Search Process Research Articles

COMMUNICATION SKILLS AND REPORT WRITING FOR BUILDING SCIENTISTS

ABSTRACT Communication Skills and Report Writing for Building Scientists addresses the primary questions of inexperienced technical writers: “How should my report be written?” and “How do I present my work in style, format, data presentation and illustration program?” An undergraduate research report is given as an example of good report writing and key elements of the report are highlighted for student learning. This concise handbook also covers writing to land a job or applying to graduate school, and a step-by-step guide is provided on how to successfully navigate the job search or graduate school application process. Several sample resumes are provided as well as effective means to communicate with prospective employers or graduate programs of interest. The last section of this guide covers writing on the job and speaks to the kinds of tasks students face when they make the transition from classroom reporting to workplace communication, where problems are often open-ended and audiences cannot be assumed to be building science professionals. Instruction is also given to students on how to use the library as a research tool and how to master various virtual communication platforms: Zoom, Microsoft Teams and LinkedIn.

Mitotic activity: A systematic literature review of the assessment methodology and prognostic value in canine tumors.

One of the most relevant prognostic indices for tumors is cellular proliferation, which is most commonly measured by the mitotic activity in routine tumor sections. The goal of this systematic review was to analyze the methods and prognostic relevance of histologically measuring mitotic activity that have been reported for canine tumors in the literature. A total of 137 articles that correlated the mitotic activity in canine tumors with patient outcome were identified through a systematic (PubMed and Scopus) and nonsystematic (Google Scholar) literature search and eligibility screening process. Mitotic activity methods encompassed the mitotic count (MC, number of mitotic figures per tumor area) in 126 studies, presumably the MC (method not specified) in 6 studies, and the mitotic index (MI, number of mitotic figures per number of tumor cells) in 5 studies. A particularly high risk of bias was identified based on the available details of the MC methods and statistical analyses, which often did not quantify the prognostic discriminative ability of the MC and only reported P values. A significant association of the MC with survival was found in 72 of 109 (66%) studies. However, survival was evaluated by at least 3 studies in only 7 tumor types/groups, of which a prognostic relevance is apparent for mast cell tumors of the skin, cutaneous melanoma, and soft tissue tumor of the skin and subcutis. None of the studies using the MI found a prognostic relevance. This review highlights the need for more studies with standardized methods and appropriate analysis of the discriminative ability to prove the prognostic value of the MC and MI in various tumor types. Future studies are needed to evaluate the influence of the performance of individual pathologists on the appropriateness of prognostic thresholds and investigate methods to improve interobserver reproducibility.

Rational satisficing heuristics as determinants of online search behavior

We design a set of satisficing heuristic algorithms that mimic the online information retrieval behavior of rational decision makers (DMs) as reflected in their click through rates (CTRs). We illustrate how basic heuristic algorithms formalizing binary decision trees composed by 21 nodes and requiring DMs to observe one satisficing alternative lack the structural capacity to characterize the retrieval process. The algorithm requiring DMs to observe two satisficing alternatives –formalizing binary decision trees composed by 111 nodes – provides a sufficient approximation to their CTRs. Adding a third alternative – accounting for 351 nodes – delivers an almost identical set of CTRs to those displayed by DMs. The mimicking quality of the heuristic algorithms prevails as alternatives are added up to include the ten ranked within the first page of search results, incorporating 2,047 nodes to formalize the corresponding retrieval process. The set of algorithms bridges the gap between purely rational decision theory and heuristic behavior, illustrating how the CTRs observed can be generated by rational DMs performing a sequential search process while aiming to observe two or three satisficing alternatives. The decision-tree algorithmic structures presented are sufficiently malleable to introduce any potential modification to the beliefs and preferences of DMs and study its consequences in terms of CTRs.

