Ill-posed problems are widely existed in signal processing. In this paper, we review popular regularization models such as truncated singular value decomposition regularization, iterative regularization, variational regularization. Meanwhile, we also retrospect popular optimization approaches and regularization parameter choice methods. In fact, the regularization problem is inherently a multiobjective problem. The traditional methods usually combine the fidelity term and the regularization term into a singleobjective with regularization parameters, which are difficult to tune. Therefore, we propose a multi-objective framework for ill-posed problems, which can handle complex features of problem such as non-convexity, discontinuity. In this framework, the fidelity term and regularization term are optimized simultaneously to gain more insights into the ill-posed problems. A case study on signal recovery shows the effectiveness of the multi-objective framework for ill-posed problems.
In the past decade, the remarkable development of high-throughput sequencing technology accelerates the generation of large amount of multiple dimensional data such as genomic, epigenomic, transcriptomic and proteomic data. The comprehensive data make it possible to understand the underlying mechanisms of biology and disease such as cancer systematically. It also provides great challenges for computational cancer genomics due to the complexity, scale and noise of data. In this article, we aim to review the recent developments and progresses of computational models, algorithms and analysis of complex data in cancer genomics. These topics of this paper include the identification of driver mutations, the genetic heterogeneity analysis, genomic markers discovery of drug response, pan-cancer scale analysis and so on.
Beamforming using sensor array is widely used in spatial signal processing since it offers better spatial focusing capability than single sensor. However, in practical applications for broadband signal, there always exists a trade-off issue between the directivity capability of an array and its robustness on system errors. In this paper, in order to combine merits of different beamformers instead of trade-off their performances, we propose a constrained minimum-power combination method. We firstly analyze two optimal beamformers that maximize Directivity Factor (DF) and White Noise Gain (WNG) respectively. Then we propose a non-linear combination method, which automatically selects the best beamformer that has the minimum output power, so as to control the unwanted white noise amplification and keep the maximum DF if possible. Two solutions to the proposed combination strategy are given. They do not need to determine the correct trade-off factor used in linear combination method, and avoid challenge estimations on noise and target statistics required in adaptive beamforming. The performance of the proposed beamformer is evaluated in ideal noise fields and complicated noise fields respectively. It is shown that the proposed beamformer integrates merits of different beamformers. It always achieves the best speech quality and biggest noise reduction compared to other popular beamformers.
3D audio effects can provide immersive auditory experience, but we often face the so-called in-head localization (IHL) problem in headphone sound reproduction. To address this problem, we propose an effective sound image externalization approach. Specifically, we consider several important factors related to sound propagation, which include image-source model based early reflections with distance decay, wall absorption and air absorption, late reverberation and other dynamic factors like head movement. We apply our sound image externalization approach to a headphone based real-time 3D audio system. Subjective listening tests show that the sound image externalization performance is significantly improved and the sound source direction is preserved as well. A/B preference test further shows that, as compared with a recent popular approach, the proposed approach is mostly preferred by the listeners.
This work presents a spoken dialog summarization system with HAPPINESS/SUFFERING factor recognition. The semantic content is compressed and classified by factor categories from spoken dialog. The transcription of automatic speech recognition is then processed through Chinese Knowledge and Information Processing segmentation system. The proposed system also adopts the part-of-speech tags to effectively select and rank the keywords. Finally, the HAPPINESS/SUFFERING factor recognition is done by the proposed point-wise mutual information. Compared with the original method, the performance is improved by applying the significant scores of keywords. The experimental results show that the average precision rate for factor recognition in outside test can reach 73.5% which demonstrates the possibility and potential of the proposed system.
Architecture-level business services are identified based on business processes; and likewise, in serviceoriented product lines, identifying the domain architecturelevel business services and their variability is preferred to be based on business processes and their variability. Identification of business services for a product line satisfying a set of given design metrics (such as cohesion and coupling) is extremely difficult for a domain architect, since there are many product configurations for which the services must be proper at the same time. This means that the identified services must have proper values for n metrics in m different configurations at the same time. The problem becomes more serious when there are high degrees of variability and complexity embedded in the business processes that are the basis for service identification.We contribute to solve the multi-objective optimization problem of identifying business services for a product line by partitioning the graph of a business process variability model utilizing Non-dominated Sorting Genetic Algorithm-II. The service specification is achieved based on the results of the partitioning. The variability of the services is then determined in terms of mandatory and optional services as well as variability relationships, which are all represented in a Service Variability Model. The method was empirically evaluated through experimentation, and showed proper levels of reusability and variability. Furthermore, the resulting models were fully consistent.
