Semantic search and composition in unstructured peer-to-peer networks

This dissertation focuses on several research questions in the area of semantic search and composition in unstructured peer-to-peer (P2P) networks. Going beyond the state of the art, the proposed semantic-based search strategy S2P2P offers a novel path-suggestion based query routing mechanism, providing a reasonable tradeoff between search performance and network traffic overhead. In addition, the first semantic-based data replication scheme DSDR is proposed. It enables peers to use semantic information to select replica numbers and target peers to address predicted future demands. With DSDR, k-random search can achieve better precision and recall than it can with a near-optimal non-semantic replication strategy. Further, this thesis introduces a functional automatic semantic service composition method, SPSC. Distinctively, it enables peers to jointly compose complex workflows with high cumulative recall but low network traffic overhead, using heuristic-based bidirectional haining and service memorization mechanisms. Its query branching method helps to handle dead-ends in a pruned search space. SPSC is proved to be sound and a lower bound of is completeness is given. Finally, this thesis presents iRep3D for semantic-index based 3D scene selection in P2P search. Its efficient retrieval scales to answer hybrid queries involving conceptual, functional and geometric aspects. iRep3D outperforms previous representative efforts in terms of search precision and efficiency.Diese Dissertation bearbeitet Forschungsfragen zur semantischen Suche und Komposition in unstrukturierten Peer-to-Peer Netzen(P2P). Die semantische Suchstrategie S2P2P verwendet eine neuartige Methode zur Anfrageweiterleitung basierend auf Pfadvorschlägen, welche den Stand der Wissenschaft übertrifft. Sie bietet angemessene Balance zwischen Suchleistung und Kommunikationsbelastung im Netzwerk. Außerdem wird das erste semantische System zur Datenreplikation genannt DSDR vorgestellt, welche semantische Informationen berücksichtigt vorhergesagten zukünftigen Bedarf optimal im P2P zu decken. Hierdurch erzielt k-random-Suche bessere Präzision und Ausbeute als mit nahezu optimaler nicht-semantischer Replikation. SPSC, ein automatisches Verfahren zur funktional korrekten Komposition semantischer Dienste, ermöglicht es Peers, gemeinsam komplexe Ablaufpläne zu komponieren. Mechanismen zur heuristischen bidirektionalen Verkettung und Rückstellung von Diensten ermöglichen hohe Ausbeute bei geringer Belastung des Netzes. Eine Methode zur Anfrageverzweigung vermeidet das Feststecken in Sackgassen im beschnittenen Suchraum. Beweise zur Korrektheit und unteren Schranke der Vollständigkeit von SPSC sind gegeben. iRep3D ist ein neuer semantischer Selektionsmechanismus für 3D-Modelle in P2P. iRep3D beantwortet effizient hybride Anfragen unter Berücksichtigung konzeptioneller, funktionaler und geometrischer Aspekte. Der Ansatz übertrifft vorherige Arbeiten bezüglich Präzision und Effizienz

Semantic search and composition in unstructured peer-to-peer networks

This dissertation focuses on several research questions in the area of semantic search and composition in unstructured peer-to-peer (P2P) networks. Going beyond the state of the art, the proposed semantic-based search strategy S2P2P offers a novel path-suggestion based query routing mechanism, providing a reasonable tradeoff between search performance and network traffic overhead. In addition, the first semantic-based data replication scheme DSDR is proposed. It enables peers to use semantic information to select replica numbers and target peers to address predicted future demands. With DSDR, k-random search can achieve better precision and recall than it can with a near-optimal non-semantic replication strategy. Further, this thesis introduces a functional automatic semantic service composition method, SPSC. Distinctively, it enables peers to jointly compose complex workflows with high cumulative recall but low network traffic overhead, using heuristic-based bidirectional haining and service memorization mechanisms. Its query branching method helps to handle dead-ends in a pruned search space. SPSC is proved to be sound and a lower bound of is completeness is given. Finally, this thesis presents iRep3D for semantic-index based 3D scene selection in P2P search. Its efficient retrieval scales to answer hybrid queries involving conceptual, functional and geometric aspects. iRep3D outperforms previous representative efforts in terms of search precision and efficiency.Diese Dissertation bearbeitet Forschungsfragen zur semantischen Suche und Komposition in unstrukturierten Peer-to-Peer Netzen(P2P). Die semantische Suchstrategie S2P2P verwendet eine neuartige Methode zur Anfrageweiterleitung basierend auf Pfadvorschlägen, welche den Stand der Wissenschaft übertrifft. Sie bietet angemessene Balance zwischen Suchleistung und Kommunikationsbelastung im Netzwerk. Außerdem wird das erste semantische System zur Datenreplikation genannt DSDR vorgestellt, welche semantische Informationen berücksichtigt vorhergesagten zukünftigen Bedarf optimal im P2P zu decken. Hierdurch erzielt k-random-Suche bessere Präzision und Ausbeute als mit nahezu optimaler nicht-semantischer Replikation. SPSC, ein automatisches Verfahren zur funktional korrekten Komposition semantischer Dienste, ermöglicht es Peers, gemeinsam komplexe Ablaufpläne zu komponieren. Mechanismen zur heuristischen bidirektionalen Verkettung und Rückstellung von Diensten ermöglichen hohe Ausbeute bei geringer Belastung des Netzes. Eine Methode zur Anfrageverzweigung vermeidet das Feststecken in Sackgassen im beschnittenen Suchraum. Beweise zur Korrektheit und unteren Schranke der Vollständigkeit von SPSC sind gegeben. iRep3D ist ein neuer semantischer Selektionsmechanismus für 3D-Modelle in P2P. iRep3D beantwortet effizient hybride Anfragen unter Berücksichtigung konzeptioneller, funktionaler und geometrischer Aspekte. Der Ansatz übertrifft vorherige Arbeiten bezüglich Präzision und Effizienz

