The present study, using quantitative and qualitative analyses, aimed at delineating the interrelationship between the knowledge of metacognition and the regulation of metacognition, along with the role of learners' regulatory ability in mediating the effects of task-induced involvement load on word learning. A total of 60 university EFL students were recruited to the study. They first completed a checklist on metacognition and were then assigned to complete three tasks with varying degrees of involvement load followed by a vocabulary (...) test. Of them, 12 students also participated in an interview. The results showed that the two main components of metacognition, i.e., the knowledge and regulation of metacognition, are closely and significantly correlated. The learners, assigned to four different ability groups (LK/LR, LK/HR, HK/LR, HK/HR), were found to benefit most by engaging in a task with the highest involvement load. Despite the benefits, their regulatory ability mediated the effects of task-induced involvement load on word learning, which was corroborated by the interview results. The relevant implications for teaching and learning words through tasks are further discussed. (shrink)

This study aimed to examine the lived experiences of multilingual students in online distance learning during the height of the COVID-19 pandemic. The study utilized the qualitative research design - transcendental phenomenology and employed purposive sampling. The study participants were ten (10) multilingual students among Senior High School Students. A researcher-made questionnaire was reviewed by research experts and was used in individual interviews with the participants. The collected data through interviews underwent thematic analyses and triangulation. Based on the findings, (...) the model was developed through thematic analyses, the Basic Instructional Strategies Aiming to Address the Problems of Multilingual Students in Online Distance Learning MODEL (in the Filipino language: Batayang Instruksiyong estratehiya na Naglalayong Tugunan at Alamin (BINTA) ang mga suliranin ng mga multilinggwal na mag-aaral sa online distance learning) was developed. The BINTA Model represents the lived experiences of multilingual students in online distance learning in terms of the themes that emerged: lack of interaction and communication, difficulty in understanding technical terms, consistency of internet connection, and effective communication. This implies that the model can help improve these students' educational outcomes and experiences by providing appropriate instructional strategies tailored to the needs of multilingual students. Educators can help mitigate these students' challenges and difficulties in an online distance learning environment. Through this, educators can help ensure that multilingual students can engage with the material, participate fully in online discussions and activities, and ultimately achieve their learning goals. (shrink)

Children acquire complex concepts like DOG earlier than simple concepts like BROWN, even though our best neuroscientific theories suggest that learning the former is harder than learning the latter and, thus, should take more time (Werning 2010). This is the Complex- First Paradox. We present a novel solution to the Complex-First Paradox. Our solution builds on a generalization of Xu and Tenenbaum’s (2007) Bayesian model of word learning. By focusing on a rational theory of concept (...) class='Hi'>learning, we show that it is easier to infer the meaning of complex concepts than that of simple concepts. (shrink)

This paper examines Augustine’s views on language, learning, and testimony in De Magistro. It is often held that, in De Magistro, Augustine is especially concerned with explanatory understanding (a complex cognitive state characterized by its synoptic nature and awareness of explanatory relations) and that he thinks testimony is deficient in imparting explanatory understanding. I argue against this view and give a clear analysis of the different kinds of cognitive state Augustine is concerned with and a careful examination of his (...) arguments concerning the deficiencies of testimony in producing these cognitive states. (shrink)

Estimating word complexity is a well-established task in computer-assisted language learning. So far, however, complexity estimation has been largely limited to comprehension. This neglects words that are easy to comprehend, but hard to produce. We introduce semantic error prediction (SEP) as a novel task that assesses the production complexity of content words. Given the corrected version of a learner-produced text, a system has to predict which content words replace tokens from the original text. We present and analyse one (...) example of such a semantic error prediction dataset, which we generate from an error correction dataset. As neural baselines, we use BERT, RoBERTa, and LLAMA2 embeddings for SEP. We show that our models can already improve downstream applications, such as predicting essay vocabulary scores. (shrink)

Triangles and trigonometry are always difficult topics for both mathematics students and teachers. Hence, students' performance in solving mathematical word problems in these topics is not only a reflection of their learning outcomes but also an indication of teaching effectiveness. This case study drew from two examples of solving word problems involving triangles by pre-service mathematics teachers in a foundation mathematics course delivered by the author. The focus of this case study was on reasoning implications of students' (...) performances on the effective training of pre-service mathematics teachers, from which a three-step interactive explicit teaching-learning approach, comprising teacher-led precise and inspiring teaching (or explicit teaching), student-driven engaged learning (or imitative learning), and student-led and teacher-guided problem-solving for real-world problems or projects (or active application), was summarized. Explicit teaching establishes a solid foundation for students to further their understanding of new mathematical concepts and to conceptualize the technical processes associated with these new concepts. Imitative learning helps students build technical abilities and enhance technical efficacy by engaging in learning activities. Once these first two steps have been completed, students should have a decent understanding of new mathematical concepts and technical efficacy to analyze, formulate, and finally solve real-world applications with assistance from teachers whenever required. Specially crafted professional development should also be considered for some in-service mathematics teachers to adopt this three-step interactive teaching-learning process. (shrink)

Modes of teaching and learning have had to rapidly shift amid the COVID-19 pandemic. As an emergency response, students from Philippine public schools were provided learning modules based on a minimized list of essential learning competencies in Biology. Using a cross-sectional survey method, we investigated students’ perceptions of the Biology self-learning modules (BSLM) that were designed in print and digitized formats according to a constructivist learning approach. Senior high school STEM students from grades 11 (n (...) = 117) and 12 (n = 104) participated in a survey using a 3-point Likert-scale questionnaire uploaded online through Google Forms. The survey results indicate that majority of the students perceived the modules positively, suggesting that aspects of the modules that were salient to students corresponded to essential elements of constructivist pedagogies. However, during interviews, students reported several difficulties in learning with BSLM as it was constrained by, to name a few, the use of unfamiliar words, lack of access to supporting resources, slow internet connection, and time constraints. To address these problems, teachers reported that they gave deadline extensions, complemented modules with other channels of support, and used online and offline platforms for reaching out to students to answer their queries and plan out their schedule for the week. The findings across the data sources point to the complex demands of emergency distance education that teachers, as curriculum designers and enactors, need to bear in mind in order to craft productive pedagogies, constructivist or otherwise, during this unprecedented time. (shrink)

