In the field of knowledge graph completion, traditional methods mainly rely on Knowledge Graph Embedding (KGE), which maps entities and relations into a low-dimensional vector space to predict missing links in the knowledge graph. However, as the embedding dimensions increase, the performance improvement is limited, while the computational resources and storage requirements significantly grow, making lightweight re-search for low-resource challenging. To address this issue, this paper proposes an innovative dual stage distillation that combines traditional knowledge distillation with self-distillation, dividing the iterative training of the model into a continuous sequence of iterations, with each pair forming a dual stage distillation unit, allowing the model to accumulate and optimize knowledge across continuous training periods. In addition, we propose a dynamic sample weight allocation mechanism, adjusting the weights of samples based on their quality, ensuring that high-quality sam-ples receive more attention and reducing the negative impact of low-quality samples. A series of experiments demonstrate that our proposed method outperforms existing baseline models on most metrics, especially in terms of accuracy metric (MRR), where we achieved state-of-the-art results.

With the rapid development of Generative Artificial Intelligence (Generative AI), Sovereign AI—a vital vehicle for national digital sovereignty—has introduced new technical challenges for value alignment. Traditional alignment methods struggle to effectively address issues such as multicultural divergence, the abstract complexity of ethical principles, and the high computational cost known as the “alignment tax” under Sovereign AI. To tackle these challenges, a multi-granular semantic reasoning method is proposed for value alignment based on the DIKWP framework, designed explicitly for Sovereign AI. By mapping the interrelations among Data, Information, Knowledge, Wisdom, and Purpose (DIKWP), a dual system of intention and behavior is constructed to resolve semantic conflicts, fragmented value expressions, and dynamic adaptation in complex contexts. To reduce alignment costs, we design a dedicated, prompt template—DIKWP Chain of Thought(DIKWP-CoT)—that explicitly embeds value-behavior logic into the model’s reasoning process, significantly minimizing retraining needs. Experimental results show that, compared to traditional approaches, the proposed method significantly improves the accuracy and robustness of value alignment on the CoreValue and CMOS datasets, especially in complex scenarios involving implicit intentions or biased expressions. This provides a novel technical pathway for building safe, trustworthy, and sovereignty-aligned generative AI systems.

Although large language models (LLMs) perform impressively in natural language tasks, they face several challenges, such as conventional new knowledge, generating accurate responses, and explaining their reasoning. To address these issues, we propose a new approach, Retrieval-Augmented Reasoning on Knowledge (RARoK), which combines Chain of Thought (CoT) prompts with Retrieval-Augmented Generation (RAG). By leveraging external information from knowledge graphs (KGs), RARoK iteratively refines the CoT to further optimize the reasoning process of the model. Our approach significantly outperformed the baseline in various Q&A tasks, especially in the medical domain. Compared to the SOTA method, RARoK improves Hits@1 by 3.3% on the WebQSP dataset, while improving the Hits@1 and F1 scores by 18.6% and 10.4% respectively on the CWQ dataset. The experimental results on the Knowledge Graph Question Answering (KGQA) datasets and the Medical Q&A datasets show that our method has better reasoning ability and interpretability compared to the vanilla LLMs and other retrieval-enhanced methods. Our code and data are publicly available (https://github.com/cskyan/RARoK).

Point clouds play a crucial role in robots and intelligent vehicles. Noise caused by fog droplets seriously degrades the quality of point clouds. Previous researches have shown that the extent of degradation is correlated with visibility. The fog attenuation coefficient is associated with visibility. In light of this background, this paper proposes a noise-distribution-based defogging method for point clouds. Our approach hinges on the estimation of the fog attenuation coefficient, facilitated by road-based prior knowledge. Subsequently, our method integrates the fog-induced noise distribution inferred from the LiDAR imaging model with the spatially non-uniform distribution of point clouds caused by LiDAR structure. The fused results are input to a statistical filter based on the relative sparsity of noise to achieve defogging. This paper is one of the early works focusing on point cloud defogging. Its core insight lies in the estimation of the attenuation coefficient and the employment of fog-induced noise distribution for defogging. Experiments demonstrate that our method can accurately mitigate the impact of fog and meanwhile enhance the performance of 3D object detection network.

Voxel is one of the common structural representation of 3D point cloud. Due to the sparsity of point cloud generated by light detection and ranging (LiDAR), there is the extreme imbalance in the foreground and background voxels. It decreases the accuracy of 3D object detection, has the negative effect on intelligent driving safety. To overcome this problem, we present a saliency prediction based 3D object detector SP-Det in this article. Although foreground voxels have the sufficient feature of object, it is difficult to localize the foreground region from voxel space with the larger background region. We design an auxiliary learning task, saliency prediction (SP). It benefits 3D detector in identifying the foreground region. SP task uses label diffusion to alleviate the label imbalance. It reduces the learning difficulty of saliency in voxel and bird's eye view (BEV) spaces. After that, to strengthen feature interaction from the sparse foreground region, we design saliency fusion (SF) module to fuse the learning result in SP task. It utilizes voxel and BEV saliency maps as progressive attention to resist the redundant feature from background region. To aggregate more foreground feature inside 3D and BEV region of interest (RoI), we design hybrid grid maps based RoI pooling (Hybrid-RoI pooling). Experiments are conducted in STF dataset. The adverse weather enlarges the sparsity of LiDAR point cloud, increasing the difficulty of object detection. SP-Det identifies and leverages foreground region, and achieves the performance better than the current methods. Hence, we believe that SP-Det benefits to LiDAR based 3D scene understanding in the adverse weather.

The revelation of cognitive knowledge, development, and planning of distinct economic societies in human civilization have mostly been influenced by historically asymmetric availability or ownership of Data Asset, Information Asset, Knowledge Asset, and Wisdom Asset. The availability of asymmetric information and the asymmetry of demand for commercial goods or services serve as the foundation for many economic models and theories. However, from the DIKWP (Data, Information, Knowledge, Wisdom, and Purpose) Capital materialization and DIKWP Governance perspective, with the rapidly development of information technology and widely progressing digital communication facilities, Asymmetric Information Economy is increasingly replaced with essentially Symmetric Knowledge Economy and Symmetric Wisdom Economy. The inevitability of the replacement of Asymmetric Economy by Symmetric Economy in terms of DIKWP-12-Chains is formalized, and proposed uniformly semantic processing crossing DIKWP.

