Deep Knowledge Research Articles

APIRec: deep knowledge and diversity-aware web API recommendation

APIRec: deep knowledge and diversity-aware web API recommendation

Deep Learning and Knowledge

This paper considers the question of what kind of knowledge is produced by deep learning. Ryle’s concept of knowledge how is examined and is contrasted with knowledge with a rationale. It is then argued that deep neural networks do produce knowledge how, but, because of their opacity, they do not in general, though there may be some special cases to the contrary, produce knowledge with a rationale. It is concluded that the distinction between knowledge how and knowledge with a rationale is a useful one for judging whether a particular application of deep learning AI is appropriate.

Effects of the General Data Protection Law (LGPD) On For-Profit Organizations

Objective: The general objective of the study is to analyze which technical and administrative measures are necessary for accounting organizations to comply with the provisions of the LGPD, with this general objective being divided into three specific objectives: a) identify the technical and administrative measures relevant to the LGPD; b) establish privacy policies on the data made available and c) define the stages of technical and administrative measures so that organizations comply with the provisions of the Law. Theoretical Framework: This topic presents the main concepts and theories that underpin the research. The General Data Protection Law - LGPD (LAW 13.709/2018) stands out, providing a solid basis for understanding the context of the investigation. Method: The methodology adopted for this research is applied, descriptive, bibliographical and documentary, with a qualitative approach. The unit of analysis was 5 managers from 5 for-profit organizations located in the northwest region of RS during the month of May 2023. Data collection was carried out through bibliographic, documentary and interview research, using documentary and descriptive analysis techniques. Results and Discussion: The findings indicate that criteria adopted for the protection of people's data were defined for the companies surveyed, considering the administrative department of the companies, where data considered sensitive is handled, divided basically into three sectors: Accounting sector, Tax sector and Human Resources sector and Personnel Department. It was found that there is a need to implement technical and administrative measures in the processes and invest in training on the subject within the organizations, in order to avoid infractions and fines as punishment for non-compliance with the provisions of the General Data Protection Law. In the discussion section, these results are contextualized in light of the General Data Protection Law - LGPD (LAW 13.709/2018), highlighting the implications and relationships identified. Possible discrepancies and limitations of the study are also considered in this section. Research Implications: The practical and theoretical implications of this research are discussed, providing insights into how the results can be applied or influence practices in the field of the General Data Protection Law - LGPD. These implications may include the deepening of knowledge, as well as from a practical point of view, since it offers a concrete solution to a specific demand of the company investigated in this study. Originality/Value: This study contributes to the literature by discussing the topic General Data Protection Law - LGPD (LAW 13.709/2018. The relevance and value of this research are evidenced by providing guidance to organizations on how to prepare the Privacy Policy and Contingency Plan, with the purpose of adapting to the LGPD.

Investigating Remote Healthcare Accessibility with AI: Deep Learning and NLP-Based Knowledge Graph for Digitalized Diagnostics

Introduction: Recently, vast generational modern AI techniques have facilitated developments for accessing digital healthcare diagnosis with capabilities of detecting illnesses. Problem: There exists a lot of scepticism for e-health couple with high similarities on health symptoms which hinder text data analysis for remote diagnosis limiting remote services and affecting tech development. Objective: This research investigates and substantiates opportunities associated with computational leverage of text data analytics and cognitive extraction of knowledge insights to improve healthcare outcomes. Significance: The study presents an overview of public, an integrated deep learning (DL), and AI knowledge graph (KG) for healthcare accessibility of remote diagnostics with NLP assist. Method: This research applied both qualitative and quantitative analysis. Questionnaires were used to understand the computational analytics and cognitive extraction of AI knowledge graphs on healthcare data. Also, an AI model was built to detect, diagnose based on text data and streamline five (5) related disease symptoms for each given text input. Results: The result of the survey was tested with hypotheses of H1, H2, H3, H4, H5. Results show that deep learning models and knowledge graphs can effectively lead to a well-defined class of data classification. Our model also exhibits a tremendous level of acceptable prediction of health symptoms based on text data. The significant group was accepted as an identified health issue and the non-significant was identified as a non-health issue. Conclusion: The study concludes that a well-defined system based on a rigorous ethical healthcare standard can easily support determining a feasible remote diagnosis.

