Early Iterations Research Articles

Curvilinear lineament extraction: Bayesian optimization of Principal Component Wavelet Analysis and Hysteresis Thresholding

Understanding deformation networks, visible as curvilinear lineaments in images, is crucial for geoscientific explorations. However, traditional manual extraction of lineaments is expertise-dependent, time-consuming, and labor-intensive. This study introduces an automated method to extract and identify geological faults from aeromagnetic images, integrating Bayesian Hyperparameter Optimization (BHO), Principal Component Wavelet Analysis (PCWA), and Hysteresis Thresholding Algorithm (HTA). The continuous wavelet transform (CWT), employed across various scales and orientations, enhances feature extraction quality, while Principal Component Analysis (PCA) within the CWT eliminates redundant information, focusing on relevant features. Using a Gaussian Process surrogate model, BHO autonomously fine-tunes hyperparameters for optimal curvilinear pattern recognition, resulting in a highly accurate and computationally efficient solution for curvilinear lineament mapping. Empirical validation using aeromagnetic images from a prominent fault zone in the James Bay region of Quebec, Canada, demonstrates significant accuracy improvements, with 23% improvement in Fβ Score over the unoptimized PCWA-HTA and a marked 300% improvement over traditional HTA methods, underscoring the added value of fusing BHO with PCWA in the curvilinear lineament extraction process. The iterative nature of BHO progressively refines hyperparameters, enhancing geological feature detection. Early BHO iterations broadly explore the hyperparameter space, identifying low-frequency curvilinear features representing deep lineaments. As BHO advances, hyperparameter fine-tuning increases sensitivity to high-frequency features indicative of shallow lineaments. This progressive refinement ensures that later iterations better detect detailed structures, demonstrating BHO's robustness in distinguishing various curvilinear features and improving the accuracy of curvilinear lineament extraction. For future work, we aim to expand the method's applicability by incorporating multiple geophysical image types, enhancing adaptability across diverse geological contexts.

A Test Report Optimization Method Fusing Reinforcement Learning and Genetic Algorithms

Filtering high-variability and high-severity defect reports from large test report databases is a challenging task in crowdtesting. Traditional optimization algorithms based on clustering and distance techniques have made progress but are limited by initial parameter settings and significantly decrease in efficiency with an increasing number of reports. To address this issue, this paper proposes a method that integrates reinforcement learning with genetic algorithms for crowdsourced testing report optimization, called Reinforcement Learning-based Genetic Algorithm for Crowdsourced Testing Report Optimization (RLGA). Its core goal is to identify distinct, high-severity defect reports from a large set. The method uses genetic algorithms to generate the optimal report selection sequence and adjusts the crossover probability (Pc) and mutation probability (Pm) dynamically with reinforcement learning based on the population’s average fitness, best fitness, and diversity. The reinforcement learning component uses a hybrid SARSA and Q-Learning strategy to update the Q-value table, allowing the algorithm to learn quickly in early iterations and expand the search space later to avoid local optima, thereby improving efficiency. To validate the RLGA method, this paper uses four public datasets and compares RLGA with six classic methods. The results indicate that RLGA outperforms BDDIV in terms of execution time and is less sensitive to the total number of test reports. In terms of optimization objectives, the test reports selected by RLGA have higher levels of defect severity and diversity than those selected by the random choice, BDDIV, and TSE methods. Regarding population diversity, RLGA effectively enhances the uniformity and diversity of individuals compared to random initialization. In terms of convergence speed, RLGA is superior to the GA, GA-SARSA, and GA-Q methods.

Early Iterations of Bisexual Activism in Toronto, Ontario: A Historical Examination of Bykes and the Ontario Bisexual Network

This article seeks to illuminate the activities of historical bisexual community groups in Toronto in the late twentieth century, a peak era of bisexual activism in Ontario. The city’s first bisexual women’s group, Bykes, was founded in 1983, and the first mixed gender group, the Ontario Bisexual Network, was established later in 1989. While the former was influenced by radical lesbian separatist politics in 1970s and 1980s Toronto, the latter demonstrated the need to amplify bisexual concerns across the gender spectrum, in addition to serving a growing membership within the burgeoning bisexual community in the city. In order to contextualize the significance of these bisexual community groups, the following research questions are addressed: what were the objectives of Toronto’s early bisexual community groups and how did activists accomplish them? How did bisexual people in these groups situate themselves in Toronto’s gay and lesbian landscape? What were the main tenets of bisexual community building in the city? Through analyses of oral history interviews and archival materials, we can observe that bisexual activists involved in Toronto’s first community groups participated in creating meeting spaces for each other and engaging in public advocacy that revolved primarily around visibility, education, and community care.

