Aim/Purpose: In this paper we propose a framework identifying many of the unintended consequences of information technology and posit that the increased complexity brought about by IT is a proximate cause for these negative effects. Background: Builds upon the three-world model that has been evolving within the informing science transdiscipline. Methodology: We separate complexity into three categories: experienced complexity, intrinsic complexity, and extrinsic complexity. With the complexity cycle in mind, we consider how increasing complexity of all three forms can lead to unintended consequences at the individual, task and system levels. Examples of these consequences are discussed at the individual level (e.g., deskilling, barriers to advancement), the task level (e.g., perpetuation of past practices), as well as broader consequences that may result from the need to function in an environment that is more extrinsically complex (e.g., erosion of predictable causality, shortened time horizons, inequality, tribalism). We conclude by reflecting on the implications of attempting to manage or limit increases of complexity. Contribution: Shows how many unintended consequences of IT could be attributed to growing complexity. Findings: We find that these three forms of complexity feed into one another resulting in a positive feedback loop that we term the Complexity Cycle. As examples, we analyze ChatGPT, blockchain and quantum computing, through the lens of the complexity cycle, speculating how experienced complexity can lead to greater intrinsic complexity in task performance through the incorporation of IT which, in turn, increases the extrinsic complexity of the economic/technological environment. Recommendations for Practitioners: Consider treating increasing task complexity as an externality that should be considered as new systems are developed and deployed. Recommendation for Researchers: Provides opportunities for empirical investigation of the proposed model. Impact on Society: Systemic risks of complexity are proposed along with some proposals regarding how they might be addressed. Future Research: Empirical investigation of the proposed model and the degree to which cognitive changes created by the proposed complexity cycle are necessarily problematic.

Aim/Purpose: This study offers an important contribution to the literature on couple social comparisons by showing how different aspects of comparisons are related to relationship quality. Background: Making social comparisons is a daily tendency of human beings that does not only occur on an individual level but also in the context of romantic relationships. This phenomenon is widespread among couples, though partners differ in terms of their propensity to make couple social comparisons. The literature has shown that all these facets of couple social comparison play an important role in relationship functioning. Methodology: In the current study of 104 young adults in a heterosexual relationship, we investigated the association of couple social comparison propensity, explicit couple social comparisons, and implicit couple social comparisons with couple relationship quality in terms of commitment and relationship satisfaction. Contribution: So far, studies have not tested all these aspects in predicting partners’ relationship quality. Findings: Results showed that commitment was negatively predicted by relationship social comparison propensity and positively predicted by implicit couple social comparisons, while relationship satisfaction was positively predicted by both implicit and explicit couple social comparisons. Recommendation for Researchers: Our results have implications for couple interventions. In preventive interventions, sustaining a positive view of one’s relationship may promote relationship satisfaction and commitment. Future Research: Future research should adopt a dyadic design to investigate cross-partner associations.

Aim/Purpose: This review paper aims to unveil some underlying machine-learning classification algorithms used for political election predictions and how stack ensembles have been explored. Additionally, it examines the types of datasets available to researchers and presents the results they have achieved. Background: Predicting the outcomes of presidential elections has always been a significant aspect of political systems in numerous countries. Analysts and researchers examining political elections rely on existing datasets from various sources, including tweets, Facebook posts, and so forth to forecast future elections. However, these data sources often struggle to establish a direct correlation between voters and their voting patterns, primarily due to the manual nature of the voting process. Numerous factors influence election outcomes, including ethnicity, voter incentives, and campaign messages. The voting patterns of successors in regions of countries remain uncertain, and the reasons behind such patterns remain ambiguous. Methodology: The study examined a collection of articles obtained from Google Scholar, through search, focusing on the use of ensemble classifiers and machine learning classifiers and their application in predicting political elections through machine learning algorithms. Some specific keywords for the search include “ensemble classifier,” “political election prediction,” and “machine learning”, “stack ensemble”. Contribution: The study provides a broad and deep review of political election predictions through the use of machine learning algorithms and summarizes the major source of the dataset in the said analysis. Findings: Single classifiers have featured greatly in political election predictions, though ensemble classifiers have been used and have proven potent use in the said field is rather low. Recommendation for Researchers: The efficacy of stack classification algorithms can play a significant role in machine learning classification when modelled tactfully and is efficient in handling labelled datasets. however, runtime becomes a hindrance when the dataset grows larger with the increased number of base classifiers forming the stack. Future Research: There is the need to ensure a more comprehensive analysis, alternative data sources rather than depending largely on tweets, and explore ensemble machine learning classifiers in predicting political elections. Also, ensemble classification algorithms have indeed demonstrated superior performance when carefully chosen and combined.

