Use Of Symbolic Representations Research Articles

VAPEX: an interactive web server for the deep exploration of natural virus and phage genomes.

Studying the genetic makeup of viruses and phages through genome analysis is crucial for comprehending their function in causing diseases, progressing medicine, tracing their evolutionary history, monitoring the environment, and creating innovative biotechnologies. However, accessing the necessary data can be challenging due to a lack of dedicated comparative genomic tools and viral and phage databases, which are often outdated. Moreover, many wet bench experimentalists may not have the computational proficiency required to manipulate large amounts of genomic data. We have developed VAPEX (Virus And Phage EXplorer), a web server which is supported by a database and features a user-friendly web interface. This tool enables users to easily perform various genomic analysis queries on all natural viruses and phages that have been fully sequenced and are listed in the NCBI compendium. VAPEX therefore excels in producing visual depictions of fully resolved synteny maps, which is one of its key strengths. VAPEX has the ability to exhibit a vast array of orthologous gene classes simultaneously through the use of symbolic representation. Additionally, VAPEX can fully analyze user-submitted viral and phage genomes, including those that have not yet been annotated. VAPEX can be accessed from all current web browsers such as Chrome, Firefox, Edge, Safari, and Opera. VAPEX is freely accessible at https://archaea.i2bc.paris-saclay.fr/vapex/.

Human Rights and Literature: A Study of The Seven Moons of Maali Almeida

This article examines the use of symbolic representations in The Seven Moons of Maali Almeida (2022) to narrate the history of Human Rights (HR) violations. The article argues that the genre of fiction has emerged as a cultural medium for promoting the discourse of HR, moving beyond legal, judicial, and political forums. Building upon the concept of Human Rights Literature (HRL) developed by Pramod K. Nayar, the article conducts a critical analysis of the novel. It analyses 1) the use of fictional narratives to depict HR violations, 2) the role of language and cultural discourse that contribute to the dehumanization and demonization of people and massacres, and 3) how the discursive description of HR violations due to riots, civil war, and massacres transforms into a popular language of fiction. The article emphasizes the significance of fiction as a valuable addition to ethical literature within the HR movement and as a tool for spreading awareness.

Construction and Validation of Symbolic Retention Ability Test

Education is a method or process of transmitting or learning universal mental processes, developing the abilities of thought and assessment, and, in general, intellectually modifying oneself or others for mature life. The ability to compare number and symbols, such as digits, efficiently is linked to mathematical competence across the life span. The teacher's use of symbolic representations becomes second nature, as they are deeply integrated with conceptual understanding and subject knowledge. Such representations, however, may place significant additional demands on learners who are already challenged by the abstract nature of concepts as well as the variety of unfamiliar substances to which these concepts are applied in the curriculum. Taking a broadly constructivist approach to learning, While the majority of students are conceptual thinkers who communicate verbally, some students have a different learning style, that the students are more interested to solve the mathematical problems by using formula and symbols. Thus, the Symbolic Retention Ability help the student to understand the mathematical problems in a meaningful way and solving them in a systematic way. This paper is an attempt to construct and validate a tool for assessing the symbolic retention ability among higher secondary students. For this purpose, a collection of 60 mathematical statements with related symbols from the content of XI standard mathematics text-book covering six dimensions viz. geometry, statistics, probability, number theory, calculus and set theory. These gathered 60 statements were undergone pilot study with 100 higher secondary students in Puducherry region and on the basis of index of difficulty and index of discrimination values 37 statements have been selected. The reliability and validness have also been found for this test. Hence, a complete and full-fledged Symbolic Retention Ability Test has been constructed and validated through this study and it will be an enough tool to measure the Symbolic Retention Ability of the higher secondary students.

