Concept Labels Research Articles

Multi-representation Analysis of General Chemistry Books on Chemical Bonding Subject

This research is a preliminary study that focuses on multi-representation analysis of the chemical bonding topic in three general chemistry of text books. The analysis was carried out to find out how concepts are explained based on the three levels of chemical representation, especially on ionic and covalent bonding, which are the basis for further research in developing intertextual-based learning strategies. Analysis was carried out at the macroscopic-symbolic and sub microscopic-symbolic levels usingmulti-representation analysis table that was adopted from Gkitzia et al., 201 for three general chemistry books, that are 1) Whitten, K.W, et.al. (2014). Chemistry 10th edition. English: Brooks/Cole Cengage Learning; 2) Chang, R. (2010). Chemistry 10th edition. New York : The McGraw-Hill Companies; and 3) Silberberg, M.S, and Amateis, P.G. (2021). Chemistry: the molecular nature of matter and change 9th edition. New York : The McGraw-Hill Companies. The analysis results show that book: Silberberg, M.S, and Amateis, P.G. (2021). Chemistry: the molecular nature of matter and change 9th edition. New York : The McGraw-Hill Companies has the most complete macroscopic-symbolic and sub microscopic-symbolic representation levels on the four main concept labels of chemical bonding, so that they can become standard reference material in developing chemical representations that will be used in intertextual learning strategies.

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.

The Importance of Developing E-Books Using The 4S-TMD Method to Improve Environmental Literacy

This preliminary study aimed to describe the importance of developing redox electronic books using the 4S-TMD method to increase environmental literacy. The 4S-TMD method consists of four stages: selection, structuring, characterization and didactic reduction. This study focused on the selection stage as a first step. Environmental literacy is the ability to understand and interpret ecological conditions. From the results of this understanding and interpretation, an individual can decide the appropriate action to maintain, restore and improve the condition of an environment. The components of environmental literacy that must be achieved include ecological knowledge, attitudes towards the environment, cognitive skills, and behavior towards the environment. This study used the Development Research (DR) method with the Design, Development and Evaluation type. This article discussed the importance of developing chemistry e-books in the selection stage. The results of the selection stage determined basic competencies, which are formulated as learning indicators and concept labels based on curriculum demands. The results of the selection stage were also related to the context of content related to everyday life and the development of basic concepts sourced from international textbooks, as well as pedagogic contexts related to achieving environmental literacy. The results of the selection stage were evaluated by expert lecturers in chemistry education who were declared valid. Based on the results and discussion of the research, it was concluded that the development of electronic books on redox material using the 4S TMD method was developed in several stages where the first stage, the selection stage, showed that development began with the selection of basic competencies, namely KD 3.9, by experiencing the development of indicators, namely eight indicators.
 Keywords: redox, electronic books, 4S-TMD method, environmental literacy

Towards Trustworthy Deep Learning

Deep neural networks (DNNs) have achieved unprecedented success across many scientific and engineering fields in the last decades. Despite its empirical success, unfortunately, recent studies have shown that there are various failure modes and blindspots in DNN models which may result in unexpected serious failures and potential harms, e.g. the existence of adversarial examples and small perturbations. This is not acceptable especially for safety critical and high stakes applications in the real-world, including healthcare, self-driving cars, aircraft control systems, hiring and malware detection protocols. Moreover, it has been challenging to understand why and when DNNs will fail due to their complicated structures and black-box behaviors. Lacking interpretability is one critical issue that may seriously hinder the deployment of DNNs in high-stake applications, which need interpretability to trust the prediction, to understand potential failures, and to be able to mitigate harms and eliminate biases in the model. To make DNNs trustworthy and reliable for deployment, it is necessary and urgent to develop methods and tools that can (i) quantify and improve their robustness against adversarial and natural perturbations, and (ii) understand their underlying behaviors and further correct errors to prevent injuries and damages. These are the important first steps to enable Trustworthy AI and Trustworthy Machine Learning. In this talk, I will survey a series of research efforts in my lab contributed to tackling the grand challenges in (i) and (ii). In the first part of my talk, I will overview our research effort in Robust Machine Learning since 2017, where we have proposed the first attack-agnostic robustness evaluation metric, the first efficient robustness certification algorithms for various types of perturbations, and efficient robust learning algorithms across supervised learning to deep reinforcement learning. In the second part of my talk, I will survey a series of exciting results in my lab on accelerating interpretable machine learning and explainable AI. Specifically, I will show how we could bring interpretability into deep learning by leveraging recent advances in multi-modal models. I'll present recent works in our group on automatically dissecting neural networks with open vocabulary concepts, designing interpretable neural networks without concept labels, and briefly overview our recent efforts on demystifying black-box DNN training process, automated neuron explanations for Large Language Models and the first robustness evaluation of a family of neuron-level interpretation techniques.

