The implementation of artificial intelligence (AI) in education is today an innovative strategy that cannot be ignored when accompanying learning processes, with the aim of transforming educational systems and taking ...
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ISBN:
(纸本)9783031752353;9783031752360
The implementation of artificial intelligence (AI) in education is today an innovative strategy that cannot be ignored when accompanying learning processes, with the aim of transforming educational systems and taking ways of learning to another level. Although there are studies on the application of AI in education, most focus on digital literacy or specific areas of knowledge, perhaps ignoring a correct integration of software that provides different teaching strategies focused on analyzing behavior patterns and interests of the student. Student, especially in early childhood, to project personalized learning strategies. Considering now the study carried out, a systematic mapping of literature about AI tools in education was carried out. Through a search in 4 digital databases, 124,210 documents on AI in education were obtained, which after applying inclusion and exclusion criteria, 64 relevant documents were selected that provide a solid basis for future research and offer a consensus on the current state. And the perspectives of AI in the educational field.
Unveiling, modeling, and comprehending the causal mechanisms underpinning natural phenomena stand as fundamental endeavors across myriad scientific disciplines. Meanwhile, new knowledge emerges when discovering causal...
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ISBN:
(纸本)1577358872
Unveiling, modeling, and comprehending the causal mechanisms underpinning natural phenomena stand as fundamental endeavors across myriad scientific disciplines. Meanwhile, new knowledge emerges when discovering causal relationships from data. Existing causal learning algorithms predominantly focus on the isolated effects of variables, overlook the intricate interplay of multiple variables and their collective behavioral patterns. Furthermore, the ubiquity of high-dimensional data exacts a substantial temporal cost for causal algorithms. In this paper, we develop a novel method called MgCSL (Multi-granularity Causal Structure learning), which first leverages sparse auto-encoder to explore coarse-graining strategies and causal abstractions from micro-variables to macro-ones. MgCSL then takes multi-granularity variables as inputs to train multilayer perceptrons and to delve the causality between variables. To enhance the efficacy on high-dimensional data, MgCSL introduces a simplified acyclicity constraint to adeptly search the directed acyclic graph among variables. Experimental results show that MgCSL outperforms competitive baselines, and finds out explainable causal connections on fMRI datasets.
In the era of big data, the high-dimensional online learning problems require huge computing power. This paper proposes a novel approach for high-dimensional online learning. Two new algorithms are developed for onlin...
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In the era of big data, the high-dimensional online learning problems require huge computing power. This paper proposes a novel approach for high-dimensional online learning. Two new algorithms are developed for online high-dimensional regression and classification problems, respectively. The problems are formulated as feedback control problems for some low dimensional systems. The novel learning algorithms are then developed via the control problems. Via an efficient polar decomposition, we derive the explicit solutions of the control problems, substantially reducing the corresponding computational complexity, especially for high dimensional large-scale data streams. Comparing with conventional methods, the new algorithm can achieve more robust and accurate performance with faster convergence. This paper demonstrates that optimal control can be an effective approach for developing high dimensional learning algorithms. We have also for the first time proposed a control-based robust algorithm for classification problems. Numerical results support our theory and illustrate the efficiency of our algorithm. (C) 2020 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
Technology has reached unprecedented scale, interoperability, and societal clout as science ushers in an unparalleled Information Era. However, if protective artificial intelligence guardrails are to be effective, pub...
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Technology has reached unprecedented scale, interoperability, and societal clout as science ushers in an unparalleled Information Era. However, if protective artificial intelligence guardrails are to be effective, public awareness of modern science and its consequences becomes paramount.
We use supervised and unsupervised machine learning techniques to investigate how optical frequency combs may be used to identify gas molecules in the atmosphere. Dual comb heterodyne detection with GHz and THz repeti...
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A desirable trait of an artificial agent acting in the visual world is to continually learn a sequence of language-informed tasks while striking a balance between sufficiently specializing in each task and building a ...