Blues as a Symbolic Resistance and Representation of Countercultural Groups in the United States in the late 19 – early 20 centuries

Introduction. This article examines the blues music tradition from the perspective of the use of symbolic representations by the creators of this form of culture, which formed a unique “hidden transcripts” transmitted by certain socio-cultural groups that lived and worked in a certain historical era. Since the blues tradition in the United States originates in black communities, in terms of the self-representation of representatives of this groups to the dominant culture, we can talk abut the music of this socio-cultural period of American history as an instrument for conveying “hidden transcripts”.Methodology and sources. The author used the comparative-historical methodology in the context of studying the relationship of domination and subordination between groups and individuals. All subordinate groups use resistance strategies that go unnoticed by superior groups. Open public interaction between dominant and oppressed groups is defined by the term “public transcripts” and criticism of power that takes place offstage by the term “hidden transcripts”. Forms of hidden transcripts are coded demonstrations by oppressed groups to resist and oppose themselves, their way of life, and the difficulties of inequality to dominant groups.Results and discussion. In the context of any dominance-subordination relationship, spaces of autonomy for oppressed racial and social groups were formed, in which there was an opportunity for self-expression as acts of resistance to existing inequality, which found its expression in songs, folk tales, clothing, language, and religious expression. The development of hidden transcripts depends not only on the creation of relatively uncontrollable physical places and free time, but also on the active human agents who create and disseminate them. The bearers are likely to be as socially marginalized as the places where they gather.Conclusion. Oppressed or marginalized groups create not just their art and culture as a social group caught up in a certain cultural context, but a culture of integration into society, as well as a culture of interaction with the social hierarchy in which these groups have a rather low position. Groups excluded from the decision-making process or weakly involved in it, develop their own models for demonstrating their presence in society, and also try to convey their content to all other members of society, whether groups and individuals close or distant in status and hierarchy.

EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: The MonuMAI cultural heritage use case

The latest Deep Learning (DL) models for detection and classification have achieved an unprecedented performance over classical machine learning algorithms. However, DL models are black-box methods hard to debug, interpret, and certify. DL alone cannot provide explanations that can be validated by a non technical audience such as end-users or domain experts. In contrast, symbolic AI systems that convert concepts into rules or symbols – such as knowledge graphs – are easier to explain. However, they present lower generalization and scaling capabilities. A very important challenge is to fuse DL representations with expert knowledge. One way to address this challenge, as well as the performance-explainability trade-off is by leveraging the best of both streams without obviating domain expert knowledge. In this paper, we tackle such problem by considering the symbolic knowledge is expressed in form of a domain expert knowledge graph. We present the eXplainable Neural-symbolic learning (X-NeSyL) methodology, designed to learn both symbolic and deep representations, together with an explainability metric to assess the level of alignment of machine and human expert explanations. The ultimate objective is to fuse DL representations with expert domain knowledge during the learning process so it serves as a sound basis for explainability. In particular, X-NeSyL methodology involves the concrete use of two notions of explanation, both at inference and training time respectively: (1) EXPLANet: Expert-aligned eXplainable Part-based cLAssifier NETwork Architecture, a compositional convolutional neural network that makes use of symbolic representations, and (2) SHAP-Backprop, an explainable AI-informed training procedure that corrects and guides the DL process to align with such symbolic representations in form of knowledge graphs. We showcase X-NeSyL methodology using MonuMAI dataset for monument facade image classification, and demonstrate that with our approach, it is possible to improve explainability at the same time as performance.

