We propose an enhancement module called depth discontinuity learning (DDL) for learning-based multi-view stereo (MVS) methods. Traditional methods are known their accuracy but struggle with completeness. While recent have improved completeness at the cost of accuracy, our DDL approach aims to improve while retaining in reconstruction process. To achieve this, we introduce joint estimation and b...

The paper presents learning mechanisms for MAS based on Distributed Default Logic (DDL), the formalism for multi-agent knowledge representation and reasoning. In the distributed environment learning processes provide measures to order default rules, which gives the agent better use of its local and external knowledge. Such mechanisms allow the system to work effectively in a changing environmen...

Subspace clustering techniques have shown promise in hyperspectral image segmentation. The fundamental assumption subspace is that the samples belonging to different clusters/segments lie separable subspaces. What if this condition does not hold? We surmise even hold original space, data may be nonlinearly transformed a space where it will into In work, we propose transformation based on tenets...

Wider coverage and a better solution to latency reduction in 5G necessitate its combination with multi-access edge computing technology. Decentralized deep learning (DDL), such as federated swarm learning, promising privacy-preserving data processing for millions of smart devices leverages distributed multilayer neural networks within the networking local clients, without disclosing original tr...

In this paper we re-evaluate Segerberg’s “full DDL” (Dynamic Doxastic Logic) from the perspective of Dynamic Epistemic Logic (DEL), in its beliefrevision-friendly incarnation. We argue that a correct version of full DDL must give up the Success Postulate for dynamic revision. Next, we present (an appropriately generalized and simplified version of) full DDL, showing that it is a generalization ...

Proteins containing the forkhead-associated domain (FHA) are known to act in biological processes such as DNA damage repair, protein degradation, and signal transduction. Here we report that DAWDLE (DDL), an FHA domain-containing protein in Arabidopsis, acts in the biogenesis of miRNAs and endogenous siRNAs. Unlike mutants of genes known to participate in the processing of miRNA precursors, suc...

As deep neural networks become more complex and input data-sets grow larger, it can take days or even weeks to train a deep neural network to the desired accuracy. Therefore, distributed Deep Learning at a massive scale is a critical capability, since it offers the potential to reduce the training time from weeks to hours. In this paper, we present a software-hardware co-optimized distributed D...

The Coronavirus pandemic during the past two years (March 2020 – March 2022) has accelerated investment in, and use of, digital distance learning (DDL). transition towards DDL in mainstream higher education this period expanded both terms of depth breadth. Few academic practitioners, observers, managers, or students could have foreseen rapid pace change. Unprecedented government interventions s...

Abstract Artificial intelligence (AI) is changing many aspects of education and gradually being introduced to language education. This article reviews the literature examine main trends common findings in relation AI technologies applications for second foreign learning teaching. With special reference computer-assisted (CALL), explores natural processing (NLP), data-driven (DDL), automated wri...

The data-driven learning model is a newer method. It not only provides learners with rich, diverse, and real-language big data, but it also creates an ideal environment for them because of its corpus-based teaching characteristics. This paper will look at how to analyze research in college English translation classes, as well describe the approach. presents problem design, then expands on conce...