The integration of Cognitive Internet of Things (IoT) sensors with autonomous aerial vehicles (AAVs) has transformed industrial sectors, such as monitoring, logistics, and infrastructure inspection. However, the advancement of visual synthesis technologies like generative adversarial networks and diffusion models has introduced significant risks by enabling the creation of highly realistic AI-manipulated content, making the detection of falsified imagery increasingly challenging. Existing detection methods, largely based on convolutional neural networks (CNNs), focus primarily on global image features and often overlook crucial relational connections, limiting their robustness and generalization. To overcome these limitations, we propose a novel dual-stream architecture that integrates global feature extraction with relational feature learning. By combining the CLIP model with a graph-based topology, our approach identifies hard-to-detect samples and processes them through a graph convolutional network (GCN) to capture both structural and relational information. Extensive evaluations validate the robustness and generalization ability of our method across various generative models and real-world perturbations. This approach offers a scalable and reliable solution to ensure data integrity in industrial IoT systems, helping to preserve societal trust in AI-driven applications.

In recent years, the Transformer model based on the self-attention mechanism has made significant progress in natural language processing and has also been applied in the text implication recognition task, achieving excellent results. However, the Transformer model still has deficiencies in modeling local information in the text. To improve the Transformer model, the QRNN-Transformer was proposed, which uses the QRNN network to divide the input text sequence into local short sequences to capture the local information of the input text. The self-attention is improved by combining the gating mechanism to make the model select tasks-related words or features. Extensive experiments have demonstrated the QRNN-Transformer model can effectively improve the accuracy of entailment relationship recognition on both English and Chinese datasets.l

In the distributed Cognitive Industrial Internet of Things (CIIoT), since industrial devices may self-organize to determine their connection and dispersion and the network may be distributed with no infrastructure, network management with self-organizing characteristics is still an unsolved and a difficult problem. To overcome this challenge, this paper proposes a hierarchical network management strategy for distributed CIIoT with imperfect channel state information (CSI) considering services with different transmission delay requirements. First, we build a two-layer architecture that supports collaborative spectrum sensing and multi-node spectrum sharing in a distributed CIIoT. Then, to improve the network management efficiency and reduce the number of backbone nodes (BNs) while achieving spectrum sharing for massively distributed nodes, we establish a minimum backbone node set mathematical model and design the simplest backbone network optimization algorithm (SBNOA). For the problem of spectrum sharing with imperfect CSI while considering the delay-sensitive industrial services, we establish a time delay model that incorporates delay-sensitive and delay-tolerant services. Based on the model, we establish a robust optimization model with multiple conditional aim-listed probability constraints that consider imperfect CSI and transform the stochastic optimization problem into a convex optimization by using the quadratic transformation method and Gaussian Q function to solve it. Simulation results show that the proposed algorithm has good performance in a distributed CIIoT.

The purpose of this paper is to explore the emotional composition, psychological characteristics, and the consistency between information behavior and attitude of social media users, and to provide reference for online public opinion monitoring, topic detection, and emotional situation evaluation. Based on big-five personality theory and self-difference theory, this paper takes 12,151 Twitter texts during Hurricane Maria as the analysis objects, extracts the personality characteristics of the texts based on convolution neural network, and analyzes the subjectivity and emotional polarity of the texts by Python. Based on the experimental results, this paper analyzes the psychological characteristics and information needs reflected by social media users' information behavior in disaster environment and further verifies and expounds the reasons for the inconsistent information behavior and attitude of social media users in disaster environments.

In relational databases and their applications, there are opportunities for evaluating a stream of K NN queries submitted one by one at different times. For this issue, we propose a new method with learning-based techniques, region clustering methods and caching mechanisms. This method uses a knowledge base to store related information of some past K NN queries, groups the search regions of the past queries into larger regions, and retrieves the tuples from the larger regions. To answer a newly submitted query, our strategy tries to obtain a majority or all of the results from the previously retrieved tuples cached in main memory. Thus, this method seeks to minimize the response time by reducing the search region or avoiding the accesses to the underlying database. Meanwhile, our method remains effective for high-dimensional data. Extensive experiments are carried out to measure the performance of this new strategy and the results indicate that it is significantly better than the state-of-the-art naïve methods of evaluating a stream of K NN queries for both low-dimensional (2, 3 and 4) and high-dimensional (25, 50 and 104) data.

