Answer Passage Ranking Enhancement Using Shallow Linguistic Features

Sustainable decision making stands for decision making which contributes to the transition to a sustainable society. It raises a number of challenging problems for which existing decision support systems (DSS) may not be equipped. The role of DSS in sustainable decision making is considered. The different models of decision making and their appropriateness in sustainable decision making are discussed. Examples from the areas of water resources and energy planning and management are presented to illustrate some of the issues in sustainable decision making and the role of DSS. Conclusions include a suggested research program for further development of models of decision making and the development of DSS for use in sustainable decision making.

Purpose The objective of this study is to offer a comprehensive review of the role of decision support systems (DSS) in enhancing performance monitoring and decision-making in public administration (PA). The study aims to address the fragmented nature of existing literature on DSS in PA and provide a holistic overview of its applications and potential benefits. Design/methodology/approach A systematic literature review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was conducted, analyzing peer-reviewed studies from Scopus and Web of Science databases published over two decades (2004–2024). A thematic synthesis approach was used to align findings with three research questions concerning DSS significance, adoption barriers and emerging trends in public governance. Findings Our review demonstrates that DSS significantly enhances administrative efficiency, transparency and policy responsiveness, contributing to Sustainable Development Goal 16. However, the literature remains fragmented, with limited understanding of implementation barriers, impact measurement and governance improvements enabled by DSS. Emerging trends highlight the integration of artificial intelligence, blockchain technologies and ethical frameworks for decision automation. Originality/value The study offers a detailed synthesis of DSS applications in PA and outlines a forward-looking research agenda that addresses key adoption challenges and technology-led governance reforms. It also provides actionable guidance for policymakers and technology developers to support the strategic implementation of DSS across various administrative settings.

Representation Learning and Learning from Limited Labeled Data for Community Question Answering

BackgroundThe potential role of DSS in CVD prevention remains unclear as only a few studies report on patient outcomes for cardiovascular disease.Methods and ResultsA systematic review and meta-analysis of randomised controlled trials and observational studies was done using Medline, Embase, Cochrane Library, PubMed, Amed, CINAHL, Web of Science, Scopus databases; reference lists of relevant studies to 30 July 2011; and email contact with experts. The primary outcome was prevention of cardiovascular disorders (myocardial infarction, stroke, coronary heart disease, peripheral vascular disorders and heart failure) and management of hypertension owing to decision support systems, clinical decision supports systems, computerized decision support systems, clinical decision making tools and medical decision making (interventions). From 4116 references ten studies met our inclusion criteria (including 16,312 participants). Five papers reported outcomes on blood pressure management, one paper on heart failure, two papers each on stroke, and coronary heart disease. The pooled estimate for CDSS versus control group differences in SBP (mm of Hg) was - 0.99 (95% CI −3.02 to 1.04 mm of Hg; I2 = 0; p = 0.851).ConclusionsDSS show an insignificant benefit in the management and control of hypertension (insignificant reduction of SBP). The paucity of well-designed studies on patient related outcomes is a major hindrance that restricts interpretation for evaluating the role of DSS in secondary prevention. Future studies on DSS should (1) evaluate both physician performance and patient outcome measures (2) integrate into the routine clinical workflow with a provision for decision support at the point of care.

