Multiple Concepts Research Articles

Ethics in AI through the practitioner’s view: a grounded theory literature review

The term ethics is widely used, explored, and debated in the context of developing Artificial Intelligence (AI) based software systems. In recent years, numerous incidents have raised the profile of ethical issues in AI development and led to public concerns about the proliferation of AI technology in our everyday lives. But what do we know about the views and experiences of those who develop these systems – the AI practitioners? We conducted a grounded theory literature review (GTLR) of 38 primary empirical studies that included AI practitioners’ views on ethics in AI and analysed them to derive five categories: practitioner awareness, perception, need, challenge, and approach. These are underpinned by multiple codes and concepts that we explain with evidence from the included studies. We present a taxonomy of ethics in AI from practitioners’ viewpoints to assist AI practitioners in identifying and understanding the different aspects of AI ethics. The taxonomy provides a landscape view of the key aspects that concern AI practitioners when it comes to ethics in AI. We also share an agenda for future research studies and recommendations for practitioners, managers, and organisations to help in their efforts to better consider and implement ethics in AI.

ANALISIS KESALAHAN SISWA DALAM MENYELESAIKAN SOAL FUNGSI KOMPOSISI DITINJAU DARI PERBEDAAN GENDER

This study was conducted with the aim of identifying errors that occur when students solve composition function problems by paying attention to gender differences. The research subjects were four students of class XI, two male students and two female students. The research method used was descriptive qualitative. Tests and interviews were conducted to collect data. The data were then analyzed by examining the students' answer sheets and classifying the errors made into four types, namely concept, principle, fact, and operation errors. The findings showed that male students made more errors than female students, especially in the category of fact and operation errors. Fact errors made by male students are errors in writing arithmetic operation signs, this is because male students do not understand the concept of multiplication and addition operation signs. While the error in determining the final answer was caused by an error in the previous process, where male students could not do the calculation correctly so that they got the wrong final result. The conclusion of this study confirms that female students have an advantage in the ability to solve math problems compared to male students.

Decoding the Atmosphere: Optimising Probabilistic Forecasts with Information Gain

Probabilistic prediction models exist to reduce surprise about future events. This paper explores the evaluation of such forecasts when the event of interest is rare. We review how the family of Brier-type scores may be ill-suited to evaluate predictions of rare events, and we offer an alternative to information-theoretical scores such as Ignorance. The reduction in surprise provided by a set of forecasts is represented as information gain, a frequent loss function in machine learning training, meaning the reduction in ignorance over a baseline having received a new forecast. We evaluate predictions of a synthetic dataset of rare events and demonstrate the differences in interpretation of the same datasets depending on whether the Brier or Ignorance score is used. While the two types of scores are broadly similar, there are substantial differences in interpretation at extreme probabilities. Information gain is measured in units of bits, an irreducible unit of information, that allows forecasts of different variables to be comparatively evaluated fairly. Further insight from information-based scores is gained via a similar reliability–discrimination decomposition as found in Brier-type scores. We conclude by crystallising multiple concepts to better equip forecast-system developers and decision-makers with tools to navigate complex trade-offs and uncertainties that characterise meteorological forecasting. To this end, we also provide computer code to reproduce data and figures herein.

Ethnomathematics: Numerical Literacy in the Dawan Language System Dialects of the Amanuban Tribe and its Relation to Mathematics Learning

This research aims to describe the numerical literacy in the Dawan language system, the dialect of the Amanuban tribe, South Central Timor Regency in terms of ethnomathematics perspective and its relation to mathematics learning. This research is qualitative with an ethnographic research type. Data collection techniques were done through observation, interviews, and literature study. The results of this research indicate that the numerical literacy in the Dawan language system, the Amanuban dialect used by the community when using the number system is very unique. The uniqueness is that the community uses the base 10 number system with the unit base number 0 to 9 and the tens base number 10, which is used to calculate the next number using the concept of multiplication and addition. The mention of the name of the day in the Dawan language of the Amanuban dialect is also based on the unit base number system. This uniqueness can be used as a reference in learning mathematics with numbers, especially the concept of number sequences, the place value of a number, and addition and multiplication operations on integers for low-grade students who are fluent in Dawan. Keywords: dawan language system dialects of the amanuban tribe, ethnomathematics, mathematics learning, numerical literacy