In the Lunyu, Confucius remarks on the implausibility—or impossibility—of a life lacking in xin 信, reliability (2.22). In existing discussions of Confucian philosophy, this aspect of life is often eclipsed by greater emphasis on Confucian values such as ren 仁 (benevolence), li 禮 (propriety) and yi 義 (rightness). My discussion addresses this imbalance by focusing on reliability, extending current debates in two ways. First, it proposes that the common translation of xin as denoting coherence between a person’s words and deeds (...) is inadequate. The translation fails to capture the longer-term consistency in a person’s actions and behaviours in different circumstances across time. Second, it explores how the Lunyu passages discuss the processes of learning that prepare a person for reliable action. (shrink)

The article examines the basics of learning a foreign language. The fundamental foundations of this process are chosen. These judgments are made at the intersection of philosophical and pedagogical platforms. Therefore, research is of importance in both aspects, both philosophical and pedagogical. A new concept called ”Intuitive language base" is put forward. With the help of this concept, judgments about the fundamental foundations are made. The main issue explained within the framework of the concept is about words. Thus, it (...) is shown that, unlike the grammatical rules, words have a different fundamental aspect. At the same time, fundamental ideas about translation and scientific language are taken from this concept. In addition, the number of possible words in the world is calculated, judgments about creativity are put forward, fundamental issues such as the concept of the minilex and the music of the language are touched upon, and the attitude is expressed within the proposed concept. (shrink)

Programming artificial intelligence to make fairness assessments of texts through top-down rules, bottom-up training, or hybrid approaches, has presented the challenge of defining cross-cultural fairness. In this paper a simple method is presented which uses vectors to discover if a verb is unfair or fair. It uses already existing relational social ontologies inherent in Word Embeddings and thus requires no training. The plausibility of the approach rests on two premises. That individuals consider fair acts those that they would be (...) willing to accept if done to themselves. Secondly, that such a construal is ontologically reflected in Word Embeddings, by virtue of their ability to reflect the dimensions of such a perception. These dimensions being: responsibility vs. irresponsibility, gain vs. loss, reward vs. sanction, joy vs. pain, all as a single vector. The paper finds it possible to quantify and qualify a verb as fair or unfair by calculating the cosine similarity of the said verb’s embedding vector against FairVec—which represents the above dimensions. We apply this to Glove and Word2Vec embeddings. Testing on a list of verbs produces an F1 score of 95.7, which is improved to 97.0. Lastly, a demonstration of the method’s applicability to sentence measurement is carried out. (shrink)

Abstract: Sarcasm is commonly used in news and detecting sarcasm in headline news is challenging for humans and thus for computers. The media regularly seem to engage sarcasm in their news headline to get the attention of people. However, people find it tough to detect the sarcasm in the headline news, hence receiving a mistaken idea about that specific news and additionally spreading it to their friends, colleagues, etc. Consequently, an intelligent system that is able to distinguish between can sarcasm (...) none sarcasm automatically is very important. The aim of the study is to build a sarcasm model that detect headline news using machine and deep learning and attempt to understand how a computer learns the patterns of sarcasm. The dataset used in this study was collected from Kaggle depository. We examined 21 algorithms of machine learning and one deep learning algorithm for detecting sarcasm in headline news. The evaluation metric used in this study are Accuracy, F1-measure, Recall, Precision, and Time needed for training and evaluation. The deep learning model achieved accuracy (95.27%), recall (96.62%), precision (94.15%), F1-score (95.37%) and time needed to train the mode (400 seconds), with loss of around 0.3398. However, the algorithm of machine learning that achieved the highest F1-Score is Passive Aggressive Classifier. It was the top classier for sarcasm detection among the machine learning algorithms with accuracy (95.50%), recall (96.09 %), precision (94.30%), F1-score (95.19%) and time needed to train the mode (0.31 seconds). (shrink)

This work investigates one of the most widespread yet elusive ideas about our experience of art: the idea that there is something cognitively valuable in engaging with great artworks, or, in other words, that we learn from them. This claim and the age-old controversy that surrounds it are reconsidered in light of the psychological and neuroscientific literature on learning, in one of the first systematic efforts to bridge the gap between philosophical and scientific inquiries on the topic. The work (...) has five chapters. Chapter 1 lays down its conceptual bases: it explains what learning is taken to be in the current philosophical debate and it points out how Bayesian cognitive science (particularly in its predictive processing formulations) might be well-suited to capture the kind of learning involved in our engagement with the arts. The following chapters test this latter hypothesis with respect to particular art forms, namely literature and literary language (Chapter 2), narrative (Chapter 3), and visual art, music and motor activities (Chapter 4). The fine-grained discussions conducted in each of these areas will enable us to see that the relationship between art and learning is indeed fundamental and pervasive. The final chapter (Chapter 5) examines the consequences of this fact for our understanding of the role of art in our epistemic practices, its ultimate usefulness and value, and its place in the interdisciplinary study of the human mind. The upshot is a novel and wide-ranging picture, both philosophically informed and empirically sound, that bypasses many of the problems and dead ends of the current philosophical debate on the topic and captures the deep sense in which art and learning are interrelated. (shrink)

