Computing generates intelligence. With this statement we do not mean computing’s capabilities of manipulating numbers, shapes, symbols, and even logics. What we mean is the ingenious design of computing structures which serve as the basis of intelligence generation during program running. In this panel discussion, we consider how to obtain such capabilities through some computing paradigms as examples, including principal computing, logic computing, discriminative computing, and generative computing. The panelists express their thoughts about the inherent advantages and disadvantages of each of these paradigms, in terms of their adaptivity, interpretability, generality and specificity, and dives into detailed discussions about Large Language Models (LLMs), a mainstream generative paradigm which leverages the strengths of large pre-trained models and downstream prompt tuning to deliver combined intelligence, superior to most existing frameworks in natural language processing. The panel outlines potential challenges of the generative paradigm, with a strong focus on LLMs, and emphasizes that future directions of such models will need to address (1) tackling bias, discrimination, and transparency challenges; (2) delivering logical answers with high specificity; (3) enabling personalized, lightweight, and rapid updating mechanisms; (4) assessing accreditation, tracing, and misusages; and (5) ensuring sustainable LLMs.

As the first step of machine-learning based protein structure and function prediction, the amino acid encoding play a fundamental role in the final success of those methods. Different from the protein sequence encoding, the amino acid encoding can be used in both residue-level and sequence-level prediction of protein properties by combining them with different algorithms. However, it has not attracted enough attention in the past decades, and there are no comprehensive reviews and assessments about encoding methods so far. In this article, we make a systematic classification and propose a comprehensive review and assessment for various amino acid encoding methods. Those methods are grouped into five categories according to their information sources and information extraction methodologies, including binary encoding, physicochemical properties encoding, evolution-based encoding, structure-based encoding, and machine-learning encoding. Then, 16 representative methods from five categories are selected and compared on protein secondary structure prediction and protein fold recognition tasks by using large-scale benchmark datasets. The results show that the evolution-based position-dependent encoding method PSSM achieved the best performance, and the structure-based and machine-learning encoding methods also show some potential for further application, the neural network based distributed representation of amino acids in particular may bring new light to this area. We hope that the review and assessment are useful for future studies in amino acid encoding.

After entering the big data era, a new term of `big knowledge' has been coined to deal with challenges in mining a mass of knowledge from big data. While researchers used to explore the basic characteristics of big data, we have not seen any studies on the general and essential properties of big knowledge. To fill this gap, this paper studies the concepts of big knowledge, big-knowledge system, and big-knowledge engineering. Ten massiveness characteristics for big knowledge and big-knowledge systems, including massive concepts, connectedness, clean data resources, cases, confidence, capabilities, cumulativeness, concerns, consistency, and completeness, are defined and explored. Based on these characteristics, a comprehensive investigation is conducted on some large-scale knowledge engineering projects, including the Fifth Comprehensive Traffic Survey in Shanghai, the China's Xia-Shang-Zhou Chronology Project, the Troy and Trojan War Project, and the International Human Genome Project, as well as the online free encyclopedia Wikipedia. We also investigate the recent research efforts on knowledge graphs, where they are analyzed to determine which ones can be considered as big knowledge and big-knowledge systems. Further, a definition of big-knowledge engineering and its life cycle paradigm is presented. All of these projects are accordingly checked to determine whether they belong to big-knowledge engineering projects. Finally, the perspectives of big knowledge research are discussed.

In machine learning, a high dimensional dataset such as the digital image of human face is often considered as a point set distributed on a (differentiable) manifold. In many cases the intrinsic dimension of this manifold is highly lower than the representation dimension. In order to ease data processing, manifold-based dimension reduction algorithms were put forward since this century. The real purpose of manifold learning (MAL) is to learn a suitable metric in the low dimensional space. One main limitation of the existing MAL algorithms is that they all do not consider the intrinsic curvature of the embedded manifold, which means that the intrinsic geodesic distance cannot be uncovered by these algorithms. The intrinsic geodesic distance on the manifold is measured by Riemannian metric which is highly affected by Riemannian curvature. With this idea in mind, our work proposes to formulate a new algorithm by adding the curvature information to metric learning. We study a new curvature flow from input dataset. By employing this curvature flow, we obtain a Mahalanobis metric which can better uncover the intrinsic structure of the embedded manifold. To show the effectiveness of our proposed method, we compare our algorithm with several traditional MAL algorithms.