2.I. Workshop: Planetary and One health policy and governance: evidence, tools and management

Abstract In the past few years, a broad perspective on the relationships between human health, animal health and environmental health has developed under different conceptual labels of EcoHealth, One Health and Planetary health. Regardless of particular frames, what they have in common is the recognition that human health is not superior to other components of the ecosystem, and therefore these are complementary approaches. In this workshop we aim to increase awareness and knowledge of the fundamental interdependencies of human health, animal health, ecology and economy. The purpose is to enhance policy actions and health management with regard to the cumulative impacts across environmental, animal and human health, and the many potential trade-offs that should be taken into account when prioritizing one action over another. Also, there are specific requirements for integrative and cross-sectoral as well as multilevel governance with respect to cross-disciplinary capacity, stakeholder acceptance and other implementation conditions. In order to set the agenda, build coalitions and advocate for One health policies and management, a first and foremost condition is the development of interconnections between existing networks and platforms to speak with one voice. This workshop presents research evidence into One Health management needs, the European Hub for Planetary Health; and a recently developed One Health policy screening tool for rapid decision-making on which potential impacts and trade-offs policy impact assessment and action should focus. After three presentations from renowned speakers in the field, we will open the floor for interactive dialogue with the audience on the categorization, prioritization, and planning of follow up activities for One Health agenda-setting, advocacy and platform expansion. We intend this workshop as a recurring workshop in the next two conferences ensuring follow up actions throughout the years, using among others the EUPHA and its PHPP and ENV sections networks and media channels to spur awareness and action. Key messages • Prevention of emerging One Health threats require a public health community mind shift creating collective action with animal, environmental as well as economic and agricultural policy sectors. • Our platforms, tools and evidence support agenda-setting, advocacy and policy screening to reduce health impacts and inequalities.

From Gibson Girl to Gibson Goddess: The World War I Illustrations of Charles Dana Gibson in Life Magazine

ABSTRACT Charles Dana Gibson’s leadership of the Division of Pictorial Publicity, part of the US government’s Committee on Public Information (CPI), during World War I has been thoroughly studied, yet scant attention has been given to his own art during the conflict. This study examines Gibson’s illustrations that appeared in the illustrated humor magazine Life during World War I, with the primary purpose of examining how he employed the female image to not only garner support for the war, but also define women’s responsibilities and limitations in time of war. Examination of Life during the war indicates that the Gibson Girl’s “Beauty” or “Sentimental” typologies had limited utility in convincing Americans that the battle in Europe was worth fighting. Instead, Gibson was more likely to use two female forms that had long ago become well-recognized American propaganda devices—the “Protecting Angel,” who assumed roles as nurses, aid workers, and, by extension, mothers of soldiers; and the “Amazon Warrior,” always attired in gowns, usually in large (if not colossal) scale, and most often bearing conceptual labels such as Freedom or Democracy.

A Dynamic Interactive Approach to Music Listening: The Role of Entrainment, Attunement and Resonance

This paper takes a dynamic interactive stance to music listening. It revolves around the focal concept of entrainment as an operational tool for the description of fine-grained dynamics between the music as an entraining stimulus and the listener as an entrained subject. Listeners, in this view, can be “entrained” by the sounds at several levels of processing, dependent on the degree of attunement and alignment of their attention. The concept of entrainment, however, is somewhat ill-defined, with distinct conceptual labels, such as external vs. mutual, symmetrical vs. asymmetrical, metrical vs. non-metrical, within-persons and between-person, and physical vs. cognitive entrainment. The boundaries between entrainment, resonance, and synchronization are also not always very clear. There is, as such, a need for a broadened approach to entrainment, taking as a starting point the concept of oscillators that interact with each other in a continuous and ongoing way, and relying on the theoretical framework of interaction dynamics and the concept of adaptation. Entrainment, in this broadened view, is seen as an adaptive process that accommodates to the music under the influence of both the attentional direction of the listener and the configurations of the sounding stimuli.