Theories of number concepts often suppose that the natural numbers are acquired as children learn to count and as they draw an induction based on their interpretation of the first few count words. In a bold critique of this general approach, Rips, Asmuth, Bloomfield [Rips, L., Asmuth, J. & Bloomfield, A.. Giving the boot to the bootstrap: How not to learn the natural numbers. Cognition, 101, B51–B60.] argue that such an inductive inference is consistent with a representational system that clearly (...) does not express the natural numbers and that possession of the natural numbers requires further principles that make the inductive inference superfluous. We argue that their critique is unsuccessful. Provided that children have access to a suitable initial system of representation, the sort of inductive inference that Rips et al. call into question can in fact facilitate the acquisition of larger integer concepts without the addition of any further principles. (shrink)

A theory of learning for the future advocates the teaching of a preparedness to respond in a creative way to difference and otherness. This includes an ability to act imaginatively in situations of uncertainties. John Dewey’s pragmatism holds the key to such a learning theory his view of the continuous meetings of individuals and environments as experimental and playful. That pragmatism has not yet been acknowledged as a relevant learning theory for the future may be due to (...) the immediate connotation and the many interpretations associated with the term ‘experience’, which is at the heart of Dewey’s educational thinking. Dewey defi ned experience in a way that is not well understood within educational research, and in a way that is easily confused with the term ‘experiential learning’. The latter refers to the importance of participants’ ‘experiences’ derived from bodily actions and stored in memory as more or less tacit knowledge. (shrink)

Machine learning systems (MLS) that model low-level processes are the cornerstones of current AI systems. These ‘indirect’ learners are good at classifying kinds that are distinguished solely by their manifest physical properties. But the more a kind is a function of spatio-temporally extended properties — words, situation-types, social norms — the less likely an MLS will be able to track it. Systems that can interact with objects at the individual level, on the other hand, and that can sustain this (...) interaction, can learn responses to increasingly abstract properties, including representational ones. This representational capacity, arguably the mark of intelligence, then, is not available to current MLS’s. (shrink)

How does philosophy learn to speak a new language? That is, how does some particular language come to serve as the means for the expression of philosophical ideas? In this paper, I present an answer grounded in four historical case studies and suggest that this answer has broad implications for contemporary philosophy. I begin with Jonathan Rée’s account of philosophical translation into English in the sixteenth century, and the debate between philosopher-translators who wanted to acquire – wholesale or with modifications (...) – foreign terms, and those who wished to take existing words and transform them from their ordinary to a philosophical use. I sketch how these twin processes of ‘acquisition’ and ‘transformation’ manifested themselves in philosophical translations from Greek to Latin, Greek to Arabic and both Greek and Arabic to Gə’əz and argue that comparative work in this vein could yield interesting and significant results. I suggest that not only is Rée’s approach useful for thinking about philosophical translation historically, but that philosophical translation between very different languages is important for contemporary philosophy insofar as it reveals the linguistic presuppositions of philosophical theories expressed in some particular language, and that this constitutes an argument against the prevailing monolingualism in philosophy. (shrink)

In American universities, even Asian Philosophy is still often taught following methods adapted from European universities of the nineteenth century. Whether or not this approach is well-suited to philosophy as it was conceived in that era, it is inadequate if the aim is to develop a deep appreciation of Japanese philosophy. To limit what we consider Japanese philosophy to only what bears a distinct resemblance to academic Western philosophy, and accordingly to approach Japanese philosophy purely theoretically, is to risk missing (...) the greater part. Much of Japanese philosophy is applied philosophy, or in other words, what Pierre Hadot calls a “way of life,” and to appropriate it meaningfully requires practice rather than mere intellectual study alone. Thus, I contend that a proper means for introducing Western students is a more holistic method grounded in practicing traditional arts, such as composing haiku. I argue that the seventeenth century poet Matsuo Bashō can serve as a valuable resource in this process. I conclude with a description of the methods that I use in my efforts at teaching Japanese philosophy to undergraduate university students in South Texas. (shrink)

This work demonstrates the implementation and use of an encoder-decoder model to perform a many-to-many mapping of video data to text captions. The many-to-many mapping occurs via an input temporal sequence of video frames to an output sequence of words to form a caption sentence. Data preprocessing, model construction, and model training are discussed. Caption correctness is evaluated using 2-gram BLEU scores across the different splits of the dataset. Specific examples of output captions were shown to demonstrate model generality over (...) the video temporal dimension. Predicted captions were shown to generalize over video action, even in instances where the video scene changed dramatically. Model architecture changes are discussed to improve sentence grammar and correctness. (shrink)

Endowing artificial systems with explanatory capacities about the reasons guiding their decisions, represents a crucial challenge and research objective in the current fields of Artificial Intelligence (AI) and Computational Cognitive Science [Langley et al., 2017]. Current mainstream AI systems, in fact, despite the enormous progresses reached in specific tasks, mostly fail to provide a transparent account of the reasons determining their behavior (both in cases of a successful or unsuccessful output). This is due to the fact that the classical problem (...) of opacity in artificial neural networks (ANNs) explodes with the adoption of current Deep Learning techniques [LeCun, Bengio, Hinton, 2015]. In this paper we argue that the explanatory deficit of such techniques represents an important problem, that limits their adoption in the cognitive modelling and computational cognitive science arena. In particular we will show how the current attempts of providing explanations of the deep nets behaviour (see e.g. [Ritter et al. 2017] are not satisfactory. As a possibile way out to this problem, we present two different research strategies. The first strategy aims at dealing with the opacity problem by providing a more abstract interpretation of neural mechanisms and representations. This approach is adopted, for example, by the biologically inspired SPAUN architecture [Eliasmith et al., 2012] and by other proposals suggesting, for example, the interpretation of neural networks in terms of the Conceptual Spaces framework [Gärdenfors 2000, Lieto, Chella and Frixione, 2017]. All such proposals presuppose that the neural level of representation can be considered somehow irrelevant for attacking the problem of explanation [Lieto, Lebiere and Oltramari, 2017]. In our opinion, pursuing this research direction can still preserve the use of deep learning techniques in artificial cognitive models provided that novel and additional results in terms of “transparency” are obtained. The second strategy is somehow at odds with respect to the previous one and tries to address the explanatory issue by avoiding to directly solve the “opacity” problem. In this case, the idea is that one of resorting to pre-compiled plausible explanatory models of the word used in combination with deep-nets (see e.g. [Augello et al. 2017]). We argue that this research agenda, even if does not directly fits the explanatory needs of Computational Cognitive Science, can still be useful to provide results in the area of applied AI aiming at shedding light on the models of interaction between low level and high level tasks (e.g. between perceptual categorization and explanantion) in artificial systems. (shrink)