Improving Ontology Requirements Engineering with OntoChat and Participatory Prompting

Past ontology requirements engineering (ORE) has primarily relied on manual methods, such as interviews and collaborative forums, to gather user requirements from domain experts, especially in large projects. Current OntoChat offers a framework for ORE that utilises large language models (LLMs) to streamline the process through four key functions: user story creation, competency question (CQ) extraction, CQ filtration and analysis, and ontology testing support. In OntoChat, users are expected to prompt the chatbot to generate user stories. However, preliminary evaluations revealed that they struggle to do this effectively. To address this issue, we experimented with a research method called participatory prompting, which involves researcher-mediated interactions to help users without deep knowledge of LLMs use the chatbot more effectively. The participatory prompting user study produces pre-defined prompt templates based on user query, focusing on creating and refining personas, goals, scenarios, sample data, and data resources for user story. These refined user stories will subsequently be converted into CQs.

Development of Idaea inquinata (Lepidoptera Geometridae) at different constant temperatures and relative humidities under controlled conditions

The rusty wave moth Idaea inquinata is an insect pest that infests dried herbs, above all when stored in warehouses. Infestations were historically localised, but the recent climatic change might increase the incidence of this pest in many areas of north Italy. The artificial regulation of the environmental conditions of warehouses is one of the most common techniques to control pests, often combined with thermal treatments just after the introduction of the stocks. The optimisation of the warehouse conditions, however, requires a deep knowledge on how the species react to variations in temperature and relative humidities. This information is to date missing for I. inquinata and this work aimed to fill this gap in knowledge. The life tables at 35% and 70% RH, and at different constant temperatures were obtained for the egg, larval, and pupal stages by combining datasets provided by 20 years of continuous rearing of this species. A second part of the study, instead, concerned the estimation of the parameters of the temperature-dependent development rate functions, laying the foundations for further formulations of mathematical models to be applied in decision support systems. Life tables showed that conditions of low relative humidities and low temperatures are a good compromise that slows down the development time of the preimaginal stages. The upper thermal limit for the development of this species, instead, is around 40 °C, a threshold that can be considered for further thermal treatments to disinfest warehouses before the introduction of pest-free stocks or as a controlling action in case of infestations.

Using historical approaches to understand contemporary student loneliness

ABSTRACT Student loneliness is a global problem, with universities struggling to tackle an issue that has important implications for student success, satisfaction, and mental health. This research uses archival material from the 1960s and 1970s alongside qualitative discussions with contemporary students to explore the ways that experiences of loneliness within British higher education have changed across recent decades. Such an approach bridges the divergent approaches taken by different scholarly disciplines, applying focus group methodologies to the consideration of archival material. For this project five focus groups were held with undergraduate students at four universities in England, Wales and Scotland. This article argues for the contemporary relevance of historical research into student loneliness, exploring student responses to their predecessors’ experiences of loneliness. It argues that equipping undergraduates with a deeper knowledge about their forerunners’ experiences of disconnection can trouble some of the stereotypes, assumptions, and expectations around the sociable ‘student experience’ today. Such an approach has widespread implications for researchers’ and policy makers’ understandings of the potential role of interdisciplinary and humanities-generated knowledge in addressing social problems within higher education.

FORMATION OF FOREIGN-LANGUAGE COMMUNICATIVE COMPETENCIES OF STUDENTS OF PROFESSIONAL PRE-HIGHER EDUCATION INSTITUTIONS

In the article, the author analyzed the pedagogical conditions for the formation of foreign language communicative competences in institutions of professional pre-higher education in the process of teaching students of 121 program subject area – Software Engineering. During the study, a comprehensive approach was used, including a deep analysis of psychological and pedagogical literature: the study of scientific papers, articles and other sources of information on the topic under study. Analytical work with information sources: synthesis and systematization of acquired knowledge, clear formulation of key concepts and aspects of the topic. It has been established that modern employers expect software engineering professionals to have not only deep technical knowledge but also a high level of English language proficiency. This includes the ability to work with English-language technical documentation, communicate effectively in international projects, and write professional documentation in English. This is especially true for professionals studying at institutions of professional pre-higher education. Foreign language training should be aimed at developing the communication competencies necessary to work effectively in an international environment. The author identifies the main pedagogical conditions, in particular, the formation of motivation in future IT specialists to learn foreign languages; systematic use of digital interactive technologies as a means of forming foreign language communicative competences; creation of professionally oriented teaching and methodological support (in particular, workbooks in in English for professional purposes for each semester); readiness of foreign language teachers to use professionally oriented teaching and methodological support.