Iterative solutions for nonlinear equations via fractional derivatives: adaptations and advances

In recent years, the utilization of fractional calculus has witnessed a notable surge across various scientific and engineering domains. This manuscript delves into the exploration of adapted iterative techniques tailored for solving nonlinear equations, capitalizing on the diverse range of derivatives available for addressing different problem contexts. We scrutinize previously developed iterative methods, enhancing their efficacy by introducing an auxiliary parameter to the root search process for nonlinear equations (NLE), alongside a fixed order of fractional derivatives. The selection of the auxiliary parameter is confined to the interval (0,1] for convenience. A thorough convergence analysis is conducted, employing a fractional power series expansion of f(x) in terms of fractional derivatives. Subsequently, a series of NLEs is solved to showcase and contrast the efficiency of our proposed methods with established iterative techniques. This refined abstract aims to succinctly elucidate the objectives and contributions of our study, providing readers with a clearer understanding of the manuscript's scope and significance.

Computational Bibliometric Analysis of Research on Bloom Digital Taxonomy and Critical Thinking

One important step towards understanding the development, trends, and effects in the context of contemporary education is to conduct a computational bibliometric analysis of research focused on Bloom Digital Taxonomy and elements of critical thinking studies. This research was conducted to perform a bibliometric analysis on the digital Bloom's taxonomy and critical thinking. The research method employed was bibliometric analysis, utilizing machine learning to map the data. The research comprised four stages of bibliometric analysis, namely: (a) data retrieval through the application of Publish or Perish, (b) data processing, (c) data mapping using machine learning, and (d) data analysis of the mapping using the R programming language. The research materials were published between 2015 and collected from the Google Scholar database in 2023. The search process involved the usage of the keywords "Taxonomy Bloom Digital" and "Critical Thinking." The results demonstrated that bibliometric analysis and mapping of 500 publications using machine learning enabled a deeper understanding of the development, trends, and crucial aspects of research in this field. By employing a bibliometric analysis approach and implementing machine learning, this study contributes to the comprehension of digital Bloom's taxonomy and critical thinking while providing an overview of research trends.

Transition path theory for diffusive search with stochastic resetting

Many chemical reactions can be formulated in terms of particle diffusion in a complex energy landscape. Transition path theory (TPT) is a theoretical framework for describing the direct (reaction) pathways from reactant to product states within this energy landscape, and calculating the effective reaction rate. It is now the standard method for analyzing rare events between long lived states. In this paper, we consider a completely different application of TPT, namely, a dual-aspect diffusive search process in which a particle alternates between collecting cargo from a source domain A and then delivering it to a target domain B. The rate of resource accumulation at the target, k AB , is determined by the statistics of direct (reactive or transport) paths from A to B. Rather than considering diffusion in a complex energy landscape, we focus on pure diffusion with stochastic resetting. Resetting introduces two non-trivial problems in the application of TPT. First, the process is not time-reversal invariant, which is reflected by the fact that there exists a unique non-equilibrium stationary state (NESS). Second, calculating k AB involves determining the total probability flux of direct transport paths across a dividing surface S between A and B. This requires taking into account discontinuous jumps across S due to resetting. We derive a general expression for k AB and show that it is independent of the choice of dividing surface. Finally, using the example of diffusion in a finite interval, we show that there exists an optimal resetting rate at which k AB is maximized. We explore how this feature depends on model parameters.

A Novel Approach to Sensor Placement: Recursive Exhaustive Search and Stochastic Optimization Parameter Impact Analysis

This study presents a comprehensive approach for single sensor placement optimization in two-dimensional and three-dimensional spaces. A traditional exhaustive search technique and a novel method called recursive exhaustive search are used to place a sensor in a way that maximizes the area coverage metric. Exhaustive search provides a baseline by methodically evaluating all potential placements, while recursive exhaustive search innovates by segmenting the search process into more manageable, recursive steps. Our findings highlight the significant impact of two key parameters, the number of evaluations and the rasterization value, on the achieved coverage and computation time. The results show that the right choice of parameters can significantly reduce the computational effort without compromising the quality of the solution. This underlines the critical need for a balanced approach that considers both computational complexity and placement efficacy. We show that exhaustive search is not feasible for three-dimensional environment models and propose to establish a modified exhaustive search method as a ground truth for the single sensor placement problem. We then explore nature-inspired genetic algorithms and the impact of the number of evaluations of the optimization function for these algorithms on both accuracy and computational cost.

