With the increasing complexity of cloud systems, resource performance fluctuation and failure have become two significant factors that affect task execution in the cloud, particularly for workflow tasks with precedence constraints. The former often leads to uncertain task execution time, while the latter can result in task abortion. Although many cloud workflow scheduling algorithms have been proposed for fault tolerance or uncertain task execution time separately, these two issues simultaneously exist in practice and have not yet been thoroughly explored. This paper proposes a hybrid fault-tolerant cloud workflow scheduling algorithm designed to study the failures of cloud resources and uncertain task execution time simultaneously. The proposed scheduling process consists of four phases: preprocessing, initial scheduling, online scheduling, and online adjustment. Two fundamental and widely recognized strategies for fault tolerance, replication and resubmission, are integrated into the algorithm. An elastic resource provisioning mechanism is also designed to adjust active resources dynamically. Performance evaluations on both randomly generated and real-world workflows demonstrate that the algorithm effectively schedules deadline-constrained workflows under uncertain task execution time while guaranteeing fault tolerance.

Wideband polarization-reconfigurable antenna is highly desired for modern intelligent IoT applications that demand both high data rates and flexible polarizations. To meet this demand, a fractured petal-shaped multi-polarization reconfigurable antenna has been developed. It is capable of switching among seven linear polarizations (LPs) with a remarkable overlapping bandwidth of over 2:1, from 3.04 to 6.72 GHz. The fractured petal-shaped antenna is formed by seven pairs of elliptical dipoles uniformly printed on a circular substrate, which enables the reconfiguration among seven LPs at a 25.7∘ interval. In contrast to the conventional narrow overlapping bandwidth of dipole-based reconfigurable antennas, an innovative Dual-dipole Activation Method is introduced, where two dipole pairs are activated simultaneously to expand the operating bandwidth. In addition, the dipole shapes were meticulously iterated, and the coupling between the activated and deactivated dipoles was carefully engineered to generate multiple modes. Combining an innovative strategy of slot etching, the operating bandwidth was significantly enhanced. Notably, due to the rotationally symmetrical configuration, the antenna maintains consistent radiation patterns and stable gains across the entire bandwidth for all reconfigurable seven LPs. Measured results of the fabricated prototype agree well with the simulated ones, validating the effectiveness of our design. To the best of our knowledge, this is the first time a multiple LP antenna with stable gains and radiation patterns has been achieved with over 2:1 bandwidth. This renders the developed antenna highly suitable for modern intelligent IoT applications, further validated via two representative experimental scenarios tailored to practical IoT environments.

Blockchain has been widely used in various industries for providing trustworthy data. On-chain data can be regarded as trusted after it is finalized by blockchain consensus, namely after the data is believed to be immutable. Unfortunately, nodes with poor/isolated network conditions are still susceptible to data spoofing attacks of blockchain view, spawning kinds of severe attacks. For example, a light node newly joining a blockchain network may request the blockchain view from a malicious full node and accept a spoof view, leading to a double spending attack. Besides, a Trusted Execution Environment (TEE), the network stack of which is fully controlled by its host, may be fed spoofed blockchain data as input, undermining the trustworthiness of TEE-based computation by cheating inputs. To resist data spoofing, existing methods rely on a trusted authority to identify trusted data, or timely provide sufficient confirmation blocks for a block b to prove the finalization of b (since the adversary holding less hash power than the honest blockchain node cannot generate the confirmation blocks timely). These methods either suffer the risks caused by centralized trust base or are only PoW-oriented and high-latency. As promising blockchains including Ethereum migrate to energy-saving consensus, e.g., PoS, designing consensus-agnostic approaches against data spoofing becomes an urgent need of the industries. In this paper, we introduce a Proof of Finalization (PoF) problem for proving the finalization of blockchain to prevent data spoofing attacks of blockchain. We also contrive a novel PoF scheme, which leverages the chain quality property of blockchain to establish a trustworthy committee for proof generation. The scheme is chain-agnostic, non-interactive, non-authority-involved, and with negligible latency. Once blockchain data is finalized, the latency of proof generation in our scheme is only 106 milliseconds. Therefore, our scheme paves the way for any system, e.g., light nodes, cross-chain bridges, and layer-2 systems, to read blockchains with various consensus securely.

