High-level Categories Research Articles

An empirical study of developers’ challenges in implementing Workflows as Code: A case study on Apache Airflow

The Workflows as Code paradigm is becoming increasingly essential to streamline the design and management of complex processes within data-intensive software systems. These systems require robust capabilities to process, analyze, and extract insights from large datasets. Workflow orchestration platforms such as Apache Airflow are pivotal in meeting these needs, as they effectively support the implementation of the Workflows as Code paradigm. Nevertheless, despite its considerable advantages, developers still face challenges due to the specialized demands of workflow orchestration and the complexities of distributed execution environments. In this paper, we manually study 1000 sampled Stack Overflow posts derived from 9591 Airflow-related questions to understand developers’ challenges and root causes while implementing Workflows as Code. Our analysis results in a hierarchical taxonomy of Airflow-related challenges that contains 7 high-level categories and 14 sub-categories. We find that the most significant obstacles for developers arise when defining and executing their workflow. Our in-depth analysis identifies 10 root causes behind the challenges, including incorrect workflow configuration, complex environmental setup, and a lack of basic knowledge about Airflow and the external systems that it interacts with. Additionally, our analysis of references shared within the collected posts reveals that beyond the frequently cited Airflow documentation, documentation from external systems and third-party providers is also commonly referenced to address Airflow-related challenges.Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.

Leveraging Greenspace to Manage Urban Flooding: An Investigation of Nature-Based Solutions Implementation in U.S. Public Parks

Many cities are looking to adopt nature-based solutions (NBS) in greenspace to manage urban flooding and provide diverse co-benefits. Yet little research exists to inform the planning and design of park NBS. This study investigated NBS adoption in 58 public parks across major U.S. cities, using a 2022 survey by the Trust for Public Lands and other secondary datasets. We developed a typology to conceptualize a wide range of park NBS into five high-level categories by size/capacity, location of the gray–green spectrum, and design objectives. We then employed this typology to explore how a park’s adopted NBS types may relate to its landscape and sociodemographic contexts. We found that the most used type of NBS in the studied parks was ECO (a typology we defined as conserving, restoring, or creating ecosystems to mitigate flooding through ecological processes and functions), while the least used NBS type was ENG (a typology we defined as imitating natural infiltration processes but having no living elements). Further, parks that adopted ECO had significantly higher percentages of greenspace in the surrounding, as well as higher flood risks. We also found notable—though not statistically significant—evidence of potential associations between the type of NBS implementation in a park and its nearby neighborhoods’ income level, poverty, and population racial and age compositions. Moreover, our findings indicated that park visitors were more privileged compared to residents living near a park. We concluded that park contextual factors deserve more explicit consideration in the planning and design of NBS and discussed key implications of this study for practice and future research around park NBS for urban flooding.

Examining Variability of High-Level Visual Categories Across Development

Examining Variability of High-Level Visual Categories Across Development

Does Stimulus Category Coherence Influence Visual Working Memory? A Rational Analysis.

Visual working memory (VWM) refers to the temporary storage and manipulation of visual information. Although visually different, objects we view and remember can share the same higher-level category information, such as an apple, orange, and banana all being classified as fruit. We study the influence of category information on VWM, focusing on the question of whether stimulus category coherence (i.e., whether all to-be-remembered items belong to the same semantic category) influences VWM performance. This question is addressed in two behavioral experiments using a change-detection paradigm and a rational analysis using an ideal observer based on a Bayesian model. Both experimental participants and the ideal observer often, but not always, performed numerically better on coherent trials (i.e., when all stimuli belonged to the same category). We hypothesize that the influence of category coherence information on VWM may be task-dependent and/or stimulus-dependent. In conditions when category coherence information is highly valuable for task performance, as indicated by the ideal observer, then participants tended to make use of it. However, when the ideal observer suggested this information was not crucial to performance, participants did not. In addition, both participants and the ideal observer showed a bias toward responding "same," and often showed a stronger influence of category coherence on change trials. The consistencies between participant and ideal observer responses suggest participants often behaved as they did because these behaviors are optimal (or approximately so) for maximizing task performance. This may help explain conflicting results reported in the scientificliterature.

Toward More Comprehensive Evaluations of 3D Immersive Sketching, Drawing, and Painting.

