Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
229
result(s) for
"Reasoning Graphic methods."
Sort by:
Diagrammatic Reasoning in AI
Pioneering work shows how using Diagrams facilitates the design of better AI systems The publication of Diagrammatic Reasoning in AI marks an important milestone for anyone seeking to design graphical user interfaces to support decision-making and problem-solving tasks. The author expertly demonstrates how diagrammatic representations can simplify our interaction with increasingly complex information technologies and computer-based information systems. In particular, the book emphasizes how diagrammatic user interfaces can help us better understand and visualize artificial intelligence (AI) systems. It examines how diagrammatic reasoning enhances various AI programming strategies used to emulate human thinking and problem-solving, including: Expert systems Model-based reasoning Inexact reasoning such as certainty factors and Bayesian networks Logic reasoning A key part of the book is its extensive development of applications and graphical illustrations, drawing on such fields as the physical sciences, macroeconomics, finance, business logistics management, and medicine. Despite such tremendous diversity of usage, in terms of applications and diagramming notations, the book classifies and organizes diagrams around six major themes: system topology; sequence and flow; hierarchy and classification; association; cause and effect; and logic reasoning. Readers will benefit from the author's discussion of how diagrams can be more than just a static picture or representation and how diagrams can be a central part of an intelligent user interface, meant to be manipulated and modified, and in some cases, utilized to infer solutions to difficult problems. This book is ideal for many different types of readers: practitioners and researchers in AI and human-computer interaction; business and computing professionals; graphic designers and designers of graphical user interfaces; and just about anyone interested in understanding the power of diagrams. By discovering the many different types of diagrams and their applications in AI, all readers will gain a deeper appreciation of diagrammatic reasoning.
eBook
The perceived value of human-AI collaboration in early shape exploration: An exploratory assessment
As a vital element of early shape exploration, divergence can be time-consuming and challenging, with iterative cycles where idea fixation and creative blocks must be overcome for fuzzy ideas to be fully expanded and understood. Despite interesting tools that have been developed for this purpose, some important challenges remain, as it appears that many designers still prefer simple freehand sketching and tend to defer the use of computational tools to later stages. This work presents an exploratory assessment of the perceived value of a new tool, Shapi, developed to assist early shape exploration by addressing some of the pitfalls reported in the literature. Shapi is envisioned as an autonomous assistant that provides local and global shape variations in the form of rough sketches based on an initial human sketch and interactive cycles. These shape variations are What-If scenarios and cognitive facilitators that may spark new ideas or enable a deeper understanding of the shape and the identification of interesting patterns. Shapi’s capabilities are explored in a diverse set of case studies with different purposes: nine implementations in industrial design, three in graphic design, and five with open-ended artistic purposes. These implementations are then used in a survey about initial perceived value in which the majority gave high ratings in terms of exploration (75.5% ≥ 4 out of 5), interpretation (83.7% ≥ 4), adaptation (77.6% ≥ 4), value (73.5% ≥ 4), creativity (69.4% ≥ 4), and general interest in the tool (79.6% ≥ 4). This work brings insight into promising functionalities, opportunities, and risks in the intersection between artificial intelligence, design, and art.
Journal Article
What part of the brain is involved in graphic design thinking in landscape architecture?
Graphic design thinking is a key skill for landscape architects, but little is known about the links between the design process and brain activity. Based on Goel’s frontal lobe lateralization hypothesis (FLLH), we used functional magnetic resonance imaging (fMRI) to scan the brain activity of 24 designers engaging in four design processes—viewing, copy drawing, preliminary ideas, and refinement—during graphic design thinking. The captured scans produced evidence of dramatic differences between brain activity when copying an existing graphic and when engaging in graphic design thinking. The results confirm that designs involving more graphic design thinking exhibit significantly more activity in the left prefrontal cortex. These findings illuminate the design process and suggest the possibility of developing specific activities or exercises to promote graphic design thinking in landscape architecture.