Amorphous infrastructure: Contesting the proposed Yuam River water diversion project in the Salween River Basin

Recent studies in human geography and political ecology have examined the anticipatory politics of proposed infrastructure and their effects. Less work analyses how speculation and rumour are mobilised to promote or resist such infrastructures. In this paper, I examine how speculative ideas about long-proposed hydropower dams and transbasin water diversions in the Salween River Basin are strategically deployed by differently placed actors working towards project development or demise. I draw on empirical research on the proposed Yuam River water diversion project in Northwest Thailand and near the Thai–Myanmar border. I develop a conceptualisation of proposed infrastructures as amorphous as they constantly change form and enrol a shifting network of actors, and development plans and imaginaries. The Yuam diversion becomes amorphous through the contested ‘facts’ of the case and over time. I illustrate this using two longitudinal examples. First, infrastructure is made amorphous through protracted development processes for the Yuam diversion and earlier iterations. Second, I examine speculative ideas about how the Yuam diversion is intertwined with proposed dams in the Basin including the Hatgyi Dam in Myanmar. In this case, the lack of confirmed project developers and financiers for the Yuam diversion, and rumours of Chinese actors’ involvement under the Belt and Road Initiative, creates strategic space for project promotion and resistance. What is at stake is not just theorising proposed infrastructures as speculative ideas but how the ‘amorphous’ qualities of infrastructure are deployed strategically, by whom, and to what effect.

Adversarial Approaches to Deepfake Detection: A Theoretical Framework for Robust Defense

The rapid improvements in capabilities of neural networks and generative adversarial networks (GANs) has given rise to extremely sophisticated deepfake technologies. This has made it very difficult to reliably recognize fake digital content. It has enabled the creation of highly convincing synthetic media which can be used in malicious ways in this era of user generated information and social media. Existing deepfake detection techniques are effective against early iterations of deepfakes but get increasingly vulnerable to more sophisticated deepfakes and adversarial attacks. In this paper we explore a novel approach to deepfake detection which uses a framework to integrate adversarial training to improve the robustness and accuracy of deepfake detection models. By looking deeper into state of art adversarial machine learning, forensic analysis and deepfake detection techniques we will explore how adversarial training can improve the robustness of deep fake detection techniques against future threats. We will use perturbations which are adversarial examples designed specifically to deceive the deepfake detection algorithms. By training deepfake detection models with these perturbations we will create detection systems that can more accurately identify deepfakes. Our approach shows promise and avenues for future research in building resilience against deepfakes and applications in content moderation, security and combating synthetic media manipulation.

From Background to Foreground: A Retrospective Self-Study of Critical Social Studies Teacher Educating

ABSTRACT In this retrospective collaborative self-study, we examined our development as justice-based social studies teacher educators. Rooted in frameworks centered on critical social studies and teacher educator knowledge and practice, an analysis of discussions and curricular-instructional materials spanning 12 years found that justice was treated as optional in early iterations of our methods courses. Yet over time, we have taken steps to move justice from the background to the foreground of our work. We discuss and offer explanations for this growth, concluding with implications for our own practice as well as those charged with structuring (and teaching within) teacher educator preparation and induction programs.

Feedback Collection and Nearest-Neighbor Profiling for Recommendation Systems in Healthcare Scenarios

The rise in the dimension and complexity of information generated in the clinical field has motivated research on the automation of tasks in personalized healthcare. Recommendation systems are a filtering method that utilizes patterns and data relationships to generate items of interest for a particular user. In healthcare, these systems can be used to potentiate physical therapy by providing the user with specific exercises for rehabilitation, albeit facing issues pertaining to low accuracy in earlier iterations (cold-start) and a lack of gradual optimization. In this study, we propose a physical activity recommendation system that utilizes a K-nearest neighbor (KNN) sampling strategy and feedback collection modules to improve the adequacy of recommendations at different stages of a rehabilitation period when compared to traditional collaborative filtering (CF) or human-constrained methods. The results from a trial show significant improvements in the quality of initial recommendations, achieving 81.2% accuracy before optimization. Moreover, the introduction of short-term adjustments based on frequent player feedback can be an efficient manner of improving recommendation accuracy over time, achieving overall better convergence periods than those of human-based systems, topping at a measured 98.1% accuracy at K = 7 cycles.

