Shape restoration aims to recover intact 3D shapes from defective ones, such as those that are incomplete, noisy, and low-resolution. Previous works have achieved impressive results in shape restoration subtasks thanks to advanced generative models. While effective for specific shape defects, they are less applicable in real-world scenarios involving multiple defect types simultaneously. Additionally, training on limited subsets of defective shapes hinders knowledge transfer across restoration types and thus affects generalization. In this paper, we address the task of general shape restoration, which restores shapes with various types of defects through a unified model, thereby naturally improving the applicability and scalability. Our approach first standardizes the data representation across different restoration subtasks using high-resolution TSDF grids and constructs a large-scale dataset with diverse types of shape defects. Next, we design an efficient hierarchical shape generation model and a noise-robust defective shape encoder that enables effective impaired shape understanding and intact shape generation. Moreover, we propose a scalable training strategy for efficient model training. The capabilities of our proposed method are demonstrated across multiple shape restoration subtasks and validated on various datasets, including Objaverse, ShapeNet, GSO, and ABO.

A compiler converts smart contract source code into bytecode, ensuring behavior consistency between them. However, as compiler is also a program, it may contain bugs that disrupt this consistency, known as Compiler-Introduced Bugs (CIBs). Of the latest 4,857 verified smart contracts coded in Solidity, approximately 58% still use compilers that contain at least one CIB. These CIBs can be exploited by attackers to bypass security checks or inject malicious data, leading to significant security issues, which becomes even more serious for smart contracts in blockchain as they cannot be modified after being deployed. To this end, this paper proposes sBugChecker, to the best of our knowledge, the first systematic framework designed to automatically and effectively detect CIBs for smart contracts coded in Solidity. sBugChecker can be readily extended with the rule customization suite we propose based on domain specific language. Additionally, it employs two static analytical methods, i.e., pattern matching, and symbolic execution, to identify CIBs’ triggering conditions and confirm their impacts, broadening its detection scope and improving its detection efficiency. To evaluate sBugChecker’s performance, we construct a CIB mutated smart contract dataset, which is the first publicly-available one for this study. According to the evaluation based on this dataset, sBugChecker performs exceptionally well, with detection precision, recall, and F-measure on average achieving 96.6%, 95.5% and 96.0%, respectively. Moreover, sBugChecker has been applied to successfully discover real-world deployed smart contracts capable of triggering CIBs.

Semi-supervised domain generalization (SSDG) aims to build a domain-generalized model using partially labeled data from source domains. Mainstream SSDG methods follow the augmentation consistency in FixMatch. However, the extraction of domain-invariant features may be challenging due to the absence of feature-based operations, further leading to overfitting of the classifier. To this end, we propose Multi-level Augmentation Consistency Learning (MACMatch), which improves the generalization of feature extractor and classifier through feature-based augmentation consistency. On the other hand, existing methods assume labeled data are class-balanced and domain-balanced, which is easily violated in practice. Based on this, we introduce Representativity and Diversity-based Sample Selection (RDSS), which models data as graphs to evaluate reasonable samples for labeling, relaxing the assumption for labeled data. Experiments on PACS and OfficeHome demonstrate that MACMatch outperforms state-of-the-art SSDG methods. Furthermore, MAC-Match with RDSS achieves competitive results without domain and class priori assumptions. Code is available at https://github.com/Y-J-Zhang/MACMatch-RDSS.

Directed Greybox Fuzzing (DGF) is a target-oriented fuzzing technique that can reproduce or discover software vulnerabilities. The goal is commonly achieved through two phases: static analysis which obtains program structural information beforehand, and dynamic execution that guides fuzzing towards target sites. However, existing DGF methods still incur heavyweight and incomplete issues. The former comes from extra efforts on identifying and approaching the target sites, while the latter refers to the incompleteness of testing on the target sites due to indirect calls or insufficient paths that recent DGF can cover.In this paper, we propose a Predecessor-aware Directed Greybox Fuzzing (PDGF) method and regard DGF as a path-searching problem. PDGF divides a given program into predecessor and non-predecessor areas, and maintains a set of predecessors by lightweight program analysis initially and augmented during the dynamic execution thereafter. Meanwhile, PDGF introduces a novel fitness metric called regional maturity to indicate the coverage rate of predecessors, and contains a simulated annealing-based power scheduling technique together with seed selection and mutation, to cover the predecessor area efficiently and extensively. We evaluate the proposed PDGF on a benchmark that contains 30 real-world program target sites, and conduct extensive comparisons with state-of-the-art DGF tools. Experimental results reveal that PDGF outperforms competitors in terms of Time-To-Exposure, path diversity, and bug finding. Besides, PDGF discovered nine new vulnerabilities, six of which have been assigned CVEs.