A protein-protein interaction (PPI) network is a biomolecule relationship network that plays an important role in biological activities. Studies of functional modules in a PPI network contribute greatly to the understanding of biological mechanism. With the development of life science and computing science, a great amount of PPI data has been acquired by various experimental and computational approaches, which presents a significant challenge of detecting functional modules in a PPI network. To address this challenge, many functional module detecting methods have been developed. In this survey, we first analyze the existing problems in detecting functional modules and discuss the countermeasures in the data preprocess and postprocess. Second, we introduce some special metrics for distance or graph developed in clustering process of proteins. Third, we give a classification system of functional module detecting methods and describe some existing detection methods in each category. Fourth, we list databases in common use and conduct performance comparisons of several typical algorithms by popular measurements. Finally, we present the prospects and references for researchers engaged in analyzing PPI networks.

Natural language question‐and‐answering is one of the most convenient means for communicating with the Semantic Web, which is typically in the form of online knowledge bases encoded in Web Ontology Language (OWL). To understand a natural language question, it is essential that it can be translated into a query that is understandable by the knowledge bases. This article is concerned with the task of semantic mapping from natural language questions to OWL queries, and proposes an automatic and domain‐independent mapping framework, called Three‐Phases Semantic Mapping (TPSM). The TPSM framework approaches the task of semantic mapping in three interrelated phases: (i) formalizing knowledge, (ii) building semantic mapping, and (iii) combining OWL queries. First, formalizing knowledge formalizes the units of mapping in the natural language and OWL knowledge. Second, semantic mapping builds the transverse mapping between questions and OWL knowledge, as formalized in the first phase, by means of working with the so‐called Fuzzy Constraint Satisfaction Problems (FCSP). Third, combining OWL queries obtains valid Resource Description Framework (RDF) models by applying predefined templates and their corresponding combining methods. We have implemented a prototype semantic mapping system based on the framework, and have conducted a series of experimental validations involving OWL knowledge bases in different domains as queried by various types of questions.

Component-Based Software Engineering (CBSE) with Open Source Software and Commercial-Off-the-Shelf (COTS) components, Open Source Software (OSS) based development, and Software Outsourcing (SO) are becoming increasingly important for the Chinese software industry. It is therefore necessary to establish pragmatic and possibly nation-specific guidelines for Chinese software companies regarding the use of CBSE, OSS, and SO. Such guidelines should be based on insights from actual practice, which are in our case, obtained through surveys. A European state-of-the-practice survey on COTS- and OSS-oriented CBSE was conducted in Germany, Italy, and Norway in 2004-2005. We repeated similar surveys in China, with an extended survey on OSS and SO. We encountered many difficulties in conducting the surveys, but in most cases managed to find working solutions. We report on the lessons learned while conducting these surveys. In particular, we address issues relating to sampling, contacting respondents, data collection, and data validation. The main lessons are: 1) it was necessary to cooperate with a third-party organization with close relations to Chinese software companies; 2) it was necessary to assign researchers to this third-party organization to facilitate data collection and to control the quality of the data collected; and 3) an email survey, after an initial telephone call to establish contact, was the best method for getting questionnaires completed by Chinese respondents.

In relational databases and their applications, an important issue is to evaluate a stream of top-N selection queries. For this issue, we propose a new method with learning-based strategies and region clustering techniques in this paper. This method uses a knowledge base to store related information of some past queries, groups the search regions of the past queries into larger regions and retrieves the tuples from the larger regions. To answer a newly submitted query, our method tries to obtain most results from the previously retrieved tuples that are still in main memory. Thus, this method seeks to minimize the response time by reducing the search regions or avoiding accesses to the underlying databases. Extensive experiments are carried out to measure the performance of this new strategy and the results indicate that it is significantly better than the naive method for both low-dimensional and high-dimensional data.

The business relationship between a software outsourcer and its suppliers is gradually moving from contract relationship to partnership. The partnership type between the outsourcer and the supplier is considered as a key predictor of outsourcing success. Although several studies have investigated the practices and benefits of building partnership from an outsourcer's perspective, few of them have studied these issues from the supplier's viewpoint, especially in the context of offshore software outsourcing. Since more and more Chinese software companies are getting outsourcing subcontracts from abroad, it is important to investigate the effect of business relationship on their performance, and to identify possible enhancements. Our study has collected data by a questionnaire-based survey from 53 finished projects in 41 Chinese software suppliers. Twenty-six of our investigated suppliers claim to have contract relationship with their outsourcers and the remaining 27 think they have partnership with outsourcers. Results from our study show, however, that 1) processes and methods used by suppliers to solve conflicts with outsourcers do not follow their self-claimed contract relationships or partnership; 2) there is no significant correlations between the type of relationship and the success of outsourced projects; 3) more personnel with proper language and communication skills need to be educated in order to facilitate Chinese companies to build and maintain a proper partnership with their outsourcers.

