This book constitutes the joint thoroughly refereed post-proceedings of the Second International Workshop on Modeling Social Media, MSM 2011, held in Boston, MA, USA, in October 2011, and the Second International Work...
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ISBN:
(数字)9783642336843
ISBN:
(纸本)9783642336836
This book constitutes the joint thoroughly refereed post-proceedings of the Second International Workshop on Modeling Social Media, MSM 2011, held in Boston, MA, USA, in October 2011, and the Second International Workshop on Mining Ubiquitous and Social Environments, MUSE 2011, held in Athens, Greece, in September 2011. The 9 full papers included in the book are revised and significantly extended versions of papers submitted to the workshops. They cover a wide range of topics organized in three main themes: communities and networks in ubiquitous social media; mining approaches; and issues of user modeling, privacy and security.
The two-volume set LNAI 6634 and 6635 constitutes the refereed proceedings of the 15th Pacific-Asia Conference on Knowledge Discovery and data Mining, PAKDD 2011, held in Shenzhen, China in May 2011. The total of 32 r...
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ISBN:
(数字)9783642208478
ISBN:
(纸本)9783642208461
The two-volume set LNAI 6634 and 6635 constitutes the refereed proceedings of the 15th Pacific-Asia Conference on Knowledge Discovery and data Mining, PAKDD 2011, held in Shenzhen, China in May 2011. The total of 32 revised full papers and 58 revised short papers were carefully reviewed and selected from 331 submissions. The papers present new ideas, original research results, and practical development experiences from all KDD-related areas including data mining, machine learning, artificial intelligence and pattern recognition, data warehousing and databases, statistics, knowledge engineering, behavior sciences, visualization, and emerging areas such as social network analysis.
Knowledge Graphs (KGs) often suffer from incompleteness and this issue motivates the task of Knowledge Graph Completion (KGC). Traditional KGC models mainly concentrate on static KGs with a fixed set of entities and r...
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Knowledge Graphs (KGs) often suffer from incompleteness and this issue motivates the task of Knowledge Graph Completion (KGC). Traditional KGC models mainly concentrate on static KGs with a fixed set of entities and relations, or dynamic KGs with temporal characteristics, faltering in their generalization to constantly evolving KGs with possible irregular entity drift. Thus, in this paper, we propose a novel link prediction model based on the embedding representation to handle the incompleteness of KGs with entity drift, termed as DCEL. Unlike traditional link prediction, DCEL could generate precise embeddings for drifted entity without imposing any regular temporal characteristic. The drifted entity is added into the KG with its links to the existing entity predicted in an incremental fashion with no requirement to retrain the whole KG for computational efficiency. In terms of DCEL model, it fully takes advantages of unstructured textual description, and is composed of four modules, namely MRC (Machine Reading Comprehension), RCAA (Relation Constraint Attentive Aggregator), RSA (Relation Specific Alignment) and RCEO (Relation Constraint Embedding Optimization). Specifically, the MRC module is first employed to extract short texts from long and redundant descriptions. Then, RCAA is used to aggregate the embeddings of textual description of drifted entity and the pre-trained word embeddings learned from corpus to a single text-based entity embedding while shielding the impact of noise and irrelevant information. After that, RSA is applied to align the text-based entity embedding to graph-based space to obtain the corresponding graph-based entity embedding, and then the learned embeddings are fed into the gate structure to be optimized based on the RCEO to improve the accuracy of representation learning. Finally, the graph-based model TransE is used to perform link prediction for drifted entity. Extensive experiments conducted on benchmark datasets in terms of evaluat
The advancement of the Internet of Medical Things (IoMT) has led to the emergence of various health and emotion care services, e.g., health monitoring. To cater to increasing computational requirements of IoMT service...
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The advancement of the Internet of Medical Things (IoMT) has led to the emergence of various health and emotion care services, e.g., health monitoring. To cater to increasing computational requirements of IoMT services, Mobile Edge Computing (MEC) has emerged as an indispensable technology in smart health. Benefiting from the cost-effectiveness of deployment, unmanned aerial vehicles (UAVs) equipped with MEC servers in Non-Orthogonal Multiple Access (NOMA) have emerged as a promising solution for providing smart health services in proximity to medical devices (MDs). However, the escalating number of MDs and the limited availability of communication resources of UAVs give rise to a significant increase in transmission latency. Moreover, due to the limited communication range of UAVs, the geographically-distributed MDs lead to workload imbalance of UAVs, which deteriorates the service response delay. To this end, this paper proposes a UAV-enabled Distributed computation Offloading and Power control method with Multi-Agent, named DOPMA, for NOMA-based IoMT environment. Specifically, this paper introduces computation and transmission queue models to analyze the dynamic characteristics of task execution latency and energy consumption. Moreover, a credit assignment scheme-based reward function is designed considering both system-level rewards and rewards tailored to each MD, and an improved multi-agent deep deterministic policy gradient algorithm is developed to derive offloading and power control decisions independently. Extensive simulations demonstrate that the proposed method outperforms existing schemes, achieving \(7.1\%\) reduction in energy consumption and \(16\%\) decrease in average delay.
This book constitutes the refereed proceedings of the 10th information Retrieval Societies Conference, AIRS 2014, held in Kuching, Malaysia, in December 2014. The 42 full papers were carefully reviewed and selected fr...
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ISBN:
(数字)9783319128443
ISBN:
(纸本)9783319128436
This book constitutes the refereed proceedings of the 10th information Retrieval Societies Conference, AIRS 2014, held in Kuching, Malaysia, in December 2014.
The 42 full papers were carefully reviewed and selected from 110 submissions. Seven tracks were the focus of the AIR 2014 and they were IR models and theories; IR evaluation, user study and interactive IR; web IR, scalability and IR in social media; multimedia IR; natural language processing for IR; machine learning and data mining for IR and IR applications.
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