To understand current practice and explore the potential for more comprehensive evaluations of 3D immersive sketching, drawing, and painting, we present a survey of evaluation methodologies used in existing 3D sketching research, a breakdown and discussion of important phases (sub-tasks) in the 3D sketching process, and a framework that suggests how these factors can inform evaluation strategies in future 3D sketching research. Existing evaluations identified in the survey are organized and discussed within three high-level categories: 1) evaluating the 3D sketching activity, 2) evaluating 3D sketching tools, and 3) evaluating 3D sketching artifacts. The new framework suggests targeting evaluations to one or more of these categories and identifying relevant user populations. In addition, building upon the discussion of the different phases of the 3D sketching process, the framework suggests to evaluate relevant sketching tasks, which may range from low-level perception and hand movements to high-level conceptual design. Finally, we discuss limitations and challenges that arise when evaluating 3D sketching, including a lack of standardization of evaluation methods and multiple, potentially conflicting, ways to evaluate the same task and user interface usability; we also identify opportunities for more holistic evaluations. We hope the results can contribute to accelerating research in this domain and, ultimately, broad adoption of immersive sketching systems.

Emotional Fertility Intention and its correlates in Ethiopia among married contraceptive user women: using linked community and health facility data from performance monitoring for action; a generalized ordered logistics regression modeling

BackgroundEmotional fertility intention and couples communication are key during pregnancy and childbirth with simultaneous minimization of reproductive coercion. Intention to conceive is an integral part of the reproductive health (RH) right and can be considered as decision making on fertility, family wellbeing and the country’s population demographic dividend and composition. However, in low and middle income countries including Ethiopia where males dominance is culturally constructed and socially accepted, males took the lead in every decision making process. In the aforementioned context, women are less likely for their voices to be heard, hence, this study aimed at determining the level of womens´ emotional fertility readiness and its correlates. The finding provided actionable evidence for the ministry and developmental partners working on reproductive and womens´ health so as to be used as an action point to empower women in terms of their reproductive health right to have control over their fertility.MethodsLinked community and facility data with nationally representation from Performance Monitoring for Action (PMA Ethiopia) 2020 Survey Ethiopia except Tigray Region were used for this study. A total of 2,069 current and/or recent contraceptive user women of child bearing age who are currently married/living together as a partner were included in this analysis. Frequency was computed to describe the study participant’s characteristics. Generalized Ordered logistics regression modeling was employed to identify correlates of the hierarchical variation in women fertility intention if they became pregnant. Results were presented in the form of percentages and odds ratio with 95% Confidence Intervals. Candidate variables were selected using p-value of 0.25. Statistical significance was declared at p-value of 0.05.ResultsThe proportion of womens´ emotional fertility intention of feeling unhappiness was 48.73% (95%CI: 46.21%, 51.23%). On the contrary, 22.88%, 11.36% and 17.03% of them reported that they felt sort of happy, very happy and mixed feeling. An increase in age,10 and above years marriage duration, the type of decision maker for contraceptive use were found to increase the odds of women emotional fertility intention across the higher level categories by (AOR: 95% CI: 6.75 (3.11, 14.62) times higher among elder women aged 35 to 49 years, (AOR: 95% CI: 3.79 (1.72, 8.31) times higher for women with a 10 or more years of marriage duration; and 1.83 (1.03,3.24) times higher for women whose contraceptive use was decided by the health care provide alone. A higher birth order lowered the cumulative odds of womens´ emotional fertility intention symmetrically across the higher level categories by 86% (AOR: 95% CI: 0.14 (0.07, 0.29). Women who wanted to have additional child and whose nearest facility provided 5 or more methods had an increased odds of being in the higher level categories of women emotional fertility intention with disproportional association across the cumulative logit. Accordingly, women whose nearest health facility provided 5 or more methods had an 49% (AOR: 95%CI:1.49 (1.01, 2.19) increased likelihood of being in the mixed or happy category than being very/sort of unhappy category of the emotional fertility intention while the number of methods had no significant association with emotional fertility intention at higher cumulative logit: 1.34 (0.87,2.10). Those who wanted to have an additional child had a 3.16 (2.28, 4.36) higher odds to be in the mixed or happy category than being in unhappy category. Further, this tendency was even stronger at higher categories of emotional fertility intention: 4.83 (3.23, 7.23).ConclusionNearly one in two women reported being unhappy while 17.03% felt mixed emotion calling up on intended and spaced pregnancies by ensuring women reproductive and economic empowerment to empower women to have control over their fertility. Activities and efforts that promote intended and spaced pregnancies; and diversifying access to contraceptive methods in the nearest health facilities are likely to improve women emotional fertility intention; and activities that enable women to decide their contraceptive as well. The finding that health care provider decides on women current/recent contraceptive use calls for activities to improve quality of contraceptive use counseling to enable women to decide their contraceptive use by the themselves while the access of diversified methods in the nearby health facility create an opportunity for women to obtain the method they preferred to use and make them emotionally well. These activities are hoped to enable women to plan their fertility thereby increasing their emotional well-being. These activities and interventions need to be tailored across regions and need to be age sensitive.

