Problem Space Research Articles

Particle Swarm Optimization-Based Unconstrained Polygonal Fitting of 2D Shapes

In this paper, we present a general version of polygonal fitting problem called Unconstrained Polygonal Fitting (UPF). Our goal is to represent a given 2D shape S with an N-vertex polygonal curve P with a known number of vertices, so that the Intersection over Union (IoU) metric between S and P is maximized without any assumption or prior knowledge of the object structure and the location of the N-vertices of P that can be placed anywhere in the 2D space. The search space of the UPF problem is a superset of the classical polygonal approximation (PA) problem, where the vertices are constrained to belong in the boundary of the given 2D shape. Therefore, the resulting solutions of the UPF may better approximate the given curve than the solutions of the PA problem. For a given number of vertices N, a Particle Swarm Optimization (PSO) method is used to maximize the IoU metric, which yields almost optimal solutions. Furthermore, the proposed method has also been implemented under the equal area principle so that the total area covered by P is equal to the area of the original 2D shape to measure how this constraint affects IoU metric. The quantitative results obtained on more than 2800 2D shapes included in two standard datasets quantify the performance of the proposed methods and illustrate that their solutions outperform baselines from the literature.

Problem Spaces and Algorithms in Data Mining

Data mining has been described severally as the best thing to have happened to data and information management, especially these days that the cost of computing technologies and storage media are falling, data gathering tools becoming varied and very efficient and the boom in network computing becoming very rewarding. The challenges presented by the management and meaningful usage of large data sets have stimulated so much research in data mining. Consequently, the birth of a number of algorithms to provide insights to these big data has equally presented more complications in information processing computing. Therefore, this paper presents different problem spaces in data mining, available algorithms to mine these data and then mapping specific algorithms to specific problem spaces. Analysis of datasets from a typical financial institution suggests that no one algorithm is necessarily better than the other, but all have strengths and weaknesses depending on the particular problem spaces in use.

Homomorphisms between Problem Spaces

Homomorphisms between Problem Spaces

Redesign of Automatic Vacuum Cleaner with Participatory Approach

While using automatic vacuum cleaners, users found dissatisfaction namely dust problem, obstacle problem, and narrow space problem. Therefore, the problems in this automatic vacuum cleaner must be fixed so that it can maximize user productivity. The methods that researchers will use to carry out this research include the participatory ergonomic which is a method for translating consumer needs into an optimal and solutive products by gain aspirations from users and stakeholders. The next method is Maynard Operation Sequence Technique which is a predetermined motion time method that aims to determine the standard time for doing work. There are also participatory usability and ergonomics methods that help this research. For the dust problem, researchers have looked for a solution to the problem, namely by adding SLAM and Artificial Intelligence logarithms to the automatic. For the narrow space problem, researchers have designed different nozzles for the automatic to use. For this exact problem, researchers have added a crevice nozzle to the automatic so that the automatic can insert suction in narrow areas such as the back of cupboards. Researchers created this feature so that users no longer need to bother cleaning dust from sofas, tables and other items in the user’s room.

Design team formation using self-assessment and observer-assessment techniques: mapping practices in a global network of universities

Abstract Design engineering education is increasingly challenge-based, which requires educators to form cohesive student teams capable of delivering desired outcomes while fostering learning and collaboration. An example is an international network in which students from different global universities collaborate. Student teams work on researching the problem space, re-framing their challenge and producing multiple prototypes. The challenge for the teaching teams is to be able to form multiple cohesive teams out of a pre-selected group of highly motivated students. Because of the exclusive nature of this educational program, it is a suitable case study for exploring student design team formation practices. The aim is to identify the methods, tools, theoretical underpinnings, challenges and limitations of student team formation. We interviewed teachers from seven universities about their practices. The interviewees had several years of experience in team building. The interviews were analyzed to contrast practices across universities as well as to the team formation literature. Our findings show that mixed methods that combine self-assessments and observer-assessment methods are the preferred means of forming teams. Our findings also show that current practices have evolved over time through trial and error, and are only partially grounded in different literatures and not necessarily in team formation literature.