Assessing the Concept Understanding Abilities of Prospective Biology Teachers

This study aimed to identify the levels of understanding of prospective biology teacher students on the concept of invertebrates based on the cognitive process dimensions of Bloom’s Taxonomy. The method used in this research was descriptive qualitative. This research was conducted on 15 prospective biology teacher students. The instrument used was a multiple choice concept mastery test. Based on data analysis, the average cognitive ability of students is 69.10. The acquisition of conceptual understanding based on the dimensions of cognitive processes includes C1: 74,69; C2: 73.67; C3: 57.34; C4: 56.08, and C5: 22.34. The average score of cognitive ability showed that students’ cognitive abilities were still below the criteria for completeness set in the invertebrate zoology course. Keywords: assessing, concept, understanding, abilities, prospective teachers, biology

Elite Attitudes to the ‘Public Sphere’ in Fifteenth-Century Castile

ABSTRACT This article examines the manifold and complex responses of fifteenth-century elite politicians and writers to ‘public’ politics in Castile. Through analysis of a variety of sources including chronicles, allegorical poems, treatises, glosses and letters, it shows how the multiple conceptions of non-elite agency and attitudes to it can nuance our understanding of Castilian politics in the late Middle Ages. It argues that fifteenth-century chronicles, glosses and allegorical poems demonstrate a new attention from the elite to wider contexts beyond the confines of the traditional political society, which responded both to literary fashions and to real changes in the political reality of late medieval society. Moreover, their complex and even contradictory responses, which denigrated, appropriated and addressed these wider ‘publics’, ought to be considered integral to the development of ‘public opinion’, as part of a set of discursive and institutional struggles for the right to express political opinions.

HaLo‐NeRF: Learning Geometry‐Guided Semantics for Exploring Unconstrained Photo Collections

AbstractInternet image collections containing photos captured by crowds of photographers show promise for enabling digital exploration of large‐scale tourist landmarks. However, prior works focus primarily on geometric reconstruction and visualization, neglecting the key role of language in providing a semantic interface for navigation and fine‐grained understanding. In more constrained 3D domains, recent methods have leveraged modern vision‐and‐language models as a strong prior of 2D visual semantics. While these models display an excellent understanding of broad visual semantics, they struggle with unconstrained photo collections depicting such tourist landmarks, as they lack expert knowledge of the architectural domain and fail to exploit the geometric consistency of images capturing multiple views of such scenes. In this work, we present a localization system that connects neural representations of scenes depicting large‐scale landmarks with text describing a semantic region within the scene, by harnessing the power of SOTA vision‐and‐language models with adaptations for understanding landmark scene semantics. To bolster such models with fine‐grained knowledge, we leverage large‐scale Internet data containing images of similar landmarks along with weakly‐related textual information. Our approach is built upon the premise that images physically grounded in space can provide a powerful supervision signal for localizing new concepts, whose semantics may be unlocked from Internet textual metadata with large language models. We use correspondences between views of scenes to bootstrap spatial understanding of these semantics, providing guidance for 3D‐compatible segmentation that ultimately lifts to a volumetric scene representation. To evaluate our method, we present a new benchmark dataset containing large‐scale scenes with ground‐truth segmentations for multiple semantic concepts. Our results show that HaLo‐NeRF can accurately localize a variety of semantic concepts related to architectural landmarks, surpassing the results of other 3D models as well as strong 2D segmentation baselines. Our code and data are publicly available at https://tau‐vailab.github.io/HaLo‐NeRF/.