Fault Diagnosis of Circuit Breakers Based on MCF-RPs and Deep Residual Knowledge Incremental Under Distillation Learning

Fault Diagnosis of Circuit Breakers Based on MCF-RPs and Deep Residual Knowledge Incremental Under Distillation Learning

Perceptions of key informant neurologists before implementing anti-amyloid drugs in the Spanish departments of neurology.

A deep knowledge of the healthcare system and the organization of neurology departments is important for planning and optimizing changes to facilitate the successful implementation of anti-amyloid antibodies treatments. We aimed to assess the necessary changes prior to introducing these therapies in our setting. We conducted a key informant survey among heads of departments of neurology from 16 hospitals in Spain. The questionnaire comprised questions about changes in the organization and functioning of the departments of neurology with the introduction of anti-amyloid drugs, changes in diagnosis and patient care, use of diagnostic techniques, patients, families and public information, resources allocation, and research. Sixteen key informants completed the survey. They strongly agreed that the introduction of anti-amyloid drugs will impact the functioning of neurology services, especially in hospitals with dementia units. Consensus was reached regarding referring all Alzheimer's disease patients eligible for therapy to dementia units. There was also agreement on the need to expand the neurology services, day hospital units, extend visit durations, and hire more professionals, especially neurologists, neuropsychologists, and nuclear medicine physicians. Furthermore, consensus was achieved on increasing the use of MRI, amyloid PET, cerebrospinal fluid biomarkers, APOE genotyping, and the necessity of advancing blood biomarkers and tau tracers. Our study highlights the need for extensive changes within Spanish neurological departments to effectively integrate anti-amyloid antibodies. Implementing these changes is essential for the timely and equitable adoption of novel therapies.

PELATIHAN KESENIAN DAN PASRAMAN KILAT SEBAGAI PENDIDIKAN NON FORMAL REMAJA DI DESA GUNAKSA

The Indonesian Hindu University in Bali, through community service in Gunaksa Village, educates local children about Balinese culture. They teach Kamasan wayang painting and crafting flash pesraman to bridge the gap between cultural understanding and practical skills. This non-formal religious education complements their formal schooling, laying a strong foundation in religious knowledge. The approach is comprehensive, and data is collected through participant observation and documentation studies. Children gain deep knowledge of Balinese ornaments, coloring skills, and crafting abilities for daily life. This initiative fosters creativity and preserves Balinese culture while instilling values rooted in Hindu religious principles. It aims to enrich Hindu children's resources in fine arts and religion, positively impacting their personal and cultural development.

Phytochemical Analysis, Antioxidant Activity of Wild Medicinal Plants of Himalayan Range

Plants that are indigenous to the Himalayas have been exploited for their therapeutic properties for over 6,500 years. Using their deep knowledge of the local flora and fauna, traditional healers in the region are able to treat a wide variety of maladies with herbal treatments. People who live in rural areas of the Himalayas rely on wild medicinal plants for their health, and we need to do everything in our power to ensure that these plants continue to thrive in the harsh desert climate of the Himalayas. Raw materials that are obtained from wild plants are in high demand all around the world, particularly among pharmaceutical companies, ethnomedics, and practitioners of traditional medicine as well as other medical practitioners. India has been a top exporter of raw herbal medications all over the world. This is mostly due to the number of medicinal plants that can be found in the Himalayas. The aim of this study was to determine the Phytochemical analysis and antioxidant activity of various parts of 20 selected wild medicinal plants, found in the Himalayan regions in India, China, Nepal, Bhutan and Pakistan including Swertia bimaculate, Ficus neriifolia, Rubus treutleri, Periploca calophylla, G. depressa, Buddleja napaulensis, Habenaria edgeworthii, Pyracantha crenulate, Roscoea procera, Allium rubellum, Berberis chitria, Capsella bursa-pastoris, Artemisia maritima, C. glanduliferum, C. distans, Juniperus macropoda, Origanum vulgare, Valeriana jatamansi, Polygonatum verticillatum, Meconopsis aculeate and Fragaria nubicolais. There were several different parts of the species that were examined to determine their total polyphenol, flavonoid, alkaloid, saponin, and tannin concentrations. One fraction of the plant extracts was found to contain a higher quantity of phytochemicals when compared to the other portions. Through the utilisation of the DPPH and FRAP model systems, the antioxidant capacity of a number of different components was assessed. The high-performance liquid chromatography (HPLC) technique can be combined with the DPPH and/or ABTS tests in order to rapidly screen extracts for the presence of active chemicals.

