The surge in online interactions and advancement in Big Data related techniques have generated vast amounts of hybrid data in the sense that the data are symbolic, numerical or missing features, and usually only a small number of data objects possess true labels due to high annotation costs. A necessary step of fully releasing the potential of these partially labeled hybrid data lies in feature selection, for which the neighborhood rough set (NRS) is an efficient mathematical method to apply. In NRS, setting proper neighborhood granules greatly influences the effectiveness and robustness of algorithms atop it. However, existing methods usually determine the optimal neighborhood radius of neighborhood granule via computationally intensive grid search, where the neighborhood radius for each object is the same, i.e., “unadaptive”. Some methods investigate adaptive granulation strategies, yet they inevitably hinge on preset parameters or a-prior knowledge. To tackle this problem, we propose an adaptive granules-enabled semi-supervised feature selection method that can adaptively generate suitable neighborhood radii for both labeled and unlabeled objects. The core idea lies in using the purity of decision labels as the threshold for granularity maximization construction. Then, by combining with neighborhood entropy and local density, a feature metric is designed to measure the feature significance. A semi-supervised feature selection algorithm is utilized to select feature subset by using the information from both labeled and unlabeled objects. Instead of hinging on expert knowledge, the proposed method only rely on the data per se. Experimental results on real-world datasets demonstrate the effectiveness of the designed method and its superiority over other state-of-the-art.

Microservice architecture has demonstrated immense advantages in solving the scalability and maintainability problems confronted by traditional monolithic systems. However, existing microservice architectures still encounter the problem of blurred boundaries, which significantly impact the system performance. The primary reason being the lack of unified evaluation indicators and efficient microservice splitting strategies. Specifically, some approaches divide entities like classes subjectively, resulting in inconsistent microservice boundaries, while others utilize objective criteria, but their complexity makes practical implementation challenging. To this end, we formalize the optimal microservice granulation problem, show its NPcompleteness and propose the granulation solution based on granular-ball computing. Our designs encompass a microservicebased information decision system to quantify the structural similarity between entities, a microservice granulation representation method based on granular-ball computing, and an optimal microservice granulation method given fixed budget constraint. Extensive experiments are conducted on open-source microservice projects to showcase the granulation result and the experimental results also show the efficacy and effectiveness.

Fine-grained image retrieval (FGIR) is a crucial task in computer vision, with broad applications in areas such as biodiversity monitoring, e-commerce, and medical diagnostics. However, capturing discriminative feature information to generate binary codes is difficult because of high intraclass variance and low interclass variance. To address this challenge, we (i) build a novel and highly reliable fine-grained deep hash learning framework for more accurate retrieval of fine-grained images. (ii) We propose a part significant region erasure method that forces the network to generate compact binary codes. (iii) We introduce a CNN-guided Transformer structure for use in fine-grained retrieval tasks to capture fine-grained images effectively in contextual feature relationships to mine more discriminative regional features. (iv) A multistage mixture loss is designed to optimize network training and enhance feature representation. Experiments were conducted on three publicly available fine-grained datasets. The results show that our method effectively improves the performance of fine-grained image retrieval.

Image anomaly detection and localization have received widespread interest in the community, and knowledge distillation (KD) has been widely explored. Recently, the reverse distillation (RD) paradigm has successfully mitigated the homogenization of anomaly representations in traditional KD arising from identical or similar teacher-student (T-S) architecture. However, in RD, the lack of an effective means to prevent anomalous patterns in the teacher encoder from being leaked into the student decoder undermines potential modeling discrepancies between the T-S model in anomaly representations. To settle this problem, we proposeREverse distillation with latent Anomaly SuppressiON(REASON) method, preventing the student decoder from receiving anomalous patterns by extra means of anomaly filtering during the inference phase, and thus, the student model can only restore representations of anomaly-free images. Specifically, we construct a Siamese teacher encoder architecture, with one branch extracting features from anomaly-free samples and the other synthesizing anomaly features with spatial noise injection from the latent feature level. Next, we design a latent anomaly suppression module to recover normal features from perturbed anomalous features. In this sense, the follow-up student decoder will receive input without abnormal patterns. Thus, representations of the anomaly-free images can be described well, while those of the anomalous images can be well-differentiated between the T-S model. Furthermore, to enhance the model’s anomaly detection and localization capabilities, we propose multi-granularity KD loss to optimize the student decoder to focus on context and local details. Extensive experiments are performed on three benchmark datasets, i.e., MVTec AD, AeBAD, and OCT2017, and the results show the effectiveness and robustness of our proposed approach, which achieves superiority over the current state-of-the-art methods.