Abnormal focal segments in left uncinate fasciculus in adults with obsessive–compulsive disorder

BackgroundAlthough the specific role of the uncinate fasciculus (UF) in emotional processing in patients with obsessive–compulsive disorder (OCD) has been investigated, the exact focal abnormalities in the UF have not been identified. The aim of the current study was to identify focal abnormalities in the white matter (WM) microstructure of the UF and to determine the associations between clinical features and structural neural substrates.MethodsIn total, 71 drug-naïve patients with OCD and 81 age- and sex-matched healthy controls (HCs) were included. Automated fiber quantification (AFQ), a tract-based quantitative approach, was adopted to measure alterations in diffusion parameters, including fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AD), along the trajectory of the UF. Additionally, we utilized partial correlation analyses to explore the relationship between the altered diffusion parameters and clinical characteristics.ResultsOCD patients showed significantly higher FA and lower RD at the level of the temporal and insular portions in the left UF than HCs. In the insular segments of the left UF, increased FA was positively correlated with the Hamilton Anxiety Scale (HAMA) score, while decreased RD was negatively correlated with the duration of illness.ConclusionWe observed specific focal abnormalities in the left UF in adult patients with OCD. Correlations with measures of anxiety and duration of illness underscore the functional importance of the insular portion of left UF disturbance in OCD patients

Agent-Supported Collaboration and Interoperability for Networked Enterprises

Abstract. The paper presents an agent-supported framework for improving solutions for enterprise interoperability and enterprise collaboration. We present the context of COIN in the European research area and explain the basic approach and system architecture COIN is aiming at. Special emphasis is put on how agents can support enterprise interoperability as well as enterprise collaboration services. The framework adopts a modeling approach for the description and execution of business processes. With this a system engineer can describe the interaction protocols that should be used at an intuitive level and transform the model down to executable code. Private partner processes can be integrated using a mapping approach to bridge technical interoperability gaps. Motivation According to the COIN vision, by 2020 enterprise collaboration and interoperability services will become an invisible, pervasive and self-adaptive knowledge and business utility at disposal of networked enterprises from any industrial sector and domain in order to rapidly set-up, efficiently manage and effectively operate different forms of business collaborations, from the most traditionally supply chains to the most advanced and dynamic business ecosystems In the area of business process modeling and enactment, progress has been made to bridge the notorious gap between business and IT. Still, the business perspective itself is split into a value perspective with regards to strategical considerations and a process perspective which concentrates on conceptual modeling of business activities. The latter perspective has received exceptional attention in research and commercial tool development. Recent empirical studies among researchers and practitioners confirmed critical areas of concern: standardization of modeling approaches and model-driven process execution are considered importan

The optimized algorithm based on machine learning for inverse kinematics of two painting robots with non-spherical wrist.