Aim/Purpose: The truism is that leadership fosters or restricts innovation behaviours in organisations, but the extent to which it does depends on the leadership style in practice. This study focuses on one of the contemporary leadership styles, knowledge-oriented leadership [KOL], which has received scant attention in research. In doing so, the contextual factors of psychological safety [PS] and employee voice [EV] were applied to determine how KOL influences are channeled to innovation at the individual level. Methodology: Data were collected from 347 academic staff in public universities in Southern Nigeria and subjected to a partial least square [PLS] analytical procedure for data treatment and hypotheses testing using the SmartPLS 3 software for variance-based structural equation modelling. Contribution: The study formed an integrated research framework that links knowledge-oriented leadership and innovation by accounting for the contextual mechanisms of psychological safety and employee voice. Findings: The PLS results demonstrated that the knowledge-oriented leadership and innovation relationship was positive and significant, and this relationship was partially mediated by two variables, namely, PS and EV. Furthermore, the two mediating variables channeled KOL’s influence on innovation in a sequence. Recommendation for Researchers: Organisations need to consider the practical application of KOL to improve innovation outcomes considerably. By this, leadership training programs should include modules, courses, or topics on KOL to engender the formation of requisite managerial skills. More so, they should consider the criterion of demonstrable KOL abilities for leadership selection and recruitment. As a personal development initiative, managers can attend leadership development programmes as well as obtain certification in knowledge management to improve their KOL abilities. This initiative should be encouraged and supported by organisations. In all, the human resource management framework should be responsive to the dynamics of the knowledge economy regarding leadership. Given that PS and EV function as mediators, organisations should actively cultivate an environment enabling interpersonal risky behaviours founded on trust, respect, and cooperation and encourage/support employees who demonstrate such behaviour accordingly. In this line, they should create and sustain a supportive environment that positively reinforces voice decisions and behaviours. Future Research: The study only determined the links between KOL, PS, EV, and innovation in public universities in Southern Nigeria. Other studies may examine the linkages in other knowledge-intensive organisations as well as expand the geographic scope to make for better generality of findings. Future studies should look at other underlying mechanisms that can affect the KOL-innovation relationship, such as psychological capital, work engagement, work commitment, etc. The role of moderators can be identified and introduced to this integrative framework to demonstrate the conditions affecting the linkages.

Table of Contents for Volume 27 of Informing Science: The International Journal of an Emerging Transdiscipline, 2024

This paper proposes an efficient solution for personalised web directories recommendation using fast FCM+DQN. At first, web directory usage file obtained from given dataset is fed into the accretion matrix computation module, where visitor chain matrix, visitor chain binary matrix, directory chain matrix and directory chain binary matrix are formulated. In this, directory grouping is accomplished based on fast FCM and matching among query and group is conducted based on Kumar Hassebrook and Kulczynski similarity. The user preferred directory is restored at this stage and at last, personalised web directories are recommended to the visitors by means of DQN. The proposed approach has received superior results with respect to maximum accuracy of 0.910, minimum mean squared error (MSE) of 0.0206 and root mean squared error (RMSE) of 0.144. Although the system offered magnificent outcomes, it failed to order web directories in the form of highly, medium and low interested directories.

Mental illnesses are common among college students as well as their non-student peers, and the number and severity of these problems are increasing. It can be difficult to identify people suffering from mental illness and get the help they need early. So in this paper, the SqueezeR_MobileNet method is proposed. It performs feature fusion and early mental health prediction. Initially, outliers in the input data are detected and removed. After that, using missing data imputation and Z-score normalisation the pre-processing phase is executed. Next to this, for feature fusion, a combination of the Soergel metric and deep Kronecker network (DKN) is used. By utilising bootstrapping data augmentation is performed. Finally, early mental health prediction is done using SqueezeR_MobileNet, which is the incorporation of residual SqueezeNet and MobileNet. The devised approach has reached the highest specificity of 0.937, accuracy of 0.911 and sensitivity of 0.907.