BACKGROUND: In structural biology area, protein residue-residue contacts play a crucial role in protein structure prediction. Some researchers have found that the predicted residue-residue contacts could effectively constrain the conformational search space, which is significant for de novo protein structure prediction. In the last few decades, related researchers have developed various methods to predict residue-residue contacts, especially, significant performance has been achieved by using fusion methods in recent years. In this work, a novel fusion method based on rank strategy has been proposed to predict contacts. Unlike the traditional regression or classification strategies, the contact prediction task is regarded as a ranking task. First, two kinds of features are extracted from correlated mutations methods and ensemble machine-learning classifiers, and then the proposed method uses the learning-to-rank algorithm to predict contact probability of each residue pair. RESULTS: First, we perform two benchmark tests for the proposed fusion method (RRCRank) on CASP11 dataset and CASP12 dataset respectively. The test results show that the RRCRank method outperforms other well-developed methods, especially for medium and short range contacts. Second, in order to verify the superiority of ranking strategy, we predict contacts by using the traditional regression and classification strategies based on the same features as ranking strategy. Compared with these two traditional strategies, the proposed ranking strategy shows better performance for three contact types, in particular for long range contacts. Third, the proposed RRCRank has been compared with several state-of-the-art methods in CASP11 and CASP12. The results show that the RRCRank could achieve comparable prediction precisions and is better than three methods in most assessment metrics. CONCLUSIONS: The learning-to-rank algorithm is introduced to develop a novel rank-based method for the residue-residue contact prediction of proteins, which achieves state-of-the-art performance based on the extensive assessment.

Animated computing is the process by which inputted data will be elaborated by the computer in order to produce the expected animation ready to be shown. It differs from other computer supported animation production paradigms in three aspects: the form of input, the design of output and the implementation of output. The form of input is not necessary computer instructions. It can be a piece of data, a piece of message, a piece of natural language text, or something else. The design of output is not only based on semantics, but more closely on pragmatics of the input. It infers the effect wanted by the input and tells this to the observer in animation form. The implementation of output is automated based on artificial intelligence techniques and an underlying knowledge base.

Developing a desirable framework for handling inconsistencies in software requirements specifications is a challenging problem. It has been widely recognized that the relative priority of requirements can help developers to make some necessary trade-off decisions for resolving con- flicts. However, for most distributed development such as viewpoints-based approaches, different stakeholders may assign different levels of priority to the same shared requirements statement from their own perspectives. The disagreement in the local levels of priority assigned to the same shared requirements statement often puts developers into a dilemma during the inconsistency handling process. The main contribution of this paper is to present a prioritized merging-based framework for handling inconsistency in distributed software requirements specifications. Given a set of distributed inconsistent requirements collections with the local prioritization, we first construct a requirements specification with a prioritization from an overall perspective. We provide two approaches to constructing a requirements specification with the global prioritization, including a merging-based construction and a priority vector-based construction. Following this, we derive proposals for handling inconsistencies from the globally prioritized requirements specification in terms of prioritized merging. Moreover, from the overall perspective, these proposals may be viewed as the most appropriate to modifying the given inconsistent requirements specification in the sense of the ordering relation over all the consistent subsets of the requirements specification. Finally, we consider applying negotiation-based techniques to viewpoints so as to identify an acceptable common proposal from these proposals.

By studying two unsharp quantum structures, namely extended lattice ordered effect algebras and lattice ordered QMV algebras, we obtain some characteristic theorems of MV algebras. We go on to discuss automata theory based on these two unsharp quantum structures. In particular, we prove that an extended lattice ordered effect algebra (or a lattice ordered QMV algebra) is an MV algebra if and only if a certain kind of distributive law holds for the sum operation. We introduce the notions of (quantum) finite automata based on these two unsharp quantum structures, and discuss closure properties of languages and the subset construction of automata. We show that the universal validity of some important properties (such as sum, concatenation and subset constructions) depend heavily on the above distributive law. These generalise results about automata theory based on sharp quantum logic.