Software-as-a-Service (SaaS) introduces multitenancy architecture (MTA). Sub-tenancy architecture (STA), is an extension of MTA, allows tenants to offer services for subtenant developers to customize their applications in the SaaS infrastructure. In a STA system, tenants can create subtenants, and grant their resources (including private services and data) to their subtenants. The isolation and sharing relations between parent-child tenants, sibling tenants or two non-related tenants are more complicated than those between tenants in MTA. It is important to keep service components or data private, and at the same time, allow them to be shared, and support application customizations for tenants. To address this problem, this paper provides a formal definition of a new tenant-based access control model based on administrative role-based access control (ARBAC) forMTA and STA in service-oriented SaaS (called TMS-ARBAC). Autonomous areas (AA) and AA-tree are proposed to describe the autonomy of tenants, including their isolation and sharing relationships. Authorization operations on AA and different resource sharing strategies are defined to create and deploy the access control scheme in STA models. TMS-ARBAC model is applied to design a geographic e-Science platform.
The defects in object-oriented models will result in poor quality of applications based on the models, and thus it is necessary to know which defects often occur in practice, to what extent they occur, why they occur, and how they can be prevented. To gain deeper insights into these problems, this paper discusses how to improve the quality of objectoriented models from novice modelers through project practice. This paper summarizes a set of typical quality defect types from a large number of the defects, and confirms them through our project practice. Moreover, the paper analyzes the improvement of the quality of object-oriented models by quantifying the level of occurrence for the defect types in different phases of the project practice, and presents preventive measures by analyzing the causes for the defects to occur in object-oriented models in the aspects of syntax, semantics, and pragmatics.
Multi-agent systems (MAS) have received extensive studies in the last decade. However, little attention is paid to investigation on reasoning about logics in MAS with hierarchical structures. This paper proposes a complete quantified temporal KBC (knowledge, belief and certainty) logic and corresponding reasoning in hierarchical multi-agent systems (HMAS). The key point is that internal beliefs and certainty, and external belief and certainty are considered in our logic. The internal beliefs and certainty show every agent is autonomous, while the external belief and certainty indicate the mutual influence of mental attitudes between two different agents on different layers in HMAS. To interpret this logic, we propose four classes of corresponding quantified interpreted systems, and define first-order KBC axiomatisations over HMAS, which are sound and complete with respect to the corresponding semantical classes. Finally, we give a case study to show the advantages in terms of expressiveness of our logic.
Two traditional recommendation techniques, content-based and collaborative filtering (CF), have been widely used in a broad range of domain areas. Both methods have their advantages and disadvantages, and some of the defects can be resolved by integrating both techniques in a hybrid model to improve the quality of the recommendation. In this article, we will present a problem-oriented approach to design a hybrid immunizing solution for job recommendation problem from applicant’s perspective. The proposed approach aims to recommend the best chances of opening jobs to the applicant who searches for job. It combines the artificial immune system (AIS), which has a powerful exploration capability in polynomial time, with the collaborative filtering, which can exploit the neighbors’ interests. We will discuss the design issues, as well as the hybridization process that should be applied to the problem. Finally, experimental studies are conducted and the results show the importance of our approach for solving the job recommendation problem.
Satisfiability problem of authorization requirements in business process asks whether there exists an assignment of users to tasks that satisfies all the requirements, and methods were proposed to solve this problem. However, the proposed methods are inefficient in the sense that a step of the methods is searching all the possible assignments, which is time-consuming. This work proposes a method to solve the satisfiability problem of authorization requirements without browsing the assignments space. Our method uses improved separation of duty algebra (ISoDA) to describe a satisfiability problem of qualification requirements and quantification requirements (Separation of Duty and Binding of Duty requirements). Thereafter, ISoDA expressions are reduced into multi-mutual-exclusive expressions. The satisfiabilities of multi-mutual-exclusive expressions are determined by an efficient algorithm proposed in this study. The experiment shows that our method is faster than the state-of-the-art methods.
Interaction detection in large-scale genetic association studies has attracted intensive research interest, since many diseases have complex traits. Various approaches have been developed for finding significant genetic interactions. In this article, we propose a novel framework SRMiner to detect interacting susceptible and protective genotype patterns. SRMiner can discover not only probable combination of single nucleotide polymorphisms (SNPs) causing diseases but also the corresponding SNPs suppressing their pathogenic functions, which provides a better prospective to uncover the underlying relevance between genetic variants and complex diseases. We have performed extensive experiments on several real Wellcome Trust Case Control Consortium (WTCCC) datasets. We use the pathway-based and the protein-protein interaction (PPI) network-based evaluation methods to verify the discovered patterns. The results show that SRMiner successfully identifies many disease-related genes verified by the existing work. Furthermore, SRMiner can also infer some uncomfirmed but highly possible disease-related genes.