人工智能(AI)已经成为各领域创新和社会进步的驱动力。然而, 其大多数工业应用集中在信号处理领域, 这依赖于不同传感器产生和收集的数据。最近, 一些研究人员提出将数字人工智能和物理人工智能结合, 这可能带来人工智能理论基础的重大进步。在本文中, 我们探讨了物理人工智能的概念并提出两个子领域: 集成式物理人工智能和分布式物理人工智能。我们还讨论了物理人工智能可持续发展和治理所面临的挑战和机遇。由于物理人工智能需要连续处理来自边缘、雾和物联网的分布式信号, 它可以被看作分布式计算连续系统在人工智能领域的延伸。

With the development of ICT (information and communications technology) and service economy, service ecosystem is emerging in a lot of fields, including E-commerce, O2O(Online To Offline) life service, healthcare service, cloud manufacturing, and so on. As a complex socio-technical system, the evolution of service ecosystem is the joint result of the interaction of the three heterogeneous networks, including social network, service network and value network. Under such circumstances, the traditional SOA (Service Oriented Architecture)-based analysis model is powerless. As a result, how to analyze the laws behind the evolution of service ecosystem is still a serious challenge in the field. This paper proposes a value oriented analysis framework (VOA) of service ecosystem, which can use value as a clue to describe the interaction of the three heterogeneous networks. In addition, a computational experiment system is established to verify the effectiveness of the VOA framework, which stimulates the effect of different intervention strategies on service ecosystem. The result shows that our analysis framework can provide new means and ideas for the analysis of service ecosystem.

Automatic matching of multimodal images remains a critical challenging task in many remote sensing and computer vision applications. Due to significant nonlinear radiation distortions (NRDs) between multimodal images, it is difficult for many traditional feature matching methods which are sensitive to NRD to achieve satisfactory matching performance. To cope with this problem, this letter proposes a novel feature matching method named nonlinear consistent feature transform (NCFT) that is robust to large NRD. There are three main contributions to NCFT. First, we propose a new consistent feature map instead of image intensity for feature point detection and description, the consistent feature map encodes the structure information and provides a rich and robust feature. Second, we propose a mean-residual maximum index map (MR-MIM) for feature description and the MR-MIM is constructed from the Log-Gabor convolution sequence on the consistent feature map. Finally, the structure descriptors are built according to the MR-MIM, and multimodal image matching is achieved by computing the correspondence. The extensive experimental results demonstrate that NCFT can effectively overcome the problem of NRD, NCFT outperforms other state-of-the-art methods and improves the matching accuracy and robustness on different multimodal image datasets.

With the development of cloud computing, service computing, IoT(Internet of Things) and mobile Internet, the diversity and sociality of services are increasingly apparent. With the increasing complexity of collaborative relationships between services, service ecosystems are beginning to emerge with the characteristics of natural ecosystems, economic systems and complex networks. Under this context, how to realize systematic evaluation of service ecosystem is of great significance to promote its sound development. Based on this, this article proposes a value entropy model that links the operating state of the system with the efficiency of value creation, which helps to clarify the performance of the service ecosystem from the perspective of multi-dimensional integration. In addition, a computational experiment system is established to verify the effectiveness of value entropy model, which stimulates the competitive evolution process of two service ecosystems with different strategies. The result shows that our model can provide new ideas for the analysis of service ecosystem evolution, and can also provide decision support for the optimization of operation strategy.

With the normalization of epidemic prevention, many hotels regard pre-sale as a "life-saving straw", but it leaves a "sequelae" of booking cancellation. Therefore, based on risk aversion theory and attribution theory, this paper studies the influencing factors of hotel booking cancellation behavior of consumers through situational experiment. Results show that: (1) Electronic word-of-mouth dispersion has a significant positive impact on booking cancellation behavior of hotel consumers. (2) Attribution selection can mediate the influence of hotel electronic word-of-mouth dispersion on booking cancellation behavior of consumers. (3) Self-construal can moderate the influence of electronic word-of-mouth dispersion on attribution selection. Findings explore the important factor that influence the booking cancellation behavior of hotel consumers, and provides theoretical guidance and reference for the management of hotel booking cancellation phenomenon.

Traditional expression of emotion generally uses tone of voice or modality to express emotion content of a sender. There are limitations in expression space and personal differences in cognition, which can lead to misunderstanding. To solve these problems, we propose an emotion communication method based on Data, Information, Knowledge and Wisdom (DIKW). Firstly, the content expression of a sender transforms into Data, Information and Knowledge type resources by conceptualizing and abstracting for personalised services. In order to reduce unnecessary input costs, analysis of a sender purpose regulates the range of Data, Information, Knowledge and the transmission range of Data, Information, Knowledge. The order of priority for transferring them is in descending order of weight. Then, reshaping emotion content forms a topology that matches a receiver cognition ability to achieve a certain level of accuracy and efficiency transmission. Finally, using value-oriented assessment of emotion communications and blocking intrusions achieves a certain accuracy and efficiency and secure transmission.

In this information and regularization era, people will encounter a variety of forms in their daily life and office, such as online shopping to fill in the recipient information, travel to fill in the epidemic prevention information and so on. People classify these as content filling problems. In the face of these forms, there are many problems, such as repeated filling, error, troublesome, leakage of information and so on. However, existing automatic filling technology often only stays at the level of data or information migration, which is difficult to deal with the above problems. Moreover, it does not analyze purpose of the preparer and the form in the filling process. This is easy to produce filling behavior contrary to purpose of the preparer. Aiming at this kind of problem, we proposes a content filling data, information, knowledge and purpose(DIKP) modeling method. This method transforms the contents of preparer and form into data, information, knowledge and purpose, and establishes DIKP system. Then, driven by the purpose of the preparer, we fully analyze and calculate the DIKP systems of preparer and form. We judge and grade the filling value. We fill the content through the methods such as comparison of purpose, reducing uncertainty, fuzzy transmission and content verification to achieve intelligence and rationality.