Eksplorasi Kesulitan Siswa dalam Menyelesaikan Soal Higher Order Thingking Skills (HOTS) dengan Menggunakan Metode Polya pada Materi Perbandingan Trigonometri

This study aims to determine the difficulties of students in solving Higher Order Thingking Skills (HOTS) problems by using the Polya method in Trigonometric comparison materials. This research is a descriptive qualitative research. The subjects of the study were 36 students in class X.1 of SMA Negeri 2 Situbondo. Data collection techniques use test questions, interviews and documentation. The test in the form of Higher Order Thingking Skills (HOTS) questions is carried out to measure students' abilities. Interviews were conducted on 6 students based on the category of ability level of test results. Data analysis was carried out using data induction and reduction theory. The test results showed that those who scored in the high-level category were 10 students (28%), the moderate-level category was 14 students (39%) and the low-level category was 12 students (33%). The conclusion of this study states that the difficulty of students in solving Higher Order Thinking Skills (HOTS) questions using the polya method, namely subjects with high-level categories, experience difficulties at the re-examination stage. Subjects with a moderate level category, experienced difficulties at the stage of implementing the plan and re-examining. Subjects with low-level categories experienced difficulties at all stages of polya, namely at the stage of understanding the problem, making a plan, implementing the plan and re-examining.

Development of problem-based learning worksheets combined with the make a match cooperative model to enhance students' critical thinking skills in biology

This study aims to (1) produce Problem Based Learning-based Learning Worksheets combined with Make A Match which is feasible to use based on expert judgment assessment (2) to determine the effectiveness of Problem Based Learning-based Learning Worksheets combined with Make A Match and (3) to determine the practicality of Problem Based Learning-based Learning Worksheets in training students' critical thinking skills in the biology subject X-MIA virus material at MA Laboratorium Uinsu. This research is a development research with the 4-D model which consists of four stages, namely, define (defining), design (planning), develope (development), and disseminate (dissemination). Data collection was obtained through product feasibility validation results, student and teacher responses, as well as pretest and posttest with N-Gain analysis. The results showed that: (1) Problem Based Learning Learning Worksheets combined with Make A Match to train critical thinking skills is declared feasible to be used by students as learning media with a 99% media validation score, 91% material validation score. (2) The effectiveness of Problem Based Learning Learning Worksheets combined with Make A Match in training students' critical thinking skills based on the results of the N-Gain analysis obtained a result of 0.7 with a high critical thinking level category with the criteria "very effective" problem-based Learning Worksheets is effective in training critical thinking skills. (3) The practicality of Problem Based Learning Learning Worksheets combined with Make a Match in training critical thinking skills based on the results of questionnaire responses by students and teachers shows that student responses obtained a final result of 90% in the "very practical" category and teacher responses obtained a final result of 91% in the very practical category.

Psychological Resilience among Orang Asli Youths in Selangor during COVID-19 and Its Associated Factors.