Ant colony optimization (ACO) algorithm has been applied to data mining recently. Aiming at Ant Miner, a classification rule learning algorithm based on ACO, this paper presents an enhanced Ant Miner, which includes two main contributions. Firstly, a rule punishing operator is employed to reduce the number of rules and the number of conditions. Secondly, an adaptive state transition rule and a mutation operator are applied to the algorithm to speed up the convergence rate. The results of experiments on some data sets demonstrate that the enhanced Ant-Miner can quickly discover better classification rules which have roughly competitive predicative accuracy and short rules

Recently, research on social network and Web intelligence (WI) has shown that social intelligence techniques act as the imperative channel for automated email-centric tasks. This paper gives a complete picture of what can we do in the global social e-mail network and how to do. Our main contributions include: (1) we describe two mechanisms for implementing applications in the global social e-mail network; (2) we design operable e-mail for communicating in that network. To our best knowledge, it is the first time to discuss how to implement social intelligence in such a network under the notion of WI. We believe that this work consequentially explores a new and absolutely necessarily needed research field of WI

Technology about ontology plays an important role in all kinds of technologies about the semantic Web. OWL, the standard Web ontology language is proposed by W3C. Vocabularies in OWL only represent certain and complete concepts and roles. In order to represent fuzzy knowledge, this paper extends existing OWL language by encoding fuzzy constructors, axioms and constraints and maps semantics of these new vocabularies to fuzzy description logic. Extended OWL can represent fuzzy ontology and resolve some fuzzy entailment tasks by constraint propagation calculus of fuzzy description logic.

One of the key research challenges that we will face in developing the Wisdom Web is to make it capable of seamlessly offering solutions to users in dealing with their real-world problems. In order to make this possible, individual contents or services should be developed and written following the syntax and semantics of a pre-defined method for distributed problem solving on the Web. In this paper, we propose a new method for solving problems in a large-scale distributed Web environment. We will also give an illustrative example in this paper to show the service process of the new method.

E‐science is about global collaboration in key areas of science such as cognitive science and brain science, and the next generation of infrastructure such as the Wisdom Web and Knowledge Grids. As a case study, we investigate human multiperception mechanism by cooperatively using various psychological experiments, physiological measurements, and data mining techniques for developing artificial systems which match human ability in specific aspects. In particular, we observe fMRI (functional magnetic resonance imaging) and EEG (electroencephalogram) brain activations from the viewpoint of peculiarity oriented mining and propose a way of peculiarity oriented mining for knowledge discovery in multiple human brain data. Based on such experience and needs, we concentrate on the architectural aspect of a brain‐informatics portal from the perspective of the Wisdom Web and Knowledge Grids. We describe how to build a data‐mining grid on the Wisdom Web for multiaspect human brain data analysis. The proposed methodology attempts to change the perspective of cognitive scientists from a single type of experimental data analysis toward a holistic view at a long‐term, global field of vision.

Personalized recommendation needs powerful Web Intelligence (WI) technologies to manage, analyze and employ various business data on the Web for e-business intelligence. This paper presents a novel recommendation framework on the Web, which is based on a multilevel customer model comprising three submodels, namely, the customer shopping model (CSM), the customer preference model (CPM), and the customer consumption model (CCM). These models capture a customer's information from different aspects. After preprocessing of raw data, we first build the CSM based on Bayesian networks by mining from customer shopping transactions, and then find the CPM by analyzing customer shopping history. Furthermore, the customer purchasing power can be formalized as a linear CCM. By combining the CSM with the present customer shopping action, a recommendation algorithm based on Bayesian probability inference is used to generate an individual recommendation set of commodities. A personalized filter including customization of the CPM and orientation of the CCM is also used to realize a more personalized recommendation. Experimental evaluation on real world data shows that the proposed approach can achieve personalized commodities recommendation efficiently and effectively.