Adding supernumerary robotic limbs (SRLs) to humans and controlling them directly through the brain are main goals for movement augmentation. However, it remains uncertain whether neural patterns different from the traditional inherent limbs motor imagery (MI) can be extracted, which is essential for high-dimensional control of external devices. In this work, we established a MI neo-framework consisting of novel supernumerary robotic sixth-finger MI (SRF-MI) and traditional right-hand MI (RH-MI) paradigms and validated the distinctness of EEG response patterns between two MI tasks for the first time. Twenty-four subjects were recruited for this experiment involving three mental tasks. Event-related spectral perturbation was adopted to supply details about event-related desynchronization (ERD). Activation region, intensity and response time (RT) of ERD were compared between SRF-MI and RH-MI tasks. Three classical classification algorithms were utilized to verify the separability between different mental tasks. And genetic algorithm aims to select optimal combination of channels for neo-framework. A bilateral sensorimotor and prefrontal modulation was found during the SRF-MI task, whereas in RH-MI only contralateral sensorimotor modulation was exhibited. The novel SRF-MI paradigm enhanced ERD intensity by a maximum of 117% in prefrontal area and 188% in the ipsilateral somatosensory-association cortex. And, a global decrease of RT was exhibited during SRF-MI tasks compared to RH-MI. Classification results indicate well separable performance among different mental tasks (88.1% maximum for 2-class and 88.2% maximum for 3-class). This work demonstrated the difference between the SRF-MI and RH-MI paradigms, widening the control bandwidth of the BCI system.

It is necessary for rescuers to localize a target trapped in a an unknown area resulting from various natural disasters or human warfare. The global navigation satellite systems and existing wireless and cellular infrastructures may have been partially or totally constrained and not available for localization in the target area. In this article, we propose a simple yet effective single-anchor mobile localization scheme, called TSAL as a potential solution in the case where traditional localization methods fail. By building three Cartesian coordinate systems and carrying out distance and/or steering-angle measurement with the off-the-shelf approaches while the target node is moving, utilizing just one of existing normally-functioning cellular Base Stations (BSs) as the only anchor or redeploying only one BS is enough to localize the target. In addition, TSAL also works with multiple anchor nodes and we propose a corresponding scheme based on TSAL, called TML, which can obtain more accurate localization. Theoretical analyses, extensive simulations and real-world experiments are conducted for evaluating the proposed schemes, with the effects of a set of parameter settings investigated, which shows the high availability and effectiveness of our schemes.

The emergence of pre-trained model-based vulnerability detection methods has significantly advanced the field of automated vulnerability detection. However, these methods still face several challenges, such as difficulty in learning effective feature representations of statements for fine-grained predictions and struggling to process overly long code sequences. To address these issues, this study introduces StagedVulBERT, a novel vulnerability detection framework that leverages a pre-trained code language model and employs a coarse-to-fine strategy. The key innovation and contribution of our research lies in the development of the CodeBERT-HLS component within our framework, specialized in hierarchical, layered, and semantic encoding. This component is designed to capture semantics at both the token and statement levels simultaneously, which is crucial for achieving more accurate multi-granular vulnerability detection. Additionally, CodeBERT-HLS efficiently processes longer code token sequences, making it more suited to real-world vulnerability detection. Comprehensive experiments demonstrate that our method enhances the performance of vulnerability detection at both coarse- and fine-grained levels. Specifically, in coarse-grained vulnerability detection, StagedVulBERT achieves an F1 score of 92.26%, marking a 6.58% improvement over the best-performing methods. At the fine-grained level, our method achieves a Top-5% accuracy of 65.69%, which outperforms the state-of-the-art methods by up to 75.17%.