This work demonstrates a hybrid deep learning (DL) model for lung cancer (LC) detection using CT images. Firstly, the input image is passed to the pre-processing stage, where the input image is filtered using a BF and the obtained filtered image is subjected to lung lobe segmentation, where segmentation is done using squeeze U-SegNet. Feature extraction is performed, where features including entropy with fuzzy local binary patterns (EFLBP), local optimal oriented pattern (LOOP), and grey level co-occurrence matrix (GLCM) features are mined. After completing the extracting of features, LC is detected utilising the hybrid efficient-ShuffleNet (HES-Net) method, wherein the HES-Net is established by the incorporation of EfficientNet and ShuffleNet. The presented HES-Net for LC detection is investigated for its performance concerning TNR, and TPR, and accuracy is established to have acquired values of 92.1%, 93.1%, and 91.3%.

This paper presents a novel deep learning technique called quantum dilated convolutional neural network-DenseNet (Q-DenseNet) for prediction of heart disease in spark framework. At first, the input data taken from the database is allowed for data partitioning using fast fuzzy C-means clustering (FFCM). The partitioned data is fed into spark framework, where pre-processed by missing data imputation and quantile normalisation. The pre-processed data is further allowed for selection of suitable features. Then, the selected features from the slave nodes are merged and fed into master node. The Q-DenseNet is used in master node for the prediction of heart disease. The performance improvement of the designed Q-DenseNet model is validated by comparing with traditional prediction models. Here, the Q-DenseNet method achieved superior performance with maximum of 92.65% specificity, 91.74% sensitivity, and 90.15% accuracy.

An optimisation-based decision-making support is proposed in this study in the form of fuzzy-probabilistic programming, which can be used to solve integrated order allocation, production planning, and inventory management problems in fuzzy and probabilistic uncertain environments. The problem was modelled in an uncertain mathematical optimisation model with two objectives: maximising the expectation of production volume and minimising the expectation of total operational cost subject to demand and other constraints. The model belongs to fuzzy-probabilistic bi-objective integer linear programming, and the generalised reduced gradient method combined with the branch-and-bound algorithm was utilised to solve the derived model. Numerical simulations were performed to illustrate how the optimal decision was formulated. The results showed that the proposed decision-making support was successful in providing the optimal decision with the maximum expectation of the production volume and minimum expectation of the total operational cost. Therefore, the approach can be implemented by decision-makers in manufacturing companies.

COVID-19 created a surge in e-commerce usage, leading to fierce channel competition between the manufacturer's online sales and the offline retailer. Hence, the imperative need for effective and innovative optimisation strategies to mitigate channel competition. Manufacturer-coupons are widely practiced in market, yet research on the importance they play in coordinating channel competition to achieve optimisation in channel distributions is scarce. This research addresses this gap by examining the effectiveness of manufacturer-coupons on the coordination of the manufacturer's online sales and offline retailer's sales. The findings indicate that issuing a manufacturer-coupon to the customers who buy from the offline retailer reduces the competition in the different channel distributions, but cost sharing of the retailer coupon is a better strategy. We thus examine if profit sharing is an effective strategy to facilitate the use of manufacturer-coupon in the market. After comparing different scenarios, we conclude that advanced profit-sharing can be effective in making manufacturer-coupon prevalent in the market and thus alleviate channel competition effectively.

Data envelopment analysis (DEA) is widely used in various fields and for various models. Inverted data envelopment analysis (inverted DEA) is an extended model of DEA. Regression analysis (RA) is a statistical process for estimating the relationships among variables based on the model of averaged image. There are no essential relations among DEA and RA and inverted DEA. We creatively combine DEA, RA and inverted DEA to propose a new model: DEA-RA-Inverted DEA model. The model realises the efficiency estimation and evaluation through a discussion of the residual variables and the residual ratio coefficients. In addition, we will demonstrate the effectiveness of the model by applying it to efficiency estimation and evaluation of 16 Chinese logistics enterprises.