The study examined the relationship between strategic sensitivity and operations efficiency of communication companies in Nigeria, as well as the moderating role of decision support systems in this relationship. The study employed a cross-sectional survey methodology, using three major communication companies in Nigeria as the target population. Data were obtained through questionnaires and summarized in their respective groups. The study applied the inferential statistical tool of Spearman's rank order correlation to test the level of significance among variables. SPSS version 21.0 was utilized for the analysis. The study found that there is a significant relationship between strategic sensitivity and operations efficiency in communication companies in Nigeria. The analysis revealed a very strong positive correlation between these variables (rho = 0.915, P = 0.000), indicating that strategic sensitivity strongly contributes to improved levels of operational efficiency. Additionally, the research discovered that decision support systems play a positive moderating role in this relationship. The indirect relationship (R = 0.480) and direct relationship (R = 0.394) both showed positive correlations, with significant p-values (PV = 0.000 for indirect relationship, P = 0.12 for direct relationship). These findings led to the rejection of both null hypotheses, demonstrating that strategic sensitivity is crucial for operational efficiency and that decision support systems enhance this relationship. The study also noted that communication companies in Nigeria actively engage in practices reflecting strategic sensitivity (mean = 3.3343) and adequately express features of operational efficiency (mean = 3.2567), suggesting a strong emphasis on these aspects in the Nigerian communication sector. The study concludes that there is a positive relationship between strategic sensitivity and operations efficiency in communication companies in Nigeria. The study recommends that communication companies in Nigeria should prioritize and maintain their strategic sensitivity activities, given the significant relationship identified between strategic sensitivity and operations efficiency. Companies should invest in developing and refining their strategic sensitivity capabilities, including market analysis, trend forecasting, and adaptive planning. Furthermore, the study recommends that communication companies should focus on improving their managerial competence in handling strategic sensitivity over the long term. Companies should also consider implementing leadership development programs that emphasize strategic sensitivity and its practical application in operational contexts. Keywords: Strategic sensitivity, agility, customer satisfaction, operations efficiency, Communication Companies, Nigeria

Chapter 5 - Decision support systems for integrated water resources management with an application to the nile basin

Open-domain question answering is an emerging field of natural language processing. Application areas of question answering (QA) systems are increasing with the availability of new datasets, more powerful machines and easy access to voice-enabled devices that interact with humans in their daily lives. Users can get into interactions with QA systems without the need to be limited by any domain. However, there is still need for QA systems that can answer questions in a specific field such as international criminal law, violence of human rights or bill of rights. The recent approaches on open-domain question answering made it possible to extend open-domain QA systems towards specific fields. This paper presents Quill-QA, the new feature of the Quill Platform. Quill Platform is a web platform providing set of tools that digitize and visualize negotiated texts, any text in the form of formal negotiations. Quill-QA is based on Facebook's DrQA system and as a result it adopts a flexible Ranker-Reader architecture. Quill-QA answers questions addressed on the samples of negotiated texts such as U.S. Constitutional Convention of 1787, Utah State Constitutional Convention of 1895 and European Union Act 2017. Without limiting the scope of the input questions to a finite set of templates, Quill-QA is the first QA system that focuses on negotiated texts.

The Role of Decision Support Systems in Attenuating Racial Biases in Healthcare Delivery

Although significant research has examined how technology can intensify racial and other outgroup biases, limited work has investigated the role information systems can play in abating them. Racial biases are particularly worrisome in healthcare, where underrepresented minorities suffer disparities in access to care, quality of care, and clinical outcomes. In this paper, we examine the role clinical decision support systems (CDSS) play in attenuating systematic biases among black patients, relative to white patients, in rates of amputation and revascularization stemming from diabetes mellitus. Using a panel of inpatient data and a difference-in-difference approach, results suggest that CDSS adoption significantly shrinks disparities in amputation rates across white and black patients—with no evidence that this change is simply delaying eventual amputations. Results suggest that this effect is driven by changes in treatment care protocols that match patients to appropriate specialists, rather than altering within physician decision making. These findings highlight the role information systems and digitized patient care can play in promoting unbiased decision making by structuring and standardizing care procedures. This paper was accepted by Stefan Scholtes, healthcare management.

The authors participate in DigiMon international project (“Digital monitoring of CO2 storage projects”), which aims to develop a next generation human centred monitoring system for CO2 geological storage sites. It includes newly developed subsurface imaging technologies based on seismic fibre optic and gravity surveying. In addition, the SEL methodology was developed, which provides an assessment framework to understand which technical and non-technical aspects still need to be improved in order to implement CCS and its monitoring system successfully. Application of the SEL methodology based on socio-economic research carried out in Norway, Netherlands, Germany and Greece, identified what do stakeholders expect from a monitoring system. In this paper, the authors provide hints on how the outcomes of the international project DigiMon can be integrated within a Decision Support System (DSS) in order to assist the operator to follow up and manage CCS operation. The role of DSS is to assist data access, reservoir modelling, results interpretation and development of alternative solutions. The key stakeholders (decision makers) that must be kept informed are determined by National legislation. Their role is to assess environmental risk and manage emergency crises, if and when they occur. Processed data can be transmitted exclusively to them and can also be presented on regional maps, highlighting possible mitigation scenarios and intervention measures.