Hybrid fuzzing combines fuzzing and concolic execution which leverages the high-throughput feature of fuzzing to explore easy-to-reach code, and the powerful constraint solving capability of concolic execution to explore code wrapped in complex constraints. Based on our observations, existing hybrid fuzzers are still not efficient for the following two reasons. First, fuzzing often gets stuck in deep paths leading to the delayed discovery of vulnerabilities. Second, coarse-grained interaction strategies cannot effectively launch concolic execution. To solve the above problems, we propose a constraint-guided hybrid fuzzing approach (CGHF) that leverages the constraints’ static analysis information and dynamic execution information. CGHF contains two main techniques: an evolutionary algorithm based on path exploration difficulty and an interaction strategy guided by the execution state of constraints. Specifically, in the fuzzing phase, we evaluate the path exploration difficulty and guide the fuzzer to explore in the order of difficulty from low to high. In addition, we design a coordinator to monitor the constraints’ dynamic execution information and select the most deserving constraints to be solved for the concolic execution. We implement a prototype called SILK and compare its effectiveness on eight open source programs with other state-of-the-art fuzzers. The results show that SILK improved path coverage by 10%-45% and branch coverage by 5%-10% compared with other fuzzers.

Multidomain Internet of Things (IoT) is faced with serious domain interoperability (DI) and compatibility issues since different intradomain authorization and authentication (A&A) mechanisms are deployed without the consideration of interdomain A&A. This article proposes a blockchain-assisted scheme to achieve flexible intra- and inter-domain A&A simultaneously and seamlessly. Specifically, we first design a contract-based mutual access control agreement on top of a consortium blockchain, where domain managers can manage their access permission without any trusted parties. Based on the agreement, a secure and privacy-preserving authentication protocol is further proposed by tailoring one-out-of-many proof techniques, which enables IoT devices to anonymously access authorized IoT domains. We additionally design a voting-based protocol by using a threshold-based cryptosystem. The protocol allows domain managers to transparently audit resource access with the assistance of the blockchain. Detailed security analysis demonstrates that the proposed scheme achieves the security properties, such as DI, privacy protection, and accountability. Finally, we develop two proof-of-concept prototypes in a physical testbed and virtual machine, respectively, based on an open-source blockchain platform to show our scheme’s efficiency in terms of computation and communication overhead.

The growing power consumption of heterogeneous clusters has attracted great interests from both academia and industry. Although there have been extensive studies on energy-efficient task scheduling algorithms to address this problem, most of them are task-centric. However, as a task is fine-grained enough, the scheduling algorithm can put more efforts on utilizing different power characteristics of heterogeneous servers, which has not been fully explored. This paper presents a Linear Programming-based Energy-efficient Load Balancing (LP-ELB) scheme for heteroge-neous clusters. The power model of each sever in the cluster is obtained in advance, and the overall optimization problem is formulated as a mixed integer linear programming problem. LP-ELB tackles this problem through a two-phase heuristic: a server selection phase for choosing a minimal set of servers, and a load partition phase for calculating load distribution. An optimal solution to the relaxed problem in the second phase can be provided finally. Experiments on both simulated and real-world testbeds validate the effectiveness of the proposed method, and reveal that LP-ELB is superior to competitor algorithms, such as Round-Robin and power-aware Weighted Round-Robin.

Public Key Infrastructure (PKI) is the cornerstone technology to solve trust issues in cyberspace. However, PKI faces a serious problem of centralized trust in Certificate Authorities (CAs). Fraudulent certificates issued by CAs due to misoperation, being deceived, or being compromised, are used to launch attacks like Man-in-the-Middle (MitM), spoofing, etc. To enhance the security of CAs, we present a domain-centric system based on blockchain called Domain Transparency (DT). Domain owners are enabled to declare issuance policies that CAs should comply with in the DT system, so that all issued certificates are authorized by them. Furthermore, we design a Domain Configuration Transaction (DCT) to manage policies and certificates of domains. To resist CAs’ misbehaviors, domain owners are involved in the certificate issuance process to balance the absolute authority of CAs. We conduct extensive security analysis and implement a prototype of DT based on Hyperledger Fabric for performance evaluations. Experimental results reveal that DT is superior to competitor schemes in terms of functionality, storage and communication cost.