DaedalusData: Exploration, Knowledge Externalization and Labeling of Particles in Medical Manufacturing - A Design Study.

In medical diagnostics of both early disease detection and routine patient care, particle-based contamination of in-vitro diagnostics consumables poses a significant threat to patients. Objective data-driven decision-making on the severity of contamination is key for reducing patient risk, while saving time and cost in quality assessment. Our collaborators introduced us to their quality control process, including particle data acquisition through image recognition, feature extraction, and attributes reflecting the production context of particles. Shortcomings in the current process are limitations in exploring thousands of images, data-driven decision making, and ineffective knowledge externalization. Following the design study methodology, our contributions are a characterization of the problem space and requirements, the development and validation of DaedalusData, a comprehensive discussion of our study's learnings, and a generalizable framework for knowledge externalization. DaedalusData is a visual analytics system that enables domain experts to explore particle contamination patterns, label particles in label alphabets, and externalize knowledge through semi-supervised label-informed data projections. The results of our case study and user study show high usability of DaedalusData and its efficient support of experts in generating comprehensive overviews of thousands of particles, labeling of large quantities of particles, and externalizing knowledge to augment the dataset further. Reflecting on our approach, we discuss insights on dataset augmentation via human knowledge externalization, and on the scalability and trade-offs that come with the adoption of this approach in practice.

The Three Worlds of Task Complexity

Aim/Purpose: To provide a systematic approach to defining task complexity using a three worlds model previously introduced in informing science research. Background: The task complexity construct presents researchers with a quandary. While it appears useful on the surface, repeated attempts to define it rigorously have failed to gain traction in the broader research community. The level of inconsistency between definitions is shown to have changed little in the past 20 years. Methodology: Using a common framework that treats task complexity as a latent construct residing between sources and outcomes, moderated by both task familiarity and task discretion, separate models for each of the three worlds are developed. Contribution: Our paper proposes a potential path forward by showing how many issues in past task complexity research can be reconciled by framing the construct according to the three worlds model: the world we experience, the world of human artifacts, and the “real world.” Findings: The framework defines experienced complexity as occurring in the mind of the task performer while performing a single task instance, intrinsic complexity as a function of the internal characteristics of the problem space used to perform a bounded set of task instances, and extrinsic complexity as the ruggedness of the fitness landscape in which the task is performed. Recommendation for Researchers: It offers a path to convergence for definitions of task complexity. Future Research: The three worlds of task complexity can potentially be applied to many practical problems.

An evolutionary crow search algorithm equipped with interactive memory mechanism to optimize artificial neural network for disease diagnosis

Artificial neural network (ANN) is an information processing paradigm that loosely models the thinking patterns of the human brain with specifications such as real-time learning, self-adaption, and self-organization. The learning process of ANNs is complex and tackles shortcomings such as a slow convergence rate, learning time-consuming, and local minimum trapping, especially when using gradient-based optimization techniques. Although many metaheuristics have been proposed to arm ANN and solve these weaknesses, ANN learning still needs solution quality. Therefore, this study proposes an evolutionary crow search algorithm (ECSA) to optimize the hyperparameters of ANNs for diagnosing chronic diseases. ECSA introduces an evolutionary search strategy, self-adaptive adaptive flight length, and an interactive memory mechanism to alleviate the canonical crow search algorithm's shortcomings. The evolutionary search strategy and self-adaptive flight length provide a meaningful search strategy in which crows effectively explore and exploit problem spaces by maintaining population diversity. During the search process, the interactive memory mechanism records the best solution obtained during optimization. The performance of ECSA was evaluated and compared with well-known metaheuristic algorithms in terms of local and global search abilities, local optima avoidance, and convergence speed towards the promising area. Then, the results were statistically analyzed. Ultimately, the effectiveness of an adaptation of ECSA named ECSA-MLP for optimizing hyperparameters of the multilayer perceptron network for diagnosing diseases including coronavirus, breast cancer, diabetes, cardiovascular, cervical cancer, Parkinson's, mammography, and acute inflammation was compared with state-of-the-art competitor algorithms. The experimental results indicated the superiority of ECSA over competitor algorithms in optimizing the network.