EVIDENCE BASED LEARNING: AN ANALYSIS OF IMPACT OF REMEDIAL TEACHING ON CONCEPTUAL UNDERSTANDING

The flow in using the language of Chemistry is an important tool for students. Chemical formula is not a descriptive tool but also a heuristic tool. Most of the Secondary School students face challenges with Chemical formula (naming and writing). Inappropriate use of symbolic representation is the most common problem faced by students in Grade nine. The objective of the present paper is to enable Grade nine students to name and write the formula of Chemical compounds correctly. It is an attempt to investigate the complications faced by the students in formative assessment to acquire intended learning outcomes. To enhance the conceptual understanding and intended learning outcomes small group remedial teaching with experiential learning activities and worksheets are used. Small group work is used as a strategy to improve the learning outcomes and conceptual understanding among the students of Grade nine. The present study is limited to the students of one school and one unit of Science. Totally 40 students participate in the study. Sample students are divided into two group study group and control group equally on the basis of their previous knowledge of the subject. Students of study group are taught by using small group remedial experiential activity. The present study is experimental in nature. Data is analyzed by using mean, mean difference and standard deviation. The vital variance between the mean score of both groups on variables of pre-formative assessment and post-formative assessment score is tested at 0.05 level by applying t-test. The result of the present study reveals that there is a significant positive impact of small group remedial teaching on conceptual understanding of naming and writing the Chemical formulae of Chemical compound in Science subject among the students of Grade nine. Based on the result of the study teachers must provide small group remedial teaching to the students facing challenges in conceptual understanding to acquire the intended learning outcomes.

Video art as digital play for young children

AbstractIn the 21st century, along with fine arts, media art has become an important genre for young children. Undoubtedly, early visual arts give children a powerful language with which to express themselves aesthetically, cognitively and creatively through the use of symbolic representations. However, digital play remains a controversial issue in early childhood education, causing many schools to delay implementing digital arts. Recently, a number of international scholars have studied how digital technologies relate to children's learning experiences at school, arguing that open‐ended digital devices (eg, tablet computers, cameras and video recorders) may allow children to produce more creative content, such as drawings, photos and films. This study explored the role of video art in early visual arts education by using digital devices in a summer workshop in Hong Kong on video making, applying the digital play framework to the data collected. The findings revealed that the children who participated were able to explore the professional device through epistemic play. Meanwhile, they were able to use film language to share their toy‐playing stories and make their own 1‐minute video through ludic play. In this study, the children engaged in concurrent exploratory activities, using a digital video recorder and toys to create innovative and imaginative play. The findings of this study increase practitioners' and leaders' awareness of the role of digital play in early childhood education. Practitioner NotesWhat is already known about this topic? Digital arts initiatives in early childhood education have been discussed around the world; however, the implementation of digital arts learning projects has been a challenging undertaking in schools. The digital play framework has been introduced in the previous literature. This framework helps teachers observe, plan for and integrate the use of still‐camera technologies in play‐based learning. What this paper adds This study aims to offer practical advice on the integration of video art in children's digital play activities. It adopts a theoretical lens related to video making to examine techniques and engagement among children in the Asia‐Pacific region. It supplements the existing digital play framework with additional indicators associated with learning to use video cameras through play. Implications for practice and policy This study sheds new light on the application of open‐ended digital devices in children's play and learning. The results highlight the value of children's play in digital arts and the possibility of integrating digital arts into the early childhood curriculum in schools.

Automated real-time damage detection strategy using raw dynamic measurements

Over the last decades, several techniques have been developed in the context of Structural Health Monitoring (SHM) programs. However, when it comes to novelty (or damage) detection, these methods are generally based on human decisions. Moreover, most strategies already published in this topic mainly focus on modal identification procedures and tracking their outputs i.e., structural modal parameters. Such approaches usually lead to high computational costs and can still be insensitive to minor changes in structural behavior, thus missing crucial damage scenarios in their initial manifestations. To circumvent these drawbacks, recent researches showed that the use of symbolic representations derived directly from raw time-domain data (e.g. acceleration measurements) could provide more damage-sensitive responses with lower computational effort. Indeed, good results were achieved by representing raw measurements in terms of their statistical distributions over time. Nevertheless, the lack of information regarding the frequency spectrum represents a decisive drawback. Therefore, this paper presents a novel symbolic object, which considers both time and frequency responses of structural dynamic measurements. The proposed methodology employs a k-medoids clustering over such objects within a moving time-window framework and uses a single-valued index to indicate whether a novelty is present in the acquired data. Numerical simulations and two practical studies – a 3D frame tested in laboratory and a motorway bridge – show that the proposed approach provide an unsupervised and adaptive scheme for SHM applications.

