In order to fully utilize lesion features and vascular structure and solve the problem of class imbalance, diabetes retinopathy (DR) grading is modeled as a dual-stage task, and the prior-guided dual-stage diabetes re...
详细信息
In description logic,axiom pinpointing is used to explore defects in ontologies and identify hidden justifications for a logical *** recent years,SAT-based axiom pinpointing techniques,which rely on the enumeration of...
详细信息
In description logic,axiom pinpointing is used to explore defects in ontologies and identify hidden justifications for a logical *** recent years,SAT-based axiom pinpointing techniques,which rely on the enumeration of minimal unsatisfiable subsets(MUSes)of pinpointing formulas,have gained increasing *** with traditional Tableau-based reasoning approaches,SAT-based techniques are more competitive when computing justifications for consequences in large-scale lightweight description logic *** this article,we propose a novel enumeration justification algorithm,working with a replicated *** replicated driver discovers new justifications from the explored justifications through cheap literals resolution,which avoids frequent calls of SAT ***,when the use of SAT solver is inevitable,we adjust the strategies and heuristic parameters of the built-in SAT solver of axiom pinpointing *** adjusted SAT solver is able to improve the checking efficiency of unexplored *** proposed method is implemented as a tool named *** experimental results show that RDMinA outperforms the existing axiom pinpointing tools on practical biomedical ontologies such as Gene,Galen,NCI and Snomed-CT.
Signed network is an important kind of complex network, which includes both positive relations and negative relations. Communities of a signed network are defined as the groups of vertices, within which positive relat...
详细信息
Signed network is an important kind of complex network, which includes both positive relations and negative relations. Communities of a signed network are defined as the groups of vertices, within which positive relations are dense and between which negative relations are also dense. Being able to identify communities of signed networks is helpful for analysis of such networks. Hitherto many algorithms for detecting network communities have been developed. However, most of them are designed exclusively for the networks including only positive relations and are not suitable for signed networks. So the problem of mining communities of signed networks quickly and correctly has not been solved satisfactorily. In this paper, we propose a heuristic algorithm to address this issue. Compared with major existing methods, our approach has three distinct features. First, it is very fast with a roughly linear time with respect to network size. Second, it exhibits a good clustering capability and especially can work well with complex networks without well-defined community structures. Finally, it is insensitive to its built-in parameters and requires no prior knowledge.
Explainable recommendation, which can provide reasonable explanations for recommendations, is increasingly important in many fields. Although traditional embedding-based models can learn many implicit features, result...
详细信息
Explainable recommendation, which can provide reasonable explanations for recommendations, is increasingly important in many fields. Although traditional embedding-based models can learn many implicit features, resulting in good performance, they cannot provide the reason for their recommendations. Existing explainable recommender methods can be mainly divided into two types. The first type models highlight reviews written by users to provide an explanation. For the second type, attribute information is taken into consideration. These approaches only consider one aspect and do not make the best use of the existing information. In this paper, we propose a novel neural explainable recommender model based on attributes and reviews (NERAR) for recommendation that combines the processing of attribute features and review features. We employ a tree-based model to extract and learn attribute features from auxiliary information, and then we use a time-aware gated recurrent unit (T-GRU) to model user review features and process item review features based on a convolutional neural network (CNN). Extensive experiments on Amazon datasets demonstrate that our model outperforms the state-of-the-art recommendation models in accuracy of recommendations. The presented examples also show that our model can offer more reasonable explanations. Crowd-sourcing based evaluations are conducted to verify our model's superiority in explainability.
We consider a discrete model that describes a linear chain of particles coupled to an isolated ring composed of N defects. This simple system can be regarded as a generalization of the familiar Fano Anderson model. It...
详细信息
We consider a discrete model that describes a linear chain of particles coupled to an isolated ring composed of N defects. This simple system can be regarded as a generalization of the familiar Fano Anderson model. It can be used to model discrete networks of coupled defect modes in photonic crystals and simple waveguide arrays in two-dimensional lattices. The analytical result of the transmission coefficient is obtained, along with the conditions for perfect reflections and transmissions due to either destructive or constructive interferences. Using a simple example, we further investigate the relationship between the resonant frequencies and the number of defects N, and study how to affect the numbers of perfect reflections and transmissions. In addition, we demonstrate how these resonance transmissions and refections can be tuned by one nonlinear defect of the network that possesses a nonlinear Kerr-like response.
We have investigated theoretically the field-driven electron transport through a single-quantum-well semiconductor heterostructure with spin-orbit coupling. The splitting of the asymmetric Fano-type resonance peaks du...