Problem space search metaheuristics with fix and optimize approach for the integrated fleet sizing and replenishment planning problem

Problem space search metaheuristics with fix and optimize approach for the integrated fleet sizing and replenishment planning problem

Machine Learning-Based Problem Space Reduction in Stochastic Programming Models: An Application in Biofuel Supply Chain Network Design

Machine Learning-Based Problem Space Reduction in Stochastic Programming Models: An Application in Biofuel Supply Chain Network Design

Covid-19 Pandemic and the Freedom-Security Tension: Calibrating their Fragile Relationship

Grounded in a will to adapt to dangers, and espouse both responsibility and resilience, voluntary measures have largely replaced one of the oldest public health strategies, quarantine. The Covid-19 pandemic, however, elicited a broad sweep of tactics from the archive of public health armoury. On a general level, this review essay addresses the common measures rolled out by various authorities against the pandemic - the lock-downs, reopening process, financial support and vaccination. By relating these measures to 1) the “plague-stricken town”, deployed during the seventeenth and eighteenth centuries in Europe by the Polizeistaat; 2) the “self-regulation strategy” that emerged with liberal ideas at the end of the eighteenth century; and 3) the “minimum security” programmed by neoliberal governmentality in the second half of the twentieth century, it is suggested that tensions between freedom and security during, and after, the pandemic can be better understood. To end, the essay noticed that the pandemic has enforced tensions in the administration and calibration of individual wishes and collective wellbeing, creating a fragile “freedom-security relationship” and new problem space for self-regulation.

Relational neurostrategy

PurposeNeurostrategy is an abductive decision-making process developed from the knowledge generated within the network of decision-makers. It links cognitive style with the team's decisional adaptability in relation to the organization's purpose. Neurostrategy differs from traditional methods, in which it addresses negative utility or decisional trauma, highlighting the variety of interests that are the main cause of team misalignment and allowing for the development of strategies to address them. Neurostrategy enables the classification of strategy deficiencies based on cohesion, coordination, communication and conduction (Co4).Design/methodology/approachThis paper introduces a novel approach called relational neurostrategy (RNS) to elucidate the knowledge processes influencing decision-making. RNS aims to “capture” the intricate processes guiding decisions, enabling the network's decisional plasticity in both forms and contents. This adaptability is crucial for effectively addressing posed challenges, while simultaneously mitigating the impact of diverse interests. The methodology also ensures transparency in the decision-making process and generates an effective solution strategy.FindingsThe RNS addresses two critical aspects of the decision-making process. Firstly, it reduces unnecessary variety stemming from multiple interpretations and secondly, it minimizes the adverse impact of diverse interests within the decision-making network. This approach results in strong and credible decisions that reflect the collective intelligence, cooperation and collaborative efforts of the network, rather than being imposed as absolute truths.Originality/valueThe RNS stands out as a distinctive decision-making method, setting itself apart from existing approaches. Its uniqueness becomes evident in its ability to address the question “what prevents the authors from … ?” from this inquiry, RNS successfully integrates unrequired variety and negative utility. By doing so, it strategically narrows down the search field to the universe of distinctions that truly constitute the problem. This innovative process not only enhances efficiency but also fosters a high level of participation in the strategic design of potential solutions. In essence, RNS brings unprecedented value by effectively navigating the intricacies of decision-making and maximizing the relevance of the identified problem space.

Quantifying Sustainable Urban Energy Solutions: Statistical Analysis of Renewable Adoption, Economic Viability, and Technological Innovations

In the context of quantifying sustainable and reliable urban microgrid energy solutions economic feasibility, and technological innovation, this study investigates the potential advantages of hybrid AC-DC microgrids (HMGs) over traditional AC microgrids by eliminating the need for voltage inverters and mitigating energy losses associated with AC-DC converters. We propose a probabilistic strategy for economic feasibility and optimizing the unit commitment of HMGs while considering the integration of renewable energy sources and batteries. To account for uncertainties stemming from load demand fluctuations, energy market price variations, and renewable energy source output power variability, we introduce a stochastic power flow model based on the 2 m point estimation method for economic feasibility and technological innovation of HMGs. Furthermore, we present a novel and robust hybrid optimization approach that combines the harmony search optimization method and genetic algorithm to explore the problem space and identify optimal solutions. This hybrid optimization method incorporates a unique two-stage modification technique, enhancing its search capabilities while preventing convergence to local optima. To assess the performance of our proposed method, we apply it to the IEEE test system. The results demonstrate the effectiveness of our approach in achieving optimal management of the microgrid system, emphasizing its relevance to the quantification of sustainable and reliable urban microgrid energy solutions.