Assessment of Student Performance on Core Concepts in Organic Chemistry

Assessments can provide instructors and students with valuable information regarding student’s level of knowledge and understanding, in order to improve both teaching and learning. In this study, we analyzed departmental assessment quizzes given to students at the start of Organic Chemistry 2, over an eight year period. This assessment quiz was designed to test students on core concepts from Organic Chemistry 1 that require the use of symbolic representations, such as drawing Lewis structures and using curved arrows. Using statistical analysis, it was found that students performed significantly lower than expected on the assessment quiz, and there was no significant difference in scores on the quiz over the eight years analyzed. Analysis of each individual question revealed that approximately one-third of the students made mistakes when converting a condensed formula or a chemical name to a chemical structure. For questions that involved more detailed answers such as drawing structures and showing simple ...

Students’ ways of understanding a proof

ABSTRACTWe present a model for describing the growth of students’ understandings when reading a proof. The model is composed of two main paths. One is focused on becoming aware of the deductive structure of the proof, in other words, understanding the proof at a semantic level. Generalization, abstraction, and formalization are the most important transitions in this path. The other path focuses on the surface-level form of the proof, and the use of symbolic representations. At the end of this path, students understand how and why symbolic computations formally establish a claim, at a syntactic level. We make distinctions between states in the model and illustrate them with examples from early secondary students’ mathematical activity. We then apply the model to one student’s developing understanding in order to show how the model works in practice. We close with some suggestions for further research.

Deep reinforcement learning using compositional representations for performing instructions

AbstractSpoken language is one of the most efficientways to instruct robots about performing domestic tasks. However, the state of the environment has to be considered to plan and execute actions successfully. We propose a system that learns to recognise the user’s intention and map it to a goal. A reinforcement learning (RL) system then generates a sequence of actions toward this goal considering the state of the environment. A novel contribution in this paper is the use of symbolic representations for both input and output of a neural Deep Q-network (DQN), which enables it to be used in a hybrid system. To show the effectiveness of our approach, the Tell-Me-Dave corpus is used to train an intention detection model and in a second step an RL agent generates the sequences of actions towards the detected objective, represented by a set of state predicates. We show that the system can successfully recognise command sequences fromthis corpus aswell as train the deep- RL network with symbolic input.We further show that the performance can be significantly increased by exploiting the symbolic representation to generate intermediate rewards.

Any reward will do: Effects of a reverse-reward contingency on size preference with pet dogs (Canis lupus familiaris).

The reverse-reward contingency (RRC) task involves presenting subjects with a choice between one plate containing a large amount of food and a second plate containing a small amount of food. Subjects are then required to select the smaller of the two options in order to receive the larger-magnitude reward. The RRC task is a commonly used paradigm for assessing complex cognition, such as inhibitory control, in subjects. To date, the RRC task has not been tested with pet dogs as subjects, and it may provide insights to their ability to perceive quantities of differing magnitudes. Nine dogs were tested in an RRC task involving three conditions. In Condition 1, plates of food were presented, and the dogs were allowed to consume their choice. In Condition 2, plates with different-sized symbols resembling the quantities of food in Condition 1 were presented, and dogs received food quantities of the same size as their choice (e.g., a larger-magnitude reward for selecting the plate with the larger shape). In Condition 3, the same plates were presented, but dogs received a reverse-sized quantity of food, relative to their choice (e.g., a smaller-magnitude reward for selecting the plate with the larger shape). A novel addition here to the traditional RRC task was the inclusion of a third, empty (control) plate that was present throughout all conditions, and no programmed consequences were provided when that plate was selected. Our results were consistent with the previous RRC literature: All dogs developed and maintained a preference for the larger stimulus option across conditions. The use of symbolic representations did not ameliorate performance on the RRC task. Applied implications are discussed.