Knowledge mining from single graph plays an important role in decision support systems on single graphs such as social networks, bioinformatics, etc. In recent years, the problem of Frequent Subgraph Mining (FSM) from a single graph have been developed and attracted several studies. However, the problem of mining association rules or links from frequent subgraphs has not had many contributions. In this article, we state the problem of direct mining association rules from frequent subgraphs. Existing approaches on this topic perform the task in two phases. First, they traverse the search space to directly discover parent-child relationships from the discovered frequent subgraphs, then association rules are generated. We propose a one-phase algorithm, named So-GPARs, to generate rules as soon as frequent supergraphs are constructed from already existing frequent subgraphs. Our experiments on three single graph datasets show that the one-phase algorithm is more efficient than the two-phase algorithm in terms runtime of the rules generating phase.

Trust models play an important role in decision support systems and computational environments in general. The common goal of the existing trust models is to provide a representation as close as possible to the social phenomenon of trust in computational domains. In recent years, the field of quantum decision making has been significantly developed. Researchers have shown that the irrationalities, subjective biases, and common paradoxes of human decision making can be better described based on a quantum theoretic model. These decision and cognitive theoretic formulations that use the mathematical toolbox of quantum theory (i.e., quantum probabilities) are referred to by researchers as quantum-like modeling approaches. Based on the general structure of a quantum-like computational trust model, in this paper, we demonstrate that a quantum-like model of trust can define a powerful and flexible trust evolution (i.e., updating) mechanism. After the introduction of the general scheme of the proposed model, the main focus of the paper would be on the proposition of an amplitude amplification-based approach to trust evolution. By performing four different experimental evaluations, it is shown that the proposed trust evolution algorithm inspired by the Grover’s quantum search algorithm is an effective and accurate mechanism for trust updating compared to other commonly used classical approaches.

Given a user question, the goal of a Question Answering (QA) system is to retrieve answers rather than full documents or even best-matching passages, as most Information Retrieval systems currently do. In this paper, we present BRUJA, a QA system for the management of multilingual collections. BRUJ rkstions (English, Spanish and French). The BRUJA architecture is not formed with three monolingual QA systems but instead uses English as Interlingua to make usual QA tasks such as question classifications and answer extractions. In addition, BRUJA uses Cross Language Information Retrieval (CLIR) techniques to retrieve relevant documents from a multilingual collection. On the one hand, we have more documents to find answers from but on the other hand, we are introducing noise into the system because of translations to the Interlingua (English) and the CLIR module. The question is whether the difficulty of managing three languages is worth it or whether a monolingual QA system delivers better results. We report on in-depth experimentation and demonstrate that our multilingual QA system gets better results than its monolingual counterpart whenever it uses good translation resources and, especially, CLIR techniques that are state-of-the-art.

An authoring tool for decision support systems in context questions of ecological knowledge

This paper presents a study of the negative effect of Machine Translation (MT) on the precision of Cross---Lingual Question Answering (CL---QA). For this research, a English---Spanish Question Answering (QA) system is used. Also, the sets of 200 official questions from CLEF 2004 and 2006 are used. The CL experimental evaluation using MT reveals that the precision of the system drops around 30% with regard to the monolingual Spanish task. Our main contribution consists on a taxonomy of the identified errors caused by using MT and how the errors can be overcome by using our proposals. An experimental evaluation proves that our approach performs better than MT tools, at the same time contributing to this CL---QA system being ranked first at English---Spanish QA CLEF 2006.