Time series are widely used in many classification and regression tasks. However, numerous time series contain unavoidable missing data, making it challenging to model the temporal dynamics of sequential data. Various data imputation methods have been proposed to infer missing values in time series. Although sequences recorded at fixed time intervals are presented in discrete form, they possess an inherent temporal continuity, which is ignored in most existing approaches. In this paper, we propose an end-to-end Attentive Continuous-Time Generative Adversarial Network (ACGANet) to estimate unobserved values in irregular sequences. ACGANet captures the temporal dynamics by transforming the discrete sequence into the continuous-time flow, thereby modeling the underlying distribution of the real data. Furthermore, ACGANet employs an adversarial learning strategy to alleviate the error introduced by imputed values, with the discriminator distinguishing between real and generated samples. Additionally, ACGANet introduces the log-density of hidden temporal states as an auxiliary loss to further optimize the generator. This allows the model to simultaneously focus on the overall temporal dynamics of the time series and the underlying distribution of the missing data. Extensive experiments on three publicly available real-world datasets demonstrate that ACGANet achieves state-of-the-art performance in imputing incomplete time series. Moreover, both qualitative and quantitative analyses validate the effectiveness of the proposed model.

Peer-to-peer (P2P) content delivery networks (CDNs) have demonstrated immense potential to mitigate escalating network traffic. Meanwhile, blockchain emerges as a promising technology that can overcome the shortcomings confronted by P2P CDNs via acting as a trusted third party (TTP) to ensure critical security properties such as fairness and offering monetary incentivization. However, existing protocols for blockchain-based P2P CDNs still encounter the delivery efficiency issue, one of the primary reasons being the absence or failure to identify the most suitable deliverers. Specifically, some overlook the process of selecting deliverers, entrusting them haphazardly, and some merely consider a single evaluation dimension for a deliverer, resulting in a lack of comprehensiveness. Even when multiple dimensions are considered, the outcome of each dimension is not verifiable, leading to unreliability. To this end, we propose an optimized deliverer selection method in blockchain-based P2P CDNs. Our designs encompass a proof of delivery quality (PoQD) protocol to quantify the accumulative verifiable contributions of a deliverer, a comprehensive credibility evaluation mechanism based on neighborhood entropy, and a provider budget constraint optimized deliverer selection algorithm. Extensive experiments are conducted on Ethereum test network to justify our adaptive selection of k optimized deliverers, desired delivery efficiency and feasible on-chain costs. The experiment results indicate the efficacy and effectiveness of our proposed method.

Smart contracts are tailored software services that provide consistency and autonomy. The emergence of blockchain has powerfully facilitated the development of smart contracts but also brought dramatic challenges to their security and trustwor-thiness. Plenty of malicious traps are hidden in smart contracts, causing irreversible damage and obstructing the progress of this technology. Although researchers have gradually emphasized the identification of trap contracts, existing approaches suffer from a few concerns, viz., the shortage of an efficient detection model, the unbalanced categories of trap contracts, and the absence of a high-quality dataset with multi-trap contracts. In this paper, we propose an architecture called TrapFormer to intelligently detect trap contracts in the blockchain solely by leveraging the opcodes of smart contracts. We introduce a densely connected transformer that can segmentally extract opcode features and thus distinguish any potential traps. Furthermore, we implement an adaptive data augmentation method to alleviate the category imbalance of trap contracts. To demonstrate the feasibility of the proposed solution, we construct a multi-trap contract dataset from Ethereum. The experimental results reveal that the proposed solution can achieve superior performance for practical use.

Peer-to-peer (P2P) content delivery is up-and-coming to provide benefits comprising cost-saving and scalable peak-demand handling compared with centralized content delivery networks (CDNs), and also complementary to the popular decentralized storage networks such as Filecoin. However, reliable P2P delivery demands proper enforcement of delivery fairness, i.e., the deliverers should be rewarded in line with their in-time delivery. Unfortunately, most existing studies on delivery fairness are on the basis of non-cooperative game-theoretic assumptions that are arguably unrealistic in the ad-hoc P2P setting. We propose an expressive yet still minimalist security requirement for desired fair P2P content delivery, and give two efficient blockchain-enabled and monetary-incentivized solutions${\mathsf {FairDownload}}$and${\mathsf {FairStream}}$for P2P downloading and P2P streaming scenarios, respectively. Our designs not only ensure delivery fairness where deliverers are paid (nearly) proportional to their in-time delivery, but also guarantee exchange fairness where content consumers and content providers are also fairly treated. The fairness of each party can be assured even when other two parties collude to arbitrarily misbehave. Our protocols provide a general design of fetching content chunk from any specific position so the delivery can be resumed in the presence of unexpected interruption. Further, our systems are efficient in the sense of achieving asymptotically optimal on-chain costs and optimal delivery communication. We implement the prototype and deploy on the Ethereum Ropsten network. Extensive experiments in both LAN and WAN settings are conducted to evaluate the on-chain costs as well as the efficiency of downloading and streaming. Experimental results show the practicality and efficiency of our protocols.

