. Although RT-qPCR is a precise molecular technique, it is not immune to false-negative results. In contrast, CRISPR-based detection methods for SARS-CoV-2 offer several advantages: they are cost-effective, time-efficient, highly sensitive, and specific, and they do not require sophisticated instruments. These methods also show promise for scalability, enabling diagnostic tests. …

Toward A Responsible Future: Recommendations For Ai-Enabled Clinical Decision Support, Steven Labkoff, Bilikis Oladimeji, Joseph Kannry, Anthony Solomonides, Russell Leftwich, Eileen Koski, Amanda L Joseph, Monica Lopez-Gonzalez, Lee A Fleisher, Kimberly Nolen, Sayon Dutta, Deborah R Levy, Amy Price, Paul J Barr, Jonathan D Hron, Baihan Lin, Gyana Srivastava, Nuria Pastor, Unai Sanchez Luque, Tien Thi Thuy Bui, Reva Singh, Tayler Williams, Mark G Weiner, Tristan Naumann, Dean F Sittig, Gretchen Purcell Jackson, Yuri Quintana

Faculty, Staff and Student Publications

BACKGROUND: Integrating artificial intelligence (AI) in healthcare settings has the potential to benefit clinical decision-making. Addressing challenges such as ensuring trustworthiness, mitigating bias, and maintaining safety is paramount. The lack of established methodologies for pre- and post-deployment evaluation of AI tools regarding crucial attributes such as transparency, performance monitoring, and adverse event reporting makes this situation challenging.

OBJECTIVES: This paper aims to make practical suggestions for creating methods, rules, and guidelines to ensure that the development, testing, supervision, and use of AI in clinical decision support (CDS) systems are done well and safely for patients.

MATERIALS AND METHODS: In May …

Analysis, Design, And Experimental Validation Of The Thermal Subsystem Of Alainsat-1 Cubesat, Ameereh Seyedzadeh

Theses

This thesis investigates the thermal management techniques of AlAinSat-1 during extremely harsh space conditions. It presents a comprehensive analysis of its thermal behavior using numerical techniques and validates the CubeSat's survivability in space via experimental testing. AlAinSat-1 is a nanocube-shaped satellite carrying Earth observation payloads, including RITA (Remote sensing and Interference detector with radiome-Try and vegetation Analysis) and Locana. Understanding how the CubeSat behaves in harsh space conditions and maintains thermal stability during its mission is critical to its success. The main aim of this study is to employ finite element analysis (FEA) techniques to simulate the thermal behavior of …

Design And Implementation Of Attitude Control System For Gnssas 6u Cubesat, Anoud Nasser Alkatheeri

Theses

This thesis presents the design and simulation of the Attitude Control System (ACS) for the GNSSaS 6U CubeSat, developed by the National Space Science and Technology Center (NSSTC) at UAE University. The CubeSat's mission is to enhance GNSS signal accuracy, which requires precise attitude control to achieve this objective. The ACS utilizes magnetorquers and reaction wheels for actuation. The primary objective of this thesis is to design an ACS that fulfills the mission’s performance requirements, including maintaining pointing accuracy in both fine and medium pointing modes. To achieve this, a comprehensive disturbance analysis was conducted, leading to the selection of …

Optimizing Uncertainty Estimation On Scientific Visualizations Using Learning Models, Clara Almeida, David Guerrero-Pantoja, Erik Pautsch, Silvio Rizzi, George Thiruvathukal, Maria Pantoja

Computer Science: Faculty Publications and Other Works

Scientific visualizations (SciVis) translate numerical and spatial data as images, enabling scientists to better understand the phenomena described by the data and gain insights that may be overlooked by statistical methods alone. In recent years, machine learning, particularly Deep Learning (DL), has driven significant advancements in SciVis. By combining classical numerical techniques with DL methods, we can create models that potentially achieve the accuracies needed for real-world industrial applications and simulations. However, DL models often exhibit undue confidence, particularly when faced with inputs that differ significantly from the data they were trained on, known as out-of-distribution (OOD) inputs. This can …