Der ‚digital-öffentliche Raum‘ als kommunikative Figuration und vergemeinschaftungsideologische Imagination

Abstract This paper assesses the notion of ‘digital public spaces’ and grasps it as a discursive conglomerate which combines multiple ideology-laden concepts: the ‘public sphere’ as an ‘open’ and ‘democratic’ space (as opposed to closed private ‘bubbles’), and ‘digital communication’, which is often strictly divided from the non-digital and perceived as either a threat or the great promise of social assemblage. From the perspective of metapragmatic sociolinguistics and the sociology of knowledge, we follow discursive traces of the ‘the public sphere’ and its suturing with ‘digital communication’ within and outside of our own fields in order to show how the conceptual conglomerate ‘digital public spaces’ is bound to evaluations, expectations and projections which need more critical reflection in media studies in order to escape ideological short circuits and simplifications that haunt the field. On the other hand, we argue that the notion of ‘digital publics spaces’ serves important discursive functions in the lifeworlds of media users, as it fuels into contingency-reducing ideological complexes: ideologies of communication (Kommunikationsideologien) which guide social actors’ interaction, and related ideologies of communification (Vergemeinschaftungsideologien) which guide them in the ways they socially align with each others.

A framework for design optimization across multiple concepts

In engineering design, there often exist multiple conceptual solutions to a given problem. Concept design and selection is the first phase of the design process that is estimated to affect up to 70% of the life cycle cost of a product. Currently, optimization methods are rarely used in this phase, since standard optimization methods inherently assume a fixed (given) concept; and undertaking a full-fledged optimization for each possible concept is untenable. In this paper, we aim to address this gap by developing a framework that searches for optimum solutions efficiently across multiple concepts, where each concept may be defined using a different number, or type, of variables (continuous, binary, discrete, categorical etc.). The proposed approach makes progressive data-driven decisions regarding which concept(s) and corresponding solution(s) should be evaluated over the course of search, so as to minimize the computational budget spent on less promising concepts, as well as ensuring that the search does not prematurely converge to a non-optimal concept. This is achieved through the use of a tree-structured Parzen estimator (TPE) based sampler in addition to Gaussian process (GP), and random forest (RF) regressors. Aside from extending the use of GP and RF to search across multiple concepts, this study highlights the previously unexplored benefits of TPE for design optimization. The performance of the approach is demonstrated using diverse case studies, including design of a cantilever beam, coronary stents, and lattice structures using a limited computational budget. We believe this contribution fills an important gap and capitalizes on the developments in the machine learning domain to support designers involved in concept-based design.

Falsificación y literatura. La levedad del escritor múltiple: el caso de Bolívar Coronado

The counterfeit works of Rafael Bolívar Coronado (1884–1924) represent a phenomenon with unusual repercussions on critical reception. This author managed to publish more than twelve volumes with false authorship and content, from “crónicas de Indias” to collections of poetry. This article presents a theoretical approach regarding literary falsification in general and highlights the concepts of lightness and multiplicity to propose an interpretation of the Bolívar Coronado case. This paper also deals with the artistic dimension of literary forgeries as creative works that use the same methods and strategies as fiction writing. The set of falsified works by this author can be interpreted as a conceptual proposal that questions the functioning of the literary field of his time. Likewise, his proposal can also be framed within the cultural criticism that the forgery tradition entails: it questions not only the difficult discernment between the concepts of truth and lies, but also the rigidity of some guardians of cultural institutions and the system of legitimation, awards, and literary reviews.

Development of m6A/m5C/m1A regulated lncRNA signature for prognostic prediction, personalized immune intervention and drug selection in LUAD.