A video preview is formed by joining a subset of video snippets from a source video. It is expected to be expressive to help enrich users' experience and hence increase the views of the full video. However, it is challenging to identify suitable video snippets to compose such video previews in support of user recommendation of full-length videos. In this paper, we formulate this problem as an optimization problem to maximize the total playback popularity of video segments based on the analysis of a large amount of users' playback records. We design an algorithm for this problem and provide proof of optimality. Furthermore, we conduct an experiment using real industrial data and recruit volunteers to assess the quality of generated video previews. The experimental results show the feasibility and applicability of our methods.

Blockchain technology has been increasingly used for decentralizing cloud-based Internet of Things (IoT) architectures to address limitations faced by centralized systems. While many existing efforts are successful in decentralization with multiple servers (i.e., full nodes) to handle faulty nodes, an important issue has arisen that external clients have to rely on a relay node to communicate with the full nodes in the blockchain. Compromization of such relay nodes may result in a security breach and even a blockage of IoT sensors from the network. In this article, we propose blockchain-based decentralized IoT management systems for censorship resistance, which include a “diffusion” function to deliver all messages from sensors to all full nodes and an augmented consensus protocol to check data losses, replicate processing outcome, and facilitate opportunistic outcome delivery. We also leverage public key aggregation to reduce communication complexity and signature verification. The experimental results from proof-of-concept implementation and deployment in a real distributed environment show the feasibility and effectiveness in achieving censorship resistance.

Modern big data computing systems exemplified by Hadoop employ parallel processing based on distributed storage. The results produced by parallel tasks such as computing modules in scientific workflows or reducers in the MapReduce framework are typically stored in a distributed file system across multiple data nodes. However, most existing systems do not provide a mechanism to compose such distributed information, as required by many big data applications. We construct analytical cost models and formulate a Distributed Information Composition problem in Big Data Systems, referred to as DIC-BDS, to aggregate multiple datasets stored as data blocks in Hadoop Distributed File System (HDFS) using a composition operator of specific complexity to produce one final output. We rigorously prove that DIC-BDS is NP-complete, and propose two heuristic algorithms: Fixed-window Distributed Composition Scheme (FDCS) and Dynamic-window Distributed Composition Scheme with Delay (DDCS-D). We conduct extensive experiments in Google clouds with various composition operators of commonly considered degrees of complexity including O(n), O(n log n), and O(n^2). Experimental results illustrate the performance superiority of the proposed solutions over existing methods. Specifically, FDCS outperforms all other algorithms in comparison with a composition operator of complexity O(n) or O(n log n), while DDCS-D achieves the minimum total composition time with a composition operator of complexity O(n^2). These algorithms provide an additional level of data processing for efficient information aggregation in existing workflow and big data systems.

Blockchain technology has been increasingly used for decentralizing cloud-based Internet of Things (IoT) architectures to address some limitations faced by centralized systems. While many existing efforts are successful in leveraging blockchain for decentralization with multiple servers (full nodes) to handle faulty nodes, an important issue has arisen that external clients (also called lightweight clients) have to rely on a relay node to communicate with the full nodes in the blockchain. Compromization of such relay nodes may result in a security breach and even a blockage of IoT sensors from the network. We propose censorship resistant decentralized IoT management systems, which include a "diffusion" function to deliver all messages from sensors to all full nodes and an augmented consensus protocol to check data loss, replicate processing outcome, and facilitate opportunistic outcome delivery. We also leverage the cryptographic tool of aggregate signature to reduce the complexity of communication and signature verification.

The tsunami of video services brings huge pressure on communication systems, especially on some bandwidth-limited scenarios like wireless networks. Represented by H.264, current video compression approaches can save the transmission bandwidth by reducing the intra- and inter-frame redundancy. However, the huge amount of video content redundancy (VCR) in the transmission is still existing. In this paper, we propose a novel Content-Slimming System (CSS) framework based on the convergence of Computing, Communication and Cache to avoid the transmission of VCRs. The main idea of CSS is to detect VCRs, generate VCR models and clip them from the original frames by Computing, then transmit the necessary video content and semantic difference description by Communication, finally reconstruct the full video based on the received video content, semantic difference description and VCR models stored by Cache. Moreover, we also investigate the Video Monitoring application based on the CSS framework (CSS-VM) in which the background of monitoring frames is modeled, detected and sheared to reduce the transmission bandwidth consumption. The simulation results show that the bandwidth consumption of CSS-VM can be reduced at least by half compared to H.264, while the video quality and visual experience of CSS-VM are even better.