With the demand for security, the Internet companies and institutions have formed independent trust domains by utilizing different cryptographic settings, which brings up the problem of cross-domain authentication. To solve the cross-domain authentication problem, researchers have proposed many schemes that can be divided into two categories: centralized and blockchain-based. However, the challenges of privacy leakage, low efficiency, and incomplete cross-domain have not been effectively overcome in existing schemes. In this paper, we propose a Blockchain-based Privacy-preserving scheme for Cross-Domain Authentication (BPCDA). To realize complete cross-domain, the blockchain is introduced for building trust between different domains. For lightweight management, the Pedersen Commitment is adopted for identity voucher generation. In addition, the Non-Interactive Zero-Knowledge (NIZK) proof algorithm is utilized for mutual and anonymous authentication. The security analysis and the performance evaluation demonstrate that the proposed BPCDA is secure, privacy-preserving, and efficient.

Internet of Things (IoT) has been ubiquitous in both industrial and living areas, but also known for its weak security. Being as the first defense line against various cyberattacks, authentication is even more critical to IoT applications. Moreover, there has been a growing demand for cross-domain collaboration, leading to an increasing need for cross-domain authentication. Recently, certificate-based authentication schemes have been extensively studied. However, many of these schemes are not efficient in computation, storage, and communication, which are highly required in IoT. In this paper, we propose a lightweight authentication scheme based on consortium blockchain and design a cryptocurrency-like digital token to build trust. Furthermore, trust lifecycle management is performed by manipulating the amount of tokens. The comprehensive analysis and evaluation demonstrate that the proposed scheme is resistant to various common attacks and more efficient than competitor schemes in terms of storage, communication, and authentication cost.

The increasing diversity of Internet-of-Things (IoT) application scenarios and explosive growth of access devices have brought more frequent exchanges of resources between different administrative domains. Cross-domain authentication has become a key to safeguard communication and resource interaction among domains. Traditional centralized authentication schemes present heavy management overhead and trust challenges in cross-domain scenarios. Most of existing studies are incapable of establishing trust relationships between domains deployed with different authentication schemes, rendering such high-cost schemes difficult to be generalized. Further, the cross-domain scenario of IoT also raises additional requirements for device privacy and system overhead. In order to tackle these issues, this paper proposes a complete cross-domain authentication and privacy protection scheme, called CCAP, for the IoT based on consortium blockchain. CCAP achieves cross-domain authentication among the IoT domains which may have different configurations from each other. Further, CCAP can be cost-effectively deployed in resource-limited IoT domains and can offer privacy protection and efficient and secure communication for IoT devices. We demonstrate the effectiveness and efficiency of the scheme through experiments in virtual and physical experiment environments as well as comparing and analyzing CCAP with state-of-the-art work.

Vehicle Ad-hoc Network (VANET) faces a large number of potential threats due to its openness and complexity. Identity authentication is the basis for resisting various attacks. Common approaches usually employ public key infrastructure, identity-based signature and cryptography-based algorithms, which either bring high computation and storage costs, have certificate issuance, revocation, and management problems, or exist centralization problems. In this paper, we propose an identity authentication scheme for VANET based on consortium blockchain, in which vehicle or Road-Side Unit (RSU) authenticity is verified by on-chain transactions instead of certificates in other blockchain schemes. To the end, a new data structure based on unspent transaction output is introduced to initiate a set of online operations. Furthermore, we put forward an RSU-aided scheme, in which only one additional step, namely fillToken operation, is required to reduce the communication delay of authentication after a vehicle first joins an RSU group through a series of online operations. We implement the proposed scheme in the Hyperledger Fabric platform and conduct security and performance analysis, which shows the effectiveness of our scheme.