This study addresses a critical chain project scheduling (CCPS) problem regarding the feeding buffer. The main contribution of this study lies in determining the critical chain when the feeding buffer is considered along with the project buffer, a less addressed issue in the critical chain literature. Using a mixed-integer nonlinear programming (MINLP) model, the critical chain of a project with no break-down and no overflow is found. Moreover, the impact of the feeding buffer on the criticality of activities is discussed. The problem is solved using the Lingo software package for validation in small-sized instances. Since the CCPS is known as an NP-hard problem, a genetic algorithm (GA) is also designed to solve large-scale instances. The algorithm's performance is confirmed using various project scheduling library test problems. Sensitivity analysis is implemented based on some crucial parameters, and the critical chain is analysed after conducting several experiments. It is shown how considering the feeding buffer makes different critical chains and how shortlisting activities and resources are optimally managed.

This paper studies a dual-channel supply chain consisting of one risk-averse manufacturer and one risk-averse retailer with stochastic demand. Herein, the manufacturer provides value-added services to enhance channel demand. First, the optimal pricing and service decisions of the channel members are investigated under different settings, i.e., the cooperative game, Bertrand game, and manufacturer Stackelberg (MS) game models. Second, the effects of channel members' risk aversion on optimal channel prices and expected utilities are analysed under the assumption that the manufacturer service is a decision variable and an exogenous variable, respectively. Third, sensitivity analysis and numerical simulation are performed to verify our propositions consistently and seek more managerial implications. The findings suggest that the manufacturer's value-added services in their direct channel will improve the direct price while decreasing the retail price. Consumers' channel loyalty degree has a great influence on the optimal price decisions and the performance of the channel members. The direct price increases while the retail price decreases in the manufacturer's value-added services. The retailer's risk aversion has a greater influence on price decisions than that of the manufacturer.

One of the main tasks in tax administration is debt management. The main goal of this function is tax due collection. Statements are processed in order to select strategies to use in the debt management process to optimise the debt collection process. This work proposes to carry out a data mining process to predict debts of taxpayers with high probability of non-payment. The data mining process identifies high-risk debts using a survival analysis on a dataset from a tax administration. Three groups of tax debtors with similar payment behaviour were identified and a success rate of up to 90% was reached in estimating the payment time of taxpayers. The concordance index (C-index) was used to determine the performance of the constructed model. The highest prediction rate reached was 90.37% corresponding to the third group.

To capture the complexity and diversity of systems with both technical and social features, modelling methods are needed that similarly provide various tools and concepts. Study of developed methods shows that despite all of their advantages and strengths, there is a need for a method that with a holistic approach integrates perspectives, strengths and tools of the developed methods and models with different aspects of socio-technical systems. The main aim of the current study is to design a method for modelling complex socio-technical systems. To achieve this goal, it is necessary to design a method that is based on creativity and existing knowledge base. Therefore, design science research is used as a research strategy to design proposed method. For the first time, design science research in the field of operations research has been used to design a modelling method. This study also presents new tools and concepts for modelling socio-technical systems.

Product innovation has become a crucial part of the sustainability of the coconut agro-industry in Indonesia, covering upstream and downstream sides. To overcome this challenge, it is necessary to create several model stages using a hybrid method that combines machine learning based on multiple criteria decision making and mass balance analysis. The research case study was conducted in Tembilahan district, Riau province, Indonesia, one of the primary coconut producers in Indonesia. The analysis results showed that potential products for domestic customers included coconut milk, coconut cooking oil, coconut chips, coconut jelly, coconut sugar, and virgin coconut oil. Furthermore, considering the experts, the most potential product to be developed was coconut sugar with a weight of 0.26. Prediction of coconut sugar demand reached 13,996,607 tons/year, requiring coconut sap as a raw material up to 97,976,249.

The Republic of India has witnessed an enormous leap in financial transactions after a sudden demonetisation in 2016. The study represents an in-depth analysis of the factors influencing e-wallets usage post-COVID situation covering the National Capital Region. The scientifically collected data were subjected to Pearson's correlation to recognise the correlation amongst the selected e-wallets. The usage of e-wallets is observed mainly during recharge, UPI payments, and utility payments. Through psychometric response and interaction analysis, six factors were selected and examined for data distribution and stable observation using standard deviation and variance coefficient. The coefficient of variance for six factors was observed ≤ 1. The weight of the factors noted to be secured way (0.184), to take advantage of cashback (0.182), low risk of theft (0.169), fast service (0.1689), ease to use (0.156), and saves time (0.139) using principal component eigenvectors analysis. Freecharge and Tez wallets reveal a maximum 99.2% correlation.