Coronavirus disease (COVID-19) has affected everyone and stress-related mental health issues affect young people more than other groups, including marginalised populations. As a result of this pandemic, society is being urged to examine indigenous psychological resilience, especially among Orang Asli (OA) communities in Malaysia. Hence, this study aims to identify factors associated with psychological resilience among OA youths of Kampung Orang Asli (KOA) in Gombak during the COVID-19 pandemic. A cross-sectional study comprising OA communities was conducted between January 2022 and September 2022 in the Gombak District, Selangor. A self-administered online questionnaire using Google Forms and a self-administered printed questionnaire was used to collect data from youths aged 18 years old-24 years old. The Malay version of Conner-Davidson Resilience Scale-25 (CD-RISC-25) was used to assess psychological resilience. Data analysis was performed using SPSS version 28.0, and multiple linear regression analyses were conducted to evaluate the associated factors and their significance level. A total of 158 participants were involved in this study. The mean score for psychological resilience was estimated at 69.28 (SD = 14.52). The social relationships domain recorded the highest mean score for quality of life (71.54, SD = 13.72). Meanwhile, the total mean score for self-esteem fell into the high-level category (35.77, SD = 4.94), and the domain of relationship and family dynamics under family environment scored the highest mean score of 18.83 (SD = 2.89). Psychological resilience was associated with youths of KOA Batu 12 (β = -14.274, P < 0.05), KOA Ulu Batu (β = -17.789, P < 0.05), less than four siblings (β = -6.495, P < 0.05), owner of residential property (β = -7.543, P < 0.05), high self-esteem (β = 0.612, P < 0.05) and good relationship and family dynamic (β = 1.391, P < 0.05). Developing interventions aimed at psychological resilience determinants may assist OA youths in coping with future threats.

JOB SATISFACTION OF AGRICULTURAL SCIENTISTS OF STATE AGRICULTURAL UNIVERSITIES

The present study was undertaken to determine the level of job satisfaction of agricultural scientists of state agricultural universities of Gujarat state. The results regarding main indicators of job satisfaction revealed that majority of the agricultural scientists were found in medium to high level categories with respect to all the main indicators viz., compensation, promotion, training &amp; development, career development, leadership style, welfare facilities, recognition/rewards, communication, working condition workload, working facilities job security and status indicating importance of all the indicators in contributing job satisfaction. The study explains that a large number (71.15 per cent) agricultural scientists moderately satisfied with their job in the university followed by highly satisfied (20.77 per cent) and less satisfied (08.08 per cent), respectively.

Natural language processing analysis of the theories of people with multiple sclerosis about causes of their disease

BackgroundWhile potential risk factors for multiple sclerosis (MS) have been extensively researched, it remains unclear how persons with MS theorize about their MS. Such theories may affect mental health and treatment adherence. Using natural language processing techniques, we investigated large-scale text data about theories that persons with MS have about the causes of their disease. We examined the topics into which their theories could be grouped and the prevalence of each theory topic.MethodsA total of 486 participants of the Swiss MS Registry longitudinal citizen science project provided text data on their theories about the etiology of MS. We used the transformer-based BERTopic Python library for topic modeling to identify underlying topics. We then conducted an in-depth characterization of the topics and assessed their prevalence.ResultsThe topic modeling analysis identifies 19 distinct topics that participants theorize as causal for their MS. The topics most frequently cited are Mental Distress (31.5%), Stress (Exhaustion, Work) (29.8%), Heredity/Familial Aggregation (27.4%), and Diet, Obesity (16.0%). The 19 theory topics can be grouped into four high-level categories: physical health (mentioned by 56.2% of all participants), mental health (mentioned by 53.7%), risk factors established in the scientific literature (genetics, Epstein-Barr virus, smoking, vitamin D deficiency/low sunlight exposure; mentioned by 47.7%), and fate/coincidence (mentioned by 3.1%). Our study highlights the importance of mental health issues for theories participants have about the causes of their MS.ConclusionsOur findings emphasize the importance of communication between healthcare professionals and persons with MS about the pathogenesis of MS, the scientific evidence base and mental health.

Modelling the impact of single vs. dual presentation on visual discrimination across resolutions.

Visual categorisation relies on our ability to extract useful diagnostic information from complex stimuli. To do this, we can utilise both the "high-level" and "low-level" information in a stimulus; however, the extent to which changes in these properties impact the decision-making process is less clear. We manipulated participants' access to high-level category features via gradated reductions to image resolution while exploring the impact of access to additional category features through a dual-stimulus presentation when compared with single stimulus presentation. Results showed that while increasing image resolution consistently resulted in better choice performance, no benefit was found for dual presentation over single presentation, despite responses for dual presentation being slower compared with single presentation. Applying the diffusion decision model revealed increases in drift rate as a function of resolution, but no change in drift rate for single versus dual presentation. The increase in response time for dual presentation was instead accounted for by an increase in response caution for dual presentations. These findings suggest that while increasing access to high-level features (via increased resolution) can improve participants' categorisation performance, increasing access to both high- and low-level features (via an additional stimulus) does not.