Large-Scale Multi-Robot Coverage Path Planning via Local Search

We study graph-based Multi-Robot Coverage Path Planning (MCPP) that aims to compute coverage paths for multiple robots to cover all vertices of a given 2D grid terrain graph G. Existing graph-based MCPP algorithms first compute a tree cover on G---a forest of multiple trees that cover all vertices---and then employ the Spanning Tree Coverage (STC) paradigm to generate coverage paths on the decomposed graph D of the terrain graph G by circumnavigating the edges of the computed trees, aiming to optimize the makespan (i.e., the maximum coverage path cost among all robots). In this paper, we take a different approach by exploring how to systematically search for good coverage paths directly on D. We introduce a new algorithmic framework, called LS-MCPP, which leverages a local search to operate directly on D. We propose a novel standalone paradigm, Extended-STC (ESTC), that extends STC to achieve complete coverage for MCPP on any decomposed graph, even those resulting from incomplete terrain graphs. Furthermore, we demonstrate how to integrate ESTC with three novel types of neighborhood operators into our framework to effectively guide its search process. Our extensive experiments demonstrate the effectiveness of LS-MCPP, consistently improving the initial solution returned by two state-of-the-art baseline algorithms that compute suboptimal tree covers on G, with a notable reduction in makespan by up to 35.7% and 30.3%, respectively. Moreover, LS-MCPP consistently matches or surpasses the results of optimal tree cover computation, achieving these outcomes with orders of magnitude faster runtime, thereby showcasing its significant benefits for large-scale real-world coverage tasks.

DCLP: Neural Architecture Predictor with Curriculum Contrastive Learning

Neural predictors have shown great potential in the evaluation process of neural architecture search (NAS). However, current predictor-based approaches overlook the fact that training a predictor necessitates a considerable number of trained neural networks as the labeled training set, which is costly to obtain. Therefore, the critical issue in utilizing predictors for NAS is to train a high-performance predictor using as few trained neural networks as possible. Although some methods attempt to address this problem through unsupervised learning, they often result in inaccurate predictions. We argue that the unsupervised tasks intended for the common graph data are too challenging for neural networks, causing unsupervised training to be susceptible to performance crashes in NAS. To address this issue, we propose a CurricuLum-guided Contrastive Learning framework for neural Predictor (DCLP). Our method simplifies the contrastive task by designing a novel curriculum to enhance the stability of unlabeled training data distribution during contrastive training. Specifically, we propose a scheduler that ranks the training data according to the contrastive difficulty of each data and then inputs them to the contrastive learner in order. This approach concentrates the training data distribution and makes contrastive training more efficient. By using our method, the contrastive learner incrementally learns feature representations via unsupervised data on a smooth learning curve, avoiding performance crashes that may occur with excessively variable training data distributions. We experimentally demonstrate that DCLP has high accuracy and efficiency compared with existing predictors, and shows promising potential to discover superior architectures in various search spaces when combined with search strategies. Our code is available at: https://github.com/Zhengsh123/DCLP.

EG-NAS: Neural Architecture Search with Fast Evolutionary Exploration

Differentiable Architecture Search (DARTS) has achieved a rapid search for excellent architectures by optimizing architecture parameters through gradient descent. However, this efficiency comes with a significant challenge: the risk of premature convergence to local optima, resulting in subpar performance that falls short of expectations. To address this issue, we propose a novel and effective method called Evolutionary Gradient-Based Neural Architecture Search (EG-NAS). Our approach combines the strengths of both gradient descent and evolutionary strategy, allowing for the exploration of various optimization directions during the architecture search process. To begin with, we continue to employ gradient descent for updating network parameters to ensure efficiency. Subsequently, to mitigate the risk of premature convergence, we introduce an evolutionary strategy with global search capabilities to optimize the architecture parameters. By leveraging the best of both worlds, our method strikes a balance between efficient exploration and exploitation of the search space. Moreover, we have redefined the fitness function to not only consider accuracy but also account for individual similarity. This inclusion enhances the diversity and accuracy of the optimized directions identified by the evolutionary strategy. Extensive experiments on various datasets and search spaces demonstrate that EG-NAS achieves highly competitive performance at significantly low search costs compared to state-of-the-art methods. The code is available at https://github.com/caicaicheng/EG-NAS.