Research growth in organisational behaviour research, has increased the importance of paying attention to anti-citizenship behaviours. The current research with the aim of quantitative combination, has examined the results of research in effect of underlying factors of organisational anti-citizenship behaviours using meta-analysis method and CMA2 software and 55 articles during the time period of 2000-2020. The results showed a positive significant link between underlying factors of organisational anti-citizenship behaviours and occurrence of these behaviours and this influence was 0.389, 0.338, 0514 and 0.498 (structural, organisational, managerial, employment and professional and socio-economic and cultural factors). The level of connection found relating to each four occurrences is '68 links, 49 links, 93 links and 71 links'. Findings indicate that minute attention has been paid to organisational anti-citizenship behaviours, especially to job and professional factors in research works. Research should be conducted to control and manage these behaviours more purposefully in organisations.

In Italy, the reimbursement provided to healthcare organisations for medical and surgical procedures is based on the diagnosis related group weight (DRGW), which is an increasing function of the complexity of the procedures. This makes the reimbursement an upper unlimited function. This model does not include the relation of the volume with the complexity. The paper proposes a mathematical model for the optimisation of the reimbursement by determining the optimal mix of volume/procedure, considering the relation volume/complexity and DRGW/complexity. The decreasing, linear, and increasing returns to scale have been defined, and the optimal solution found. The comparison of the model with the traditional approach shows that the proposed model helps the healthcare system to discern the quantity of the reimbursement to provide to health organisations, while the traditional approach, neglecting the relation between the volume and the complexity, can result in an overestimation of the reimbursement.

Underpinned by stakeholder and agency theories, this study adopts a qualitative multiple-case study approach to explore and analyse various stakeholder interests and how they affect digitalisation in the health sector of a developing country (DC). The study's findings revealed that four key stakeholder interests - political, regulatory, leadership, and operational - affect digitalisation in the health sector of DCs. Further, the study found that operational and leadership interests were emergent and were triggered by some digitalisation initiatives, which included, inter alia, the use of new eHealth software and the COVID-19 vaccination exercise, which established new structures and worked better through digitalisation. Conversely, political and regulatory interests were found to be relatively enduring since they existed throughout the pre- and post-digitalisation eras. The study also unearthed principal-agent conflicts arising from technological, organisational and regulatory factors that contribute to the paradoxical outcomes of digitalisation in the health sector.

The objective of this study was to explore through the Model of Theory of Planned Behaviour the most important variables that influence the practice of physical and sports activity at home supported by virtual training in the context of the COVID-19 pandemic. A cross-study was proposed between countries from three continents, distributing the questionnaire in Spain (Europe), Pakistan (Asia), and Colombia (South America) to ensure a comprehensive study. The methodology of structural equations using partial least squares was used. The empirical exploratory study supported the hypotheses proposed, with the most important result that confinement due to the COVID-19 pandemic has been a factor causing the practice of physical and sports activity at home. This is one of the first studies to examine sports practice at home and the new context of sports practice that has generated disruptive technologies and the global crisis of the COVID-19 pandemic.

Recent innovations in additive manufacturing (AM) have proven its efficacy for not only the manufacturing industry but also the healthcare industry. Researchers from Cal Poly, San Luis Obispo, and California State University Long Beach are developing a model that will determine the optimal locations for additive manufacturing hubs that can effectively serve both the manufacturing and healthcare industries. This paper will focus on providing an overview of the healthcare industry's unique needs for an AM hub and summarise the specific inputs for the model. The methods used to gather information include extensive literature research on current practices of AM models in healthcare and an inclusive survey of healthcare practitioners. This includes findings on AM's use for surgical planning and training models, the workflow to generate them, sourcing methods, and the AM techniques and materials used. This paper seeks to utilise the information gathered through literature research and surveys to provide guidance for the initial development of an AM hub location model that locates optimal service locations.