Summary Energy efficiency in heterogeneous computing systems has attracted increasing interests due to its economic and environmental impacts during recent decades. Based on power‐aware hardware techniques, such as dynamic voltage frequency scaling, efforts have been made through task scheduling to reduce the total energy consumption for executing a parallel application while maintaining its time efficiency. In this case, energy conscious task scheduling refers to a bi‐objective optimization that aims to minimize the overall completion time (makespan) and the total energy consumption, simultaneously. Existing energy conscious scheduling algorithms conduct energy optimization by means of slack reclamation or a trade‐off function. However, the performance of slack reclamation has been proved to be upper‐bounded and methods relying on trade‐off functions cannot guarantee bi‐objective optimization. In this article, an energy conscious task scheduling algorithm is proposed to tackle the above issues based on the framework of variable neighborhood search. Two neighborhood structures are designed to reduce makespan and the total energy consumption, respectively. Furthermore, a pruning technique is incorporated into the algorithm to accelerate the searching process. Extensive experimental results on both randomly generated and real‐world applications demonstrate that the proposed algorithm improves the time‐efficient schedules on average by 22.4% for the energy consumption and 1.2% for the makespan.

Duty-cycling techniques have been widely adopted to save energy for energy-constrained wireless sensor networks, while they also cause the sleep latency issue, especially in a multihop linear sensor network (LSN). So the duty-cycling and pipelined-forwarding (DCPF) techniques have been proposed to alleviate this issue. However, most of existing DCPF protocols have no effective scheme to handle the contention and interference among those proximately-located nodes which maintain the same sleep-wakeup schedule. As a result, the network performance degrades with low energy efficiency and high packet delivery latency, particularly when experiencing a heavy traffic load. To this end, this paper proposes an enhanced pipelined data collection (EPDC) MAC protocol for LSN. In EPDC, three algorithms are proposed to guarantee that those nodes located within the interference range of each other have mutually staggered sleep-wakeup schedules, so that the contention and interference among them can be eliminated. The extensive OP-NET simulations show that EPDC significantly outperforms an existing DCPF protocol in terms of packet delivery ratio, network throughput, packet delivery latency, and energy efficiency.

The authentication protocol is commonly served as the first defense line against various attacks in vehicular ad hoc networks (VANETs). Conventional schemes usually employ public key infrastructure or cryptography-based algorithms, which suffer from high computational and storage cost. In this paper, we propose a privacy-preserving authentication scheme for VANETs based on consortium blockchain. The authenticity of a vehicle or a road-side unit is represented by its transaction capability on blockchain instead of a certificate or a cryptographic key. In support of that, we design a novel data structure based on the unspent transaction output (UTXO) combined with a set of online operations, including issue, transfer, query and revocation. Thus, the authentication between two entities is accomplished by on-chain verification and corresponding communications. We conduct a set of security and privacy analysis as well as implementing a prototype on the Hyperledger Fabric platform, to evaluate the effectiveness and the efficiency of the proposed scheme.

Energy consumption in heterogeneous clusters has attracted lots of attention since it results in high operating cost and environmental pollution. Task scheduling is considered as an effective software approach to reduce energy dissipation on DVS-enabled clusters. However, single-objective optimization focusing on energy reduction lacks the consideration on makespan while recent bi-objective schedulers can hardly guarantee the performance on both makespan and energy-saving. In this paper, we present a bi-objective approach that can ensure the performance by iterated local search, namely ILS-DVS. The algorithm is started from an initial solution produced by a time-effective scheduler and explores more solution space by perturbation to find more feasible candidates. Then, a hill climbing method is employed to optimize these candidate solutions. After a certain number of iterations, a Pareto-efficient schedule can be produced by ILS-DVS. Experimental results demonstrate that the proposed algorithm can significantly improve both makespan and energy-saving, and has superior performance to other competitors.

