In partial label learning (PLL), every sample is associated with a candidate label set comprising the ground-truth label and several noisy labels. The conventional PLL assumes the noisy labels are randomly generated (instance-independent), while in practical scenarios, the noisy labels are always instance-dependent and are highly related to the sample features, leading to the instance-dependent partial label learning (IDPLL) problem. Instance-dependent noisy label is a double-edged sword. On one side, it may promote model training as the noisy labels can depict the sample to some extent. On the other side, it brings high label ambiguity as the noisy labels are quite undistinguishable from the ground-truth label. To leverage the nuances of IDPLL effectively, for the first time we create class-wise embeddings for each sample, which allow us to explore the relationship of instance-dependent noisy labels, i.e., the class-wise embeddings in the candidate label set should have high similarity, while the class-wise embeddings between the candidate label set and the non-candidate label set should have high dissimilarity. Moreover, to reduce the high label ambiguity, we introduce the concept of class prototypes containing global feature information to disambiguate the candidate label set. Extensive experimental comparisons with twelve methods on six benchmark data sets, including four fine-grained data sets, demonstrate the effectiveness of the proposed method. The code implementation is publicly available at https://github.com/Yangfc-ML/CEL.

This paper addresses the problem of partial multi-label learning (PML), a challenging weakly supervised learning framework, where each sample is associated with a candidate label set comprising both ground-true labels and noisy labels. We theoretically reveal that an increased number of noisy labels in the candidate label set leads to an enlarged generalization error bound, consequently degrading the classification performance. Accordingly, the key to solving PML lies in accurately removing the noisy labels within the candidate label set. To achieve this objective, we leverage prior knowledge about the noisy labels in PML, which suggests that they only exist within the candidate label set and possess binary values. Specifically, we propose a constrained regression model to learn a PML classifier and select the noisy labels. The constraints of the model strictly enforce the location and value of the noisy labels. Simultaneously, the supervision information provided by the candidate label set is unreliable due to the presence of noisy labels. In contrast, the non-candidate labels of a sample precisely indicate the classes to which the sample does not belong. To aid in the selection of noisy labels, we construct a competitive classifier based on the non-candidate labels. The PML classifier and the competitive classifier form a competitive relationship, encouraging mutual learning. We formulate the proposed model as a discrete optimization problem to effectively remove the noisy labels, and we solve it using an alternative algorithm. Extensive experiments conducted on 6 real-world partial multi-label data sets and 7 synthetic data sets, employing various evaluation metrics, demonstrate that our method significantly outperforms state-of-the-art PML methods. The code implementation is publicly available at https://github.com/Yangfc-ML/NLR.

In partial label learning (PLL), each sample is associated with a group of candidate labels, among which only one label is correct. The key of PLL is to disambiguate the candidate label set to find the ground-truth label. To this end, we first construct a constrained regression model to capture the confidence of the candidate labels, and multiply the label confidence matrix by its transpose to build a second-order similarity matrix, whose elements indicate the pairwise similarity relationships of samples globally. Then we develop a semantic dissimilarity matrix by considering the complement of the intersection of the candidate label set, and further propagate the initial dissimilarity relationships to the whole data set by leveraging the local geometric structure of samples. The similarity and dissimilarity matrices form an adversarial relationship, which is further utilized to shrink the solution space of the label confidence matrix and promote the dissimilarity matrix. We finally extend the proposed model to a kernel version to exploit the non-linear structure of samples and solve the proposed model by the inexact augmented Lagrange multiplier method. By exploiting the adversarial prior, the proposed method can significantly outperform state-of-the-art PLL algorithms when evaluated on 10 artificial and 7 real-world partial label data sets. We also prove the effectiveness of our method with some theoretical guarantees. The code is publicly available at https://github.com/Yangfc-ML/DPCLS.

To improve the users satisfaction requesting services in Computing Force Network(CFN), the issues and challenges of existing service scheduling mechanisms are examined firstly in this paper. Then in view of users' specific preference and expectation for services, a service optimization scheduling algorithm based on Bkd-tree is proposed, taking service response time, service scheduling cost, availability, and successability into consideration. The method covers a search algorithm and a reconstructing strategy. To improve the efficiency of selecting the service instances that satisfy user expectation, a pruning strategy is used to narrow search space. To support dynamic updates of the service instances, a reconstructing strategy is proposed. The experimental results show that the improved service scheduling algorithm can schedule service instances with the best overall performance while maintaining higher efficiency.