Collaboration between various stakeholders is crucial for healthcare digitalisation and eHealth utilisation. Given that valuable outcomes can emerge from collaborative interactions among multiple stakeholders, exploring a quadruple helix (QH) approach to collaboration may be fruitful in involving the public sector, business, citizens, and academia. Therefore, this study aimed to explore stakeholder views on eHealth collaboration from a QH perspective using the grounded theory methodology. First, an inductive qualitative study involving all stakeholders in the QH was conducted. Subsequently, the findings were related to the actor-resource-activity (ARA) model of business relationships. The results emphasise the role of considering diverse perspectives on collaboration because digitalisation and eHealth require teamwork to benefit the end users within various settings. A model depicting the various aspects of the ARA model related to digitalisation in a healthcare QH setting is presented.

Background and Objective: In response to the challenges of poor mapping outcomes and susceptibility to obstacles encountered by indoor mobile vehicles relying solely on pure cameras or pure LiDAR during their movements, this paper proposes an obstacle avoidance method for indoor mobile vehicles that integrates image and LiDAR data, thus achieving obstacle avoidance for mobile vehicles. Materials and Methods: This method combines data from a depth camera and LiDAR, employing the Gmapping SLAM algorithm for environmental mapping, along with the A* algorithm and TEB algorithm for local path planning. In addition, this approach incorporates gesture functionality, which can be used to control the vehicle in certain special scenarios where “pseudo-obstacles” exist. The method utilizes the YOLO V3 algorithm for gesture recognition. Results: This paper merges the maps generated by the depth camera and LiDAR, resulting in a three-dimensional map that is more enriched and better aligned with real-world conditions. Combined with the A* algorithm and TEB algorithm, an optimal route is planned, enabling the mobile vehicles to effectively obtain obstacle information and thus achieve obstacle avoidance. Additionally, the introduced gesture recognition feature, which has been validated, also effectively controls the forward and backward movements of the mobile vehicles, facilitating obstacle avoidance. Conclusion: The experimental platform for the mobile vehicles, which integrates depth camera and LiDAR, built in this study has been validated for real-time obstacle avoidance through path planning in indoor environments. The introduced gesture recognition also effectively enables obstacle avoidance for the mobile vehicles.

Automated Data Augmentation via Wikidata Relationships Oyesh Singh, UMBC10:30-11:30 Monday, 21 October 2019, ITE 346 With the increase in complexity of machine learning models, there is more need for data than ever. In order to fill this gap of annotated data-scarce situation, we look towards the ocean of free data present in Wikipedia and other […]

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A semantically rich framework to enable real-time knowledge extraction from short length semi-structured documents Lavana Elluri 10:30-11:30 Monday, 4 November 2019, ITE346 Knowledge is currently maintained as a large volume of unstructured text data in books, laws, regulations and policies, news and social media, academic and scientific reports, conversation and correspondence, etc. Most of these […]

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In my knowledge graph class yesterday we talked about the SPARQL query language and I illustrated it with DBpedia queries, including an example getting data about the movie Double Indemnity. I had brought a google assistant device and used it to compare its answers to those from DBpedia. When I asked the Google assistant “Who […]

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Ph.D. Dissertation Defense Modeling and Extracting Information about Cybersecurity Events from Text Taneeya Satyapanich 9:30-11:30 Monday, 18 November, 2019, ITE346? People now rely on the Internet to carry out much of their daily activities such as banking, ordering food, and socializing with their family and friends. The technology facilitates our lives, but also comes with […]

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Reinforcement Quantum Annealing: A Quantum-Assisted Learning Automata Approach We introduce the reinforcement quantum annealing (RQA) scheme in which an intelligent agent interacts with a quantum annealer that plays the stochastic environment role of learning automata and tries to iteratively find better Ising Hamiltonians for the given problem of interest. As a proof-of-concept, we propose a […]

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Results using the reinforcement learning technique on two SAT benchmarks using a D-Wave 2000Q quantum processor showed significantly better solutions with fewer samples compared to the best-known quantum annealing techniques.

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This paper how to apply dynamic topic models to a set of cybersecurity documents to understand how the concepts found in them are changing over time.

The post paper: Temporal Understanding of Cybersecurity Threats appeared first on UMBC ebiquity.

A new paper describing a system integrating a GDPR Ontology with blockchain to support checking data operations for compliance.

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The PALS system captures physical context from sensed data, reasons about the context and associated context-driven policies to make access-control decisions and detect intrusions into smart home systems based on both network and behavioral data

The post paper: Context Sensitive Access Control in Smart Home Environments appeared first on UMBC ebiquity.

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