A continuing problem with inductive logic programming (ILP) has been the poor handling of numbers. Constraint inductive logic programming (CILP) aims to solve this problem with ILP. We propose a new approach to generating numerical constraints in CILP, and describe an implementation of the CILP system (namely, BPU-CILP). In our approach, methods from pattern recognition and multivariate data analysis, such as Fisher's linear discriminant, dynamic clustering and principal component analysis, are introduced into CILP. The BPU-CILP can generate various forms of polynomial constraints of multiple dimensions, without additional background knowledge. As a result, the constraint logic program covering all positive examples and consistent with all negative examples can be derived automatically.

Peculiarity rules are a new type of interesting rules which can be discovered by searching the relevance among peculiar data. A main task of mining peculiarity rules is the identification of peculiarity. Traditional methods of finding peculiar data are attribute-based approaches. This paper extends peculiarity oriented mining to relational peculiarity oriented mining. Peculiar data are identified on record level, and peculiar rules are mined and explained in a relational mining framework. The results from preliminary experiments show that relational peculiarity oriented mining is very effective.

TMS (Targeted Marketing System) is an integrated system and toolkit for profit-driven and cost-effective marketing. The system consists of three components: a Web-based user interface, a market value inference engine, and a presentation and evaluation module. It supports marketing decision making for a company or an organization by combining results from information retrieval, data mining, information theory, and utility theory.

Web Intelligence (WI) is a new and active research field in current AI and IT. Personalized recommendation in an intelligent B2C portal is an important research topic in WI. In this paper, we first investigate the architecture of a B2C portal from the viewpoint of conceptual levels of WI. Aiming at data mining of knowledge-level in a B2C portal, we present a new improved learning algorithm of Bayesian Networks, which consists of two major contributions, namely, making the best of lower order Conditional Independence (CI) tests and accelerating search process by means of sort order for parent nodes. By a number of experiments on ALARM datasets, we find that the proposed algorithm is both more efficient and effective than others. We have applied this algorithm to a commodity recommendation system in a B2C portal. Our experimental results demonstrate that the recommendation method based on a Customer Shopping Model (CSM) produced by the new algorithm outperforms some traditional ones in rates of coverage and precision.

This paper presents some Web Intelligence related systems developed at the WIC Beijing Research Center, such as Web-Based Intelligent Tutoring System, Recommended System in e-Business and Web-Text Mining System. In addition, we also introduce our other related work including Constraint Inductive Logic Programming (CILP), Genetic Inductive Logic Programming (GILP), association rules mining and Bayesian network learning.

The linear model of market value functions is a new method for direct marketing. Just like other methods in direct marketing, attribute reduction is very important to deal with large databases. We apply the algorithm of attribute reduction, which is based on the combination of rough set theory with the boosting algorithm, to the linear model of market value functions. Experimental results compared with the ELSA/ANN model show that the proposed algorithms can be used effectively in the linear model of market value functions.

As e-commerce developing rapidly, it is becoming a research focus about how to capture or find customer's behavior patterns and realize commerce intelligence by use of Web mining technology. Recommendation system in electronic commerce is one of the successful applications that are based on such mechanism. We present a new framework in recommendation system by finding customer model from business data. This framework formalizes the recommending process as knowledge representation of the customer shopping information and uncertainty knowledge inference process. In our approach, we firstly build a customer model based on Bayesian network by learning from customer shopping history data, then we present a recommendation algorithm based on probability inference in combination with the last shopping action of the customer, which can effectively and in real time generate a recommendation set of commodity.

Targeted marketing typically involves the identification of customers or products having potential market values. We propose a linear model for solving this problem by drawing and extending results from information retrieval. It is assumed that each object is represented by values of a finite set of attributes. A market value function, which is a linear combination of utility functions on attribute values, is used to rank objects. Several methods are examined for mining market value functions. The main advantage of the model is that one can rank objects of interest according to their market values, instead of classifying the objects. Both the theoretical and experimental results are reported in this paper. It establishes a basis on which further studies and experimental evaluation can be carried out.

