In the last years, research on Web mining has reached maturity and has broadened in scope. Two different but interrelated research threads have emerged, based on the dual nature of the Web: – The Web is a practically...
详细信息
ISBN:
(数字)9783540301233
ISBN:
(纸本)9783540232582
In the last years, research on Web mining has reached maturity and has broadened in scope. Two different but interrelated research threads have emerged, based on the dual nature of the Web: – The Web is a practically in?nite collection of documents: The acquisition and - ploitation of information from these documents asks for intelligent techniques for information categorization, extraction and search, as well as for adaptivity to the interests and background of the organization or person that looks for information. – The Web is a venue for doing business electronically: It is a venue for interaction, information acquisition and service exploitation used by public authorities, n- governmental organizations, communities of interest and private persons. When observed as a venue for the achievement of business goals, a Web presence should be aligned to the objectives of its owner and the requirements of its users. This raises the demand for understandingWeb usage, combining it with other sources of knowledge inside an organization, and deriving lines of action. ThebirthoftheSemanticWebatthebeginningofthedecadeledtoacoercionofthetwo threadsintwoaspects:(i)theextractionofsemanticsfromtheWebtobuildtheSemantic Web;and(ii)theexploitationofthesesemanticstobettersupportinformationacquisition and to enhance the interaction for business and non-business purposes. Semantic Web mining encompasses both aspects from the viewpoint of knowledge discovery.
As an important enabler for changing people’s lives, advances in artificial intelligence (AI)-based applications and services are on the rise, despite being hindered by efficiency and latency issues. By focusing on dee...
详细信息
ISBN:
(数字)9789811561863
ISBN:
(纸本)9789811561856;9789811561887
As an important enabler for changing people’s lives, advances in artificial intelligence (AI)-based applications and services are on the rise, despite being hindered by efficiency and latency issues. By focusing on deep learning as the most representative technique of AI, this book provides a comprehensive overview of how AI services are being applied to the network edge near the data sources, and demonstrates how AI and edge computing can be mutually beneficial. To do so, it introduces and discusses: 1) edge intelligence and intelligent edge; and 2) their implementation methods and enabling technologies, namely AI training and inference in the customized edge computing framework. Gathering essential information previously scattered across the communication, networking, and AI areas, the book can help readers to understand the connections between key enabling technologies, e.g. a) AI applications in edge; b) AI inference in edge; c) AI training for edge; d) edge computing for AI; and e)using AI to optimize edge. After identifying these five aspects, which are essential for the fusion of edge computing and AI, it discusses current challenges and outlines future trends in achieving more pervasive and fine-grained intelligence with the aid of edge computing.
Finding knowledge – or meaning – in data is the goal of every knowledge d- covery e?ort. Subsequent goals and questions regarding this knowledge di?er amongknowledgediscovery(KD) projectsandapproaches. Onecentralque...
详细信息
ISBN:
(数字)9783540476986
ISBN:
(纸本)9783540476979
Finding knowledge – or meaning – in data is the goal of every knowledge d- covery e?ort. Subsequent goals and questions regarding this knowledge di?er amongknowledgediscovery(KD) projectsandapproaches. Onecentralquestion is whether and to what extent the meaning extracted from the data is expressed in a formal way that allows not only humans but also machines to understand and re-use it, i. e. , whether the semantics are formal semantics. Conversely, the input to KD processes di?ers between KD projects and approaches. One central questioniswhetherthebackgroundknowledge,businessunderstanding,etc. that the analyst employs to improve the results of KD is a set of natural-language statements, a theory in a formal language, or somewhere in between. Also, the data that are being mined can be more or less structured and/or accompanied by formal semantics. These questions must be asked in every KD e?ort. Nowhere may they be more pertinent, however, than in KD from Web data (“Web mining”). Thisis due especially to the vast amounts and heterogeneity of data and ba- ground knowledge available for Web mining (content, link structure, and - age), and to the re-use of background knowledge and KD results over the Web as a global knowledge repository and activity space. In addition, the (Sem- tic) Web can serve as a publishing space for the results of knowledge discovery from other resources, especially if the whole process is underpinned by common ontologies.
The rapid development of computer vision technology for detecting anomalies in industrial products has received unprecedented attention. In this paper, we propose a dual teacher–student-based discrimination model (DT...