Trust in Philanthropy: An Examination of Multidisciplinary Factors

This article explores and consolidates the factors that influence the donor’s trust from cross-disciplinary literature, and we put factors from these different fields to the test in one topic: the donor’s trust in the fundraiser organisation. Grounded on the literature, we can say that there are nine themes of constructs that influence trust. This research utilised a quantitative survey approach and PLS-SEM as an analysis platform to analyse the data collected. We have discovered that our respondents who represent donors strongly agree that their trust is heavily influenced by perceived security, rule of law, organisational personnel personality, communication effectiveness, reputation, perceived integrity, and perceived competence. Cross-disciplinary studies are profoundly helpful towards deeper knowledge and enhance the learning experience and enable the transfer of ideas across many areas of study easier. The findings should contribute to the theoretical knowledge specifically on trust antecedent in the philanthropy setting and other disciplines. The findings presented in this paper may also be useful as input for fundraisers to build and strengthen the level of their donor’s trust. This article explores and consolidates all factors that influence donor trust, which has been published in the literature and presents the results from the cross-examination of these factors.

Mind-Sets in Brazil Regarding the Perceptions of Stevia and Sucralose to Replace Sugar in Food: Using Generative AI and Mind Genomics Thinking to Generate Hypotheses and Insights

The paper presents the use of generative AI to explore the mind-sets of consumers in Brazil regarding the increasing use of high potency sweeteners. Through simulation and synthesis by AI, the paper shows how to generate deep knowledge regarding the mind-sets, how to simulate a town-hall question and answer session with these mind-sets, and what happens when these mind-sets go shopping (shop-along).

DKVMN&MRI: A new deep knowledge tracing model based on DKVMN incorporating multi-relational information.

Knowledge tracing is a technology that models students' changing knowledge state over learning time based on their historical answer records, thus predicting their learning ability. It is the core module that supports the intelligent education system. To address the problems of sparse input data, lack of interpretability and weak capacity to capture the relationship between exercises in the existing models, this paper build a deep knowledge tracing model DKVMN&MRI based on the Dynamic Key-Value Memory Network (DKVMN) that incorporates multiple relationship information including exercise-knowledge point relations, exercise-exercise relations, and learning-forgetting relations. In the model, firstly, the Q-matrix is utilized to map the link between knowledge points and exercises to the input layer; secondly, improved DKVMN and LSTM are used to model the learning process of learners, then the Ebbinghaus forgetting curve function is introduced to simulate the process of memory forgetting in learners, and finally, the prediction strategies of Item Response Theory (IRT) and attention mechanism are used to combine the similarity relationship between learners' knowledge state and exercises to calculate the probability that learners would correctly respond during the subsequent time step. Through extensive experiments on three real-world datasets, we demonstrate that DKVMN&MRI has significant improvements in both AUC and ACC metrics contrast with the latest models. Furthermore, the study provides explanations at both the exercise level and learner knowledge state level, demonstrating the interpretability and efficacy of the proposed model.

Understanding the effects of weather parameters on the population dynamics of an endangered geophyte supports monitoring efficiency.

Due to their complex life cycles geophytes are often neglected in conservation programs, despite they are important elements of early spring communities. Their life cycle is strongly affected by weather parameters, i.e. temperature, precipitation, and light, but the effects of these parameters are often contradictory and show high intra-annual variability even within species. Deeper knowledge about the abiotic factors affecting the population dynamics of geophytes is needed to support the designation of effective conservation plans. We aimed to explore the link between weather parameters and population dynamics of Colchicum bulbocodium, an endangered and strictly protected geophyte. We monitored three life cycle stages (flowering, growing, fruiting) of 1069 individuals in permanent plots for six consecutive years. Our results showed that life cycle of C. bulbocodium was strongly related to the actual weather parameters; the lagged effect of the previous year was weaker. Increasing temperature and lack of cold periods had negative effect on all life stages. We highlighted that population estimation based on the number of flowering individuals in a single year can underestimate population size by 40-83%. Monitoring in years following wet and cold springs and cold winters could increase the accuracy of population estimations of the flowering individuals.