On the idea of freedom in modern African political philosophy

AbstractWhen Liberia, at its inception, adopted for her motto, “The Love of Freedom Brought Us Here,” they were registering their embrace of a core tenet of modernity: the idea that freedom was the primary state of humanity and that any form of social living that turned this free being into a bonded phenomenon, forces it to obey dictates and laws not of its own choosing, to bow before rulers it had no hand in electing, and to live at the behest of another must stand condemned. They were merely the latest, back then, instantiation of Africans as singers of freedom's song in the modern age. Unfortunately, neither theirs nor earlier iterations in the Haitian Revolution, much less later contributions by other Africans in the continent, are to be found routinely, if at all, in the annals of modern philosophy. In this essay, I bring to our awareness how African thinkers have domesticated the idea of freedom as a legacy of modernity in their world. This is one of the bodies of work that is obscured by the wall that I argue, in Against Decolonisation: Taking African Agency Seriously (Hurst Publishers, 2022), decolonizers have erected in their mistaking, wittingly or unwittingly, modernity for colonialism and/or Westernization.

Generative Pre-trained Transformer for Pediatric Stroke Research: A Pilot Study

BackgroundPediatric stroke is an important cause of morbidity in children. Although research can be challenging, large amounts of data have been captured through collaborative efforts in the International Pediatric Stroke Study (IPSS). This study explores the use of an advanced artificial intelligence program, the Generative Pre-trained Transformer (GPT), to enter pediatric stroke data into the IPSS. MethodsThe most recent 50 clinical notes of patients with ischemic stroke or cerebral venous sinus thrombosis at the UTHealth Pediatric Stroke Clinic were deidentified. Domain-specific prompts were engineered for an offline artificial intelligence program (GPT) to answer IPSS questions. Responses from GPT were compared with the human rater. Percent agreement was assessed across 50 patients for each of the 114 queries developed from the IPSS database outcome questionnaire. ResultsGPT demonstrated strong performance on several questions but showed variability overall. In its early iterations it was able to match human judgment occasionally with an accuracy score of 1.00 (n = 20, 17.5%), but it scored as low as 0.26 in some patients. Prompts were adjusted in four subsequent iterations to increase accuracy. In its fourth iteration, agreement was 93.6%, with a maximum agreement of 100% and minimum of 62%. Of 2400 individual items assessed, our model entered 2247 (93.6%) correctly and 153 (6.4%) incorrectly. ConclusionsAlthough our tailored generative model with domain-specific prompt engineering and ontological guidance shows promise for research applications, further refinement is needed to enhance its accuracy. It cannot enter data entirely independently, but it can be employed in tandem with human oversight contributing to a collaborative approach that reduces overall effort.

Utilizing large language models (LLMs) to pre-label candidate concepts from in conceptual development landscape reviews in oncology.