Journal Article
Ecarnet: enhanced clue-ambiguity reasoning network for multimodal fake news detection
The growing popularity of social media platforms has simplified the creation of news articles, and fake news being spread on these platforms have a disruptive impact on our lives. With the prevalence of multimodal data in social networks, automatic detection of multimodal fake news plays an important role in the prevention of its negative effects. To the best of our knowledge, existing multimodal fake news detection works still face two challenging roadblocks: the lack of ambiguous clue reasoning across different modalities and insufficient fusion of multimodal information. To alleviate these concerns, this paper presents an Enhanced Clue-Ambiguity Reasoning Network (ECARnet) for multimodal fake news detection. The proposed model first extracts the fine-grained semantic features from salient image regions and semantic textual words, and further utilizes a cross-modal weighted residual network to learn their similar clue features. Subsequently, an efficient bidirectional clue-ambiguity reasoning module is constructed to explicitly excavate the ambiguous clue features. Specifically, forward reasoning branch employs the two-branch network with clue correlation analysis to maximally distinguish the cross-modal ambiguous clues, while backward reasoning branch utilizes uncertainty learning to eliminate the uncertain clue features. Through the joint exploitation of the above, the proposed ECARnet model can adaptively learn the cross-modal ambiguous clues to benefit various multimodal fake news detection tasks. Extensive experiments verify the superiority of the proposed framework and show its competitive performances with the state-of-the-arts.
Journal Article
DSSA-TCN: Exploiting adaptive sparse attention and diffusion graph convolutions in temporal convolutional networks for traffic flow forecasting
Accurate traffic flow forecasting is essential for intelligent transportation systems, yet the nonlinear and dynamically evolving spatio-temporal dependencies in urban road networks make reliable prediction challenging. Existing graph-based and attention-based approaches have improved performance but often decouple spatial and temporal learning, which leads to redundant computation and weak directional interpretability. To address these limitations, we propose DSSA-TCN, a unified framework that establishes an alternating spatio-temporal coupling mechanism, where each temporal convolutional block is tightly integrated with an adaptive spatial module that combines sparse attention with diffusion-based graph convolution. Within this mechanism, adaptive sparse attention dynamically selects the most informative neighbors to reduce spatial complexity, and bidirectional diffusion convolution enforces physically consistent directional and multi-hop propagation over the road topology. Temporal patterns are modeled with gated dilated convolutions to preserve parallelism and stability. Comprehensive experiments on six real-world datasets demonstrate that DSSA-TCN achieves superior forecasting accuracy and computational efficiency while providing interpretable spatial reasoning. These results indicate that layer-wise coupling of adaptive sparsity and diffusion within a causal temporal backbone offers a scalable and physically grounded paradigm for spatio-temporal traffic prediction.
Journal Article
HGLER: A hierarchical heterogeneous graph networks for enhanced multimodal emotion recognition in conversations
This research has proposed a new Emotion Recognition in Conversation (ERC) model known as Hierarchical Graph Learning for Emotion Recognition (HGLER), built to go beyond the existing approaches that find it difficult to request long-distance context and interaction across different data types. Rather than simply mixing different kinds of information, as is the case with traditional methods, HGLER uses a 2-part graph technique whereby conversations are represented in a 2-fold manner: one aimed at illustrating how various parts of the conversation relate and another for enhancing learning from various types of data. This dual-graph system can represent multimodal data value for value by exploiting the benefits of each type of data yet tracking their interactions. The HGLER model was applied to two widely used datasets, IEMOCAP and MELD, with many varieties of information, texts, pictures, or sounds, hence, to see to what extent the model can understand emotions in conversations. Preprocessing methods common in practice were done to make things consistent, and the datasets were set aside for training, validation, and testing informed by previous works. The model was tested using two standard datasets, including IEMOCAP and MELD. On IEMOCAP, HGLER posted an F1-score of 96.36% and accuracy of 96.28%; on MELD, it posted an F1-score of 96.82% and accuracy of 93.68%, surpassing some state-of-the-art methods. The model also showed some superb performance in terms of its convergence, generalization, and convergence stability during training. These findings demonstrate that hierarchical graph-based learning can be applied in enhancing emotional comprehension in systems dealing with several forms of information in handling conversations. However, slight changes in validation loss observed suggest there are areas of model stability and generalization to be improved on. These results validate that using hierarchical graph-based learning in multimodal ERC does well and promises to enhance emotional understanding in conversational AI systems.