In this position paper, we set out to explore the possibilities of formulating problems in requirements engineering with concepts and frameworks from cybernetics. The objective is to seek practical synergies and to understand notable differences between the two areas. We try to understand to what extent known research results from cybernetics can be applied to address problems encountered at the requirements stage in software life cycle. In particular, we try to formulate the goal-oriented requirements analysis process as a feedback control system, in which a classical "divide and conquer" design philosophy is turned into a continuous augmentation process to existing design towards an optimal one. Similarly, control frameworks for the requirements elicitation process and requirements inconsistency management process are briefly explored. Potential control variables such as information entropy value for elicitation and quantitative measurements for inconsistencies are discussed.

In recent years, scientists in China have made significant contributions in many fields of artificial intelligence and knowledge engineering. The aim of this special issue is to help international colleagues have a better understanding of the current research activities in this area carried out in China. We solicited six papers from active research groups. Each paper has been reviewed rigorously by three international referees. Finally, we selected four papers for this special issue. The first paper by Wei Li introduces first-order languages to describe beliefs, and defines a formal inference system named R-calculus for conducting belief revision in the first-order logic. It is one of the few efforts of using first-order logic rather than propositional logic in this direction. Using the R-calculus, all maximal contractions can be derived formally from a given base of belief set with respect to its refutation by facts. The reachability, soundness and completeness of the R-calculus system are proved in this paper.

Common substring problems allowing errors are known to be NP-hard. The main challenge of the problems lies in the combinatorial explosion of potential candidates. In this paper, we propose and study a generalized center string (GCS) problem, where not only all models (center strings) of any length, but also the positions of all their (degenerative) instances in input sequences are searched for. Inspired by frequent pattern mining techniques in data mining field, we present an exact and efficient method to solve GCS. First, a highly parallelized Trie-like structure, consensus tree, is proposed. Based on this structure, we present three Bpriori algorithms step by step. Bpriori algorithms can solve GCS with reasonable time and/or space complexities. We have proved that GCS is fixed parameter tractable with respect to fixed symbol set size and fixed length of input sequences. Experiment results on both artificial and real data have shown the correctness of the algorithms and the validity of our complexity analysis. A comparison with some current algorithms for solving common approximate substring problems is also given

Many approaches have been proposed to enhance software productivity and reliability. These approaches typically fall into three categories: the engineering approach, the formal approach and the knowledge‐based approach. But the optimal gain in software productivity cannot be obtained if one relies on only one of these approaches. This paper describes the work in knowledge‐based software engineering conducted by the authors for the past 10 years. The final goal of the research is to develop a paradigm for software engineering which integrates the three approaches mentioned above. A knowledge‐based tool which can support the whole process of software development is provided in this paper.

This paper proposes the concept of generalized L systems, GL systems for short, which can describe asynchronized concurrent phenomena. We have proved that the GL systems are proper extensions of the traditional L systems. We have also defined a classification of GL systems and proved a sufficient and necessary condition for the equivalence of two subclasses of GL systems: two GPD0L (a class of deterministic GL systems) systems L[m 1, m 2, …, m j ] and L[n 1, n 2, …, n k ] are equivalent, iff k = j and there exists a common divisor g of all m l and a common divisor h of all n l such that ∀ i: m i/g = n i/h.

PROMIS, a knowledge-based tool for automatically prototyping MIS provided by us, is intended to deliver over the task of the requirement specification to the end users. It provides a pseudo-natural language for the end users to describe their own needs. PROMIS can then convert their requirements into an internal schema and then a MIS prototype automatically with the help of an internal domain knowledge base. After presenting an overview of PROMIS, this paper mainly focuses on the design and implementation of the requirement specification environment for the pseudo-natural language, including the syntax and the semantics of the language, the understanding and the analysing of it and the resulting internal conceptual model of MIS.