e23198 Background: Labeling and identifying concepts from qualitative studies and patient interviews in oncology can be challenging and time consuming, often requiring several iterations. Large language models (LLMs) may be tailored to serve as a ‘co-pilot’, thus reducing errors and time to completion. We therefore aimed to examine whether utilizing LLMs to pre-label concepts (i.e., how patient’s feel or function) from medical oncology literature text can be done at comparable accuracy as human. To examine whether such LLM pre-labelling can dramatically reduce the time required for manual labor in conceptual development landscape reviews in oncology. Methods: We developed GPT-4 (OpenAI) prompts to pre-label concepts reported in abstracts of qualitative study references. We further developed GPT-4 prompts to categorize each pre-labeled concept into one of the following relevant high-level category: clinician reported symptoms, cognitive impacts, symptoms due to treatment, symptoms due to diagnosis, emotional impacts, impact on caregiver, other impacts, physical appearance, physical function, and social impacts. The prompts were extended to also capture diagnosis, treatments, patient reported outcomes, as well as generic terms or irrelevant terms that would otherwise erroneously be captured under the concept category in the early iterations of the prompt-engineering. The prompts were tested on 48, 37, and 38 (total of 123) eligible references from three conceptual development reviews covering colorectal cancer (CRC), non-small cell lung cancer (NSCLC), and acute myeloid leukemia (AML), respectively. Results: The LLM pre-labelled a total of 153, 131, and 159 candidate concepts across the CRC, NSCLC, and AML reviews, respectively. In all three reviews, clinical reported symptoms, emotional impacts, and social impacts all accounted for 18-25% each of the pre-labelled concepts, whereas physical function and social impact accounted for 5-9% each. A total of 2 (1.3%), 3 (2.3%) and 4 (2.5%) pre-labels were predicted to belong to the wrong high-level category. Incorrectly categorized labels were dispersed across most categories and should have been categorized as ‘other impacts’. In the NSCLC review, one pre-label categorized as impact on caregiver and two as other impacts were erroneously extracted concepts. Conclusions: LLMs are effective at capturing and pre-labelling concepts for further review, evident by the high number of concept labels extracted. Further high-level categorization with LLMs to guide manual reviewers also appears highly accurate.

A Web-Based Peer Support Network to Help Care Partners of People With Serious Illness: Co-Design Study.

Care partners of people with serious illness experience significant challenges and unmet needs during the patient's treatment period and after their death. Learning from others with shared experiences can be valuable, but opportunities are not consistently available. This study aims to design and prototype a regional, facilitated, and web-based peer support network to help active and bereaved care partners of persons with serious illness be better prepared to cope with the surprises that arise during serious illness and in bereavement. An 18-member co-design team included active care partners and those in bereavement, people who had experienced serious illness, regional health care and support partners, and clinicians. It was guided by facilitators and peer network subject-matter experts. We conducted design exercises to identify the functions and specifications of a peer support network. Co-design members independently prioritized network specifications, which were incorporated into an early iteration of the web-based network. The team prioritized two functions: (1) connecting care partners to information and (2) facilitating emotional support. The design process generated 24 potential network specifications to support these functions. The highest priorities included providing a supportive and respectful community; connecting people to trusted resources; reducing barriers to asking for help; and providing frequently asked questions and responses. The network platform had to be simple and intuitive, provide technical support for users, protect member privacy, provide publicly available information and a private discussion forum, and be easily accessible. It was feasible to enroll members in the ConnectShareCare web-based network over a 3-month period. A co-design process supported the identification of critical features of a peer support network for care partners of people with serious illnesses in a rural setting, as well as initial testing and use. Further testing is underway to assess the long-term viability and impact of the network.

Immigrant Threat or Institutional Context? Examining Police Agency and County Context and the Implementation of the 287(g) Program

ABSTRACT During the early 2000s, the U.S. government began to partner with local law enforcement agencies for assistance with the enforcement of immigration laws. Participation in the voluntary 287(g) program by law enforcement agencies was an important early iteration of this new strategy. Although relatively few agencies adopted 287(g), its implementation was a critical turning point in the nation’s approach to immigration enforcement and has been linked to a wide range of negative consequences for Latinos, including higher levels of violent victimization, and decreases in police trust. This study advances knowledge about the factors associated with 287(g) program adoption during the late 2000s. We examined the role of agency ethnoracial diversity and fiscal capacity and several theoretically relevant indicators of the broader county context. Based on a sample of 880 law enforcement agencies located in 233 counties, rare event logistic regression models indicate that the strongest predictors of 287(g) adoption were the share of county population estimated to be undocumented immigrants, the degree of support for the Republican Presidential candidate, and the relative unemployment of Hispanic and non-Hispanic White residents. Overall, the results suggest that 287(g) was primarily a political response to concerns about the potential threats associated with undocumented immigration.