Research indicates that there are links between m6A, m5C and m1A modifications and the development of different types of tumours. However, it is not yet clear if these modifications are involved in the prognosis of LUAD. The TCGA-LUAD dataset was used as for signature training, while the validation cohort was created by amalgamating publicly accessible GEO datasets including GSE29013, GSE30219, GSE31210, GSE37745 and GSE50081. The study focused on 33 genes that are regulated by m6A, m5C or m1A (mRG), which were used to form mRGs clusters and clusters of mRG differentially expressed genes clusters (mRG-DEG clusters). Our subsequent LASSO regression analysis trained the signature of m6A/m5C/m1A-related lncRNA (mRLncSig) using lncRNAs that exhibited differential expression among mRG-DEG clusters and had prognostic value. The model's accuracy underwent validation via Kaplan-Meier analysis, Cox regression, ROC analysis, tAUC evaluation, PCA examination and nomogram predictor validation. In evaluating the immunotherapeutic potential of the signature, we employed multiple bioinformatics algorithms and concepts through various analyses. These included seven newly developed immunoinformatic algorithms, as well as evaluations of TMB, TIDE and immune checkpoints. Additionally, we identified and validated promising agents that target the high-risk mRLncSig in LUAD. To validate the real-world expression pattern of mRLncSig, real-time PCR was carried out on human LUAD tissues. The signature's ability to perform in pan-cancer settings was also evaluated. The study created a 10-lncRNA signature, mRLncSig, which was validated to have prognostic power in the validation cohort. Real-time PCR was applied to verify the actual manifestation of each gene in the signature in the real world. Our immunotherapy analysis revealed an association between mRLncSig and immune status. mRLncSig was found to be closely linked to several checkpoints, such as IL10, IL2, CD40LG, SELP, BTLA and CD28, which could be appropriate immunotherapy targets for LUAD. Among the high-risk patients, our study identified 12 candidate drugs and verified gemcitabine as the most significant one that could target our signature and be effective in treating LUAD. Additionally, we discovered that some of the lncRNAs in mRLncSig could play a crucial role in certain cancer types, and thus, may require further attention in future studies. According to the findings of this study, the use of mRLncSig has the potential to aid in forecasting the prognosis of LUAD and could serve as a potential target for immunotherapy. Moreover, our signature may assist in identifying targets and therapeutic agents more effectively.

Assessing the impact of marine renewable energy in Portugal: an analysis based on ACO-TCN-attention

As the global demand for renewable energy continues to increase, marine renewable energy has attracted much attention as a potential source of clean energy. As a country with rich marine resources, Portugal’s marine environment is of great significance to the development of marine energy. However, the current impact assessment of marine renewable energy projects has shortcomings such as incomplete understanding of ecosystems, incomplete consideration of fishery resources and socioeconomic impacts, lack of accuracy, and failure to consider geographical differences, thus lacking comprehensiveness and accuracy. To this end, we propose the ACO-TCN-Attention model to address these shortcomings in current impact assessments of marine renewable energy projects. The goal of this model is to provide a more comprehensive, precise and nuanced analysis to better understand the impacts of these projects on ecosystems, socio-economics and local communities. “ACO-TCN-Attention” is a model architecture that combines multiple machine learning and deep learning concepts. It includes three main parts: Ant Colony Optimization (ACO), Temporal Convolutional Network (TCN) and Attention mechanism. The ant colony optimization model simulates the behavior of ants and is used to optimize the operating strategies of marine renewable energy projects. Temporal Convolutional Network specializes in processing time series data and improves the prediction accuracy of the model. The attention mechanism allows the model to dynamically focus on the pieces of information that are most important for the current task. Extensive experimental evaluation shows that our method performs well on multiple datasets, significantly outperforming other models. This research is of great significance as it provides new methods and tools for improving the environmental impact assessment of marine renewable energy projects. By understanding the potential impacts of projects more accurately, we can better balance the relationship between the development of renewable energy and environmental protection, supporting the achievement of the Sustainable Development Goals. This research also provides useful guidance and reference for future research and practice in the field of marine energy.

Diabetes and obesity pathophysiology as a teaching tool to emphasize physiology core concepts.

Diabetes mellitus and obesity are major public health issues that significantly impact the health care system. The next generation of health care providers will need a deep understanding of the pathophysiology of these diseases if we are to prevent, treat, and eventually cure these diseases and ease the burden on patients and the health care system. Physiology core concepts are a set of core principles, or "big ideas," identified by physiology educators that are thought to promote long-term retention, create a deeper understanding, and help with formation of critical thinking skills. Here we describe our scaffolded teaching approach in an upper year undergraduate pathophysiology course to educate students about these two diseases and discuss how learning about the basis of these highly integrative diseases from the biochemical to whole body level is a meaningful tool in the physiology educator toolbox to reinforce physiology core concepts. This teaching strategy is designed to engage students in the scientific process and hone their problem-solving skills such that they are hopefully equipped to treat and eventually cure these diseases as they move forward in their careers.NEW & NOTEWORTHY Students often struggle with integration of physiological systems. Type 2 diabetes mellitus and obesity are two related diseases that are useful to explore the interdependence of physiological systems and multiple physiology core concepts. Deep learning about these diseases has the potential to dramatically improve the health care system of the future.