The crucial blockchain privacy and scalability demand has boosted off-chain contract execution frameworks for years. Some have recently extended their capabilities to transition blockchain states by off-chain multi-party computation while ensuring public verifiability. This new capability is defined as acrfull mpt. However, existing MPT solutions lack at least one of the following properties crucially valued by communities: data availability, financial fairness, delivery fairness, and delivery atomicity. This paper proposes a novel MPT-enabled off-chain contract execution framework, Decloak. Using TEEs, Decloak solves identified properties with lower gas costs and a weaker assumption. Notably, Decloak is the first to achieve data availability and also achieve all of the above properties. This achievement is coupled with its ability to tolerate all-but-one Byzantine parties and TEE executors. Evaluating 10 MPTs in different businesses, Decloak reduces the gas cost of the SOTA, Cloak, by 65.6%. This efficiency advantage further amplifies with an increasing number of MPT’s parties. Consequently, we establish an elevated level of secure and cheap MPT, being the first to demonstrate the feasibility of achieving gas costs comparable to Ethereum transactions while evaluating MPTs.

With an increasing complexity and functionality in cloud data centers, fault tolerance becomes an essential requirement for tasks executed in clouds, especially for workflows with task precedences. Hosts and network devices are the main physical components in a cloud data center. The PB (Primary-Backup) model is a desirable approach to fault tolerance. Many PB-based workflow scheduling algorithms have been proposed for host faults. However, only a few studies focus on cloud workflow scheduling considering network device faults. This paper analyzes the fault-tolerant properties for scheduling dependent tasks and migrating VMs based on the PB model, considering both host and network device faults in a cloud data center. A failure-aware elastic cloud workflow scheduling algorithm is designed for both host and network device fault tolerance. Additionally, an elastic resource provisioning mechanism is proposed and incorporated into the proposed algorithm to improve resource utilization. Performance evaluations on both randomly generated and real-world workflows show that the proposal effectively improves resource utilization while guaranteeing fault tolerance.

In recent years, the confidentiality of smart contracts has become a fundamental requirement for practical applications. While many efforts have been made to develop architectural capabilities for enforcing confidential smart contracts, a few works arise to extend confidential smart contracts to Multi-Party Computation (MPC), i.e., multiple parties jointly evaluate a transaction off-chain and commit the outputs on-chain without revealing their secret inputs/outputs to each other. However, existing solutions lack public verifiability and require O(n) transactions to enable negotiation or resist adversaries, thus suffering from inefficiency and compromised security.

Among multiple fault-tolerant strategies, resubmission, and replication are fundamental and widely recognized in distributed computing systems. In recent years, many algorithms based on replication or resubmission have been proposed. However, few of them consider these two techniques together, especially in Cloud systems. In this article, we propose a Hybrid Fault-Tolerant Scheduling Algorithm (HFTSA) for independent tasks with deadlines by integrating the above techniques in virtualized Cloud systems. During the task scheduling process, HFTSA selects fault-tolerant strategies from resubmission and replication for each accepted task based on the characteristics of both task and Cloud resources and then reserves suitable resources. During the task execution process, HFTSA adopts an online adjustment scheme for fault-tolerant strategies of some tasks if necessary while providing an online scheduling scheme for faults. Moreover, an elastic resource provisioning mechanism is designed and incorporated into HFTSA to dynamically adjust the provided resources to improve resource utilization. Experiments on a real cloud platform and a simulated platform are conducted to verify the effectiveness of the proposed HFTSA. The results demonstrate that HFTSA can provide an efficient fault-tolerant scheduling strategy for deadline-constrained tasks with high resource utilization and performs better than corresponding competitors.

Nuclear instance segmentation is a challenging task due to a large number of touching and overlapping nuclei in pathological images. Existing methods cannot effectively recognize the accurate boundary owing to neglecting the relationship between pixels (e.g., direction information). In this paper, we propose a novel Centripetal Direction Net-work (CDNet) for nuclear instance segmentation. Specifically, we define centripetal direction feature as a class of adjacent directions pointing to the nuclear center to rep-resent the spatial relationship between pixels within the nucleus. These direction features are then used to construct a direction difference map to represent the similarity within instances and the differences between instances. Finally, we propose a direction-guided refinement module, which acts as a plug-and-play module to effectively integrate auxiliary tasks and aggregate the features of different branches. Experiments on MoNuSeg and CPM17 datasets show that CDNet is significantly better than the other methods and achieves the state-of-the-art performance. The code is available at https://github.com/honglianghe/CDNet.