Content-based unsupervised segmentation of recurrent TV programs using grammatical inference

TV program segmentation raised as a major topic in the last decade for the task of high quality indexing of multimedia content. Earlier studies of TV program segmentation are either highly supervised (e.g., event detection) or too specific to a certain type of program (e.g., cluster-based methods), which is not practically usable for indexing tasks because of the lack of generality of programs types. In this paper, we address the problem of unsupervised TV program segmentation by leveraging grammatical inference, i.e., discovering a common structural model shared by a collection of episodes of a recurrent TV program by finding an optimal alignment of structural elements across episodes. Structural elements referring to a video segment with a particular syntactic meaning with respect to the video structure. The use of symbolic representation of structural elements makes grammatical inference feasible to be applied on TV program modeling, and makes TV program segmentation possible to rely on only minimal domain knowledge. The proposed approach is operated in two phases. The first phase aims at obtaining a symbolic representation of each episode, where the elements relevant to the structure are discovered based on recurrence mining. The second phase is that of grammatical inference from the symbolic representation of episodes. We investigate two inference techniques, one based on multiple sequence alignment and one relying on uniform resampling, to infer structural grammars for TV programs. A model of the structure is derived from the structural grammars and used to predict the structure of new episodes. Comparative evaluation on two grammar inference approaches demonstrates that the models obtained can reflect the structure of the program and predict the structure of unseen episodes, which is the main application of the proposed approach in industry, i.e., to assist librarians for segmentation tasks.

A Life-Saving Therapy: The Theory-Building Case of "Cora"

The present paper presents a case study of long-term (121 sessions over three years), integrative therapy with "Cora," a client with a history of severe relational trauma that had led to difficulty in forming close relationships, and a history of hospitalization resulting from a series of suicide attempts. Despite these challenges, Cora reported a highly satisfactory outcome at the end of therapy. Using the format of a theory-building case study (McLeod, 2010; Stiles, 2007), qualitative analysis of session evaluations, session transcripts, and post therapy interviews were used to investigate how both the client and therapist understood the process of therapy and why it had been successful. Data from quantitative process and outcome measures were analyzed to provide contextual information. In spite of repeated challenges and difficult therapeutic situations, Cora and the therapist were able to establish a recurring pattern of interaction that they found relevant and productive and that was characterized by persistence. Other helpful aspects of the therapy process included acting with courage, and the use of symbolic representations of their accomplishments. The results contribute to our understanding of the process of therapy with clients who have been exposed to severe relational trauma. The discussion includes reflection on methodological issues associated with mixed-methods case study research in psychotherapy.

Survey of classroom use of representations: development, field test and multilevel analysis

Because of the multimodal nature of learning, doing and reporting science, it is important that students learn how to interpret, construct, relate and translate scientific representations or, in other words, to develop representational competence. Explicit instruction about multimodal representations is needed to foster students’ representational competence in the classroom. However, only a handful studies have surveyed how representations are actually used in science classes. This might be because of the fact that economical instruments for assessing the use of representations in classrooms are not available. To bridge that gap, an instrument was developed, field-tested in biology classes with 175 and 931 students, respectively, and analysed using exploratory and (multilevel) confirmatory factor analyses. Results supported an instrument with six scales and 21 items at the individual and classroom levels covering the following dimensions: (1) interpretation of visual representations, (2) construction of visual representations, (3) use of scientific texts (verbal representations), (4) use of symbolic representations, (5) number of terms used in class, and (6) the extent to which active social construction of knowledge is possible in the class. The scales showed satisfactory discriminant validity and reliability at each level. Further applications of this instrument for researchers and teachers are discussed.

Do student perceptions of teaching predict the development of representational competence and biological knowledge?