Dynamic-mode-decomposition-based gradient prediction for adjoint-based aerodynamic shape optimization

Accurate and efficient gradient computation is the key to aerodynamic shape optimization. In this paper, dynamic mode decomposition (DMD) is employed to analyze the dynamic characteristics of the early pseudo-time marching of adjoint equations and to predict the gradient. Besides the first-order zero-frequency mode, other zero-frequency modes also contribute to the pseudo iterations of the adjoint equations in the early iterations. Hence, different from existing methods, all zero-frequency modes are retained to reconstruct adjoint fields for gradient prediction. Moreover, to further improve the modeling accuracy, an improved DMD (IDMD) is proposed by omitting the initial snapshots in early iterations. The effect of pseudo-time step on modeling accuracy is also studied. By solving the adjoint equations of the transonic and subsonic flows, the accuracy of the proposed method is verified. Results indicate that the proposed method still works despite the conventional solution process diverges. Through aerodynamic shape optimization examples of transonic flow over an airfoil, the number of adjoint pseudo-time steps is remarkably reduced by 83%, which indicates the proposed IDMD-based gradient prediction method has great potential for improving the efficiency of aerodynamic shape optimization.

The Multitude, the People, and Popular Sovereignty: Pufendorf and Locke in Reply to Hobbes

Abstract In the early iterations of his political thought, The Elements of Law and De Cive, Hobbes proposed a new account of the nature of the people. In Section 2 I describe Pufendorf’s critical response. Pufendorf’s theory of the people is a neglected aspect of the political argument of the De Jure. Just as neglected is Locke’s theory of the people in Two Treatises of Government, though there is better reason for neglect in Locke’s case, in so far as he fails in his major work of political philosophy to present anything resembling a theory of the people at all. In Section 3 I bring Locke’s thinking about the people into clearer focus. In the concluding Section 4 I explore some of the weaknesses of his position.

Multi-Strategy Improved Particle Swarm Optimization Algorithm and Gazelle Optimization Algorithm and Application

In addressing the challenges associated with low convergence accuracy and unstable optimization results in the original gazelle optimization algorithm (GOA), this paper proposes a novel approach incorporating chaos mapping termed multi-strategy particle swarm optimization with gazelle optimization algorithm (MPSOGOA). In the population initialization stage, segmented mapping is integrated to generate a uniformly distributed high-quality population which enhances diversity, and global perturbation of the population is added to improve the convergence speed in the early iteration and the convergence accuracy in the late iteration. By combining particle swarm optimization (PSO) and GOA, the algorithm leverages individual experiences of gazelles, which improves convergence accuracy and stability. Tested on 35 benchmark functions, MPSOGOA demonstrates superior performance in convergence accuracy and stability through Friedman tests and Wilcoxon signed-rank tests, surpassing other metaheuristic algorithms. Applied to engineering optimization problems, including constrained implementations, MPSOGOA exhibits excellent optimization performance.

Evolution inspired binary flower pollination for the uncapacitated facility location problem

The present paper introduces a modified flower pollination algorithm (FPA) enhanced by evolutionary operators to solve the uncapacitated facility location problem (UFLP), which is one of the well-known location science problems. The aim in UFLP is to select some locations to open facilities among a certain number of candidate locations so as to minimize the total cost, which is the sum of facility opening costs and transportation costs. Since UFLP is a binary optimization problem, FPA, which is introduced to solve real-valued optimization problems, is redesigned to be able to conduct search in binary domains. This constitutes one of the contributions of the present study. In this context, some evolutionary operators such as crossover and mutation are adopted by the proposed FPA. Next, the mutation operator is further enhanced by making use of an adaptive procedure that introduces greater level of diversity at earlier iterations and encourages intensification toward the end of search. Thus, while premature convergence and local optima problems at earlier iterations are avoided, a more intensified search around the found promising regions is performed. Secondarily, as demonstrated in this study, by making use of the reported evolutionary procedures, FPA is able to run in binary spaces without employing any additional auxiliary procedures such as transfer functions. All available benchmarking instances are solved by the proposed approach. As demonstrated by the comprehensive experimental study that includes statistically verified results, the developed approach is found as a promising algorithm that can be extended to numerous binary optimization problems.

Proficiency Development and Learning Curve in Robot-Assisted Spine Surgery Using the ExcelsiusGPS® System: Experience From a Single Institution.