Role of Clay Minerals in Natural Media Self-Regeneration from Organic Pollution-Prospects for Nature-Inspired Water Treatments.

Pollution from organic molecules is a major environmental issue that needs to be addressed because of the negative impacts of both the harmfulness of the molecule structures and the toxicity that can spread through natural media. This is mainly due to their unavoidable partial oxidation under exposure to air and solar radiation into diverse derivatives. Even when insoluble, the latter can be dispersed in aqueous media through solvatation and/or complexation with soluble species. Coagulation-flocculation, biological water treatments or adsorption on solids cannot result in a total elimination of organic pollutants. Chemical degradation by chlorine and/or oxygen-based oxidizing agents is not a viable approach due to incomplete mineralization into carbon dioxide and other oxides. A more judicious strategy resides in mimicking natural oxidation under ambient conditions. Soils and aqueous clay suspensions are known to display adsorptive and catalytic properties, and slow and complete self-regeneration can be achieved in an optimum time frame with a much slower pollution throughput. A deep knowledge of the behavior of aluminosilicates and of oxidizing species in soils and aquatic media allows us to gain an understanding of their roles in natural oxidative processes. Their individual and combined contributions will be discussed in the present critical analysis of the reported literature.

DHKFN

Knowledge tracing is effective in modeling learners' knowledge levels to predict future answering situations based on their past learning history and interaction processes. However, current methods often overlook the impact of knowledge point correlations on answer prediction. This paper design a model based on deep hierarchical knowledge fusion networks (DHKFN). It utilizes statistical approaches to construct correlation matrices to capture the correlations between knowledge points. Subsequently, it constructs a topology graph structure of questions and knowledge points and utilizes a multi-head attention network to learn student interaction information from multiple perspectives, effectively constructing adjacency matrices. Finally, it uses GCN to learn the interaction information representation between deep-level knowledge points from dynamically constructed a topology graph structures. Experimental results on three large public datasets show that DHKFN effectively considers the influence of correlations between different knowledge points, thus showing promising effectiveness.

Reflecting on the powers, possibilities and constraints of geography curricula in England, Finland and Sweden

ABSTRACT National curriculum statements found within the Official Recontextualising Field (ORF) provide an insight into how geography as a school subject is conceptualized in a country’s education system. National curricula can shape teachers’ agency in curriculum making and what, how and where children and young people study and learn geography. This paper engages with the lower secondary national geography curriculum for England, Finland and Sweden. We examine the structure and nature of the national geography curricula in each country, before drawing on the threefold arrangement of geographical knowledge as a tool for the analysis of the curricula. Our analysis found that deep and descriptive world knowledge forms the largest proportion of all three national curricula documents, and we argue that this can lead to a potentially limited conceptualization of geographical knowledge and representation of geography. We also suggest that the threefold arrangement could more actively engage with political dimensions when considering futures, and that there should be greater attention paid to the histories and geographies of the discipline (geography) in school geography.

An Investigation into the Behavior of Cathode and Anode Spots in a Welding Discharge

The effective development of modern welding technologies and the improvement of equipment and materials inevitably require deep theoretical knowledge about the physical phenomena occurring in the electric arc column and in the near-electrode region. However, there is still no convincing theoretical description of an arc discharge. This article demonstrates, through the generalization of known experimental facts and studies using a high-speed camera, that the conductive channel of an electric arc has a discrete structure, consisting of a set of thin channels through which the main discharge current passes. The cathode spot of an arc discharge is a highly heated and brightly glowing area on the cathode’s surface. Electron emission occurs from this region, which supports the discharge as well as the removal of the cathode material. We propose a new technique to study the reverse side of the cathode spot, revealing a structure consisting of individual cells or fragments of the cathode spot. For the first time, we present the anode spots captured by a high-speed camera. We carry out an analysis of the spots’ structure. We determine the parameters affecting the mobility of cathode and anode spots. We propose a hypothesis based on the obtained experimental facts about the heterogeneous structure of cathode and anode spots in an arc discharge and the existence of current filaments that affect the mobility of spots during arc combustion.