Journal Article
A Relational Reasoning Approach to Text-Graphic Processing
We propose that research on text-graphic processing could be strengthened by the inclusion of relational reasoning perspectives. We briefly outline four aspects of relational reasoning: analogies, anomalies, antinomies, and antitheses. Next, we illustrate how textgraphic researchers have been conducting research aligned with aspects of relational reasoning, although not deliberately. We call for future research on intentionally designed textgraphic pairings that should be empirically tested for their ability to support relational reasoning. Finally, we argue that relational reasoning may help explain some of the mixed results and unintended outcomes that often appear in the text-graphic research literature.
Journal Article
Comparative value of a simulation by gaming and a traditional teaching method to improve clinical reasoning skills necessary to detect patient deterioration: a randomized study in nursing students
Background
Early detection and response to patient deterioration influence patient prognosis. Nursing education is therefore essential. The objective of this randomized controlled trial was to compare the respective educational value of simulation by gaming (SG) and a traditional teaching (TT) method to improve clinical reasoning (CR) skills necessary to detect patient deterioration.
Methods
In a prospective multicenter study, and after consent, 2nd year nursing students were randomized into two groups:
Simulation by gaming “SG”: the student played individually with a serious game consisting of 2 cases followed by a common debriefing with an instructor;
Traditional Teaching “TT”: the student worked on the same cases in text paper format followed by a traditional teaching course with a PowerPoint presentation by an instructor.
CR skill was measured by script concordance tests (80 SCTs, score 0–100) immediately after the session (primary outcome) and on month later. Other outcomes included students’ satisfaction, motivation and professional impact.
Results
One hundred forty-six students were randomized. Immediately after training, the SCTs scores were 59 ± 9 in SG group (
n
= 73) and 58 ± 8 in TT group (
n
= 73) (
p
= 0.43). One month later, the SCTs scores were 59 ± 10 in SG group (
n
= 65) and 58 ± 8 in TT group (
n
= 54) (
p
= 0.77). Global satisfaction and motivation were highly valued in both groups although significantly greater in the SG group (
p
< 0.05). The students declared that the training course would have a positive professional impact, with no difference between groups.
Conclusions
In this study assessing nursing student CR to detect patient deterioration, no significant educational difference (SCT), neither immediate nor 1 month later, was observed between training by SG and the TT course. However, satisfaction and motivation were found to be greater with the use of SG.
Trial registration
ClinicalTrials.gov;
NCT03428269
. Registered 30 january 2018.
Journal Article
Efficient and self-adaptive rationale knowledge base for visual commonsense reasoning
Visual commonsense reasoning (VCR) task leads to a cognitive level of understanding between vision and linguistic domains. Three sub-tasks, i.e.,
Q
→
A
,
Q
A
→
R
, and
Q
→
A
R
, require the ability to predict the correct answer and rational explanation according to the given image and question. Different from other visual reasoning tasks, such as VQA and GQA, VCR focuses on the exploration of the facts that clarify the causes, context, and consequences of the image and questions, which is the process of acquiring knowledge and thorough understanding. In this paper, we propose a rationale knowledge base (RKB) incorporating the convolution fusion mechanism to import the VCR-related knowledge. We emphasize that (1) the RKB is extracted and then trained over VCR’s dataset (VCR-set) itself, and (2) the convolution fusion mechanism is subtly designed to be self-adaptive and computationally efficient. Experiments on the large-scale VCR-set demonstrate the effectiveness of our proposed method with respect to the three sub-tasks.
Journal Article