A typology of artificial intelligence data work

This article provides a new typology for understanding human labour integrated into the production of artificial intelligence systems through data preparation and model evaluation. We call these forms of labour ‘AI data work’ and show how they are an important and necessary element of the artificial intelligence production process. We draw on fieldwork with an artificial intelligence data business process outsourcing centre specialising in computer vision data, alongside a decade of fieldwork with microwork platforms, business process outsourcing, and artificial intelligence companies to help dispel confusion around the multiple concepts and frames that encompass artificial intelligence data work including ‘ghost work’, ‘microwork’, ‘crowdwork’ and ‘cloudwork’. We argue that these different frames of reference obscure important differences between how this labour is organised in different contexts. The article provides a conceptual division between the different types of artificial intelligence data work institutions and the different stages of what we call the artificial intelligence data pipeline. This article thus contributes to our understanding of how the practices of workers become a valuable commodity integrated into global artificial intelligence production networks.

Land rush

ABSTRACT Studies and debates on the issue of land grabs have generated multiple terms and concepts that are used loosely and interchangeably, such as large-scale land acquisitions, land grab, land deals, land enclosure, and land rush, among others. Explanations and critiques of such terms have built and enriched our knowledge. Yet, there is one term that has been under-explored: land rush. Here, we offer our own definition of a ‘land rush’, which has a different meaning from the other commonly used terms. These differences have important theoretical, methodological and political extensions and implications. Applying the lens of ‘land rush’ requires us to adjust our focus, and shows that the literature and public debates have focused on ‘concluded, operational land acquisitions’; they have tended to be dismissive of ‘failed land deals’, blind to ‘pin prick’ types of land accumulation, and have rarely taken the ‘land rush’ itself as the unit of inquiry and subject of public debate in any systematic way. This has led to a fragmented knowledge. Using the concept of the ‘land rush’ enables us to connect the issue of land grabs to broader global social life more effectively. As such, it is a key concept in Critical Agrarian Studies.

Analisis Kesulitan Pemahaman Matematika Materi Kelipatan dan Faktorisasi Bilangan di Kelas 5 Sekolah Dasar 1 Loram Kulon

This research explores the difficulty of understanding mathematics in multiples and number factorization material in grade 5 of the 1 Loram Kulon Elementary School. Qualitative research methods are used to achieve a holistic understanding of student problems. Data was collected through classroom observations and teacher interviews, involving grade 5 students as research participants. Data analysis uses qualitative analysis techniques. The research results revealed that the majority of students faced difficulties in internalizing the concepts of multiples and number factorization. Factors such as a lack of understanding of basic multiplication concepts, limited understanding of patterns, and difficulty applying concepts in real contexts are the main obstacles. This research not only identifies difficulties, but also proposes solutions by designing and implementing pedagogical interventions. The intervention involves a contextual learning approach, the use of learning media, and adjustments to teaching methods. The research results also showed a significant increase in students' understanding of multiples and number factorization material through pedagogical intervention.

Understanding multiplication concept: Exploring PMRI-enhanced thematic learning using traditional games for primary teachers

Involving concrete situations and utilizing contexts that are closely related to students' daily lives is highly promising in creating enjoyable and meaningful mathematics learning experiences. One approach that incorporates these ideas is Pendidikan Matematika Realistik Indonesia (PMRI), which is oriented towards the mathematization of everyday experiences. This research aims to investigate how the traditional game 'maggalaceng' could support students’ understanding of multiplication in thematic learning using the PMRI approach. The method employed in this study was design research in the form of workshops involving eight teachers at a public primary school in Bone, South Sulawesi. Data were collected through classroom observations, interviews, and documentation. The results show that the teachers responded positively to integrating PMRI in thematic learning, making learning more enjoyable and meaningful. Specifically, in mathematics, the designed learning trajectory fully assists teachers in exploring alternative perspectives for teaching the concept of multiplication, progressing from the informal to the formal level as a form of repeated addition.