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.

Knowledge-guided generative artificial intelligence for automated taxonomy learning from drug labels.

To automatically construct a drug indication taxonomy from drug labels using generative Artificial Intelligence (AI) represented by the Large Language Model (LLM) GPT-4 and real-world evidence (RWE). We extracted indication terms from 46421 free-text drug labels using GPT-4, iteratively and recursively generated indication concepts and inferred indication concept-to-concept and concept-to-term subsumption relations by integrating GPT-4 with RWE, and created a drug indication taxonomy. Quantitative and qualitative evaluations involving domain experts were performed for cardiovascular (CVD), Endocrine, and Genitourinary system diseases. 2909 drug indication terms were extracted and assigned into 24 high-level indication categories (ie, initially generated concepts), each of which was expanded into a sub-taxonomy. For example, the CVD sub-taxonomy contains 242 concepts, spanning a depth of 11, with 170 being leaf nodes. It collectively covers a total of 234 indication terms associated with 189 distinct drugs. The accuracies of GPT-4 on determining the drug indication hierarchy exceeded 0.7 with "good to very good" inter-rater reliability. However, the accuracies of the concept-to-term subsumption relation checking varied greatly, with "fair to moderate" reliability. We successfully used generative AI and RWE to create a taxonomy, with drug indications adequately consistent with domain expert expectations. We show that LLMs are good at deriving their own concept hierarchies but still fall short in determining the subsumption relations between concepts and terms in unregulated language from free-text drug labels, which is the same hard task for human experts.

Predicting high-level visual areas in the absence of task fMRI.

The ventral visual stream is organized into units, or functional regions of interest (fROIs), specialized for processing high-level visual categories. Task-based fMRI scans ("localizers") are typically used to identify each individual's nuanced set of fROIs. The unique landscape of an individual's functional activation may rely in large part on their specialized connectivity patterns; recent studies corroborate this by showing that connectivity can predict individual differences in neural responses. We focus on the ventral visual stream and ask: how well can an individual's resting state functional connectivity localize their fROIs for face, body, scene, and object perception? And are the neural processors for any particular visual category better predicted by connectivity than others, suggesting a tighter mechanistic relationship between connectivity and function? We found, among 18 fROIs predicted from connectivity for each subject, all but one were selective for their preferred visual category. Defining an individual's fROIs based on their connectivity patterns yielded regions that were more selective than regions identified from previous studies or atlases in nearly all cases. Overall, we found that in the absence of a domain-specific localizer task, a 10-min resting state scan can be reliably used for defining these fROIs.

Methods and Applications of Space Understanding in Indoor Environment—A Decade Survey

The demand for digitizing manufacturing and controlling processes has been steadily increasing in recent years. Digitization relies on different techniques and equipment, which produces various data types and further influences the process of space understanding and area recognition. This paper provides an updated view of these data structures and high-level categories of techniques and methods leading to indoor environment segmentation and the discovery of its semantic meaning. To achieve this, we followed the Systematic Literature Review (SLR) methodology and covered a wide range of solutions, from floor plan understanding through 3D model reconstruction and scene recognition to indoor navigation. Based on the obtained SLR results, we identified three different taxonomies (the taxonomy of underlying data type, of performed analysis process, and of accomplished task), which constitute different perspectives we can adopt to study the existing works in the field of space understanding. Our investigations clearly show that the progress of works in this field is accelerating, leading to more sophisticated techniques that rely on multidimensional structures and complex representations, while the processing itself has become focused on artificial intelligence-based methods.

Modified three-layer vesicourethral reconstruction in robot-assisted radical prostatectomy can change cystography pattern and improve early recovery of continence.