FlexiBO: A Decoupled Cost-Aware Multi-objective Optimization Approach for Deep Neural Networks (Abstract Reprint)

The design of machine learning systems often requires trading off different objectives, for example, prediction error and energy consumption for deep neural networks (DNNs). Typically, no single design performs well in all objectives; therefore, finding Pareto-optimal designs is of interest. The search for Pareto-optimal designs involves evaluating designs in an iterative process, and the measurements are used to evaluate an acquisition function that guides the search process. However, measuring different objectives incurs different costs. For example, the cost of measuring the prediction error of DNNs is orders of magnitude higher than that of measuring the energy consumption of a pre-trained DNN as it requires re-training the DNN. Current state-of-the-art methods do not consider this difference in objective evaluation cost, potentially incurring expensive evaluations of objective functions in the optimization process. In this paper, we develop a novel decoupled and cost-aware multi-objective optimization algorithm, which we call Flexible Multi-Objective Bayesian Optimization (FlexiBO) to address this issue. For evaluating each design, FlexiBO selects the objective with higher relative gain by weighting the improvement of the hypervolume of the Pareto region with the measurement cost of each objective. This strategy, therefore, balances the expense of collecting new information with the knowledge gained through objective evaluations, preventing FlexiBO from performing expensive measurements for little to no gain. We evaluate FlexiBO on seven state-of-the-art DNNs for image recognition, natural language processing (NLP), and speech-to-text translation. Our results indicate that, given the same total experimental budget, FlexiBO discovers designs with 4.8% to 12.4% lower hypervolume error than the best method in state-of-the-art multi-objective optimization.

Gaussian cross-entropy and organizing intelligence for design optimization of the outrigger system with inclined belt truss in real-size tall buildings

This research explores the optimal structural design for tall buildings with an outrigger and belt truss system. The study employs Gaussian Cross-Entropy with Organizing Intelligence (GCE-OI), a novel optimization approach that utilizes a self-organizing map as a machine learning algorithm, and Gaussian probability distribution in Cross-Entropy optimization. This approach is used to predict promising solutions and to guide the search process for swift convergence. The optimization encompasses member sizing (weight) and outrigger placement (topology) while introducing inclined belt trusses alongside traditional horizontal trusses for enhanced performance. The process involves optimizing a 25-story real-size model subjected to wind load, and the results are compared against multiple well-known algorithms. The results show that the proposed optimizer, supported by machine learning, outperforms alternative algorithms, offering superior solutions with enhanced convergence. Considering the efficiency of the inclined belt trusses and the proposed robust optimization method (GCE-OI), the optimally-placed outrigger system minimizes the constructional cost and enhances structural stability by limiting the responses.

Transformer-Empowered Multi-Modal Item Embedding for Enhanced Image Search in E-commerce

Over the past decade, significant advances have been made in the field of image search for e-commerce applications. Traditional image-to-image retrieval models, which focus solely on image details such as texture, tend to overlook useful semantic information contained within the images. As a result, the retrieved products might possess similar image details, but fail to fulfil the user's search goals. Moreover, the use of image-to-image retrieval models for products containing multiple images results in significant online product feature storage overhead and complex mapping implementations. In this paper, we report the design and deployment of the proposed Multi-modal Item Embedding Model (MIEM) to address these limitations. It is capable of utilizing both textual information and multiple images about a product to construct meaningful product features. By leveraging semantic information from images, MIEM effectively supplements the image search process, improving the overall accuracy of retrieval results. MIEM has become an integral part of the Shopee image search platform. Since its deployment in March 2023, it has achieved a remarkable 9.90% increase in terms of clicks per user and a 4.23% boost in terms of orders per user for the image search feature on the Shopee e-commerce platform.

