Contrastive learning has been widely applied into sentence representation to obtain high quality embedding vectors through pulling close positive samples and pushing apart irrelevant negative samples. Subsequently, various knowledge distillation methods have been employed to enhance the performance of contrastive learning framework. However, current distillation techniques focus on transferring knowledge through similarity of embedding vectors in a mini-batch, thus ignoring the relational structural information of them. To address this, we propose a novel framework, RKDCSE(Relational Knowledge Distillation Contrastive learning of Sentence Embedding) for sentence representation learning, designed to transferring structural information between sentences from teacher model to student model. We also introduce a random features masking mechanism, which mitigates the issues of overfitting via masking features in embedding vectors randomly. Finally, we conduct extensive experiments on Semantic Textual Similarity(STS) and Transfer Tasks(TR). The experimental results demonstrate that our proposed RKDCSE is effective and outperforms baseline method.

This work focuses on enhancing the generalization performance of deep reinforcement learning-based robot navigation in unseen environments. We present a novel data augmentation approach called scenario augmentation, which enables robots to navigate effectively across diverse settings without altering the training scenario. The method operates by mapping the robot’s observation into an imagined space, generating an imagined action based on this transformed observation, and then remapping this action back to the real action executed in simulation. Through scenario augmentation, we conduct extensive comparative experiments to investigate the underlying causes of suboptimal navigation behaviors in unseen environments. Our analysis indicates that limited training scenarios represent the primary factor behind these undesired behaviors. Experimental results confirm that scenario augmentation substantially enhances the generalization capabilities of deep reinforcement learning-based navigation systems. The improved navigation framework demonstrates exceptional performance by producing near-optimal trajectories with significantly reduced navigation time in real-world applications.

Summary Web services (or Web APIs) on the Internet tends to encounter various unexpected runtime failures because of their dynamicity and distribution. Self‐adaptation technologies for the service‐based business process can effectively repair runtime failures and improve its success rate. However, the same failures may occur on subsequent invocations because relevant processes do not evolve after failures. This makes the response time of the business processes too long. We proposed a self‐adaptation and distributed knowledge‐based evolution model (SDKEM) to guarantee business processes' stabilities, that is, low failure rates and stable response time. SDKEM adopts a service knowledge base (SKB) to organize services from a provider and uses bridge rules to eliminate semantic conflicts among multiple distributed SKBs. It can automatically trigger the evolution of a service ecosystem through the designed self‐adaptation mechanism. We adopt the “survival of the fittest” principle for crucial elements in the ecosystem during evolution so that ultimately, service‐based processes and services with high stability remain. Experiments show that, with the developed evolution mechanism, runtime failures of business processes significantly reduce. In most cases, their response time and success rates are comparable to those under the running situation where no runtime failure occurs, meaning the runtime failures within a service‐based process are automatically repaired.

In this paper, the design of multichannel noise reduction filters in the short-time-Fourier-transform (STFT) domain is addressed. By investigating the structure of the linear filter, a set of multichannel iterative noise reduction filters are developed based on the Kronecker product decomposition. Instead of computing a long noise reduction filter, we compute three much shorter sub-filters which are separately applied in the spatial, temporal and frequency dimensions. Consequently, compared with the traditional multichannel STFT-domain noise reduction filters, the proposed approaches have two advantages: 1) significantly lower computational complexity; 2) less past observations are needed to construct the iterative filters, which leads to better tracking ability for the temporal/spatial signal nonstationarity. Experimental results demonstrate the advantages of the developed iterative filters over the traditional ones.

In this letter, we develop an approach to the design of beamformers with small-spacing uniform linear microphone arrays by incorporating sparseness constraints for attenuating scattered interference incident from some pre-specified ranges of directions of arrival. The design process is formulated as a constrained LASSO problem. By adjusting the value of a tuning parameter, the proposed method can make compromises among three important yet conflicting (especially at low frequencies) performance measures of small-spacing microphone arrays, i.e., the directivity factor (DF), which quantifies the array spatial gain, the white noise gain (WNG), which evaluates the robustness of the beamformer, and the signal-to-interference-ratio (SIR) gain with respect to scattered interference. Simulation results illustrate the properties of the developed approach.

This paper is devoted to beamforming with small-spacing microphone arrays for processing broadband and scattered acoustic sources. It presents a maximum diffuse noise gain (MDNG) beamformer in this context using the joint diagonalization technique, which is effective in suppressing diffuse and directional noise, but at a price of low white noise gain (WNG). We also introduce a maximum WNG (MWNG) beamformer, which is robust to the array imperfections, but paying a price of sacrificing the diffuse noise gain (DNG). To make a tradeoff between WNG and DNG so that the beamformer, on the one hand, can achieve high directivity and, on the other hand, is robust to implement, we propose a generalized MDNG beamformer, which includes both the MDNG and MWNG beamformers as particular cases. Simulations are conducted to illustrate the properties and advantages of the proposed beamformers.