Dealing with representations is a crucial skill for students and such representational competence is essential for learning science. This study analysed the relationship between representational competence and content knowledge, student perceptions of teaching practices concerning the use of different representations, and their impact on students' outcome over a teaching unit. Participants were 931 students in 51 secondary school classes. Representational competence and content knowledge were interactively related. Representational aspects were only moderately included in teaching and students did not develop rich representational competence although content knowledge increased significantly. Multilevel regression showed that student perceptions of interpreting and constructing visual-graphical representations and active social construction of knowledge predicted students' outcome at class level, whereas the individually perceived amount of terms and use of symbolic representations influenced the students' achievement at individual level. Methodological and practical implications of these findings are discussed in relation to the development of representational competence in classrooms.

A new approach to qualitative learning in time series

In this paper the k-nearest-neighbours (KNN) based method is presented for the classification of time series which use qualitative learning to identify similarities using kernels. To this end, time series are transformed into symbol strings by means of several discretization methods and a distance based on a kernel between symbols in ordinal scale is used to calculate the similarity between time series. Hence, the idea proposed is the consideration of the simultaneous use of symbolic representation together with a kernel based approach for classification of time series. The methodology has been tested and compared with quantitative learning from a television-viewing shared data set and has yielded a high success identification ratio.

El aprendizaje del concepto de campo en física: una investigación exploratoria a luz de la teoría de Vergnaud

Se presentan resultados de una investigación del concepto de campo, con alumnos de Bachillerato y primer año de Universidad, en el marco de la teoría de campos conceptuales de Vergnaud, adoptada como referencial teórico para comprender y explicar el proceso de aprendizaje de este concepto fundamental de la física. El propósito fue explorar vínculos entre la estructura cognitiva de los estudiantes y la estructura del concepto de campo construido por la física, y describir niveles de conceptualización del concepto. Los resultados confirman potencialidades de la teoría para abordar estos vínculos, que se manifiestan en el uso de representaciones simbólicas y una explicitación parcial de significados, que dan forma a invariantes operatorios que usan para enfrentar situaciones y problemas que demandan una conceptualización científica aceptable. Se observa una ausencia notoria de invariantes para enfrentar situaciones y problemas propuestos. El 92,7 % de los estudiantes se ubica en los niveles más bajos de conceptualización del concepto de campo (niveles 1, 2 y 3 definidos en este estudio), donde el nivel 3 corresponde a un reconocimiento y explicación parcial de significados del concepto. Este bajo nivel es concordante con bajos desempeños en las categorías de análisis, de operación (17,6 %), expresión escrita (17,3 %), representación (13,6 %) y resolución (5,5 %), en acuerdo con la teoría de Vergnaud, que afirma que un bajo nivel de conceptualización se caracteriza por bajos niveles de explicitación de invariantes y sus representaciones, con predominio de aspectos procedimentales de las operaciones sobre el uso de predicados de mayor riqueza conceptual.

Symbolic fusion of luminance-hue-chroma features for region segmentation

Segmentation is a very important pre-processing step towards image analysis or image compression. In this paper, we propose an original region segmentation method applied to color images. The two key elements of this method are: 1. the use of a color space where hue is explicitly defined and processed according to its relevance which can be linked to chroma, 2. the use of symbolic representations and rule-base systems combining color and luminance features in order to define homogeneity between pixels. As an illustration, this approach is applied to an iterative region growing method classically used in grey level image processing. Experimental results on real images are given.

Quantity-based interference and symbolic representations in chimpanzees (Pan troglodytes).

Five chimpanzees with training in counting and numerical skills selected between 2 arrays of different amounts of candy or 2 Arabic numerals. A reversed reinforcement contingency was in effect, in which the selected array was removed and the subject received the nonselected candies (or the number of candies represented by the nonselected Arabic numeral). Animals were unable to maximize reward by selecting the smaller array when candies were used as array elements. When Arabic numerals were substituted for the candy arrays, all animals showed an immediate shift to a more optimal response strategy of selecting the smaller numeral, thereby receiving the larger reward. Results suggest that a response disposition to the high-incentive candy stimuli introduced a powerful interference effect on performance, which was effectively overridden by the use of symbolic representations.