With the development of cloud computing, service computing, IoT(Internet of Things) and mobile Internet, the diversity and sociality of services are increasingly apparent. With the increasing complexity of collaborative relationships between services, service ecosystems are beginning to emerge with the characteristics of natural ecosystems, economic systems and complex networks. Under this context, how to realize systematic evaluation of service ecosystem is of great significance to promote its sound development. As shown in Fig.1 , the value creation of service ecosystem consists of three elements: Input, Output, and Operation. Input means customers’ value demands, which drives the constant evolution of service ecosystem. Output means the value created by the service ecosystem in a certain period of time. Operation means the value creation ability of service ecosystem.

With the development of Internet technology, the number of Web services is growing rapidly, and various types of service recommendation systems emerge in a rapid stream. Although all major service recommendation systems show efficient data processing and service recommendation performance, most of the existing service recommendation systems are developed based on a centralized platform, with functions and data concentrated on a central server. There are still many problems with this over-centralized authority, such as data tamper, data leakage and so on. In response to the above problems, we designed and implemented a service recommendation system based on smart contracts and DIKW (Data, Information, Knowledge and Wisdom). The system data is stored in the blockchain, which effectively prevents data from being tampered. At the same time, the operating environment of the system is the decentralized environment of the Ethereum alliance chain, which overcomes the centralization drawbacks of the traditional service recommendation system and provides a new solution to the existing problems in the service recommendation domain.

Service-oriented architecture (SOA) provides a flexible IT infrastructure in order to deal with global competition. However, real-world services change their architectures and functions frequently. This work uses data integration, information synthesis, and knowledge fusion to analyze SOA evolution and complexity. A quantificational approach is provided to estimate the change effects on a service-oriented system. We established a cloud service community to provide rich cloud services and a service mashup platform that connects API service providers and service users. The platform user can invoke all API services on the platform without jumping to the third-party website. The platform users can filter and accept the services recommended by the platform, and also freely compose the services which are real-world services. Data integration, information synthesis, and knowledge fusion are applied to mashups that users constructed, helping users avoid complexity and unreliability that is our objective. We also showed that our method achieved significant quantitative results.

Data, information, knowledge, and wisdom forms a progressive relationship. Information is formed by data collation. Knowledge is filtered, refined, and processed from relevant information. Wisdom is based on knowledge and is accumulated through experience. This paper uses the progressive relationship of service data, information, knowledge, and wisdom to explain the expression of service knowledge graph. It is an increasingly challenging demand to discover trusted Cloud service providers with service data, information, and knowledge. We propose an efficient method of trusted service provider discovery based on service knowledge graphs, called PDG (Provider Discovery based on Graphs), to ensure that each service instance of composite services in Cloud systems is trustworthy. PDG evaluates the outputs of service providers in service classes with the help of additional service information. According to the additional service information, service knowledge is generated and trusted service providers can be found easily. PDG improves the accuracy of processing results by automatically replacing data provided by untrusted service providers with results provided by trusted service providers.

An energy-efficient anomaly detection is fundamental to maintain a healthy status of domain applications in edge-cloud collaboration networks. Generally, various kinds of multimodal sensory data capture heterogeneous attributes, where a certain attribute, called the primary one, may be more significant in detecting certain anomaly. This observation drives us to propose a novel energy-efficient anomaly detection mechanism, where attributes sensed by multimodal smart things (msts) are categorized as primary and secondary ones according to their relevance with the characteristic of this anomaly. This technique includes two steps: 1) an initial anomaly detection in single edge networks. Edge nodes associated with the primary attribute adopt a lightweight object detection model to initially detect the potential occurrence of this anomaly. Certain edge networks are determined where an anomaly is suspected and 2) an anomaly refinement with multimodal and multiattribute smart things in marginal edge networks. The cloud identifies and issues a specific query request to gather anomaly-aware sensory data from smart things with secondary attributes, for refining the detection accuracy of this anomaly, where an adaptive weighted fusion model is developed to analyze sensory data coupling of msts. The experimental results show that this technique performs better than the state of the art on the reduction of energy consumption and query time.

Summary With the ever‐increasing popularity of web application programming interfaces (APIs) sharing communities, it is becoming a promising way for software developers to design and create their interesting Apps through composing a set of selected web APIs that can collectively fulfill the App functions expected by the App developer. However, the App developer's web APIs selection decision‐makings are often nontrivial due to the massive candidate APIs as well as their diverse functions. Furthermore, it is difficult to guarantee that the selected web APIs are compatible enough. Moreover, traditional web APIs recommendation approaches only return a recommended APIs list, which are often not sufficient to accommodate the App developer's undetermined and fuzzy personalized preferences. Considering the above challenges, a novel keywords‐driven web APIs recommendation approach called keywords‐driven and compatibility‐aware multiple API group recommendation is proposed in this article for green and compatible software, which cannot only satisfy the App developer's functional requirements, but also return a group of web APIs recommended lists. Each returned list includes a set of compatible web APIs. Finally, we design a series of experiments based on a real‐world web APIs dataset, that is, PW dataset crawled from www.programmableWeb.com. Experimental reports compared with other competitive approaches in existing literatures indicate the effectiveness and efficiency of our proposal in this work.