The interplay of the innovation cycle, build time, lifetime, and deployment rate of new energy technologies: a case study of nuclear fusion energy

Abstract This article analyses how a forced transition to low-carbon energy impacts the innovation of new energy technologies. We apply the insights to nuclear fusion, potentially a large provider of carbon-free energy currently attracting billions in private investments. We discuss the ‘fastest-feasible-growth (FFG)’ curve for transitions: exponential growth followed by linear growth, where the rate of latter is limited by the inverse lifetime of the installation. We analyse how innovation is affected if, during rapid deployment, a technology progresses through several generations. We identify key timescales: the learning time, the generation time, the build time, and the exponential growth time of the early deployment phase and compare these for different energy technologies. We distinguish learning rate-limited and generation-time-limited innovation. Applying these findings to fusion energy, we find that a long build time may slow deployment, slow learning, and promote early technology lock-in. Slow learning can be remedied by developing multiple concepts in parallel. Probabilistic analysis of value implies that the optimal strategy is to parallelize the development of many concepts. This concurs with the present surge in private investment in multiple concepts. For this strategy to be successful, the build time of the power plant must be minimized. This requirement favours concepts that lend themselves to modularization and parallelization of production and assembly.

Recruitment-to-inflation ratio for bedside PEEP selection in acute respiratory distress syndrome.

In acute respiratory distress syndrome, the role of positive end-expiratory pressure (PEEP) to prevent ventilator-induced lung injury is controversial. Randomized trials comparing higher versus lower PEEP strategies failed to demonstrate a clinical benefit. This may depend on the inter-individually variable potential for lung recruitment (i.e. recruitability), which would warrant PEEP individualization to balance alveolar recruitment and the unavoidable baby lung overinflation produced by high pressure. Many techniques have been used to assess recruitability, including lung imaging, multiple pressure-volume curves and lung volume measurement. The Recruitment-to-Inflation ratio (R/I) has been recently proposed to bedside assess recruitability without additional equipment. R/I assessment is a simplified technique based on the multiple pressure-volume curve concept: it is measured by monitoring respiratory mechanics and exhaled tidal volume during a 10-cmH<inf>2</inf>O one-breath derecruitment maneuver after a short high-PEEP test. R/I scales recruited volume to respiratory system compliance, and normalizes recruitment to a proxy of actual lung size. With modest R/I (<0.3-0.4), setting low PEEP (5-8 cmH<inf>2</inf>O) may be advisable; with R/I>0.6-0.7, high PEEP (≥15 cmH<inf>2</inf>O) can be considered, provided that airway and/or transpulmonary plateau pressure do not exceed safety limits. In case of intermediate R/I (≈0.5), a more granular assessment of recruitability may be needed. This could be accomplished with advanced monitoring tools, like sequential lung volume measurement with granular R/I assessment or electrical impedance tomography monitoring during a decremental PEEP trial. In this review, we discuss R/I rationale, applications and limits, providing insights on its clinical use for PEEP selection in moderate-to-severe acute respiratory distress syndrome.

Online Machine Learning from Non-stationary Data Streams in the Presence of Concept Drift and Class Imbalance: A Systematic Review

In IoT environment applications generate continuous non-stationary data streams with in-built problems of concept drift and class imbalance which cause classifier performance degradation. The imbalanced data affects the classifier during concept detection and concept adaptation. In general, for concept detection, a separate mechanism is added in parallel with the classifier to detect the concept drift called a drift detector. For concept adaptation, the classifier updates itself or trains a new classifier to replace the older one. In case, the data stream faces a class imbalance issue, the classifier may not properly adapt to the latest concept. In this survey, we study how the existing work addresses the issues of class imbalance and concept drift while learning from nonstationarydata streams. We further highlight the limitation of existing work and challenges caused by other factors of class imbalance alongwith concept drift in data stream classification. Results of our survey found that, out of 1110 studies, by using our inclusion and exclusion criteria, we were able to narrow the pool of articles down to 35 that directly addressed our study objectives. The study found that issues such as multiple concept drift types, dynamic class imbalance ratio, and multi-class imbalance in presence of concept drift are still open for further research. We also observed that, while major research efforts have been dedicated to resolving concept drift and class imbalance, not much attention has been given to with-in-class imbalance, rear examples, and borderline instances when they exist with concept drift in multi-class data. This paper concludes with some suggested future directions.