Study Of The Efficacy Of Neural Mobilizations To Improve Sensory And Functional Responses Of Lower Extremities In Older Adults With Diabetic Peripheral Neuropathy Using Plithogenic N-Superhypergraphs, Lisbeth J. Reales-Chacón, María E. Lucena De Ustáriz, Francisco J. Ustáriz-Fajardo, Andrea C. Peñafiel Luna, Gabriela J. Bonilla-Ayala, Pablo Djabayan-Djibeyan, José L. Erazo-Parra, Mónica A. Valdiviezo-Maygua

Neutrosophic Sets and Systems

Diabetic Peripheral Neuropathy (DPN) is an aggravation of diabetes mellitus, which consists of ulcers and loss of sensitivity in the lower limbs. In its most severe stage, the person's limb may be amputated, which causes disability. This is a disease with multiple causes and whose treatment requires several so-lutions, including drug treatment, physiotherapy, and re-education of the person to change to a healthy lifestyle, if necessary, where the need for adequate nutritional habits appears. It is due to such complexity that we selected the Plithogenic n- SuperHyperGraph as a tool to represent the collected data, which were processed with the …

Antibacterial And Antifungal Properties Of Hedyosmum Cuatrecazanum Occhioni Essential Oil: A Promising Natural Alternative Studied Using Neutrosophic Superhypersoft Sets, María Eugenia Lucena De Ustáriz, Francisco Javier Ustáriz-Fajardo, Adriana Monserrath Monge Moreno, Adriana Isabel Rodríguez Basantes, Lisbeth Josefina Reales Chacón, Rosa Elisa Cruz Tenempaguay, Verónica Paulina Cáceres Manzano, Nathaly Kassandra Moscoso Moreno

Neutrosophic Sets and Systems

Antimicrobial resistance is a global public health issue that, exacerbated by inappropriate antibiotic use, leads to reduced bactericidal effects, multidrug resistance, increased medical costs, prolonged hospital stays, and higher mortality rates. This study evaluates the antibacterial and antifungal activity of the es-sential oil of Hedyosmum cuatrecazanum Occhioni against various clinically relevant bacterial and fungal strains. The essential oil was extracted by hydrodistillation, yielding a 0.26% extract. Its components were characterized by gas chromatography coupled with mass spectrometry (GC-MS). The results demonstrate that the essential oil exhibits significant antibacterial activity, comparable to commercial antibiotics. Hedyosmum cuatrecazanum oil showed the highest antibacterial …

Gender Gap In Sme Ownership: An Analysis Of Female Entrepreneurship In Santa Elena Canton, Ecuador, Using Treesoft Sets, Lorena Gisella Reyes Tomala, Lilia Esther Valencia Cruzaty, Arturo Gustavo Benavides Rodríguez, Jacqueline Del Rocío Bacilio Bejeguen

Neutrosophic Sets and Systems

The paper we present here is an approach to the problem of gender inequality in the artisanal production association called ASOPROMAHER, located in Santa Elena Canton, Ecuador. It is a pretext to address the issue and extrapolate it to the current situation of the entire country. Without a doubt, gender inequality persists in Ecuador today, including an important sphere of economic development in the country such as entrepreneurship in SMEs. There are multiple issues to address in this situation, ranging from psycho-logical, economic, social, cultural, and political issues. Each of these spheres is related to each other in a complex …

Impact Of Irrigation Water Technification On Seven Directories Of The San Juan-Patoa River Using Plithogenic N-Superhypergraphs Based On Environmental Indicators In The Canton Of Pujilí, 2021, Yenson Vinicio Mogro Cepeda, Marco Antonio Riofrío Guevara, Emerson Javier Jácome Mogro, Rachele Piovanelli Tizano