Summary The continually increasing energy consumption represents a critical issue in modern heterogeneous computing systems. With the aid of dynamic voltage frequency scaling (DVFS), task scheduling is considered an effective software‐based technique for reducing the total energy consumption and minimizing the overall schedule length (makespan). A natural solution is to reclaim the slack time in a given time‐efficient schedule, which is also referred to as a “two‐pass” method or a “rescheduling” method. A number of studies have focused on slack reclamation to achieve energy reductions through heuristics; although, these methods offer suboptimal solutions. In this article, the rescheduling optimization problem is formulated as a linear program for minimizing an energy objective function subject to precedence and deadline constraints implied in the given schedule. Two types of decision variables, ie, frequency duty factors and task intervals, are defined to set up the linear model. Consequently, an optimal solution to the problem can be provided in a straightforward manner by a linear programming solver, which suggests that such a rescheduling problem belongs to the P (polynomial time) class. The experimental results show the effectiveness of the proposed approach and demonstrate that the performance is superior to that of other competitive algorithms in terms of both energy saving and runtime efficiency.

With the increasing development of network technologies and the growing number of new service requirements in Internet, the problem of service extensibility in traditional network is being challenged constantly. Thus, we propose the extensible network service model. In this paper, the basic principle of the extensible network service model is described firstly. Then, on the basis of service composition operator, the service extensibility of the service model is analyzed in detail. Finally, through the experimental comparison, the difference of performance between the traditional network and the extensible network service model is given when providing the same service. From the experiment results, it can be verified that the extensible network service model does well in service extensibility.

The energy consumption in heterogeneous computing systems (HCS) has attracted a great deal of attention in both scientific and commercial fields due to operating and environmental concerns. Based on the technique of dynamic voltage and frequency scaling (DVFS), many studies have investigated and developed efficient task scheduling algorithms for energy reduction. However, most of them provide only one refined frequency for each task to perform slack reclamation. Moreover, the total energy-saving is accumulated by individual local minimum of energy consumption with less or no global consideration. In this paper, we use a linear combination of processor frequencies to execute each task and allocate time slices for these frequencies by a linear programming approach. The goal of energy reduction is represented by a global function associated with the set of time slices while the constraint declarations are given by runtime precedence-constraints and processor-constraints, respectively. In this case, the problem of energy-efficient task scheduling becomes a linear program which can be solved by a mature set of linear programming solvers. The experimental results show the effectiveness of our proposed method and demonstrate the superior performance over existing approaches without sacrificing the schedule length.

Energy consumption imposes a significant cost for data centers in providing cloud services. Many studies explore the opportunities to save power by energy-efficient task scheduling based on the technique of dynamic voltage and frequency scaling (DVFS). However, most of them assume that energy budgets and/or deadline constraints are known in advance. But these information can hardly be acquired in general computing environments, such as cloud computing, and job rejections caused by restricted constraints are intolerable to guarantee the service-level agreement (SLA). Moreover, previous works prefer to provide “black-box” algorithms with little consideration on adjustability, and cannot satisfy runtime requirements in performance and energy-saving. This paper proposes an elastic energy-efficient algorithm called CloudFreq for bag-of-tasks scheduling in DVFS-enabled clouds. CloudFreq enables a model of elastic, adjustable energy-efficient scheduling without any prior knowledge of constraints, and then eliminates job rejections accordingly. CloudFreq also provides an entry for operators to scale system performance at runtime. Experimental results demonstrate that the proposed algorithm can effectively perform energy-efficient scheduling without constraints, and has the capability of making an appropriate tradeoff to improve the weighted balance between schedule length and energy-saving.

Heterogeneous Networks (HetNets) has attracted considerable attention since they can improve the system capacity and user throughput in Long Term Evolution (LTE)-Advanced system. However, due to the different coverage sizes for macro eNodeB (eNB) and pico eNB, the handover performance of UE will be impacted especially when the user equipment (UE) moves in medium or high speed in HetNets. Three metrics are agreed in 3GPP RAN WG2 to evaluate the handover performance: radio link failure (RLF) rate, handover failure (HOF) rate, and short time of stay (short ToS). In this paper, firstly, based on the three metrics, the handover performance in HetNets is analyzed. Then, we propose two schemes to solve the issue of handover performance deterioration when UE moves in medium speed. One scheme is to optimize pico-macro handover, and the other is to optimize macro-pico handover. In other words, two schemes can separately optimize pico cell leaving and attaching. Furthermore, the two schemes can be used jointly to further improve the mobility robustness in HetNets. System level simulation is employed to present the handover performance improvement.