In recent years, as blockchain adoption has been expanding across a wide range of domains, e.g., digital asset, supply chain finance, etc., the confidentiality of smart contracts is now a fundamental demand for practical applications. However, while new privacy protection techniques keep coming out, how existing ones can best fit development settings is little studied. Suffering from limited architectural support in terms of programming interfaces, state-of-the-art solutions can hardly reach general developers. In this paper, we proposed the CLOAK framework for developing confidential smart contracts. The key capability of Cloak is allowing developers to implement and deploy practical solutions to multi-party transaction (MPT) problems, i.e., transact with secret inputs and states owned by different parties by simply specifying it. To this end, CLOAK introduced a domain-specific annotation language for declaring privacy specifications and further automatically generating confidential smart contracts to be deployed with trusted execution environment (TEE) on blockchain. In our evaluation on both simple and real-world applications, developers managed to deploy business services on blockchain in a concise manner by only developing CLOAK smart contracts whose size is less than 30% of the deployed ones.

Smart contracts on the Ethereum blockchain are notoriously known as vulnerable to external attacks. Many of their issues led to a considerably large financial loss as they resulted from broken payments by digital assets, e.g., cryptocurrency. Existing research focused on specific patterns to find such problems, e.g., reentrancy bug, nondeterministic recipient etc., yet may lead to false alarms or miss important issues. To mitigate these limitations, we designed the SafePay analysis framework to find unfair payments in Ethereum smart contracts. Compared to existing analyzers, SafePay can detect potential blockchain transactions with feasible exploits thus effectively avoid false reports. Specifically, the detection is driven by a systematic search for violations on fair value exchange (FVE), i.e., a new security invariant introduced in SafePay to indicate that each party “fairly” pays to others. The preliminary evaluation validated the efficacy of SafePay by reporting previously unknown issues and decreasing the number of false alarms.

While smart contracts have enabled a wide range of applications in many public blockchains, e.g., Ethereum, their security issues have been raising an increasing number of threats on the stability of blockchain ecosystem. In practice, many external attacks on smart contracts result from broken payments with digital assets, e.g., cryptocurrencies. While an increasing number of research works have been focusing on such problems, many of them adopted pattern-based heuristics (e.g., reentrancy) to find payment-related attacks thus can incur a considerably large portion of both false positives and negatives. To overcome these limitations and achieve better payment security on blockchain, we introduced a new class of payment attacks in this paper, i.e., unfair payment (UP). Compared to existing heuristics, UP semantically captures a wider range of payment attacks. Furthermore, we highlighted the general framework SAFEPAY to systematically detect UP. The key insight behind is a novel security invariant, i.e., fair value exchange (FVE), which models the fairness for blockchain payments between multiple parties. More specifically, SAFEPAY systematically explores the transaction space of a given smart contract and generates a bounded set of transaction sequences. For each of the sequence, SAFEPAY reports a UP attack once a violation on FVE is confirmed. We have further instantiated SAFEPAY for Ethereum and applied it in real-world smart contracts. In the empirical evaluation, SAFEPAY managed to identify previously unreported UP attacks and effectively avoid false alarms compared to analyzers in the literature as well.

This paper describes an efficient graph-based method to optimize data-flow expressions for best hardware implementation. The method is based on factorization, common subexpression elimination (CSE) and decomposition of algebraic expressions performed on a canonical representation, Taylor Expansion Diagram. The method is generic, applicable to arbitrary algebraic expressions and does not require specific knowledge of the application domain. Experimental results show that the DFGs generated from such optimized expressions are better suited for high level synthesis, and the final, scheduled implementations are characterized, on average, by 15.5% lower latency and 7.6% better area than those obtained using traditional CSE and algebraic decomposition.

An efficient graph-based method to optimize polynomial expressions in data-flow computations is presented. The method is based on the factorization, common-subexpression elimination, and decomposition of algebraic expressions performed on a canonical Taylor expansion diagram representation. It targets the minimization of the latency and hardware cost of arithmetic operators in the scheduled implementation. The generated data-flow graphs are better suited for high-level synthesis than those extracted directly from the initial specification or obtained with traditional algebraic decomposition methods. Experimental results show that the resulting implementations are characterized by better performance and smaller datapath area than those obtained using traditional algebraic decomposition techniques. The described method is generic, applicable to arbitrary algebraic expressions, and does not require any knowledge of the application domain.

This paper describes an efficient method to perform factorization of DSP transforms based on Taylor expansion diagram (TED). It is shown that TED can efficiently represent and manipulate mathematical expressions. We demonstrate that it enables efficient factorization of arithmetic expressions of DSP transforms, resulting in a simplification of the computation