An Automated Timetable Generation System for Academic Expertise Matching

For educators, creating time tables is a laborious task that requires a lot of manpower and time. It will be easier to produce time tables automatically if an automatic time table generator is available. Our project's proposed solution will aid in its automatic generation and time-saving. It does away with the difficulty of manually creating and maintaining a schedule. We want to employ resource scheduling, heuristic, and genetic algorithms in our project to lessen the challenges associated with creating timetables. Several strategies are incorporated into these algorithms with the goal of making the search process more efficient. The number of subjects, teachers, workload of a teacher, semester, and subject priority are just a few of the inputs that the system will get. Based on these inputs, it will produce potential schedules for the working days.

Analyzing of IDUKA as Partner to Improve the Quality of Graduates

This article aims to analyze of Industri Dunia Usaha Dan Dunia Kerja (IDUKA) as partner to improve the quality of Graduates at Vocational High School PGRI 3 Malang. The aim of building a partnership between IDUKA with schools is to guide knowledgeable students into the industrial realm of IDUKA which is in line with the student's major. This research uses qualitative research methods with the research design used in this research is a case study which aims to describe IDUKA's search as a partner to improve the quality of graduates at SMK PGRI 3 Malang. Data was obtained through observation, interviews and documentation. The data analysis technique is carried out in several steps, namely: data condensation, data presentation and drawing conclusions/verification. The results showed that the IDUKA search process was carried out by the Industrial Cooperation Division by submitting field work practice proposals, the school also attended activities at job fairs that followed the principles of link and match, the quality and quantity obtained by schools and industry in the world of business and work (IDUKA) is in accordance with what is expected and needed by partners.

Estimation of Distribution Algorithm for Grammar-Guided Genetic Programming.

Genetic variation operators in grammar-guided genetic programming are fundamental to guide the evolutionary process in search and optimization problems. However, they show some limitations, mainly derived from an unbalanced exploration and local-search trade-off. This article presents an estimation of distribution algorithm for grammar-guided genetic programming to overcome this difficulty and thus increase the performance of the evolutionary algorithm. Our proposal employs an extended dynamic stochastic context-free grammar to encode and calculate the estimation of the distribution of the search space from some promising individuals in the population. Unlike traditional estimation of distribution algorithms, the proposed approach improves exploratory behavior by smoothing the estimated distribution model. Therefore, this algorithm is referred to as SEDA, smoothed estimation of distribution algorithm. Experiments have been conducted to compare overall performance using a typical genetic programming crossover operator, an incremental estimation of distribution algorithm, and the proposed approach after tuning their hyperparameters. These experiments involve challenging problems to test the local search and exploration features of the three evolutionary systems. The results show that grammar-guided genetic programming with SEDA achieves the most accurate solutions with an intermediate convergence speed.

Mayfly Optimization for Real-World Problems: A Review of Applications and Challenges

The Mayfly Optimization Algorithm (MO), inspired by the swarming and mating behavior of adult mayflies, has emerged as a powerful tool for tackling optimization problems, particularly those seeking minimum values. This review paper delves into the core principles of MO, exploring its stages of swarming, velocity update, movement, mating, and selection. We analyze the strengths of MO, including its ability to balance exploration and exploitation during the search process, leading to well-converged solutions. Additionally, the paper examines recent advancements in MO that address potential limitations. We discuss how incorporating techniques like greedy selection and auto-termination can enhance the convergence speed and efficiency of the algorithm. Furthermore, the review explores various applications of MO across diverse fields, highlighting its potential for solving real-world minimization problems. Finally, we identify and discuss ongoing research directions, including hybridization with other algorithms and exploration of advanced termination strategies. This review paper aims to provide a comprehensive understanding of the Mayfly Optimization Algorithm, its capabilities, and promising areas for future development. Index Terms— Mayfly optimization, particle swarm optimization, swarm intelligence, nature inspired algorithm.