The emergence of mobile service composition meets the current needs for real-time eCommerce. However, the requirements for eCommerce, such as safety and timeliness, are becoming increasingly strict. Thus, the cloud-edge hybrid computing model has been introduced to accelerate information processing, especially in a mobile scenario. However, the mobile environment is characterized by limited resource storage and users who frequently move, and these characteristics strongly affect the reliability of service composition running in this environment. Consequently, applications are likely to fail if inappropriate services are invoked. To ensure that the composite service can operate normally, traditional dynamic reconfiguration methods tend to focus on cloud services scheduling. Unfortunately, most of these approaches cannot support timely responses to dynamic changes. In this article, the cloud-edge based dynamic reconfiguration to service workflow for mobile eCommerce environments is proposed. First, the service quality concept is extended. Specifically, the value and cost attributes of a service are considered. The value attribute is used to assess the stability of the service for some time to come, and the cost attribute is the cost of a service invocation. Second, a long short-term memory (LSTM) neural network is used to predict the stability of services, which is related to the calculation of the value attribute. Then, in view of the limited available equipment resources, a method for calculating the cost of calling a service is introduced. Third, candidate services are selected by considering both service stability and the cost of service invocation, thus yielding a dynamic reconfiguration scheme that is more suitable for the cloud-edge environment. Finally, a series of comparative experiments were carried out, and the experimental results prove that the method proposed in this article offers higher stability, less energy consumption, and more accurate service prediction.

Understanding the brand competition pattern is extremely important for automakers and dealers to identify emerging competitors and respond agilely to market changes. However, the traditional methods are subject to the analysts' experiences and analytical dimensions, which lack a reliable methodology and ignore the value of user behaviors. Therefore, in this paper, we incorporate the complex network into our model to explore and analyze the brand competition patterns in China's auto market with users' sales leads in detail. Our investigation involves 188,085,987 sales leads of 213 brands from a vertical auto website, and it has three important contributions. First, we propose an innovative visualization approach to help automakers quickly identify competitors and market positioning. Second, we analyze both the static and dynamic characteristics of the auto brand competition network, and reveal characteristics including the small-world phenomenon, disassortativeness, and preferential attachment. Finally, we discover the core-periphery structure in the network, and introduce a novel method to identify the core/periphery characteristics of brands with centrality metrics and K-Means clustering. Besides, features such as the number of sales leads, the number of models of the brand, and the sales price range are found to accurately predict the core/periphery characteristics of the brand.

Process models as knowledge graph representation have been widely used in various domains to create products and deliver services. Although different process model discovery approaches have been proposed in recent years, few of them are designed for distributed computing environments. Specifically, none of them has been studied in the emerging edge computing application scenarios. In this paper, based on the requirements of some real-time process services, we propose a system design for learning process models in IoT edge. We present the details of our solution and our preliminary results on a simulated IoT network show that our method can discover real-time process models in less than a second.

Knowledge representation is considered one of the main challenges in knowledge-based system. Currently, knowledge graph is the most modern knowledge representation model, which has been utilized in critical knowledge based systems. On the other hand, the Entity Relationship Diagram (ERD) for relational database is the most used model in information systems. In this paper, an efficient approach for mapping ERD to knowledge graph is presented with a case study for demonstrating the mapping process. First, relational tables have been represented by knowledge graph, then reification has been implemented to convert the knowledge graph to knowledge base. Eventually, extracting knowledge from these knowledge bases is processed by Prolog. Our proposed model has been empirically evaluated by comparing the Prolog results with the conventional SQL results.

Cloud computing and service computing are merging together rapidly. This process brings large challenges to create knowledge graphs for service data. Our aim is to develop a knowledge graph for service analysis, and recommendation to boost API economy. In this paper, knowledge extraction and knowledge discovery are shown for the knowledge graph.

Conceptual identification and semantic formalization of concepts are necessary for sharing thought among individuals and accumulating cognition of concepts in discrete minds. The extent of how well concepts are conceptually identified, clarified and semantically formalized essentially determines in positive ratio to how well these concepts can function effectively, and perform efficiently. However, after a long struggle with various effort, we still are not fully satisfied with the conceptual revelation and semantic formalization of some fundamental concepts in natural language, e.g., software Style, Structure, Pattern, Framework, Architecture, Service, etc. Based on our proposed Existence Computation, which enables conceptual revelation and formalizing semantics of concepts from the extreme beginning of cognition on existence, and the ideology of Relationship Defined Everything of Semantic, which provides a global unification of constructive semantic expression, we proposed a Periodic Table like conceptualization and formalization mechanism of concepts, called Existence Periodic Table, based on a two level existence evaluation architecture: atomic existence level referring to independence semantic, and typed existence level bundled with semantic of completeness. We show the cases of utilization of our proposed Existence Periodic table in identification and formalization of concepts.

Towards tackling the challenge of clarification of the conceptual difference of DIKW concepts of data, information, knowledge and wisdom, we proposed the debut of existence computation (EC) which reveals the ultimate semantics argumentation on concepts in areas including software engineering, service computing, requirement specification, philosophy, etc, from an existence semantic based foundation and organized upwards from this foundation. The description model of the target concepts and their composition is built on our conceptual formalization of the semantic expression space in terms of relationship defined everything of semantics (RDEX) where all semantics of concepts are mapped to relationships. We proposed the axioms and theorems of transitive existence value scopes locating and evaluation for implementation of reasoning and computation of existence value of concepts in relative and absolute manner covering both top down and bottom up tracing. We show the effectiveness of EC in RDE through conceptual reasoning for the revelation of the semantic of key concepts of “Entity vs. Relationship” on the existence level difference in terms of existence value of False, denoted with E=0, and existence value of True, denoted with E=1.

Based on our proposed modeling approach of relationship defined everything of semantics (RDXS) which prioritizes relationship as comprising entity and equalized semantic and relationship, from existence value level investigation which we specialized with Existence Computation (EC), we present in this work a few conceptual application of semantic revelation of implicit modes such as implicit pornographic identification, and majority dominating challenge increasingly prevailing in AI applications implicitly. The work is expected to contribute to natural language form common sense reasoning in AI expression, modeling and reasoning for currently challenging expressions which don't have deterministic conceptual semantics available theoretically due to implicit inconsistency and incompleteness of existence assumptions of targeted content.

Abstract With the rapid development of RGB‐D cameras and pose estimation techniques, action recognition based on three‐dimensional skeleton data has gained significant attention in the artificial intelligence community. In this paper, we incorporate temporal pattern descriptors of joint positions with the currently popular long short‐term memory (LSTM)–based learning scheme to obtain accurate and robust action recognition. Considering that actions are essentially formed by small subactions, we first utilize a two‐dimensional wavelet transform to extract temporal pattern descriptors in the frequency domain for each subaction. Afterward, we design a novel LSTM structure to extract deep features, which model a long‐term spatiotemporal correlation between body parts. Since temporal pattern descriptors and LSTM deep features can be regarded as multimodal representations for actions, we fuse them with an autoencoder network to achieve a more effective feature descriptor for action recognition. Experimental results on three challenging data sets with several comparative methods demonstrate the effectiveness of the proposed method for three‐dimensional action recognition.