This paper applies rough set theory to Inductive Logic Programming (ILP, a relatively new method in machine learning) to deal with imperfect data occurring in large real‐world applications. We investigate various kinds of imperfect data in ILP and propose rough problem settings to deal with incomplete background knowledge (where essential predicates/clauses are missing), indiscernible data (where some examples belong to both sets of positive and negative training examples), missing classification (where some examples are unclassified) and too strong declarative bias (hence the failure in searching for solutions). The rough problem settings relax the strict requirements in the standard normal problem setting for ILP, so that rough but useful hypotheses can be induced from imperfect data. We give simple measures of learning quality for the rough problem settings. For other kinds of imperfect data (noise data, too sparse data, missing values, real‐valued data, etc.), while referring to their traditional handling techniques, we also point out the possibility of new methods based on rough set theory.

How to increase both autonomy and versatility of a knowledge discovery system is a core problem and a crucial aspect of KDD (Knowledge Discovery and Data Mining). Within the framework of the KDD process and the GLS (Global Learning Scheme) system recently proposed by us, this paper describes a way of increasing both autonomy and versatility of a KDD system by dynamically organizing KDD processes. In our approach, the KDD process is modeled as an organized society of KDD agents with multiple levels. We propose an ontology to describe KDD agents, in the style of OOER (Object Oriented Entity Relationship) data model. Based on this ontology of KDD agents, we apply several AI planning techniques, which are implemented as a meta-agent, so that we might (1) solve the most difficult problem in a multiagent KDD system: how to automatically choose appropriate KDD techniques (KDD agents) to achieve a particular discovery goal in a particular application domain; (2) tackle the complexity of KDD process; and (3) support evolution of KDD data, knowledge and process. The GLS system, as a multistrategy and multiagent KDD system based on the methodology, increases both autonomy and versatility.

Constraint Databases (CDBs) are the combination of Constraint Logic Programming (CLP, which is one of the strategic directions in computing research) and database technology. CDBs represent a new kind of post-relational databases, naturally extending relational, deductive, or object-oriented databases by making feasible the use of constraints to represent possibly infinite but representable complex data. This paper discusses constraint query languages and their expressive powers, constraint query, evaluation and optimization techniques, CDBs prototype systems and promising directions of research on CDBs.

The ability to handle changes is a characteristic feature of successful software projects. The problem addressed in this paper is what should be done in project planning and iterative replanning so that the project can react effectively to changes. Thus the work presents research results in software engineering, as well as transfer of methods in knowledge engineering to software engineering, applying the AI planning technique to software process modeling and software project management. Our method is based on inter-project experience and evolution patterns. We propose a new classification of software projects, identifying and characterizing ten software process evolution patterns and link them to different project profile. Based on the evolution patterns, we discuss the planning support for process evolution and propose several methods that are new or significantly extend existing work, e.g. cost estimation of process changes, evolution pattern analysis, and a coarse process model for the initial planning and the iterative replanning process. The preliminary results have shown that the study of evolution patterns, based on inter-project experience, can provide valuable guidance in software process understanding and improvement.

In our GLS (global learning scheme) system, KDD (knowledge discovery in databases) process is modeled as an organized society of intelligent agents (called KDD agents). KDD process planning is a meta-agent in this framework, and some basic AI planning techniques have been used to organize dynamically the KDD process so that the GLS system increases both autonomy and versatility. In this paper, we address some deeper issues concerning KDD process planning. Especially we propose a mechanism integrating process planning with process controlling to formalize process iteration (that is, the re-execution of some agents). In this mode, execution failures causing process iteration, feedback paths and re-execution of the agents in the paths (and even the iterating number for each loop) are determined dynamically and automatically. With this and other deeper issues (such as process evolution) being properly handled, the GLS system is more complete in KDD process modeling, and more flexible and robust for practical use.

Presents a new procedure of partial deduction of various constraint logic programs. The procedure takes a program-goal pair (P/sub 0/, Q/sub 0/) as input and outputs a resultant program-goal pair (P/sub n/, Q/sub n/). The procedure always terminates and the computational equivalence of the program-goal pairs (P/sub 0/, Q/sub 0/) and (P/sub n/, Q/sub n/) is preserved. P/sub n/ is more efficient in solving Q/sub n/ than P/sub 0/ in solving Q/sub 0/. Our procedure is based on the idea of program transformation, using transformation rules such as unfolding, constraint simplification, code deletion with respect to a goal, introduction of a newly defined clause, and folding. We have implemented the procedure in an integrated partial deduction system, which can work with Prolog, CLP(Bool), and CLP(R) programs. Our results are compared with those resulting from other partial deduction methods, and it is shown that our method generates more succinct and efficient resultant programs in general.