In my dissertation, I develop a novel approach to thought experiments and literary learning. It’s novel primarily because, unlike many prominent approaches, it has us refrain from advancing theories, from giving logical analyses, and from explicating. We are, instead, to proceed in a way inspired by Wittgenstein’s writings. We are, that is, to clarify words that give rise to problems and to clear those problems away. To clarify words, we may compare language games in which figure terms like “thought (...) experiment.” Thereby, we might see that the concept these terms express has a family resemblance character. To clear away problems, we may describe how such a concept, if not illuminated, yields philosophical problems about thought experiments and literary learning. -/- After I develop this approach, I bring it to bear on two problems, and I achieve two main results. One problem concerns the nature of thought experiments. It is: Why do we have trouble explaining what we know them to be? I find that, despite appearances, we have no such trouble. Central to this result are two claims about thought experiments. One is that imaginings aren’t common to them. The other is that our unreflective concept of them has a family resemblance character. -/- The other problem concerns stories in works of literary fiction. It is: How could we possibly learn about the world from them? To solve it, you might claim that we learn by performing thought experiments, and then appeal to a theory of them. I find that you’d risk explaining the wrong thing. That is, you may well explain only how we learn—not how we do so from literature itself. Central to this result are three claims, which concern how these stories differ from thought experiments. They differ, I claim, (i) in how we count imaginings as experiences of them, (ii) in how free we are to interpret them, and (iii) in how complex they may be. This done, I’ve twice taken my novel approach and achieved results. (shrink)

This paper is both systematic and historical in nature. From a historical viewpoint, I aim to show that to establish Wittgenstein’s claim that “an ‘inner process’ stands in need of outward criteria” (PI §580) there is an enthymeme in Wittgenstein’s private language argument (henceforth PLA) overlooked in the literature, namely Wittgenstein’s suggestion that both perceptual and bodily experiences are transparent in the relevant sense that one cannot point to a mental state and wonder “What is that?” From a systematic viewpoint, (...) I aim to show that Wittgenstein’s PLA teaches us that the prevailing picture of the nature of phenomenal concepts (henceforth PCs) is upside down: we can only introspectively know what is going on inside our heads after we learn of what is going on outside (PI §580). In this regard, I aim to defend two associate claims against the prevailing view of PCs on the basis of PLA. First, by means of transparency, I aim to show that there is no de re awareness of our private sensation that could determine the meaning of sensation words; for example, I am never aware of the phenomenal blueness of my experience of something blue. The second associated claim is that introspective self-knowledge of our private sensations is always de dicto. We can only know introspectively that phenomenal blueness is the phenomenal character of the experience we are undergoing after we have learned that (de dicto knowledge) blue is the color that usually causes us that kind of experience. Likewise, we can only introspectively know that pain is the phenomenal character of the experience we are undergoing after we have learned that pain is what usually causes some typical pain behavior. (shrink)

The Internet of Things (IoT) connects schemes, programs, data management, and operations, and as they continuously assist in the corporation, they may be a fresh entryway for cyber-attacks. Presently, illegal downloading and virus attacks pose significant threats to IoT security. These risks may acquire confidential material, causing reputational and financial harm. In this paper hybrid optimization mechanism and deep learning,a frame is used to detect the attack prevention in IoT. To develop a cybersecurity warning system in a huge data (...) set, the cybersecurity warning systems index system is first constructed, then the index factors are picked and measured, and finally, the situation evaluation is done.Numerous bio-inspired techniques were used to enhance the productivity of an IDS by lowering the data dimensionality and deleting unnecessary and noisy input. The Grey Wolf Optimization algorithm (GWO) is a developed bio-inspired algorithm that improves the efficacy of the IDS in detecting both regular and abnormal congestion in the network. The smart initialization step integrates the different pre-processing strategies to make sure that informative features are incorporated in the early development stages, has been improved. Researchers pick multi-source material in a big data environment for the identification and verification of index components and present a parallel reduction approach based on the classification significance matrix to decrease data underlying data characteristics. For the simulation of this situation, grey wolf optimization and whale optimization were combined to detect the attack prevention and the deep learning approach was presented. Utilizing system software plagiarism, the TensorFlow deep neural network is intended to classify stolen software. To reduce the noise from the signal and to zoom the significance of each word in the perspective of open-source plagiarism, the tokenization and weighting feature approaches are utilized. Malware specimens have been collected from the Mailing database for testing purposes. The experimental findings show that the suggested technique for measuring cyber security hazards in IoT has superior classification results to existing methods. Hence to detect the attack prevention in IoT process Whale with Grey wolf optimization (WGWO) and deep convolution network is used. (shrink)

The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify and classify (...) cyberbullying behavior in real-time, analyzing textual data from social media posts to detect harmful content. The model is trained on a large dataset of labeled instances of bullying and non-bullying content, using supervised ML algorithms such as Support Vector Machines (SVM), Decision Trees, and Random Forest. NLP techniques, including sentiment analysis, keyword extraction, and text vectorization, are employed to preprocess and transform the data into a format suitable for machine learning. To optimize the performance of the detection model, techniques such as Grid Search, Genetic Algorithms, and Particle Swarm Optimization are used to fine-tune hyperparameters, resulting in improved accuracy and reduced false positives. The system's effectiveness is validated through experiments conducted on various social media platforms, demonstrating its potential to detect cyberbullying with high precision. Future work will focus on enhancing the model's adaptability to emerging slang and evolving language patterns in social media. Key words: Cyberbullying Detection, Social Media, Supervised Machine Learning, Natural Language Processing (NLP), Optimization Techniques. (shrink)