This paper proposes a multi-request cooperative-integrating mechanism to optimize concurrent multi-applications in service-oriented wireless sensor networks (WSNs). Specifically, a sensor node is encapsulated as one or multiple WSN services, which can be categorized into service classes. A service network is constructed by considering the invocation relationship between service classes. Candidate service class chains are recommended. These service classes chains will be instantiated by available WSN services, which can be reduced to a multi-objective and multi-constraint optimization problem, where the spatial-and temporal-constraints, and energy efficiency of the network, are taken into consideration. This combinational optimization problem is solved by adopting heuristic algorithms. Experimental results show that this technique improves the shareability and energy efficiency for supporting concurrent applications.

This paper presents a three-tier framework to facilitate the composition of Internet-of-Things (IoT) services, where these IoT services represent functionalities provided by heterogenous smart things. Various IoT services are categorized into service classes through the categorization of their functionalities. A service network is constructed by considering the invocation relationship between service classes, and service class chains are generated using traditional Web service composition techniques to satisfy the requirement from the functional perspective only. Considering the factors, including spatial and temporal constraints, energy efficiency, and the functional configurability, IoT service composition can be reduced to a multi-objective optimization problem. Heuristic algorithms, such as genetic algorithm (GA), ant colony optimization (ACO), and particle swarm optimization (PSO), are adopted to search for optimal IoT service compositions. Experimental results show that PSO performs better than GA and ACO in searching for approximately optimal IoT service compositions and reducing the energy consumption of smart things in the network.

We present privacy resources in IoT as data, information, and knowledge.We construct a privacy protection architecture on our previously proposed DIKW graphs: Data Graph, Information Graph, and Knowledge Graph.On this architecture, we search privacy protection target resources both as they appear explicitly in their original types and as they appear implicitly which means that they are expressed not in their original types.For a single privacy protection target, it may have various concrete compositions in various layers of DIKW Graph.It becomes more complex since the implementation of a privacy target might also be intertwined with the implementation of other privacy targets.We propose to protect target resources according to their types by either isolating the elements comprising an implementation, or weakening relationships among elements comprising an implement.To optimize among several choices of implementing a protection in a business environment, we introduced the tradeoff between customers' expectations/investment and privacy providers' expectation.Thereafter we proposed to prioritize implementation according to their ratio of cost/benefit.

We proposed an algorithm design approach based on semantics analysis of the problem description and analysis. We showed this approach with the designing of a median selection algorithm. Following the principles of our semantics analysis, we designed a median selection algorithm to find medians for an even amount of numbers with upper bound time complexity of O(3n).

Wide application of the Internet of Things (IoT) system has been increasingly demanding more hardware facilities for processing various resources including data, information, and knowledge. With the rapid growth of generated resource quantity, it is difficult to adapt to this situation by using traditional cloud computing models. Fog computing enables storage and computing services to perform at the edge of the network to extend cloud computing. However, there are some problems such as restricted computation, limited storage, and expensive network bandwidth in Fog computing applications. It is a challenge to balance the distribution of network resources. We propose a processing optimization mechanism of typed resources with synchronized storage and computation adaptation in Fog computing. In this mechanism, we process typed resources in a wireless‐network‐based three‐tier architecture consisting of Data Graph, Information Graph, and Knowledge Graph. The proposed mechanism aims to minimize processing cost over network, computation, and storage while maximizing the performance of processing in a business value driven manner. Simulation results show that the proposed approach improves the ratio of performance over user investment. Meanwhile, conversions between resource types deliver support for dynamically allocating network resources.

Web service is a popular solution to integrate components when building a software system, or to allow communication between a system and third-party users, providing a flexible and reusable mechanism to access its functionalities. Various web service based systems are prevailing in health service provision. We propose a framework of construction, searching and protection of typed health resources in terms of data, information and knowledge through a hierarchy consisting of Data Graph, Information Graph and Knowledge Graph. We use cases to illustrate the mechanism of the framework.

The boundary detection of continuous objects has become an important research challenge in Wireless Sensor Networks (WSNs), where improving the accuracy of boundary and reducing the energy consumption are the primary factors to be considered. To address this challenge, this article proposes a tow-stage boundary area detection scheme in duty-cycled WSNs, and sensor nodes are deployed in a dense fashion. Experimental evaluation result shows that the refinement procedure can refine the boundary area, where half of the initial boundary faces area should be reduced in most situations.

Knowledge graphs have been widely adopted, in large part owing to their schema-less nature.It enables knowledge graphs to grow seamlessly and allows for new relationships and entities as needed.Natural language questions are the most intuitive way of formulating an information need.People can formulate questions to express their information needs.Natural language questions as a query language present an ideal compromise between keyword and structured querying.Questions can be used to express complex information needs that cannot be expressed as keywords without a significant loss in structure and semantics.Knowledge graph has abundant natural semantics and can contain various and more complete information.Its expression mechanism is closer to natural language.We propose to clarify the expression of knowledge graph as a whole.We use knowledge graph to solve the Five Ws problems respectively which are guided by interrogative words such as who/when, what, how and why.We also propose to specify knowledge graph in a progressive manner as four basic forms including data graph, information graph, knowledge graph and wisdom graph.

From the probabilistic perspective, we present an analysis on potential incurred chances of division by “2” without reminder during a single process of the conjuncture of “3x + 1” in different sections of the binary expression of an input number. We revealed the difference of the tail section and the other parts for a large input number. Based on the ratio of the difference of the change of different sections of the expression with randomly assigned values in a single process, we identified the decreasing tendency of the input number.