This paper studies the inverse kinematics of two non-spherical wrist configurations of painting robot. The simplest analytical solution of orthogonal wrist configuration is deduced in this paper for the first time. For the oblique wrist configuration, there is no analytical solution for the configuration. So it is necessary to solve by general method, which cannot achieve high precision and high speed as analytic solution. Two general methods are optimized in this paper. Firstly, the elimination method is optimized to reduce the solving speed to 20% of the original one, and the completeness of the method is supplemented. Based on the Gauss damped least squares method, a new optimization method is proposed to improve the solving speed. The enhanced step length coefficient is introduced to conduct studies with the machine learning correlation method. It has been proved that, on the basis of ensuring the stability of motion, the number of iterations can be effectively reduced and the average number of iterations can be less than 5 times, which can effectively improve the speed of solution. In the simulation and experimental environment, it is verified

Two Optimized General Methods for Inverse Kinematics of 6R Robots Based on Machine Learning

For the 6R robot, there is no analytical solution for some configurations, so it is necessary to analyse inverse kinematics (IK) by the general solution method, which cannot achieve high precision and high speed as the analytical solution. With the expansion of application fields and the complexity of application scenarios, some robots with special configuration have become the research hotspot, and more high-speed and high-precision general algorithms are still being explored and studied. The present paper optimized two general solutions. Elimination is a numerical solution, which has high accuracy, but the solution process is complex and time-consuming. The present paper optimized the elimination method, derived the final matrix expression directly through complex coefficient extraction and simplifying operation, and realized one-step solution. The solving speed was reduced to 15% of the original, and the integrity of the method was supplemented. This paper proposed a new optimization method for the Gaussian damped least-squares method, in which the variable step-size coefficient is introduced and the machine learning method is used for the research. It was proved that, on the basis of guaranteeing the stability of motion, the average number of iterations can be effectively reduced and was only 4-5 times, effectively improving the solving speed

Combating QR-Code-Based Compromised Accounts in Mobile Social Networks

Cyber Physical Social Sensing makes mobile social networks (MSNs) popular with users. However, such attacks are rampant as malicious URLs are spread covertly through quick response (QR) codes to control compromised accounts in MSNs to propagate malicious messages. Currently, there are generally two types of methods to identify compromised accounts in MSNs: one type is to analyze the potential threats on wireless access points and the potential threats on handheld devices’ operation systems so as to stop compromised accounts from spreading malicious messages; the other type is to apply the method of detecting compromised accounts in online social networks to MSNs. The above types of methods above focus neither on the problems of MSNs themselves nor on the interaction of sensors’ messages, which leads to the restrictiveness of platforms and the simplification of methods. In order to stop the spreading of compromised accounts in MSNs effectively, the attacks have to be traced to their sources first. Through sensors, users exchange information in MSNs and acquire information by scanning QR codes. Therefore, analyzing the traces of sensor-related information helps to identify the compromised accounts in MSNs. This paper analyzes the diversity of information sending modes of compromised accounts and normal accounts, analyzes the regularity of GPS (Global Positioning System)-based location information, and introduces the concepts of entropy and conditional entropy so as to construct an entropy-based model based on machine learning strategies. To achieve the goal, about 500,000 accounts of Sina Weibo and about 100 million corresponding messages are collected. Through the validation, the accuracy rate of the model is proved to be as high as 87.6%, and the false positive rate is only 3.7%. Meanwhile, the comparative experiments of the feature sets prove that sensor-based location information can be applied to detect the compromised accounts in MSNs

Transient response improvement of half‐bridge LLC resonant converter with full‐bridge rectifier for DC microgrid

Abstract LLC resonant converter has the features of low noise, high efficiency and power density, which is suitable to be integrated into DC microgrid. Due to severe stresses in the resonant tank, achieving fast and safe transient performance has been challenging for resonant converters. With less switch number and voltage stress, half‐bridge LLC resonant converter with full‐bridge rectifier (H‐F LLC resonant converter) are more suitable for high‐voltage application. To improve the transient response of the converter, this paper proposes a method based fixed center state trajectory for the converter to achieve soft start‐up. To reduce dynamic response time during start‐up, each step of fixed center state‐trajectory is designed optimally for the shortest path with symmetrical current limitation. In addition, the proposed method is also applicable to full‐bridge LLC converters. Moreover, according to the state‐plane analysis, load stepping‐up and stepping‐down transient state trajectories with the shortest path are carried out as well. To minimize the adverse effect of digital delay, fast load transient control based on 3D look‐up table is given based on FPGA‐EP3C25E144I7, which would also significantly reduce the computation requirement of the hardware controllers. The experimental results are verified on a 380V/12V LLC converter prototype at 130 kHz