The presence of the Arabic language is without a doubt the primary reason underpinning the persistence of Islamic culture and Islamic identity. Although languages are thought of as a component of communication, new research in linguistics and philosophy suggests that languages actually constitute the most fundamental component in the development of identity and the acceptance of knowledge. The significance of language in the construction of identities and the learning dissemination of knowledge has grown, particularly since the American philosopher Edward (...) Sapir. By modifying the structure of languages, Sapir attempted to explain the fundamental components of language, such as meaning, word, and speech. Language thus serves as an embodiment of culture in linguistics, opening up the prospect of a successful scientific analysis of society. As a result, my major objective in writing this paper is to highlight the connection between the Islamic identity and the Arabic language and to shed light on the role that the latter had in the development of the former. (shrink)

Yoga is a unique form of expert movement that promotes an increasingly subtle interpenetration of thought and movement. The mindful nature of its practice, even at expert levels, challenges the idea that thought and mind are inevitably disruptive to absorbed coping. Building on parallel phenomenological and ethnographic studies of skilful performance and embodied apprenticeship, we argue for the importance in yoga of mental access to embodied movement during skill execution by way of a case study of instruction and practice in (...) two related traditions, Iyengar and Anusara. Sharing a pose repertoire, they are based on distinctive philosophical systems with different teaching styles and metaphoric structures. To address relations between pedagogy and practice in embodied expertise, and to investigate the reciprocal influences of embodiment and thought, we explore in detail the linguistically mediated learning context where practitioners work with yoga teachers. Here, the mind/body problem comes to practical life. We demonstrate the effects of words on bodies, as knowledge is literally incorporated. We show why interpersonal influence on our movement capacities is sometimes needed to enhance expertise. We theorize and identify ?signature patterns of tension? among practitioners. These patterns have four sources: ghost gestures, innate differences in bodily form, functional fusing, and signature patterns of affective experience, modulation and expression. These patterns of tension produce ?silent zones?, cognitively impenetrable actions, functional fusing of a skilful, compensatory form, and signature patterns of pain and damage. We show how instruction can disrupt these silent zones, enhancing mental and physical flexibility. (shrink)

While representation learning techniques have shown great promise in application to a number of different NLP tasks, they have had little impact on the problem of ontology matching. Unlike past work that has focused on feature engineering, we present a novel representation learning approach that is tailored to the ontology matching task. Our approach is based on embedding ontological terms in a high-dimensional Euclidean space. This embedding is derived on the basis of a novel phrase retrofitting strategy through (...) which semantic similarity information becomes inscribed onto fields of pre-trained word vectors. The resulting framework also incorporates a novel outlier detection mechanism based on a denoising autoencoder that is shown to improve performance. An ontology matching system derived using the proposed framework achieved an F-score of 94% on an alignment scenario involving the Adult Mouse Anatomical Dictionary and the Foundational Model of Anatomy ontology (FMA) as targets. This compares favorably with the best performing systems on the Ontology Alignment Evaluation Initiative anatomy challenge. We performed additional experiments on aligning FMA to NCI Thesaurus and to SNOMED CT based on a reference alignment extracted from the UMLS Metathesaurus. Our system obtained overall F-scores of 93.2% and 89.2% for these experiments, thus achieving state-of-the-art results. (shrink)

What does Wittgenstein say about the learning child? In the Philosophical Investigations, he writes, ‘An important part … will consist in the teacher’s pointing to the objects, directing the child’s attention to them, and at the same time uttering a word.’ Here Wittgenstein is describing what is called ‘joint attention’ which is agreed to be a rich resource for learning in children. In this essay, I explore the developmental significance of this passage particularly with regards the (...) class='Hi'>learning that occurs in the pre-linguistic child during joint attention. I refer to critical remarks elsewhere in the PI which can help further convey what Wittgenstein means when he talks about attentional capacities and how they develop, including the relevant ability for responsiveness required for the learning to proceed. These considerations should motivate a defence of the social initiation model of learning contra Luntley’s mentalist-individualist account, this despite conceding that certain prior capacities do play a role, though not a central one since these do not pick out the relevant ability (responsiveness) required for learning to take place in the child. (shrink)

How political opinions are spread on social media has been the subject of many academic researches recently, and rightly so. Social platforms give researchers a unique opportunity to understand how public discourses are perceived, owned and instrumentalized by the general public. This paper is instead focussing on the political discourses themselves, and how a specific machine learning technique - vector space models (VSMs) -, can be used to make systematic and more objective discourse analysis. Political footprints are vector-based representation (...) of a political discourse in which each vector represents a word, they are produced thanks to the training of the English lexicon on large corpora of text. This paper describes a simple implementation of political footprints, some heuristics on how to use them, and their application to four cases: the U.N. Kyoto Protocol and Paris Agreement, the 2008 and 2016 U.S. presidential elections. The reader will be given some reasons to believe that political footprints produce meaningful results, suggestions on how to improve them and validate the results. (shrink)

This work demonstrates the implementation and use of an encoder-decoder model to perform a many-to-many mapping of video data to text captions. The many-to-many mapping occurs via an input temporal sequence of video frames to an output sequence of words to form a caption sentence. Data preprocessing, model construction, and model training are discussed. Caption correctness is evaluated using 2-gram BLEU scores across the different splits of the dataset. Specific examples of output captions were shown to demonstrate model generality over (...) the video temporal dimension. Predicted captions were shown to generalize over video action, even in instances where the video scene changed dramatically. Model architecture changes are discussed to improve sentence grammar and correctness. (shrink)