Knowledge graphs have been widely adopted, in large part owing to their schema-less nature. It enables knowledge graphs to grow seamlessly and allows for new relationships and entities as needed. Knowledge graph has become a powerful tool to represent knowledge in the form of a labelled directed graph and to give semantics to textual information. A knowledge graph is a graph constructed by representing each item, entity and user as nodes, and linking those nodes that interact with each other via edges. Knowledge graph has abundant natural semantics and can contain various and more complete information. Its expression mechanism is close to natural language. However, we still lack a unified definition and standard expression form of knowledge graph. We propose to clarify the expression of knowledge graph as a whole. We clarify the architecture of knowledge graph from data, information, knowledge, and wisdom aspects respectively. We also propose to specify knowledge graph in a progressive manner as four basic forms including data graph, information graph, knowledge graph and wisdom graph.

Value-Driven Design enables rational decisions to be made in terms of the optimum business and technical solution at every level of engineering design by employing economics in decision making. In order to maximize the business profitability, we propose to bridge bidirectional value driven design between economic planning and technology implementation on the basis of the data graph, information graph and knowledge graph. We use data graph, information graph and knowledge graph to analyze problems that have negative impact on activities of software development including requirement analysis, summary design and detail design. We propose to improve system reliability and robustness by managing data and information reuse, redundancy as well as structure.

Business process modeling is a way to the organizational change management, which provides abstract workflows to describe business logics for customer demands analysis and IT infrastructures improvement. In order to reuse business process for covering different application scopes, it requires the capacity of configuring each task of business process by selecting appropriate services from the candidate services set, and then assembling them together to properly work with each other as domain-specific software. Considering choosing an executable business process with high quality service (QoS), the service selection is regarded as the pivotal step of determining process instances since service displays probabilistic behaviors under the uncertainty of Internet making business process shown different reliability. In this paper, it proposes an approach to the service selection for business process modeling. In the first phase, the function similarity method is used to pick out services from service repository in order to build a set of candidate services, which checks the functions description to find matching services, especially service may publish one or more functions through multiple interfaces. In the second phase, the probabilistic model checking-based method is employed to the quantitative verification of process instances, which involves services composition and stochastic behaviors computing according to workflow structures. Then, corresponding algorithms are discussed for service selection purposes. Furthermore, it introduces the probabilistic model checking-based framework for prototype design and implementation. Finally, experiments are conducted to demonstrate the effectiveness and efficiency of the proposed method comparing with traditional methods.

Service Economics has been successfully proposed and implemented for promoting the macro service market especially in Global value chains which however does not provide guidance and integrate directly with the IT side implementation. Therefore there leaves a gap of the refinement from the strategic business planning to ubiquitous services implementation in the API (Application Program Interface) economy. Recently Gio Wiederhold attributed this gap to the knowledge difference between economists and the IT professionals and proposed to solve it from the IT side. As an instance of this guidance in the practice of Value Driven Design, we proposed a systemic formalization from the value calculation to the design quality measurement which binds the modification and change on the design artifacts with the business value strategy through a framework of managed quality properties in a service design process.

Given a new issue request, suitable developers should be arranged to implement it. Technologies such as developer recommendation have been proposed to tackle this issue. These techniques tend to recommend experienced developers, i.e., the more experienced the developer is, the more possible he/she is recommended. However, if the experienced developers are hectic, the junior developers may be employed to finish the incoming issue. But they may have difficulty in finishing these tasks for lack of developing experience. In this paper, we propose a novel approach, DR_PSF (Developer Recommendation with Personalized Source-code Files), to enhance developer recommendation by leveraging personalized source-code files. DR_PSF uses the collaborative topic modeling (CTM) technique to analyze developer expertise and triage some personalized files for the recommended developers. An empirical study is conducted, and the results show that DR_PSF can effectively recommend useful personalized source-code files for them to refer when they implement the incoming issue.

Topic models are shown to be effective to mine unstructured software engineering (SE) data. In this paper, we give a simple survey of exploring topic models to support various SE tasks between 2003 and 2015. The survey results show that there is an increasing concern in this area. Among the SE tasks, source code comprehension and software history comprehension are the mostly studied, followed by software defects prediction. However, there is still only a few studies on other SE tasks, such as feature location and regression testing.

Program comprehension is one of the first and most frequently performed activities during software maintenance and evolution. In a program, there are not only source code, but also comments. Comments in a program is one of the main sources of information for program comprehension. If a program has good comments, it will be easier for developers to understand it. Unfortunately, for many software systems, due to developers’ poor coding style or hectic work schedule, it is often the case that a number of methods and classes are not written with good comments. This can make it difficult for developers to understand the methods and classes, when they are performing future software maintenance tasks. To deal with this problem, in this paper we propose an approach which assesses the quality of a code comment and generates suggestions to improve comment quality. A user study is conducted to assess the effectiveness of our approach and the results show that our comment quality assessments are similar to the assessments made by our user study participants, the suggestions provided by our approach are useful to improve comment quality, and our approach can improve the accuracy of the previous comment quality analysis approaches.

For several years now, scientists have been proposing numerous models for defining anything "as a service (aaS)", including discussions of products, processes, data & information management, and security as a service. In this paper, based on a thorough literature survey, we investigate the vast stream of the state of the art in Everything as a Service (XaaS). We then use this investigation to explore an integrated view of XaaS that will help propose approaches for migrating applications to the cloud and exposing them as services.

Recent growth of cloud computing has driven the development of Heterogeneous Cloud Federation (HCF) that has been considered an approach of improving the diversity of cloud systems by enable cloud users to select resources provided by different service providers. Using this mechanism can divide a complicate task into a group of sub-tasks in order to increase the efficiency of application executions, which can support simultaneously executing different cloud systems. One of the resource allocation mechanisms is to schedule tasks among cloud providers. However, current challenge of implementing this mechanism is to generate an efficient task scheduling. We consider mobile cloud computing an adaptive approach for increasing the utilizations of mobile devices. Addressing this dimension, we propose our solution named Mobile Cloud-based Heterogeneous Resource Allocation Model (MC-HRAM), which is designed to minimize the execution time. The main algorithm used in the proposed model is Dynamic Heterogeneous Task Assignments Algorithm (DHTA). Our experimental evaluation has proved that our proposed schema has an advantage of saving execution time.

