Computing is a critical driving force in the development of human
civilization. In recent years, we have witnessed the emergence of intelligent
computing, a new computing paradigm that is reshaping traditional computing and
promoting digital revolution in the era of big data, artificial intelligence
and internet-of-things with new computing theories, architectures, methods,
systems, and applications. Intelligent computing has greatly broadened the
scope of computing, extending it from traditional computing on data to
increasingly diverse computing paradigms such as perceptual intelligence,
cognitive intelligence, autonomous intelligence, and human-computer fusion
intelligence. Intelligence and computing have undergone paths of different
evolution and development for a long time but have become increasingly
intertwined in recent years: intelligent computing is not only
intelligence-oriented but also intelligence-driven. Such cross-fertilization
has prompted the emergence and rapid advancement of intelligent computing.
Intelligent computing is still in its infancy and an abundance of innovations
in the theories, systems, and applications of intelligent computing are
expected to occur soon. We present the first comprehensive survey of literature
on intelligent computing, covering its theory fundamentals, the technological
fusion of intelligence and computing, important applications, challenges, and
future perspectives. We believe that this survey is highly timely and will
provide a comprehensive reference and cast valuable insights into intelligent
computing for academic and industrial researchers and practitioners.

智能计算是一种新的计算范式，在数据，人工智能和物联网方面推动数字革命。本文是对智能计算领域的第一次综述，涵盖了其理论基础、技术融合、重要应用、挑战和未来发展前景。

智能计算：最新进展，挑战和未来

Intelligent Computing: The Latest Advances, Challenges and Future

In recent years, the Neurosymbolic framework has attracted a lot of attention
in various applications, from recommender systems and information retrieval to
healthcare and finance. This success is due to its stellar performance combined
with attractive properties, such as learning and reasoning. The new emerging
Neurosymbolic field is currently experiencing a renaissance, as novel
frameworks and algorithms motivated by various practical applications are being
introduced, building on top of the classical neural and reasoning problem
setting. This article aims to provide a comprehensive review of significant
recent developments in real-world applications of Neurosymbolic Artificial
Intelligence. Specifically, we introduce a taxonomy of common Neurosymbolic
applications and summarize the state-of-the-art for each of those domains.
Furthermore, we identify important current trends and provide new perspectives
pertaining to the future of this burgeoning field.

本文综述了神经符号人工智能在真实世界应用中的最新发展，介绍了常见神经符号应用的分类法，总结了各个领域的最新技术现状，并且提供了对未来发展的新视角。

神经符号人工智能应用调查

Survey on Applications of Neurosymbolic Artificial Intelligence

A substantial progress in development of new and efficient tensor
factorization techniques has led to an extensive research of their
applicability in recommender systems field. Tensor-based recommender models
push the boundaries of traditional collaborative filtering techniques by taking
into account a multifaceted nature of real environments, which allows to
produce more accurate, situational (e.g. context-aware, criteria-driven)
recommendations. Despite the promising results, tensor-based methods are poorly
covered in existing recommender systems surveys. This survey aims to complement
previous works and provide a comprehensive overview on the subject. To the best
of our knowledge, this is the first attempt to consolidate studies from various
application domains in an easily readable, digestible format, which helps to
get a notion of the current state of the field. We also provide a high level
discussion of the future perspectives and directions for further improvement of
tensor-based recommendation systems.

本文为了弥补目前推荐系统领域中对于基于张量方法的研究欠缺的问题，全面回顾和讨论了基于张量的推荐模型的应用以及未来的发展方向。