Interactive Concept Bottleneck Models

Concept bottleneck models (CBMs) are interpretable neural networks that first predict labels for human-interpretable concepts relevant to the prediction task, and then predict the final label based on the concept label predictions. We extend CBMs to interactive prediction settings where the model can query a human collaborator for the label to some concepts. We develop an interaction policy that, at prediction time, chooses which concepts to request a label for so as to maximally improve the final prediction. We demonstrate that a simple policy combining concept prediction uncertainty and influence of the concept on the final prediction achieves strong performance and outperforms static approaches as well as active feature acquisition methods proposed in the literature. We show that the interactive CBM can achieve accuracy gains of 5-10% with only 5 interactions over competitive baselines on the Caltech-UCSD Birds, CheXpert and OAI datasets.

What is a mental disorder? Evaluating the lay concept of Mental Ill Health in the United States

PurposeHow “mental disorder” should be defined has been the focus of extensive theoretical and philosophical debate, but how the concept is understood by laypeople has received much less attention. The study aimed to examine the content (distinctive features and inclusiveness) of these concepts, their degree of correspondence to the DSM-5 definition, and whether alternative concept labels (“mental disorder”, “mental illness”, “mental health problem”, “psychological issue”) have similar or different meanings.MethodsWe investigated concepts of mental disorder in a nationally representative sample of 600 U.S. residents. Subsets of participants made judgments about vignettes describing people with 37 DSM-5 disorders and 24 non-DSM phenomena including neurological conditions, character flaws, bad habits, and culture-specific syndromes.ResultsFindings indicated that concepts of mental disorder were primarily based on judgments that a condition is associated with emotional distress and impairment, and that it is rare and aberrant. Disorder judgments were only weakly associated with the DSM-5: many DSM-5 conditions were not judged to be disorders and many non-DSM conditions were so judged. “Mental disorder”, “mental illness”, and “mental health problem” were effectively identical in meaning, but “psychological issue” was somewhat more inclusive, capturing a broader range of conditions.ConclusionThese findings clarify important issues surrounding how laypeople conceptualize mental disorder. Our findings point to some significant points of disagreement between professional and public understandings of disorder, while also establishing that laypeople’s concepts of mental disorder are systematic and structured.

CRIME HOTSPOT EMERGENCE IN MEXICO CITY: A COMPLEXITY SCIENCE PERSPECTIVE

We present a dynamic model based on the theories proposed by environmental criminologists to explain the emergence of crime hotspots within cities; a pervasive phenomenon that is largely independent of cities size and cultural differences. The model is defined on a multiplex network that represents a city spatial tiling with its corresponding urban transport infrastructure, allowing to explore the relation between crime hotspot locations and the network topological features. It also allows to explore the effects that cities time evolution and police checkpoints might have on the emergence of crime hotspots. For Mexico City, the model shows that heterogeneous distributions of criminal activity arise from a diffusion-driven instability, as a self-organizing process. The results obtained for this city are in line with several insights from environmental criminology, such as the relationship between urban layout and crime hotspots locations, or the conceptual label assigned to specific locations as crime generators. They also uncover new relationships between cities design and crime hotspot locations, and suggest that routine activity theory alone cannot explain the emergence of heterogeneous crime distributions.

Digital “x”—Charting a Path for Digital-Themed Research

We live in a time when digital technologies reshape most aspects of business and social life. This challenges received assumptions about modes of operation in organizations. As a result, scholars and practitioners increasingly use the label “digital” to signify that something has changed to the extent that a plethora of long-established management concepts are expressed in a new formulaic form of “digital x,” and x can stand for innovation, strategy, transformation, infrastructure, etc. In the information systems discipline and beyond, “digital” has emerged as an oft-used conceptual label to characterize age-long phenomena hitherto described by the IT (or x) label. There is a sense among academic and practitioner communities that digital and IT are not mere synonyms, but beyond the hype, something fundamentally different is being signaled when the “digital” label is invoked. This paper traces the intellectual roots and foundations of the growing use of “digital” as a conceptual label, identifies when the label use is warranted as well as outlines implications that the moniker holds for future scholarship, policy, and practice. In particular, the paper offers actionable guidance that enables more reflective use of the term “digital” as we move forward.