Problem spaces in STEM inquiry: a case analysis of an integrated curriculum

ABSTRACT Many empirical studies about STEM (science, technology, engineering, and mathematics) curriculum present problems for students to solve. This paper draws upon the data collected from the enactment of an integrated STEM curriculum to discuss problems as constitutive of problem spaces where four Grade 5 Singapore students engage with the materialities and relationships to generate solutions from these spaces. The study seeks to address the nature of the problem spaces in STEM inquiry. Derived from the emergent coding of 12 hours of lesson videos, the findings illuminated that problem spaces emerged from familiar and dissimilar contexts in the curriculum. These problem spaces embodied epistemic infrastructures and epistemic emotions within which students exercised agency to collaborate productively and learn. Implications for STEM curriculum making that foregrounded epistemic considerations, rather than outcome-based learning, were discussed. This paper helps to further the field of curriculum studies in STEM education by contributing to the theorization of STEM curriculum by applying a process lens to deepen understanding of problems as problem spaces.

“These Somalis are not Somalis:” Cup of Art Italian Coffeehouse, Authentic Identities, and Belonging in Hargeisa, Somaliland

Abstract Through ethnography of a performatively Western-inspired coffeehouse in a “traditionally” identifying town of Hargeisa in Somaliland, Serunkuma uses Cup of Art Italian Coffeehouse to debate the political-conceptual dilemma—and potential dangers—of the renewed longing for cultural authenticity and “total revolution” in post-colonial Africa. While acknowledging the failure of the dreams that animated the anti-colonial struggle, manifest in the collapse in public service delivery, governance challenges, and civil war, Serunkuma contends that taking this to be the product of the “legacy of late colonialism,” and thus seeking to protect supposedly “authentic” African traditions and religious practices from Western corruption is itself a poisoned chalice. Using Cup of Art, Serunkuma urges humility toward the permanence of a colonial modernity and constant awareness of the new “problem space,” in which actors exercise their agency.

Simultaneous Sizing of a Rocket Family with Embedded Trajectory Optimization

This paper presents a sizing procedure for a rocket family capable of fulfilling multiple missions, considering the commonalities between the vehicles. The procedure aims to take full advantage of sharing a common part across multiple rockets whose payload capability differs entirely, ultimately leading to cost savings in designing a rocket family. As the foundation of the proposed rocket family design method, an integrated sizing method with trajectory optimization for a single rocket is first formulated as a single optimal control problem. This formulation can find the optimal sizing along with trajectory results in a tractable manner. Building upon this formulation, the proposed rocket family design method is developed to 1) determine the feasible design space of the rocket family design problem (i.e., commonality check), and 2) if a feasible design space is determined to exist, minimize the cost function within that feasible space by solving an optimization problem in which the optimal control problem is embedded as a subproblem. A case study is carried out on a rocket family composed of expendable and reusable launchers to demonstrate the novelty of the proposed procedure.