With the popularity of cloud computing and micro service architectures, various service ecosystems including services, venders, and service-based processes continuously emerge on Internet or in an enterprise. Semantics of services from different venders may be described by distributed domain ontologies. Distributed knowledge brings difficulty to competition and cooperation among services, and hampers the evolution of a service ecosystem. In this paper, we propose a distributed knowledge based evolution model (DKEM) to promote competition and cooperation among services from different venders. DKEM considers stability as key factor in competition, and a stability evaluation model is designed to compute stability of services, venders, and service-based processes according to service invocation histories. Based on the evaluation model, two evolution patterns are given, and they can automatically explore new and more stable cooperation among services by means of runtime self-adaption mechanism. A prototype system for DKEM is implemented and a series of experiments show that DKEM is effective for competition and cooperation among services with distributed knowledge, and, evolved processes have higher stability and response efficiency.

This paper deals with the problem of single-channel noise reduction. Thanks to the eigenvalue decomposition, we arrange the eigenvalues of the speech correlation matrix in such a way that all the spectral mode signal-to-noise ratios (SNRs) of the noisy speech are ordered in a descending manner. By maintaining no speech distortion in the spectral modes with high input SNRs while allowing some degree of speech distortion in the modes with low input SNRs, we develop a minimum variance partially distortionless response (MVPDR) filter. We first formulate the problem and derive this filter within the general filtering framework. Then, the MVPDR filter is applied to the single-channel noise reduction problem in both the time and time-frequency domains. In comparison with the minimum variance distortionless response (MVDR) filter based on the subspace decomposition, the developed MVPDR filter can provide much more freedom for controlling the compromise between noise reduction and speech distortion to achieve higher speech quality. Simulations are conducted and preliminary results justify the advantages of the deduced MVPDR filter.

Summary For service‐based process, there is a frequent failure when it runs under a loose‐coupled and uncertain environment, such as mobile Internet. The runtime self‐adaptation mechanism is urgently needed to automatically achieve the goal of a process. Therefore, this article presents a self‐adaptation framework to automatically monitor and self‐adapt to various failures. On the basis of the presented self‐adaptation exceptions framework, the service‐based process is automatically extended with these exception definitions. In particular, the local self‐adaptation exceptions monitor the failures over each web service instance, whereas the global self‐adaptation exceptions are designed to explore the process‐level failures. For each exception, the self‐adaptation strategy is dynamically generated according to the running environment. Finally, we implement the prototype system running on the Apache ODE. The experimental results show that our framework can automatically recover a process instance from various failures, and the proposed approach can identify more failure types than previous works and apparently improve the running success rate of process instances.

This paper develops a single-channel noise cancelation filter in the short-time Fourier transform (STFT) domain by combining the subspace method and the optimal filtering technique via joint diagonalization of the desired clean speech and noise signal correlation matrices. This filter is shown to be flexible in controlling the compromise between the output signal-to-noise ratio (oSNR) and the amount of speech distortion. Simulations are performed to justify the property of this filter.

AHP (Analytic Hierarchy Process) is used to make decision in many fields. There are many tasks to be completed artificially in an AHP evaluation. Traditional AHP evaluation systems rarely support collaborative evaluation among many experts and the decision maker. The paper presents the design and implementation of a collaboration-based AHP evaluation system. In its architecture, workflow technology is used to implement collaboration among different experts, MVC framework based on web is adopted to organize modules, and Ajax technology is used to provide friend user interface. Based on the architecture, a prototype is developed, using Struts2, jBPM and jQuery.

Information Model is one of the important models that describe the Virtual Enterprise in order to construct and manage the VE (Virtual Enterprise). In the paper, a method of VE building is discussed in the view of information modeling. Then it is proposed how to build an organization model of cross domain and how to construct an information model of multi-domain of VE. Based on the information model, the service-oriented operating mode and the service-oriented function model of VE are put forward.

In collaborative environment, to complete a business often needs cross-organizational cooperation. Traditional workflow modeling methods cannot express this kind of cooperation completely. Furthermore, current workflow systems lack adaptability and flexibility. Firstly, this paper made use of the basic cooperative unit and virtual cooperative unit to model cooperation within one organization or among organizations. Then, it used knowledge related to them to express the cooperation. After that, a collaborative process based on cooperative units meta-model was proposed. On the basis of the meta-model, a knowledge-based adaptive collaborative work system implementation framework was designed. In this framework, knowledge management for cooperative units was involved, which give collaborative process better adaptability and flexibility.