Computational integrative analysis has become a significant approach in the data-driven exploration of biological problems. Many integration methods for cancer subtyping have been proposed, but evaluating these methods has become a complicated problem due to the lack of gold standards. Moreover, questions of practical importance remain to be addressed regarding the impact of selecting appropriate data types and combinations on the performance of integrative studies. Here, we constructed three classes of benchmarking datasets of nine cancers in TCGA by considering all the eleven combinations of four multi-omics data types. Using these datasets, we conducted a comprehensive evaluation of ten representative integration methods for cancer subtyping in terms of accuracy measured by combining both clustering accuracy and clinical significance, robustness, and computational efficiency. We subsequently investigated the influence of different omics data on cancer subtyping and the effectiveness of their combinations. Refuting the widely held intuition that incorporating more types of omics data always produces better results, our analyses showed that there are situations where integrating more omics data negatively impacts the performance of integration methods. Our analyses also suggested several effective combinations for most cancers under our studies, which may be of particular interest to researchers in omics data analysis.

This paper addresses QoS routing in an integrated network architecture based on the software-defined networking and edge computing technologies. Our study leverages the benefits of edge computing to differentiate data flows of different service types, as well as the programmable features of the software-defined networking to manipulate the forwarding behavior on data flows. Joint QoS-specific factors of latency and packet loss are used to design a novel path selection method for data delivery in such an integrated network. Simulation under heavy traffic patterns shows that our method performs better with lower latency and packet loss rate simultaneously as compared with typical OSPF and some greedy-like routing methods.

Dual-Structural Network (DSN), pioneered by our group for content sharing, is a networking paradigm with the Internet as primary structure and the broadcast-storage network (BSN) as secondary structure. In order to quantitatively evaluate its content sharing capability, in this paper, we generally adopt a deductive methodology, namely that DSN is formalized as a complex network and then its sharing capability is derived according to graph theory. In specific, according to bipartite graph theory, we first construct a bipartite dual-structural network model to obtain an abstract content sharing graph through top-down projection, and then content sharing hops (CSH) in the graph is capitalized as a metric to evaluate the sharing capability between any two content nodes. Furthermore, we leverage the general random graph theory to generate the sharing graph for deriving quantitative upper bounds on average content sharing hops (ACSH) and maximum content sharing hops (MCSH) of DSN. Lastly, the theoretical derivations are validated by numerical simulation. Moreover, compared with content delivery network (CDN), content centric network (CCN) and information-centric mobile ad hoc networks (ICMANET), DSN is demonstrated to be superior in terms of the sharing capability.

Exploiting camera sensors to conduct intruder detection has attracted a lot of research attention. Different from the traditional sensor with omni-directional sensing model, a camera sensor usually has a specified direction with a fixed sensing angle of sensing area. The sensing quality of coverage is critical to the application of coverage scheme. In this paper, we first investigate the complete coverage issue with two alternative layouts of sensing area by 2 camera sensors. Considering the weighted image quality and the importance of sensing area, we propose a quality-enhancing coverage scheme for camera sensor networks, QCC, to improve the coverage performance. We then mathematically present a geometrical probability-based analysis to theoretically evaluate the performance of intruder detection approach. Furthermore, the extension of QCC scheme, QCC-D, is proposed to cover the sensing areas with differentiated importance. Through extensive simulations, our scheme is demonstrated to outperform the best known coverage algorithms, in terms of both overlap area ratio and total weighted quality.