Prepositions as function words and single monomorphemic words are the most basic words of the human language, especially in the context of maintaining daily life. They are probably the first lexes/words entered to the human’s linguistic repertoire, as their requirements in the language are so essential. Prepositions shows various relationships between lexes or phrases in sentences. Among these relationships time, points, position, direction and various degrees of mental or emotional attitudes seem to be significant. The purpose of the research is (...) try to reveal what kind of challenges English prepositions have for the learners and also to confirm if they are as difficult as some linguists have stated before. Another purpose is to find out which features are more difficult among all their entity properties. The results of this study disclose that English prepositions possess very complex structures, different meanings and an associating duty of various types of part of speeches. In particular, those that construct the phrasal verbs require great effort for learning due to the loss of the meaning (sometimes partially, sometimes completely) they undergo. It has been understood that their properties such as steadily changing meaning and function make them quite difficult elements for learners of English. (shrink)

The author’s practice-led research explores “the act of living.” In order to advance this idea, the author has acquired skills in investigation and expressed her thinking through a descriptive and explanatory visual language. The author’s learning journey, while not unique, has not been an ordinary one. Initial academic failure to achieve in the school education system contributes to choosing a life working on the land and harbouring the belief that she is unable to learn academically. Still, the author has (...) gained a rich base of physical knowledge and experience through the traditional oral route including learning interpersonal communication through body language and vocal tonality. The author has used this intuitive knowledge to develop an arts practice where she explores the bio-cultural links between people and the lands they inhabit, creating works that aim to extend knowing through emphasising the experience and atmosphere of landscape. At this time, when our lives have become increasingly encoded and intellectually based, the author shares a belief with American philosopher Eugene Gendlin (b. 1926) that the “felt sense” can be developed in order to enable us to engage more fully with the world around us. The author explores this idea in her visual art but also realizes the need to express it in writing, both in order to reach a wider public and because of the possibilities offered by the written word to make public which is private and held deep within. (shrink)

Text-guided image generation models can be prompted to generate images using nonce words adversarially designed to robustly evoke specific visual concepts. Two approaches for such generation are introduced: macaronic prompting, which involves designing cryptic hybrid words by concatenating subword units from different languages; and evocative prompting, which involves designing nonce words whose broad morphological features are similar enough to that of existing words to trigger robust visual associations. The two methods can also be combined to generate images associated with more (...) specific visual concepts. The implications of these techniques for the circumvention of existing approaches to content moderation, and particularly the generation of offensive or harmful images, are discussed. (shrink)

This is a 2000-word review of Eva Kittay's recent book on cognitive disability.

J. J. OʼDonnell is one those scholars whose learning is assumed rather than displayed. As a result, his brief approach to the long-terms effects of the computer revolution onreading and higher education feels like a bracing, sophisticated exchange of ideas. Like conversation, O'Donnellʼs thesis is not terribly unified or orderly. He often makessidetracks from his focus on high technology and literacy into explaining such interestingthings as how we choose our cultural ancestry instead of merely evolving out of it, the (...) errors of current education, and perhaps more than you ever wanted to know aboutother avatars of the word such as St. Jerome, St. Augustine, and Cassiodorus. Greatcover too. (shrink)

Concept maps are found to be useful in eliciting knowledge, meaningful learning, evaluation of understanding and in studying the nature of changes taking place during cognitive development, particularly in the classroom. Several experts have claimed the effectiveness of this tool for learning science. We agree with the claim, but the effectiveness will improve only if we gradually introduce a certain amount of discipline in constructing the maps. The discipline is warranted, we argue, because science thrives to be an (...) unambiguous and rigorously structured body of knowledge. Since learning science may be seen as a process where a novice is expected to be transformed into an expert, we use the context of learning science for making the proposal. Further, we identify certain anomalies in the evaluation of concept maps, and suggest that the evaluation should be based on semantics of the linking words (relation types) and not on graphical criteria alone. (shrink)

Clark and Chalmers (1998) defend the hypothesis of an ‘Extended Mind’, maintaining that beliefs and other paradigmatic mental states can be implemented outside the central nervous system or body. Aspects of the problem of ‘language acquisition’ are considered in the light of the extended mind hypothesis. Rather than ‘language’ as typically understood, the object of study is something called ‘utterance-activity’, a term of art intended to refer to the full range of kinetic and prosodic features of the on-line behaviour of (...) interacting humans. It is argued that utterance activity is plausibly regarded as jointly controlled by the embodied activity of interacting people, and that it contributes to the control of their behaviour. By means of specific examples it is suggested that this complex joint control facilitates easier learning of at least some features of language. This in turn suggests a striking form of the extended mind, in which infants’ cognitive powers are augmented by those of the people with whom they interact. (shrink)

One of the important components considered in this research approach is given by the study on teaching-learning strategies, methods and techniques in the lessons aimed at organizing economic activities. It is understood that such a topic takes into account, on the one hand, a series of aspects related to an economic issue, and on the other hand, issues of a methodological / didactic nature found in the teaching-learning-assessment activity. At the same time, the approach that is intended to (...) be highlighted reflects the practical-theoretical dimension of such a possible approach in class. Under these conditions, the research approach assumed involves an analysis focused, on the one hand, on the theoretical aspects related to the approached topic, and on the other hand, on the conceptual-theoretical operationalization with practical implications. In other words, there is an interest in valorizing and capitalizing on the contents approached both theoretically and practically. Therefore, it is considered that we will analyze, from a theoretical and pragmatic perspective, the strategies, methods and teaching-learning techniques at the level of economic-themed classes, also revealing the impact it has in the economic-educational dimension. (shrink)