详细信息
We have investigated theoretically the field-driven electron transport through a single-quantum-well semiconductor heterostructure with spin-orbit coupling. The splitting of the asymmetric Fano-type resonance peaks due to the Dresselhaus spin-orbit coupling is found to be highly sensitive to the direction of the incident electron. The splitting of the Fano-type resonance induces the spin-polarization dependent electron current. The location and the line shape of the Fano-type resonance can be controlled by adjusting the energy and the direction of the incident electron, the oscillation frequency, and the amplitude of the external field. These interesting features may be used to devise tunable spin filters and realize pure spin transmission currents.
Modal logics are good candidates for a formal theory of agents. The efficiency of reasoning method in modal logics is very important, because it determines whether or not the reasoning method can be widely used in sys...
详细信息
Modal logics are good candidates for a formal theory of agents. The efficiency of reasoning method in modal logics is very important, because it determines whether or not the reasoning method can be widely used in systems based on agent. In this paper, we modify the extension rule theorem proving method we presented before, and then apply it to P-logic that is translated from modal logic by functional transformation. At last, we give the proof of its soundness and completeness.
Model-based diagnosis(MBD)with multiple observations shows its significance in identifying fault *** existing approaches for MBD with multiple observations use observations which is inconsistent with the prediction of...
详细信息
Model-based diagnosis(MBD)with multiple observations shows its significance in identifying fault *** existing approaches for MBD with multiple observations use observations which is inconsistent with the prediction of the *** this paper,we proposed a novel diagnosis approach,namely,the Diagnosis with Different Observations(DiagDO),to exploit the diagnosis when given a set of pseudo normal observations and a set of abnormal *** ideas are proposed in this ***,for each pseudo normal observation,we propagate the value of system inputs and gain fanin-free edges to shrink the size of possible faulty ***,for each abnormal observation,we utilize filtered nodes to seek surely normal ***,we encode all the surely normal components and parts of dominated components into hard clauses and compute diagnosis using the MaxSAT solver and MCS *** tests on the ISCAS'85 and ITC'99 benchmarks show that our approach performs better than the state-of-the-art algorithms.
Locomotor intent classification has become a research hotspot due to its importance to the development of assistive robotics and wearable *** work have achieved impressive performance in classifying steady locomotion ...
详细信息
Locomotor intent classification has become a research hotspot due to its importance to the development of assistive robotics and wearable *** work have achieved impressive performance in classifying steady locomotion ***,it remains challenging for these methods to attain high accuracy when facing transitions between steady locomotion *** to the similarities between the information of the transitions and their adjacent steady ***,most of these methods rely solely on data and overlook the objective laws between physical activities,resulting in lower accuracy,particularly when encountering complex locomotion modes such as *** address the existing deficiencies,we propose the locomotion rule embedding long short-term memory(LSTM)network with Attention(LREAL)for human locomotor intent classification,with a particular focus on transitions,using data from fewer sensors(two inertial measurement units and four goniometers).The LREAL network consists of two levels:One responsible for distinguishing between steady states and transitions,and the other for the accurate identification of locomotor *** classifier in these levels is composed of multiple-LSTM layers and an attention *** introduce real-world motion rules and apply constraints to the network,a prior knowledge was added to the network via a rule-modulating *** method was tested on the ENABL3S dataset,which contains continuous locomotion date for seven steady and twelve transitions *** results showed that the LREAL network could recognize locomotor intents with an average accuracy of 99.03%and 96.52%for the steady and transitions states,*** is worth noting that the LREAL network accuracy for transition-state recognition improved by 0.18%compared to other state-of-the-art network,while using data from fewer sensors.
Community structure is an important property of network. Being able to identify communities can provide invaluable help in exploiting and understanding both social and non-social networks. Several algorithms have been...
详细信息
Community structure is an important property of network. Being able to identify communities can provide invaluable help in exploiting and understanding both social and non-social networks. Several algorithms have been developed up till now. However, all these algorithms can work well only with small or moderate networks with vertexes of order 104. Besides, all the existing algorithms are off-line and cannot work well with highly dynamic networks such as web, in which web pages are updated frequently. When an already clustered network is updated, the entire network including original and incremental parts has to be recalculated, even though only slight changes are involved. To address this problem, an incremental algorithm is proposed, which allows for mining community structure in large-scale and dynamic networks. Based on the community structure detected previously, the algorithm takes little time to reclassify the entire network including both the original and incremental parts. Furthermore, the algorithm is faster than most of the existing algorithms such as Girvan and Newman's algorithm and its improved versions. Also, the algorithm can help to visualize these community structures in network and provide a new approach to research on the evolving process of dynamic networks.
暂无评论