Counterfactual Explanation for Fairness in Recommendation

Fairness-aware recommendation alleviates discrimination issues to build trustworthy recommendation systems. Explaining the causes of unfair recommendations is critical, as it promotes fairness diagnostics, and thus secures users’ trust in recommendation models. Existing fairness explanation methods suffer high computation burdens due to the large-scale search space and the greedy nature of the explanation search process. Besides, they perform feature-level optimizations with continuous values, which are not applicable to discrete attributes such as gender and age. In this work, we adopt counterfactual explanations from causal inference and propose to generate attribute-level counterfactual explanations, adapting to discrete attributes in recommendation models. We use real-world attributes from Heterogeneous Information Networks (HINs) to empower counterfactual reasoning on discrete attributes. We propose a Counterfactual Explanation for Fairness (CFairER) that generates attribute-level counterfactual explanations from HINs for item exposure fairness. Our CFairER conducts off-policy reinforcement learning to seek high-quality counterfactual explanations, with attentive action pruning reducing the search space of candidate counterfactuals. The counterfactual explanations help to provide rational and proximate explanations for model fairness, while the attentive action pruning narrows the search space of attributes. Extensive experiments demonstrate our proposed model can generate faithful explanations while maintaining favorable recommendation performance.

Surrogate-assisted differential evolution using manifold learning-based sampling for high- dimensional expensive constrained optimization problems

To address the challenges of high-dimensional constrained optimization problems with expensive simulation models, a Surrogate-Assisted Differential Evolution using Manifold Learning-based Sampling (SADE-MLS) is proposed. In SADE-MLS, differential evolution operators are executed to generate numerous high-dimensional candidate points. To alleviate the curse of dimensionality, a Manifold Learning-based Sampling (MLS) mechanism is developed to explore the high-dimensional design space effectively. In MLS, the intrinsic dimensionality of the candidate points is determined by a maximum likelihood estimator. Then, the candidate points are mapped into a low-dimensional space using the dimensionality reduction technique, which can avoid significant information loss during dimensionality reduction. Thus, Kriging surrogates are constructed in the low-dimensional space to predict the responses of the mapped candidate points. The candidate points with high constrained expected improvement values are selected for global exploration. Moreover, the local search process assisted by radial basis function and differential evolution is performed to exploit the design space efficiently. Several numerical benchmarks are tested to compare SADE-MLS with other algorithms. Finally, SADE-MLS is successfully applied to a solid rocket motor multidisciplinary optimization problem and a re-entry vehicle aerodynamic optimization problem, with the total impulse and lift to drag ratio being increased by 32.7% and 35.5%, respectively. The optimization results demonstrate the practicality and effectiveness of the proposed method in real engineering practices.

Lessons from a scoping review: Clinical presentations of central centrifugal cicatricial alopecia

Central centrifugal cicatricial alopecia (CCCA) nomenclature describes a typical clinical presentation of cicatricial hair loss that begins on the vertex scalp with progressive, symmetric, and centrifugal evolution. However, atypical presentations have been noted clinically by the authors and reported in the literature. We sought to characterize the distribution of hair loss in published cases of adult patients with CCCA. A 3-step search process was used to evaluate research articles in Cumulative Index to Nursing & Allied Health, EMBASE, Google Scholar, MEDLINE, Scopus, and Web of Science databases. Studies with scalp photography or description of hair loss distribution were included. Three researchers evaluated eligible studies for clinical subtypes. Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Review was used to report results. Ninety-nine studies consisting of 281 cases of CCCA were included. Hair loss distributions included variants of the classic presentation along with distinct subtypes such as patchy, occipital, parietal, frontal, temporal, and trichorrhexis. Studies had significant homogeneity, as the classic distribution of CCCA was commonly reported. Additionally, clinically diagnosed cases may have concurrent diagnoses, and numerous studies did not report trichoscopy findings. CCCA terminology may not always be reflective of clinical presentation. Understanding atypical presentations is essential to inform appropriate and targeted treatment.