There is substantial evidence from many domains that visual representations aid various forms of cognition. We aimed to determine whether visual representations of argument structure enhanced the acquisition and development of critical thinking skills within the context of an introductory philosophy course. We found a significant effect of the use of argument diagrams, and this effect was stable even when multiple plausible correlates were controlled for. These results suggest that natural⎯and relatively minor⎯modifications to standard critical thinking courses could provide substantial (...) increases in student learning and performance. (shrink)

Nobody doubts that culture plays a decisive role in understanding human forms of life. But it is unclear how this decisive role should be integrated into a comprehensive explanatory model of human behaviour that brings together naturalistic and social-scientific perspectives. Cultural difference, cultural learning, cultural determination do not mix well with the factors that are normally given full explanatory value in the more naturalistic approaches to the study of human behaviour. My purpose in this paper is to alert to (...) some of the theoretical vulnerabilities or concerns that the cross-cultural study of mind and behaviour might entail. I classify these theoretical concerns into three, loosely defined, categories: epistemological, ontological and ethical. The first have to do with what in anthropology was once labelled as ‘butterfly collecting’. What kind of supplementary, or additional, general theoretical knowledge do we produce when we add to the research different, particularistic, culturally determined, ways of knowing? Ontological concerns refer to the underlying reality that those ways of knowing are meant to disclose. If there are so many ways of knowing the world, where is the reality to be known? Ethical concerns are those entailed in the forms of ‘othering’ that unqualified cross-cultural research is likely to produce in research participants. (shrink)

Amid the debates over the meaning and usefulness of the word “culture” during the 1980s and 90s, practice theory emerged as a framework for analysis and criticism in cultural anthropology. While theorists have gradually begun to explore practice-oriented frameworks as promising vistas in cultural anthropology and the study of religion, these remain relatively recent developments that stand to be historically explicated and conceptually refined. This article assesses several ways that practice theory has been articulated by some of its chief (...) expositors and critics, and places these developments in conversation with comparable accounts of “social practices” by recent pragmatist philosophers. My aim in generating such a conversation is to illuminate the ways that Ludwig Wittgenstein’s later work provides important resources for cultural analysis that are already implicit in practice theory, yet either frequently overlooked or dismissed by practice theorists. To demonstrate its relevance for cultural theorists in the study of religion, I show how such a Wittgensteinian understanding of practice theory coheres with, and illuminates, Clifford Geertz’s account of meaning, thick description and religious practices. (shrink)

Cognitive flexibility, the adaptation of representations and responses to new task demands, improves dramatically in early childhood. It is unclear, however, whether flexibility is a coherent, unitary cognitive trait, or is an emergent dimension of task-specific performance that varies across populations with divergent experiences. Three-to 5-year-old English-speaking U.S. children and Tswana-speaking South African children completed two distinct language-processing cognitive flexibility tests: the FIM-Animates, a word-learning test, and the 3DCCS, a rule-switching test. U.S. and South African children did not (...) differ in word-learning flexibility but showed similar age-related increases. In contrast, U.S. preschoolers showed an age-related increase in rule-switching flexibility but South African children did not. Verbal recall explained additional variance in both tests but did not modulate the interaction between population sample (i.e., country) and task. We hypothesize that rule-switching flexibility might be more dependent upon particular kinds of cultural experiences, whereas word-learning flexibility is less cross-culturally variable. (shrink)

Philosophers conceptualize a lot, generate, analyze, and engineer concepts. They often either equate ‘words’ and ‘concepts’ or consider words only as means of expression of their ‘concepts’. I suggest that philosophers should learn to distinguish between ‘words’ and ‘concepts’. Philosophers’ terms are often incompatible or partially incompatible with philosophers’ ‘concepts’. Examples are ‘isms’ in philosophy and the conception of reason. If ‘talking about the same matter’ is a prerequisite for fruitful philosophical debates, words and their analysis should have at least (...) some limiting and guiding power over concepts. (shrink)

The success of deep learning in natural language processing raises intriguing questions about the nature of linguistic meaning and ways in which it can be processed by natural and artificial systems. One such question has to do with subword segmentation algorithms widely employed in language modeling, machine translation, and other tasks since 2016. These algorithms often cut words into semantically opaque pieces, such as ‘period’, ‘on’, ‘t’, and ‘ist’ in ‘period|on|t|ist’. The system then represents the resulting segments in a (...) dense vector space, which is expected to model grammatical relations among them. This representation may in turn be used to map ‘period|on|t|ist’ (English) to ‘par|od|ont|iste’ (French). Thus, instead of being modeled at the lexical level, translation is reformulated more generally as the task of learning the best bilingual mapping between the sequences of subword segments of two languages; and sometimes even between pure character sequences: ‘p|e|r|i|o|d|o|n|t|i|s|t’ → ‘p|a|r|o|d|o|n|t|i|s|t|e’. Such subword segmentations and alignments are at work in highly efficient end-to-end machine translation systems, despite their allegedly opaque nature. The computational value of such processes is unquestionable. But do they have any linguistic or philosophical plausibility? I attempt to cast light on this question by reviewing the relevant details of the subword segmentation algorithms and by relating them to important philosophical and linguistic debates, in the spirit of making artificial intelligence more transparent and explainable. (shrink)

The words "violence" and "nonviolence" are increasingly misleading translations for the Sanskrit words hiṃsā and ahiṃsā -- which were used by Gandhi as the basis for his philosophy of satyāgraha. I argue for re-reading hiṃsā as “maleficence” and ahiṃsā as “beneficence.” These two more mind-referring English words – associated with religiously contextualized discourse of the past -- capture the primacy of intention implied by Gandhi’s core principles, better than “violence” and “nonviolence” do. Reflecting a political turn in moral accountability detectable (...) through linguistic data, both the scope and the usage of the word “violence” have expanded dramatically. The expanded scope of “violence” reflects greater consciousness of the various forms that serious harm can take, but also makes it harder to convincingly characterize people and actions as “nonviolent.” New translations could clarify the distinction between hiṃsā and ahiṃsā, and thereby prevent some misunderstandings of Gandhi. Training in beneficence would reflect Gandhi’s psychological path to reducing avoidable harm: detachment from the ego, learning to love universally, and seeking truth by experiment. (shrink)