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ISBN:
(纸本)9798891760615
A desirable trait of an artificial agent acting in the visual world is to continually learn a sequence of language-informed tasks while striking a balance between sufficiently specializing in each task and building a generalized knowledge for transfer. Selective specialization, i.e., a careful selection of model components to specialize in each task, is a strategy to provide control over this trade-off. However, the design of selection strategies requires insights on the role of each model component in learning rather specialized or generalizable representations, which poses a gap in current research. Thus, our aim with this work is to provide an extensive analysis of selection strategies for visually grounded continual language learning. Due to the lack of suitable benchmarks for this purpose, we introduce two novel diagnostic datasets that provide enough control and flexibility for a thorough model analysis. We assess various heuristics for module specialization strategies as well as quantifiable measures for two different types of model architectures. Finally, we design conceptually simple approaches based on our analysis that outperform common continual learning baselines.(1) Our results demonstrate the need for further efforts towards better aligning continual learning algorithms with the learning behaviors of individual model parts.
Task-oriented dialog (TOD) agents often ground their responses on external knowledge bases (KBs). These KBs can be dynamic and may be updated frequently. Existing approaches for learning TOD agents assume the KB snaps...
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ISBN:
(纸本)9781959429623
Task-oriented dialog (TOD) agents often ground their responses on external knowledge bases (KBs). These KBs can be dynamic and may be updated frequently. Existing approaches for learning TOD agents assume the KB snapshot contemporary to each individual dialog is available during training. However, in real-world scenarios, only the latest KB snapshot is available during training and as a result, the train dialogs may contain facts conflicting with the latest KB. These dialog-KB inconsistencies in the training data may potentially confuse the TOD agent learning algorithm. In this work, we define the novel problem of learning a TOD agent with dialog-KB inconsistencies in the training data. We propose a Dialog-KB Arbitration Framework (DKAF) which reduces the dialog-KB inconsistencies by predicting the contemporary KB snapshot for each train dialog. These predicted KB snapshots are then used for training downstream TOD agents. As there are no existing datasets with dialog-KB inconsistencies, we systematically introduce inconsistencies in two publicly available dialog datasets. We show that TOD agents trained with DKAF perform better than existing baselines on both these datasets.
The goal of this study was to assess the feasibility of three models for detecting hydronephrosis through ultrasound images using state-of-the-art deep learning algorithms. The diagnosis of hydronephrosis is challengi...
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Automata learning is an approach for extracting a model in the shape of an automaton from a black-box system. This approach has recently gained much attention in both industry and academia. In this paper, we introduce...
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Data from commercial off-the-shelf (COTS) wearables leveraged with machine learning algorithms provide an unprecedented potential for the early detection of adverse physiological events. However, several challenges in...
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Data from commercial off-the-shelf (COTS) wearables leveraged with machine learning algorithms provide an unprecedented potential for the early detection of adverse physiological events. However, several challenges inhibit this potential, including (1) heterogeneity among and within participants that make scaling detection algorithms to a general population less precise, (2) confounders that lead to incorrect assumptions regarding a participant's healthy state, (3) noise in the data at the sensor level that limits the sensitivity of detection algorithms, and (4) imprecision in self-reported labels that misrepresent the true data values associated with a given physiological event. The goal of this study was two-fold: (1) to characterize the performance of such algorithms in the presence of these challenges and provide insights to researchers on limitations and opportunities, and (2) to subsequently devise algorithms to address each challenge and offer insights on future opportunities for advancement. Our proposed algorithms include techniques that build on determining suitable baselines for each participant to capture important physiological changes and label correction techniques as it pertains to participant-reported identifiers. Our work is validated on potentially one of the largest datasets available, obtained with 8000+ participants and 1.3+ million hours of wearable data captured from Oura smart rings. Leveraging this extensive dataset, we achieve pre-symptomatic detection of COVID-19 with a performance receiver operator characteristic (ROC) area under the curve (AUC) of 0.725 without correction techniques, 0.739 with baseline correction, 0.740 with baseline correction and label correction on the training set, and 0.777 with baseline correction and label correction on both the training and the test set. Using the same respective paradigms, we achieve ROC AUCs of 0.919, 0.938, 0.943 and 0.994 for the detection of self-reported fever, and 0.574, 0.611, 0.601, an
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