Triple competitive differential evolution for global numerical optimization

As optimization problems become more and more complex in real-world scenarios, the effectiveness of many existing differential evolution (DE) methods is critically challenged. To circumvent this predicament, this paper proposes a triple competitive DE (TCDE) to tackle increasingly complicated optimization problems. Specifically, a triple competition mechanism is devised to first randomly arrange individuals into exclusive triples and then let individuals in each triple compete with each other. Subsequently, heterogeneous mutation schemes are designed to update the three individuals in each triple based on the competition results. With the cooperation between these two techniques, on the one hand, better individuals in the triples tend to exploit the areas where they lie to find high-accuracy solutions, while worse individuals in the triples prefer to explore the intricate problem space to locate promising regions. On the other hand, different individuals are mutated by following the guidance of different leading exemplars, which ensures that individuals traverse the sophisticated solution space in diverse directions along with slight intensification to optimal regions. Furthermore, a modified adaptive parameter adjustment scheme is designed for individuals in each triple by considering their differences so as to meet the expectation that better individuals focus on exploitation while worse individuals concentrate on exploration. Finally, experiments are performed to first compare TCDE with totally 10 advanced DE variants and then further compare it with totally 10 CEC winners and their latest variants on the CEC'2014 and the CEC'2017 problem suites with totally 4 different dimensionality settings. Experimental results prove that TCDE achieves highly competitive or even much better performance than the totally 20 compared methods. Particularly, TCDE is demonstrated to be especially capable of solving complicated optimization problems. Furthermore, the efficacy of TCDE in tackling real-world optimization problems is substantiated through experiments conducted on the CEC'2011 real-world optimization problem set.

School Reform: New Future-Ready Quality Outcomes and Proposed Measures

As we increasingly emphasise the importance of developing future-ready outcomes for learners, we will need to also expand new capabilities to measure such outcomes. AI, big data, and analytics are examples of such new capabilities. Ideation is one of six habits of practice we have identified that will prepare students for the future. In this paper, we present a means to computationally appraise ideation quality as one such capability. We have developed a heuristic to appraise the ideation quality of university student essays using natural language processing, a branch of artificial intelligence concerned with the understanding of human languages. Our heuristic allows for ideation quality to be quickly quantified in the form of an ideation score. So, instead of going about the process blindly, we now have a means to provide a point of reference to allow students to give measured consideration to their ideation. Unlike a learning outcome, a future-ready habit is more of a predisposition. Consequently, it is not coherent with conventional assessments, which rather seek to evaluate than to guide. This heuristic represents an outcome of our evaluation of a new problem space in education and is, at the same time, a novel expansion into a space that exploits new capabilities.

MLBRSA: Multi-Learning-Based Reptile Search Algorithm for Global Optimization and Software Requirement Prioritization Problems.

In the realm of computational problem-solving, the search for efficient algorithms tailored for real-world engineering challenges and software requirement prioritization is relentless. This paper introduces the Multi-Learning-Based Reptile Search Algorithm (MLBRSA), a novel approach that synergistically integrates Q-learning, competitive learning, and adaptive learning techniques. The essence of multi-learning lies in harnessing the strengths of these individual learning paradigms to foster a more robust and versatile search mechanism. Q-learning brings the advantage of reinforcement learning, enabling the algorithm to make informed decisions based on past experiences. On the other hand, competitive learning introduces an element of competition, ensuring that the best solutions are continually evolving and adapting. Lastly, adaptive learning ensures the algorithm remains flexible, adjusting the traditional Reptile Search Algorithm (RSA) parameters. The application of the MLBRSA to numerical benchmarks and a few real-world engineering problems demonstrates its ability to find optimal solutions in complex problem spaces. Furthermore, when applied to the complicated task of software requirement prioritization, MLBRSA showcases its capability to rank requirements effectively, ensuring that critical software functionalities are addressed promptly. Based on the results obtained, the MLBRSA stands as evidence of the potential of multi-learning, offering a promising solution to engineering and software-centric challenges. Its adaptability, competitiveness, and experience-driven approach make it a valuable tool for researchers and practitioners.

On the solvability and the dimension of the solution set of abstract linear boundary value problems and applications to ODE'S

A solvability theory for linear abstract boundary value problems, that is of linear operator equations subject to an additional (“boundary”) condition given by a linear operator, is developed using a new approach based on normal solvability of the induced map or a pseudoinverse of the boundary map when restricted to the null space of the homogeneous problem. Uniqueness and dimension of their solution sets is also established. Applications to BVP's for finite-dimensional as well as infinite-dimensional systems of ordinary differential equations defined on the half line or the whole line are given. Systems having exponential dichotomy, or satisfying Riccati type inequalities are studied, as well as systems with asymptotically hyperbolic linear part and systems between pairs of admissible Banach spaces.