详细信息
The rapid development of computer vision technology for detecting anomalies in industrial products has received unprecedented attention. In this paper, we propose a dual teacher–student-based discrimination model (DTSD) for anomaly detection, which combines the advantages of both embedding-based and reconstruction-based methods. First, the DTSD builds a dual teacher-student architecture consisting of a pretrained teacher encoder with frozen parameters, a student encoder and a student decoder. By distillation of knowledge from the teacher encoder, the two teacher-student modules acquire the ability to capture both local and global anomaly patterns. Second, to address the issue of poor reconstruction quality faced by previous reconstruction-based approaches in some challenging cases, the model employs a feature bank that stores encoded features of normal samples. By incorporating template features from the feature bank, the student decoder receives explicit guidance to enhance the quality of reconstruction. Finally, a segmentation network is utilized to adaptively integrate multiscale anomaly information from the two teacher–student modules, thereby improving segmentation accuracy. Extensive experiments demonstrate that our method outperforms existing state-of-the-art approaches. The code of DTSD is publicly available on https://***/Math-Computer/DTSD.
Embark on a captivating exploration of human-centered computing and AI, where the convergence of technology and human interaction unveils a world of endless possibilities. In the age of wireless communication, pervasi...
详细信息
ISBN:
(数字)9783031613753
ISBN:
(纸本)9783031613746;9783031613777
Embark on a captivating exploration of human-centered computing and AI, where the convergence of technology and human interaction unveils a world of endless possibilities. In the age of wireless communication, pervasive computing, and the Internet of Things, the synergy between humans and machines has never been more profound. This book delves deep into the heart of this symbiotic relationship, shedding light on the intricate dynamics that define our digital landscape.
From the humble human-computer communications, via simple interaction points, to the complex web of virtual networks, every aspect of this journey is meticulously examined. Through a lens of innovation and insight, we navigate within the complex terrain of user actions, individual differences, and algorithmic computations. At the core of our exploration lies a quest for understanding—a quest that transcends the boundaries of traditional research and ventures into the realm of cutting-edge technology.
Special emphasis is placed on promoting original insights and paradigms, incorporating aspects derived from psychological theory and individual differences in adaptive computational systems and recommenders. Our goal is simple yet ambitious: by enhancing explainability, fairness, transparency, and decreasing bias during interactions, to empower users with greater control and understanding of the technologies that shape their lives.
Through a blend of visual storytelling and specialized textual contributions, we invite you to join us on this extraordinary journey. Whether you're a researcher, practitioner, or enthusiast in the field of AI and human-computer interaction, this book offers valuable insights and perspectives. Prepare to be inspired, enlightened, and empowered through this transformative journey as we unlock the true potential of technology in service of humanity.
Graph pattern mining is essential for deciphering complex networks. In the real world, graphs are dynamic and evolve over time, necessitating updates in mining patterns to reflect these changes. Traditional methods us...
详细信息
Graph pattern mining is essential for deciphering complex networks. In the real world, graphs are dynamic and evolve over time, necessitating updates in mining patterns to reflect these changes. Traditional methods use fine-grained incremental computation to avoid full re-mining after each update, which improves speed but often overlooks potential gains from examining inter-update interactions holistically, thus missing out on overall efficiency *** this paper, we introduce Cheetah, a dynamic graph mining system that processes updates in a coarse-grained manner by leveraging exploration domains. These domains exploit the community structure of real-world graphs to uncover data reuse opportunities typically missed by existing approaches. Exploration domains, which encapsulate extensive portions of the graph relevant to updates, allow multiple updates to explore the same regions efficiently. Cheetah dynamically constructs these domains using a management module that identifies and maintains areas of redundancy as the graph changes. By grouping updates within these domains and employing a neighbor-centric expansion strategy, Cheetah minimizes redundant data accesses. Our evaluation of Cheetah across five real-world datasets shows it outperforms current leading systems by an average factor of 2.63 ×.
After a thorough peer-review process, the 17th SOCO 2022 International Program Committee selected 64 papers which are published in these conference proceedings and represent an acceptance rate of 60%. In this relevant...
详细信息
ISBN:
(数字)9783031180507
ISBN:
(纸本)9783031180491
After a thorough peer-review process, the 17th SOCO 2022 International Program Committee selected 64 papers which are published in these conference proceedings and represent an acceptance rate of 60%. In this relevant edition, a particular emphasis was put on the organization of special sessions. Seven special sessions were organized related to relevant topics such as machine learning and computer vision in Industry 4.0; time series forecasting in industrial and environmental applications; optimization, modeling, and control by soft computing techniques; soft computing applied to renewable energy systems; preprocessing big data in machine learning; tackling real-world problems with artificial intelligence.
暂无评论