Prolegomena for the concept of constructionism in architecture

This prolegomena initially consolidates ?meta-architectural constructionism? as a conceptual label for the operationalization of the architectural modalities of the philosophy of synthesis. After the heuristic definition of its key aspects was carried out through four key terms - reconstruction, relationism, processualism and projectivism, the second part of the article initiates a proposal of understanding and chronology according to the changing ontology of modernity, following Berthelot?s (Jean- Michel Berthelot) thesis about the three poles of the contemporary program of historical epistemology - the naturalistic, intentional and symbolic poles. His metatheoretical form of constructivism applies a version of the familiar scientific hypothetico-deductive model to cognition in general. As a kind of general vector of time, appropriation of epochal consciousness or a kind of sensibility in architecture, this post-positivist dogma deviates from theoretically too stabilized paradigms, categorical schemes or even less norms, but functions as a variable form of discursive, transdisciplinary, phenomenological and metaphysical identifications. Naturalistic metaarchitectural constructs indicate the core presence of the aspectuality of reconstruction and relationism, intentional constructs indicate the strong presence of metaarchitectural procedurality and projectivism, while the symbolic metaarchitectural pole is based on a complex combination of all heuristically recognized aspectualities as a modality of complex and combinatorial thinking and a methodology that is essentially postmodern scripture. The ontological vocations of metatheory of synthesis in the first two historical epistemologies of metaarchitectural constructionism are directed towards analytical constructs, while the third pole is directed towards the descriptive nature of constructs, but in all aspects of meta-architectural constructionism the role of some kind of structure or system appears. The intention is to theoretically standardize the ambiguity of constructionism in architecture as a text of culture, the consequences of which are rationalistic shaping of relativistic phenomena, as a meta-architectural procedure, protocol or a specific, simultaneously ontologically, epistemologically and methodologically based platform of thought.

Learning High-Dimensional Evolving Data Streams With Limited Labels.

In the context of streaming data, learning algorithms often need to confront several unique challenges, such as concept drift, label scarcity, and high dimensionality. Several concept drift-aware data stream learning algorithms have been proposed to tackle these issues over the past decades. However, most existing algorithms utilize a supervised learning framework and require all true class labels to update their models. Unfortunately, in the streaming environment, requiring all labels is unfeasible and not realistic in many real-world applications. Therefore, learning data streams with minimal labels is a more practical scenario. Considering the problem of the curse of dimensionality and label scarcity, in this article, we present a new semisupervised learning technique for streaming data. To cure the curse of dimensionality, we employ a denoising autoencoder to transform the high-dimensional feature space into a reduced, compact, and more informative feature representation. Furthermore, we use a cluster-and-label technique to reduce the dependency on true class labels. We employ a synchronization-based dynamic clustering technique to summarize the streaming data into a set of dynamic microclusters that are further used for classification. In addition, we employ a disagreement-based learning method to cope with concept drift. Extensive experiments performed on many real-world datasets demonstrate the superior performance of the proposed method compared to several state-of-the-art methods.

FAIR Versus Open Data: A Comparison of Objectives and Principles

Abstract This article assesses the difference between the concepts of ‘open data’ and ‘FAIR data’ in data management. FAIR data is understood as data that complies with the FAIR Guidelines—data that is Findable, Accessible, Interoperable and Reusable—while open data was born out of awareness of the need to democratise data by improving its accessibility, based on the idea that data should not have limitations that prevent people from using it. This study compared FAIR data with open data by analysing relevant documents using a coding analysis with conceptual labels based on Kingdon's theory of agenda setting. The study found that in relation to FAIR data the problem stream focuses on the complexity of data collected for research, while open data primarily emphasises giving the public access to non-confidential data. In the policy stream, the two concepts share common standpoints in terms of making data available and reusable, although different approaches are adopted in practice to accomplish these goals. In the politics stream, stakeholders with different objectives support FAIR data and from those who support open data.

ANALISIS MATERI HIDROLISIS GARAM DALAM BUKU TEKS PELAJARAN KIMIA SMA/MA KELAS XI BERDASARKAN KRITERIA TAHAP SELEKSI 4S TMD

This study is aims to analyze the salt hydrolysis material in the SMA/MA class XI textbooks based on the selection criteria of the 4S TMD. The selection of the 4S TMD used has criteria of the suitability of material with curriculum 2013, the truth of the concept, and influcation of values. The suitability of material with curicculum 2013 in term of breadth of the material an the depth of concept. Based of the result of the analysis of the breadth of material in textbook A there are 8 concept labels declared approprite. While the textbook B There are 8 concept labels which are stated to be approprite, and concept labels which is stated to be too broad. The results of the analysis of the depth of the concept of textbook A there is 1 concept that is declared approprite, 4 concept are not deepth, 3 concept are too deep. In the textbook B there are 2 concepts that are stated to be approprite, 3 concepts that are not deep enough, and 3 concepts are to deep. The results of the analysis of the truth of the concept of textbook A there are 2 concept labels which are stated according to the scientists, and 6 concept label which were stated not to be approprite. In the textbook B there are 8 concept labels which are stated to e scientifcally correct. The result of the analysis of the instillation of the value of textbook A there are 8 value that are instilled namely, religious, tolerance, democratic, friendly/communicative, hard work, curiosity, discipline, and responsibility. In the textbook B there are 7 values that are instilled namely, tolerance, democracy, friendly/communicative, hard work ,curiosity, discipline, and responsibility. The result of the selection analysis show that textbook B it is better than textbook A can be used in learning activities.