Comments to describe the intent of the code is crucial to measure the program readability, especially for the methods and their comments in a program. Existing program readability techniques mainly focus on matching method and its comments on whether there is the same content between them. But these techniques cannot accurately analyze polysemy and synonyms in the program. In this paper, we propose an approach to analyze program readability based on WordNet, which is able to expand the range of keyword search and solve the problem of semantic ambiguity. Based on the same semantic query function of WordNet, we match keywords between comments and methods, and analyze the readability of the classes and packages in a program.

With the increasing of the popularity of Service oriented Software Development, we have identified there is a need to systemically reduce the complexity and increase the robustness of developed Service systems through a guided development process. We choose under design (UD) and over design (OD) as core value assets to uniformly represent the target of managing the human errors and deficiencies during a development process. In the study of a Web Service architectural selection case, based on the ideology of Value Driven Design, we proposed the Problem-Value-Constraint (PVC) approach as the framework of minimizing under design and over design covering both IT concerns including functionalities and qualities and Business concerns including value, cost, price and usage. Our PVC solution connects the characteristics including knowledge management, information transformation and business value. Partially through service design patterns, our study showed that PVC is able to outline both business and technical characteristics at the same time keep the simplicity of model structures.

Numerous service models have been proposed in the form of ”as a Service” or ”aaS” in the past. This is especially eminent in the era of the Cloud under the term of anything as a service or everything as a service(XaaS). An unified view of XaaS can aid efficient classification of services for service registration, discovery, and composition. In view of that there lacks such an unified view which is demanded by systematic application of XaaS in the Web Service Ecosystem, towards proposing a taxonomy of ”aaS”, we present in this work a collection of ”aaS” based on a throughout literature survey.

Source code search plays an important role in software maintenance. The effectiveness of source code search not only relies on the search technique, but also on the quality of the query. In practice, software systems are large, thus it is difficult for a developer to format an accurate query to express what really in her/his mind, especially when the maintainer and the original developer are not the same person. When a query performs poorly, it has to be reformulated. But the words used in a query may be different from those that have similar semantics in the source code, i.e., the synonyms, which will affect the accuracy of code search results. To address this issue, we propose an approach that extends a query with synonyms generated from WordNet. Our approach extracts natural language phrases from source code identifiers, matches expanded queries with these phrases, and sorts the search results. It allows developers to explore word usage in a piece of software, helps them quickly identify relevant program elements for investigation or quickly recognize alternative words for query reformulation. Our initial empirical study on search tasks performed on the JavaScript/ECMAScript interpreter and compiler, Rhino, shows that the synonyms used to expand the queries help recommend good alternative queries. Our approach also improves the precision and recall of Conquer, a state-of-the-art query expansion/reformulation technique, by 5% and 8% respectively.

Service brokering has an increasingly prominent role in bridging the gap between business requirement and technology enablement. We propose the concept of service value brokering (SVB) to fulfil the possible missing linkages between business and technology layer. In this paper, we modeled a SVB Web service as an integration of two layers with the business interface (BIF) and the technical interface (TIF). With this distinction, Web service compositions can map to two layers of compositions at both BIF and TIF levels. We notice that any partial consideration on the consistency of either BIF or TIF layers would likely leave mismatching situations on the other layer. We employ SVB to solve these mismatching situations. With the help of SVB, we address the needs of coherent business planning and IT implementation in a model drivenmanner. Finally, we illustrate the feasibility of our approach in the development of a modern cloud-based tourism e-commerce platform.

Automating business collaboration promises efficiency and effectiveness increases with means of service-oriented cloud computing. For automating cross-organizationally connected business processes operation, existing choreography languages are typically technology driven and focus less on their socio-technical suitability and expressiveness that recognizes the interaction between people in organizations and technology in workplaces. The eSourcing Markup Language (eSML) addresses the gap, extracted from our studies of suitability and expressiveness for supporting the automation of business collaboration. eSML has been applied in case studies and its development approach is replicable for exploring strengths and weaknesses of other choreography languages.

We identify that to improve the reusability of implementation in service engineering, we need to collect and modulate reusable strategy, knowledge and experience crossing business modeling, knowledge management and economic analysis simultaneously from an interdisciplinary perspective. Strategically to ease the complexity, we adopt the existing experience from design patterns by forming a service design pattern called service value broker(SVB). SVB can efficiently integrate the three domains with relieved complexity, enhanced reusability and efficiency, etc, catering different abstraction levels. In this paper, we focus on modeling the foundational aspects of SVB presentation and value transaction, etc.

E-Service related technologies have a profound and revolutionary impact on many traditional areas. We try to outline its influence on economics which we call E-service Economics at fundamental level. We model the new characteristics of this trend along two themes: value driven decision making which covers both reuse based value added analysis and service value broker (SVB) pattern which embodies the integration of information technology and business wisdoms, and E-contract based realization which equally supports collaborative and competitive business activities. An initial case based on E-contract to show the attained flexibility, dynamic and consistency of automation is demonstrated.

In our previous work, we have introduced various service value broker (SVB) patterns which integrate business modeling, knowledge management and economic analysis. In this paper, working towards the target of maximizing the potential usage of available resource to achieve the optimization of the satisfaction on both the service provider side and the service consumer side under the guidance of the public administrative, we propose to build the E-Tourism platform based on SVB. This paper demonstrates the mechanism for SVB based E-Tourism framework. The advantages of employing SVB include that the SVB can help to increase the value added in a realtime and balanced manner which conforms to the economical goal of both long run and short run. An experiment is shown using a personnel recommendation system.