Retrospective Cohort Study Objectives: Robot-assisted spine surgery (RASS) is a rapidly evolving technique with potential benefits for improving surgical outcomes. A number of studies on RASS learning curve have focused on early iterations of the Mazor robot. Limited research exists on the learning curve associated with using the Globus Medical ExcelsiusGPS® system. In this retrospective study, we aimed to evaluate the learning curve of RASS using the ExcelsiusGPS® system at a single institution. A total of 95 patients (541 screws) who underwent RASS between 2021 and 2022 were included. Variables including operative time, robot registration time, screw placement time, fluoroscopy utilization, and complications were analyzed. Statistical analysis was performed using descriptive statistics and two-sample t-tests. The average operative time significantly decreased after the first 14 cases, indicating a learning curve. However, no significant improvement was observed in robot registration time. Notably, screw placement time significantly improved after approximately 13 cases. When controlling for the number of levels fused, the trends remained consistent. Our study confirmed the presence of a learning curve in RASS using the ExcelsiusGPS® system and demonstrated rapid proficiency development. Our findings highlight the relatively quick learning curve of 1 RASS system.

ProcessGallery: Contrasting Early and Late Iterations for Design Principle Learning

Traditional design galleries enable users to search for examples based on surface attributes (e.g., color or style), and largely obscure underlying principles (e.g., hierarchy or readability). We conducted three studies to explore how galleries could be constructed to help novices learn key design principles. Study 1 revealed that novices gain perspective by observing how designs evolve throughout a process. Study 2 found that novices are better at identifying design issues when viewing iterations that show improvements for just one principle at a time, rather than multiple. Building on these insights, we created ProcessGallery, a tool that enables users to browse contrasting pairs of early-and-late iterations of designs that highlight key improvements organized by design principles. In Study 3, a within-subjects experiment, sixteen participants iterated on a seed design after viewing examples in ProcessGallery versus a traditional gallery. Using ProcessGallery, participants found more appropriate examples, assessed designs better, and preferred ProcessGallery for learning compared to a traditional gallery.

Strategic styles of hardware product development could accelerate commercialization in cleantech startups

Hardware-based startups risk having longer times-to-market, deterring investment in the clean technologies that are critical to a sustainable future. We interviewed 55 leaders at hardware startups, 20 of which are cleantech, mapped their development timelines, and found prototyping to be the longest development step (median of 19 weeks per prototype) regardless of prototype complexity or iteration. Qualitative interview analysis reveals the prototyping team’s choice of development style is a major factor affecting timeline. We define two development styles: natural and structured, typified by free-form exploration and rule-based execution, respectively. On average, natural development takes 35% less time than structured, and is thus preferred for early iterations, but adopting structure at strategic points is needed for timely commercialization. Critical points of transition to a structured style include adding new team members or engaging external partners, which demand clear communication and expectations. When pivoting to a new product or market, returning to a natural style is beneficial.

AI based Identification of Students Dress Code in Schools and Universities

Automatic dress code verification (ADCV) uses advanced technology to quickly and fairly check if people are following the dress code. It is more accurate than humans and does not have personal biases. This can be helpful in places like schools, universities, and offices where dress codes are important for safety. One of the great things about it is that it saves time and money. It does not require people to manually check the dress codes, which can be slow and not always consistent. This technology can be adjusted to fit different dress code rules, whether it's a formal office dress or a school uniform. The technology can be customized for different dress code rules, from formal office attire to school uniforms. One notable advantage of ADCV is its capacity to save both time and financial resources. The system gets rid of the requirement for people to manually check dress codes, which can be slow and inconsistent. The technology's flexibility extends to customization for different dress code rules, addressing the unique needs of diverse settings. It is important to highlight the evolution of ADCV from previous models, where limitations existed in terms of singular application focus. In earlier iterations, functionalities such as face recognition or object detection could only be implemented individually, creating a gap in the comprehensive identification of individuals that encompassed both facial features and attire. The proposed system aims to rectify these shortcomings by integrating capabilities, ensuring a more holistic approach to dress code verification. This enhancement signifies a significant stride toward a more versatile and effective solution for monitoring and enforcing dress codes in various environments. In the previous models the only one application could be designed. For instance, the face recognition or the object detection is only possible individually. It cannot do both identification of person’s face and attire of the person. Considering these disadvantages, the rectification of these problems will be achieved through the implementation of the proposed system. ADCV's impact extends beyond its immediate application in dress code enforcement. As a pioneering example of the intersection between computer vision and artificial intelligence.