The ‘globalhood’ as an urban district of the 21st-century city: the case of the St Vincent’s Quarter, Sheffield

ABSTRACT The ‘globalhood’ is a conceptual label that has been used to describe parts of cities that are very popular with global tourists, often characterised by high levels of short-let/holiday rentals. In these neighbourhoods locals can feel overwhelmed by visitors. This article extends the concept of the globalhood to those areas of cities that play host to populations of thousands of students from around the world who are attracted to study at universities outside of their home country. Using the St Vincent’s Quarter, Sheffield, as a case study of the globalhood, this article explores the factors that led to its creation over the first two decades of the 20th century. In identifying the student globalhood, it posits that this type of neighbourhood should be considered when formulating an understanding of the urban morphology of cities in the UK and around the world where globally attractive higher education institutions attract large numbers of internationally diverse students.

Exhibition as cinema, cinema as exhibition: Philippe Parreno and the moving image in the museum

This article situates a number of Philippe Parreno’s moving image installations within the developments that led in the 1990s to the establishment of film as one of the pre-eminent forms of display in the museum of contemporary art. From here, a clear yet unexplored relation is established between cinema’s relocation to the museum and artistic practices promoted under the conceptual label of relational aesthetics, with which Parreno was closely associated throughout the 1990s. Conceived as a part of the structural whole comprising an exhibition and embedded in a specific location, film installation was used by Parreno to raise questions concerning the visibility of the artwork and the duration of the viewing encounter. The fluid interactions between temporal and spatial categories in these installations invite a re-examination of the categories of museum and exhibition, as well as the material limits of the medium of the moving image.

Concept Bottleneck Model With Additional Unsupervised Concepts

With the increasing demands for accountability, interpretability is becoming an essential capability for real-world AI applications. However, most methods utilize post-hoc approaches rather than training the interpretable model. In this article, we propose a novel interpretable model based on the concept bottleneck model (CBM). CBM uses concept labels to train an intermediate layer as the additional visible layer. However, because the number of concept labels restricts the dimension of this layer, it is difficult to obtain high accuracy with a small number of labels. To address this issue, we integrate supervised concepts with unsupervised ones trained with self-explaining neural networks (SENNs). By seamlessly training these two types of concepts while reducing the amount of computation, we can obtain both supervised and unsupervised concepts simultaneously, even for large-sized images. We refer to the proposed model as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">concept bottleneck model with additional unsupervised concepts</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CBM-AUC</i> ). We experimentally confirmed that the proposed model outperformed CBM and SENN. We also visualized the saliency map of each concept and confirmed that it was consistent with the semantic meanings.

Transfer-based taxonomy induction over concept labels

Given a domain-specific set of concepts, taxonomy induction is the problem of inducing a taxonomy from the set of concepts. The problem, despite having practical importance, has not received as much research attention, in contrast with related problems such as link prediction, due to its difficulty and lack of domain-specific benchmarks. In this paper, we present a principled approach for taxonomy induction in the e-commerce domain over a set of concept-labels, given background resources such as a pre-trained language representation learning model and examples of other taxonomies, induced over other concept-sets, but no example links for the target concept-set. Our approach, developed as an academic-industrial collaboration, is significantly more competitive than seven different baselines, including the transformer-based RoBERTa model, on three real-world and widely used e-commerce concept-sets.

Forum: Conflict Delegation in Civil Wars

AbstractThis forum provides an outlet for an assessment of research on the delegation of war to non-state armed groups in civil wars. Given the significant growth of studies concerned with this phenomenon over the last decade, this forum critically engages with the present state of the field. First, we canvass some of the most important theoretical developments to demonstrate the heterogeneity of the debate. Second, we expand on the theme of complexity and investigate its multiple facets as a window into pushing the debate forward. Third, we draw the contours of a future research agenda by highlighting some contemporary problems, puzzles, and challenges to empirical data collection. In essence, we seek to connect two main literatures that have been talking past each other: external support in civil wars and proxy warfare. The forum bridges this gap at a critical juncture in this new and emerging scholarship by offering space for scholarly dialogue across conceptual labels.