Neutrosophic Sets and Systems

The study analyzed interactions among economic, environmental, and sociocultural factors in agricul-tural irrigation systems to evaluate and promote integral sustainability, using the application of plitho-genic n-SuperHyperGraphs. Significant interactions between vertex attributes were identified during the study's development. Results highlighted that optimizing economic profitability, implementing ecologi-cal pest management, and fostering social cohesion are essential for improving system sustainability. It is concluded that an interdisciplinary approach is critical for achieving comprehensive agricultural sus-tainability, proposing an evaluative model adaptable to local settings to strengthen sector resilience. Ad-ditionally, the application of plithogenic n-SuperHyperGraphs provides a useful tool across various study areas for management and …

Neutrosophic Statistical Analysis Of Cardiovascular Disease Prevention From A Physical Culture In Urban Communities, Dennys Raúl Dupotey Hernández, Darvin Manuel Ramíerez Guerra

Neutrosophic Sets and Systems

The research presented here considers the educational potential of physical activities for disease preven-tion. Its objective is to elaborate a physical-educational intervention strategy for the prevention of cardio-vascular diseases in urban communities in the department of Cundinamarca, Colombia. The research was carried out in the municipalities of Funza, Fusagasugá Albán, and Supatá in the department of Cundinamarca in Colombia, based on the theoretical foundations of prophylactic and therapeutic phys-ical culture and related sciences for disease prevention. Theoretical, empirical, and mathematical-statistical scientific methods were applied. In addition, neutrosophic techniques were used to validate the study. The results indicate an adequate level …

English For Specific Purposes In The Medical Sciences To Strengthen The Professional Profile Of The Higher Education Medicine Student: A Knowledge Representation Using Superhypersoft Sets, Oswaldo Edison García Brito, Andrea Sofía Ribadeneira Vacacela, Carmen Hortensia Sánchez Burneo, Mónica Cecilia Jimbo Galarza

Neutrosophic Sets and Systems

The English language is an important added value for the professional profile of students in all higher education courses due to the demands of the current century, in which mastering more than one lan-guage is undoubtedly one of the preponderant skills to access better and more opportunities within the workplace, especially in careers such as medicine. Therefore, the curricular design and the implementa-tion of a program focused on the development of the linguistic skills of the future health professional is pertinent and necessary to strengthen their professional profile; not only in the discourse at the level of talks, congresses, training, …

Uncertain Automata And Uncertain Graph Grammar, Takaaki Fujita, Florentin Smarandache

Neutrosophic Sets and Systems

Graph theory has been widely studied, resulting in numerous applications across various felds. Among its many topics, Automata and Graph Grammar have emerged as signifcant areas ofresearch. This paper delves into these concepts, emphasizing their adaptation o uncertainframeworks likeFuzzy, Neutro- sophic, Vague, Turiyam Neutrosophic, and Plithogenic systems. By integrating uncertaintyinto traditional graph theoretical models, the paper aims to address ongoing researchchallengesand expand thescope of thesemodels.

A Neutrosophic Framework For Artificial Immune Systems, Antonios Paraskevas, Florentin Smarandache

Neutrosophic Sets and Systems

Artificial immune systems (AIS) draw inspiration from the mechanisms of the natural immune system. They extract ideas from the functioning of the natural immune system in order to use them to build computer models to solve real-world problems. While traditional AIS models mimic the biological immune system’s capacity to distinguish between self and non-self entities, they often face challenges in environments where data may be incomplete or ambiguous. In this paper we introduce neutrosophic AIS that includes degrees of truthiness, falsehood and indeterminacy to better address ambiguity within pattern recognition tasks. Within this neutrosophic framework we discuss and redefine main …

Evaluation Of The Impact Of Government Audit On Quality Management, Based On Neutroalgebra Generated By The Prospector Function, Alexander Santos Silva Bernardo, Jeri Gloria Ramón Ruffner, David Sánchez Cruz, Patricia Padilla-Vento