To determine early continence outcomes after three-layer vesicourethral reconstruction during robot-assisted radical prostatectomy (RARP) and the role of postoperative cystography pattern. Between May 2015 and January 2019, a total of 170 consecutive patients with localized prostate cancer who underwent RARP, were divided into one- and three-layer groups based on the method of vesicourethral reconstruction. Continent status, preoperative, intraoperative, postoperative, clinicopathological variables, and cystography parameters were analyzed. The patients were followed up for at least 12 months. Of the 170 consecutive patients, 85 with one-layer vesicourethral anastomosis, and 85 with three-layer reconstruction. The continence rates immediately after catheter removal, 4, 12, and 24 weeks after RARP were 47.1%, 75.3%, 92.9%, and 98.8% in the three-layer group; compared to 15.3%, 60%, 78.8%, and 90.6% in the one-layer group, respectively. In the multivariate analysis, three-layer reconstruction was the only independent variable with a 42% risk reduction of postprostatectomy incontinence (hazard ratio (HR): 0.58, 95% confidence interval (CI) = 0.42-0.80, p = 0.001). Cystography in the three-layer group revealed less anastomotic leakage, less sharp bladder neck angle, and higher bladder neck level category. Three-layer anatomical reconstruction demonstrated promising early continence outcomes, and postoperative cystography revealed a specific pattern more associated with continence.

Utilizing BERT for Information Retrieval: Survey, Applications, Resources, and Challenges

Recent years have witnessed a substantial increase in the use of deep learning to solve various natural language processing (NLP) problems. Early deep learning models were constrained by their sequential or unidirectional nature, such that they struggled to capture the contextual relationships across text inputs. The introduction of bidirectional encoder representations from transformers (BERT) leads to a robust encoder for the transformer model that can understand the broader context and deliver state-of-the-art performance across various NLP tasks. This has inspired researchers and practitioners to apply BERT to practical problems, such as information retrieval (IR). A survey that focuses on a comprehensive analysis of prevalent approaches that apply pretrained transformer encoders like BERT to IR can thus be useful for academia and the industry. In light of this, we revisit a variety of BERT-based methods in this survey, cover a wide range of techniques of IR, and group them into six high-level categories: (i) handling long documents, (ii) integrating semantic information, (iii) balancing effectiveness and efficiency, (iv) predicting the weights of terms, (v) query expansion, and (vi) document expansion. We also provide links to resources, including datasets and toolkits, for BERT-based IR systems. Additionally, we highlight the advantages of employing encoder-based BERT models in contrast to recent large language models like ChatGPT, which are decoder-based and demand extensive computational resources. Finally, we summarize the comprehensive outcomes of the survey and suggest directions for future research in the area.

Explainable Reinforcement Learning: A Survey and Comparative Review

Explainable reinforcement learning (XRL) is an emerging subfield of explainable machine learning that has attracted considerable attention in recent years. The goal of XRL is to elucidate the decision-making process of reinforcement learning (RL) agents in sequential decision-making settings. Equipped with this information, practitioners can better understand important questions about RL agents (especially those deployed in the real world), such as what the agents will do and why. Despite increased interest, there exists a gap in the literature for organizing the plethora of papers—especially in a way that centers the sequential decision-making nature of the problem. In this survey, we propose a novel taxonomy for organizing the XRL literature that prioritizes the RL setting. We propose three high-level categories: feature importance, learning process and Markov decision process, and policy-level. We overview techniques according to this taxonomy, highlighting challenges and opportunities for future work. We conclude by using these gaps to motivate and outline a roadmap for future work.

Comparative Analysis of Socio-Economic Characteristics of Aquaculture Farmers in Borno State, Nigeria

The study was undertaken with the main objective of comparative analysis of socioeconomic characteristics of aquaculture fish farmers with specific objective of determining the level of influence of socioeconomic variables on the status of aquaculture fish farmers for subsequent identification and formulation of policy framework that will suit the peculiar problem of the aquaculture fish farmers with the aim of embarking on sustainable large-scale aquaculture fish production in Borno State of Nigeria. Data for the study were obtained from both primary and secondary sources on socioeconomic characteristics of aquaculture fish farmers. Quantitative technique with descriptive statistics was employed in the comparative analysis and interpretation of the data on the socioeconomic characteristics of aquaculture fish farmers in the study area. Comparatively, the result shows Konduga local Government has more influence on the socioeconomic status of the aquaculture fish farmers at the high and lowest level categories of the comparative analysis than Jere local government at 80% and 20% level respectively in the study area. Recommendation were made for the transformation of the socioeconomic characteristics of the aquaculture fish farmers for the upliftment of the general well-being of the farmers by attainment of the economic, social and environmental requirement for truly sustainable large-scale aquaculture fish farming in the study area.