Big data analytics and knowledge management is becoming a hot topic with the emerging techniques of cloud computing and big data computing model such as MapReduce. However, large-scale adoption of MapReduce applications on public clouds is hindered by the lack of trust on the participating virtual machines deployed on the public cloud. In this paper, we extend the existing hybrid cloud MapReduce architecture to multiple public clouds. Based on such architecture, we propose IntegrityMR, an integrity assurance framework for big data analytics and management applications. We explore the result integrity check techniques at two alternative software layers: the MapReduce task layer and the applications layer. We design and implement the system at both layers based on Apache Hadoop MapReduce and Pig Latin, and perform a series of experiments with popular big data analytics and management applications such as Apache Mahout and Pig on commercial public clouds (Amazon EC2 and Microsoft Azure) and local cluster environment. The experimental result of the task layer approach shows high integrity (98% with a credit threshold of 5) with non-negligible performance overhead (18% to 82% extra running time compared to original MapReduce). The experimental result of the application layer approach shows better performance compared with the task layer approach (less than 35% of extra running time compared with the original MapReduce).

As an emerging interdisciplinary subject which crosscuts business modeling, knowledge management and economic analysis, service engineering is increasingly demanded to take care of various stakeholders' proting goals of the short run vs. long run. We propose to work towards a value driven design based solution through introducing a form of service design patterns: the service value broker(SVB) patterns to shorten the distance from economical analysis to IT implementation. SVB patterns allow us to not only study the value added in terms of functional and business aspects, but also reason about the need for brokerage across various domains.

The service value broker(SVB) pattern integrates business modeling, knowledge management and economic analysis with relieved complexity, enhanced reusability and efficiency, etc. The study of SVB is an emerging interdisciplinary subject which will help to promote the reuse of knowledge, strategy and experience in service based designs and solutions. In this paper, we focus on enumerating collected SVBs empirically with initial analysis on their composition manners. The results from this paper will play a dominating role in fueling a coming E-service Economics era.

Service contracts function as interfaces which bridge IT implementation, business modeling, and economical analysis among stakeholder. We report a review of recent research/practice(to be completed) with focus on contract content and contract management. Dimensions of { Quality of data(QoD), Quality of service(QoS), Legal issue, Context, Business term } and { Description of contract, Monitor/Control, Selection, Matchmaking, Composition } are empirically chosen for evaluation purpose. We also propose future directions.

Value is the premier pursuing which drives a service implementation and dominates service selection. Added value will help to maximize the business profiting from existing service system centering positive reuse or redundancy reducing. In view of absence of a general calculating mechanism of service value which focus on added value in the XaaS (everything as a service), we firstly model XaaS in a multiple semantics background. Then we identified the critical target object for value computation. Hence after we propose a calculation mechanism of service value and added value for situations including data as a service (DaaS), information as a service (FaaS), composition scenarios, optimization and their mix with usage explanations. Ongoing complicated implementation which centers service contracts is outlined.

Service contracts function as interfaces which bridge IT implementation, business modeling, and economical analysis for service systems among various stakeholder from a holistic perspective. This paper conducts a review of recent research and practice on service contracts with focus on service contract content and service contract management. Dimensions of {Quality of data (QoD), Quality of service (QoS), Legal issue, Context, Business term} are introduced to evaluate service contract content. Dimensions of {Description of contract, Monitor/Control, Selection, Matchmaking, Composition} are introduced to evaluate service contract management Based on the survey, we propose possible directions for future efforts.

Modeling of the value transfer of service based business systems is often based on the value normalization in terms of the value of money. We identify that the normalization is neither applicable for not normalizable properties of services of XaaS/EaaS (everything as a service) nor contributing to modeling the business nature of service based systems. We explore from a knowledge engineering approach the foundation of value modeling factors including "unit of account" and "value normalization". We propose a holistic formula for assisting tradeoffs on the modeling factors. We identify possible not normalizable situations and introduce a service value broker mode as solution initiatives with demonstrative cases.

This work aims at bound geometrical detection of 3D objects from a point cloud using semantic descriptors to improve reusability of architectural building reconstruction and aid automatic reasoning in building information modeling (BIM). Based on exploring cognitive origins of spatial semantics representations, semantics conceptualization and classification is proposed for management of architectural objects. The knowledge classification is formalized with transformations among closed world assumption (CWA) and open world assumption (OWA). Initial case study of a building prototype complying with the IFC standard reveals the organization of empirical knowledge rules and semantics scopes both in a bottom up manner of geometry→topology→semantics, and vice versa.

This paper presents an ongoing research which aims at combining geometrical analysis of point clouds and semantic rules to detect 3D building objects. Firstly by applying a previous semantic formalization investigation, we propose a classification of related knowledge as definition, partial knowledge and ambiguous knowledge to facilitate the understanding and design. Secondly an empirical implementation is conducted on a simplified building prototype complying with the IFC standard. The generation of empirical knowledge rules is revealed and semantic scopes are addressed both in the bottom up manner along the line of geometry → topology → semantic, and a vice versa top down manner. Concrete implementation is on the platform of protégé with Semantic Web Rule Language (SWRL).

Archi3D project is a very successfully practice well proved by real engineering practices. In this paper, we propose to reconstruct semantics for the purpose of reconstruct 3D architecture in Archi3D fundamentally. The formalization approach starts from several hypotheses on semantics which include: there is a core mechanism of semantics which is not limited to conceptual expressions; and a complete expression of semantics necessaries the ¿implicita¿explicit¿ of human side knowledge, etc. The necessity and feasibility concerning applying the proposed method and technology to the practice of Archi3D is discussed systemically by way of semantics revelations on some cases related to natural language and logic expressions, etc.

Firstly this draft presents a discussion of the possible challenges of model transformation (MT) approaches and related issues. Then after acknowledging the great achievements and successes in MTs, we propose to study MTs from an open and general cognitive background which differs from most current practice. Issues under study cover the broad scope of openness, incompleteness, uncertainty, quality management, human vs. machine aspects, etc, in the evolving process composed of MTs. A solution framework is proposed for the advancement of the state of art.

The information transformation problem of model driven software development (MDSD) processes is gaining increasing attentions. In this paper, we present an indepth analysis of the major causes leading to the ambiguities of existing transformation. A metamodel based approach is then proposed to address these causes. A mechanism to generate transformation rules is devised based on a classification of the UML metamodel elements to be used in subsequent transformation processes. Some other critical issues relating to the generated transformation system are also discussed.