The K Nearest Neighbor (KNN) technique is very simple, highly efficient and effective in the field of text categorization, pattern recognition, object recognition etc. However, its efficiency is challenged by Big Data, which is characterized with volume, variety and velocity. All these characters require a new KNN algorithm with better time complexity, which is lower than O(n) at least Additionally, the characters witness that centralized variants executed in a single thread are no longer reasonable. In this paper, we propose an efficient and scalable strategy, which we called as BALLKNN, for KNN classifier. In BALLKNN, since computation of some samples are reduced by replacing complicated quadratic sum with simple subduction and comparison, time efficiency improvement is obtained; the disadvantage of dependency on global data structure is overcome with the help of temporary variable(s), BALLKNN can be implemented with multiple threads easily, and it scales well with the size of the dataset. Meanwhile, as all samples are handled, the classification accuracy is guaranteed different from traditional sample reduction methods. Furthermore, detailed theoretical analysis on relationship between dimensionality of input and time complexity is provided. Finally, extensive experiments in large real datasets, with tens or hundreds of thousands of records and up to 618 dimensions, have demonstrated the efficiency and scalability of our methods.

Recommendation systems, as efficient measures to handle the information overload and personalized service problems, have attracted considerable attention in research community. Collaborative filtering is one of the most successful techniques based on the user-item matrix in recommendation systems. Usually the matrix is extremely sparse due to the massive number of users and items. And the sparsity of users and items tends to differ significantly in degree. The feature of the matrix changes with the variation of users/items data and hence, leads to poor scalability of the recommendation method. This paper proposes a dynamic-weighted collaborative filtering approach (DWCF) to address sparsity and adaptivity issues. In this approach, the relationship between the distributions of similar users and items is considered to get better recommendation, i.e., the contributions of the user part and the item part to recommendation results depend on their similarity ratios. Moreover, the effect strength of different parts is controlled by an averaging parameter. Experiments on MovieLens dataset illustrate that the DWCF approach proposed in this paper can obtain good recommendation result given different conditions of data sparsity and perform better than a user-based predictor, an item-based predictor and a conventional hybrid approach.

Opportunistic routing with intra-session network coding (NCOR) is a promising communication paradigm in wireless multi-hop networks with lossy links. Unlike traditional routing protocols, which use a single path to route traffic between node pairs, NCOR broadcasts data packets to a set of forwarding candidates. Each candidate combines the overheard packets to generate linearly independent packets, which are then forwarded to the destination. The focus of this paper is on the fundamental problem of how to select the candidate forwarder set (CFS) and how to allocate traffic among candidate forwarders to achieve optimal routing. In current literature, CFS selection and traffic allocation have typically been addressed separately. In this paper, we take an integrated approach and propose a minimum cost NCOR model, MIC-NCOR, which addresses the two aspects of the problem jointly. Based on the optimal substructure of MIC-NCOR, we derive a provable algorithm that can be implemented in a distributed fashion, to compute both the optimal CFS and traffic portion for each candidate. An extensive simulation study indicates that MIC-NCOR accurately predicts the quality of NCOR routes. The simulation results also show that the MIC-NCOR algorithm achieves significant throughput improvement over existing NCOR routing schemes, especially in networks with low NCOR cost and high node density.

Opportunistic routing with network coding (NCOR) has recently emerged as a promising unicast paradigm in lossy wireless multihop networks. By combining the multi-user diversity advantage of the broadcast links and the erasure-codes property of random network coding, it raises an interesting question on the broadcast channel access control algorithm, since the reliability is guaranteed by upper-layer coding and thus it is not necessary to wait for all the receivers to be ready. In this paper, we formalize it as the opportunistic broadcast channel access control problem. By appealing to the theory of optimal stopping, we develop a strategy which can balance between the access delay and instantaneous delivery ability of the broadcast link. This strategy turns out to be a threshold-based policy, which allowing fully distributed implementation. Simulation results show that the proposed strategy outperforms existing schemes significantly in terms of end to end throughput under various traffic loads.

This paper introduces an extensible SOA-based platform that facilitates implementation of various e-collaboration systems. First, it reviews the roadmap of SOA evolution and examines the trend of service orientation in network architecture field, then it constructs a holistic service-oriented platform integrating into Web services, component-based and messaging-based technologies. After analyzing the requirements of collaboration manufacturing (CM), a typical CM model for virtual manufacturing enterprise alliance is presented. It also defines four levels of CM services according to the degree of process-centric. Finally, it illustrates how to dynamically compose a service-oriented application solution using BPEL4WS