Mathematicians and software developers use the word "function" very differently, and yet, sometimes, things that are in practice implemented using the software developer's "function", are mathematically formalized using the mathematician's "function". This mismatch can lead to inaccurate formalisms. We consider a special case of this meta-problem. Various kinds of agents might, in actual practice, make use of private memory, reading and writing to a memory-bank invisible to the ambient environment. In some sense, we humans do this when we silently (...) subvocalize thoughts about the actions we are taking (at least when the environment we're in is too primitive to probe the contents of our brains). Mathematical function formalizations of agents often ignore this ability. We show that in a general agent-environment framework (of which reinforcement learning is a special case), in a technical sense, such private memories do not enable qualitatively different agent behavior. (shrink)

The study aimed to analyze lexical items underpinned in the textbooks used in the current teaching of ESP and GE. Using content analysis, a systematic evaluation of texts to examine nuances to bridge the gap between quantitative and qualitative data. This was such of importance, however, difficult to study due to issues of interest like in the study, frequency of lexical items in ESP, and GE textbooks. Results found 13,713 lexical items in Hospitality Management, 17,561 in Criminology, 4576 in Tourism, (...) 7167 in Marine Engineering, and 512 in Information Technology. Furthermore, the overall percentage of ESP lexical items fell in Tier 2 (with multiple-meaning while the least was in Tier 3, specifically on context-specific vocabulary. It is the core of vocabulary learning to ensure English language teaching. It is its goal to help learners better understand language, allowing them to understand others as they want to express themselves as well. This applies not only in speaking but also in writing and reading. Wilkins (1972, p. 111-112) stated that without grammar very little understanding can be acquired and without vocabulary there can be no learning at all. Thus, even without good grammar, so long as you equipped with useful words and expression, one can still manage to communicate. Lewis (1993) argued that “lexis is the heart of language” and that it develops a better fluency and expression in English. He also added the significance to the learners of acquiring a more productive vocabulary knowledge, also, their eagerness to develop their own personal vocabulary strategies. Thus, a proposed bridge model program was recommended to highlight the study findings using the lexicons found from the different respective courses. (shrink)

Since its emergence in the 1960s, Artifcial Intelligence (AI) has grown to conquer many technology products and their felds of application. Machine learning, as a major part of the current AI solutions, can learn from the data and through experience to reach high performance on various tasks. This growing success of AI algorithms has led to a need for interpretability to understand opaque models such as deep neural networks. Various requirements have been raised from diferent domains, together with numerous (...) tools to debug, justify outcomes, and establish the safety, fairness and reliability of the models. This variety of tasks has led to inconsistencies in the terminology with, for instance, terms such as interpretable, explainable and transparent being often used interchangeably in methodology papers. These words, however, convey diferent meanings and are “weighted" diferently across domains, for example in the technical and social sciences. In this paper, we propose an overarching terminology of interpretability of AI systems that can be referred to by the technical developers as much as by the social sciences community to pursue clarity and efciency in the defnition of regulations for ethical and reliable AI development. We show how our taxonomy and defnition of interpretable AI difer from the ones in previous research and how they apply with high versatility to several domains and use cases, proposing a—highly needed—standard for the communication among interdisciplinary areas of AI. (shrink)

This study was conducted to assess the reading comprehension among the grade 4 pupils in an integrated school in the District of Tangalan, Philippines. The result of the study was the basis on the development of an instructional reading material during the School Year 2023-2024. The design based research was used in this study using IPO model. The participants were the 66 grade 4 pupils and 10 teacher experts. Reading Comprehension Test and Learning Material Evaluation Tool were the data (...) gathering instruments. The data were analyzed and interpreted using descriptive and inferential data analysis. The reading comprehension in English of grade 4 pupils is “low”. Based on the result of the study, an instructional material was developed offering a variety of engaging activities, including word boxes, missing letter challenges, collaborative group activities, reading experiences, interactive games, and visual aids for descriptive analysis. The level of acceptability of the developed instructional material for grade 4 was “very highly acceptable” as to content, instructional quality, prints, design and layouts. The level of reading comprehension of grade 4 pupils before the intervention were “low” and after using the developed instructional material, it became “moderate”. Hence, there is a significant difference on the reading comprehension of grade 4 pupils before and after using the instructional material. -/- . (shrink)

The goal of creating Artificial General Intelligence (AGI) – or in other words of creating Turing machines (modern computers) that can behave in a way that mimics human intelligence – has occupied AI researchers ever since the idea of AI was first proposed. One common theme in these discussions is the thesis that the ability of a machine to conduct convincing dialogues with human beings can serve as at least a sufficient criterion of AGI. We argue that this very ability (...) should be accepted also as a necessary condition of AGI, and we provide a description of the nature of human dialogue in particular and of human language in general against this background. We then argue that it is for mathematical reasons impossible to program a machine in such a way that it could master human dialogue behaviour in its full generality. This is (1) because there are no traditional explicitly designed mathematical models that could be used as a starting point for creating such programs; and (2) because even the sorts of automated models generated by using machine learning, which have been used successfully in areas such as machine translation, cannot be extended to cope with human dialogue. If this is so, then we can conclude that a Turing machine also cannot possess AGI, because it fails to fulfil a necessary condition thereof. At the same time, however, we acknowledge the potential of Turing machines to master dialogue behaviour in highly restricted contexts, where what is called “narrow” AI can still be of considerable utility. (shrink)