Neutrosophic Sets and Systems

The present study focuses on the evaluation of the impact of government audit on quality management, using a novel approach based on the neutralalgebra generated by the PROSPECTOR function. The central question of the research lies in the need to improve the effectiveness of government audits to ensure more efficient and transparent quality management in public institutions. The relevance of this issue is accentuated in the current context, where the pressure to optimize government processes is crucial to guarantee efficiency and public responsibility. Although there are various studies on government auditing, a significant gap has been identified in the literature …

Neutrosophic Analysis Of Strategies To Improve Productivity In An Restaurant Chain, Efrén Gonzalo Montenegro Cueva, Milena Alejandra Napa Alcívar

Neutrosophic Sets and Systems

The study analyzed productivity in an Ecuadorian restaurant chain using a multicriteria approach within a neu-trosophic framework. The research focused on identifying effective strategies to optimize processes and address-ing specific challenges such as staff turnover and compensation management. The TOPSIS method, combined with bipolar neutrosophic numbers, was employed to evaluate a group of strategies based on high-impact crite-ria. The results highlighted the implementation of economic incentives, training and retention programs, and performance management systems as the most effective alternatives. In contrast, internal communication cam-paigns and flexible schedules were found to be less prioritized. The research demonstrated the utility of neutro-sophic …

Assessment Of Academic Integrity In University Students Using A Hybrid Fuzzy-Neutrosophic Model Under Uncertainty, Lorenzo Cevallos-Torres, Roger Martínez, Rosangela Caicedo-Quiroz, Rosa Hernández-Magallanes, Douglas Iturburu-Salvador, Franklin Parrales-Bravo, Maikel Leyva-Vázquez

Neutrosophic Sets and Systems

This study presents a model based on a fuzzy-neutrosophic approach to evaluate the academic integrity of university students in the context of uncertainty. The central issue of the research lies in the abrupt transition from in-person education to virtual learning caused by the pandemic, which increased fraudulent behaviors in teaching and learning environments, making it difficult to determine the level of academic integrity among students in higher education institutions. To address this issue, variables related to dishonesty—motivation, justification, and opportunities—were analyzed. Three evaluation scenarios were conducted, yielding positive results. The proposed approach, based on fuzzy logic and neutrosophy, effectively measured …

Applying Neutrosophic Natural Language Processing To Analyze Complex Phenomena In Interdisciplinary Contexts, Diego Fernando Coka Flores, Ignacio Fernando Barcos Arias, María Elena Infante Miranda, Omar Mar Cornelio

Neutrosophic Sets and Systems

The paper addresses the challenge of integrating natural language processing (NLP) with neutrosophic logic to solve problems where uncertainty, ambiguity, and indeterminacy play a critical role. This approach becomes particularly relevant in an increasingly digitalized world, where human-machine interaction requires a deeper understanding of linguistic and contextual nuances. Despite advances in conventional NLP, many existing methodologies lack tools to effectively manage undefined or contradictory elements present in real data. In this context, neutrosophic logic offers an innovative framework to model and analyze complex and multifaceted human perceptions. The study applies a methodology that combines advanced NLP techniques with neutrosophic analytical …

Using The Neutrosophic N-Ahp Method To Prioritize Variables And Expert Analysis, Oswaldo Lıber Andrade Salazar, Lenı Cecılıa Campaña Muñoz, Second Huacón Farm, Angel Braulio Martinez Vasquez

Neutrosophic Sets and Systems

The study focuses on addressing a growing challenge in complex decision-making: how to prioritize key variables in an environment of inherent uncertainty and contradiction. In this context, the research explores the implementation of the neutrosophic N-AHP method, a variant of Hierarchical Process Analysis (HPA) that incorporates neutrosophic logic to handle ambiguity and indeterminacy in the evaluation of critical variables. Although traditional HPA has been widely used to prioritize options, it has been limited in contexts where expert perceptions are not clear-cut or are marked by high levels of uncertainty. This work seeks to overcome those limitations, proposing a more flexible …