Transformer-based language models power many cloud services, but inference on sensitive data raises confidentiality concerns. Fully Homomorphic Encryption (FHE) enables computation on encrypted inputs while preserving privacy, but at high computational cost, making Transformers difficult to deploy. This paper presents PriTran, an efficient CKKS-based library for privacy-preserving Transformer inference on CPUs. Complementing the only prior work, RoLe, which supports only Berttiny(2 encoders), PriTran introduces two novel algorithms with optimized data layouts that accelerate ciphertext–plaintext (CP) and ciphertext–ciphertext (CC) matrix multiplications (MMs) across all Bert models by reducing costly rotations and multiplications. On the MNLI dataset, RoLe fails on inputs longer than 36 tokens within a 5-hour per-token budget, while PriTran achieves average speedups of 29.3% and 22.2% for CP- and CC-MMs, respectively, and 24.1% end-to-end. We further evaluate PriTran on scaled Berttinyvariants with additional encoders and on Bertmini(4 encoders), demonstrating correctness and scalability beyond RoLe’s limits. Within current FHE limits, these gains and RoLe’s failure on longer inputs underscore PriTran’s promise as a practical approach for FHE-based Transformer inference.

Decentralized Autonomous Organizations (DAOs) signify a groundbreaking approach to Internet-based management, enabled by blockchain technology and cryptocurrencies, and are viewed as fundamental elements of the Web3 ecosystem. In this study, we delve into the concept of DAOs by thoroughly investigating their underlying structure, ideology, and operational principles. Furthermore, we present a novel DAO framework derived from a technical and organizational assessment and provide an overview of cutting-edge DAO tools currently available. This research enables the swift implementation of DAO creation or transformation customized to an organization’s specific stage. Additionally, we recognize current challenges and shortcomings in existing DAOs and propose areas for future exploration.

• Propose spatial and temporal encoders for container-level autoscaling decisions • Design a hierarchical action network adaptable to changing container numbers • Achieve higher QoS and cost savings than four state-of-the-art autoscaling methods • Validate effectiveness on real-world user request traces • Advance expert systems for complex and large-scale cloud environments Autoscaling is an important technique for cloud computing that dynamically adjusts resources allocated to cloud applications in response to fluctuating user requests to maintain Quality of Service (QoS) and adhere to a given budget. Recent advancements in Deep Reinforcement Learning (DRL) have shown promise in achieving effective autoscaling approaches. However, prior DRL-based approaches struggle to simultaneously consider the spatial dependencies within an application and the changing historical workload patterns, limiting their ability to make accurate scaling decisions. Moreover, existing approaches lack the fine-grained resource adjustment, leading to suboptimal autoscaling performance. To address these limitations, we propose a new DRL-based autoscaling approach with a novel spatial-temporal autoscaling policy, which jointly captures spatial and temporal features of cloud applications by Graph Neural Networks and Transformers. Meanwhile, this policy enables fine-grained resource adjustment. Extensive experiments on real-world user request traces show that the proposed approach significantly outperforms existing state-of-the-art methods, achieving up to a 78.23% reduction in mean response time without violating the cost budget.

Recent years have witnessed a surge in deep learning research, marked by the introduction of expensive generative models like OpenAI’s SORA and GPT, Meta AI’s LLAMA series, and Google’s FLAN, BART, and Gemini models. However, the rapid advancement of large models (LM) has intensified the demand for computing resources, particularly GPUs, which are crucial for their parallel processing capabilities. This demand is exacerbated by limited GPU availability due to supply chain delays and monopolistic acquisition by major tech firms. Distributed Machine Learning (DML) methods, such as Federated Learning (FL), mitigate these challenges by partitioning data and models across multiple servers, though implementing optimizations like tensor and pipeline parallelism remains complex. Blockchain technology emerges as a promising solution, ensuring data integrity, scalability, and trust in distributed computing environments, but still lacks guidance on building practical DML systems. In this paper, we propose a trustworthy distributed machine learning (TDML) framework that leverages blockchain to coordinate remote trainers and validate workloads, achieving privacy, transparency, and efficient model training across public remote computing resources. Experimental validation demonstrates TDML’s efficacy in overcoming performance limitations and malicious node detection, positioning it as a robust solution for scalable and secure distributed machine learning.

Federated learning (FL) has emerged as a promising paradigm within edge computing (EC) systems, enabling numerous edge devices to collaboratively train artificial intelligence (AI) models while maintaining data privacy. To overcome the communication bottlenecks associated with centralized parameter servers, decentralized federated learning (DFL), which leverages peer-to-peer (P2P) communication, has been extensively explored in the research community. Although researchers design a variety of DFL approaches to ensure model convergence, its iterative learning process inevitably incurs considerable cost along with the growth of model complexity and the number of participants. These costs are largely influenced by the dynamic changes in topology in each training round, particularly its sparsity and connectivity conditions. Furthermore, the inherent resources heterogeneity in the edge environments affects energy efficiency of the learning process, while data heterogeneity degrades model performance. These factors pose significant challenges to the design of an effective DFL framework for EC systems. To this end, we propose Hat-DFed, a heterogeneity-aware and cost-effective decentralized federated learning framework. In Hat-DFed, the topology construction is formulated as a dual optimization problem, which is then proven to be NP-hard, with the goal of maximizing model performance while minimizing cumulative energy consumption in complex edge environments. To solve this problem, we design a two-phase algorithm that dynamically constructs optimal communication topologies while unbiasedly estimating their impact on both model performance and energy cost. Additionally, the algorithm incorporates an importance-aware model aggregation mechanism to mitigate performance degradation caused by data heterogeneity. Extensive experiments demonstrate that Hat-DFed outperforms state-of-the-art baselines, achieving an average 1.8% improvement in test accuracy while reducing total energy cost by 36.9% throughout the learning process.

Model training is increasingly offered as a service for resource-constrained data owners to build customized models. Split Learning (SL) enables such services by offloading training computation under privacy constraints, and evolves towardserverlessandmulti-clientsettings where model segments are distributed across training clients. This cooperative mode assumes partial trust: data owners hide labels and data from trainer clients, while trainer clients produce verifiable training artifacts and ownership proofs. We presentCliCooper, a multi-clientcooperative SL framework tailored for cooperative model training services in heterogeneous and partially trusted environments, where one client contributes data, while others collectively act as SL trainers.CliCooperbridges the privacy and trust gaps through two new designs. First, Differential Privacy–based activation protection and secret label obfuscation safeguard data owners' privacy without degrading model performance. Second, a dynamic chained watermarking scheme cryptographically links training stages on model segments across trainers, ensuring verifiable training integrity, robust model provenance, and copyright protection. Experiments show thatCliCooperpreserves model accuracy while enhancing resilience to privacy and ownership attacks. It reduces the success rate of clustering attacks (which infer label groups from intermediate activation) to 0%, decreases inversion-reconstruction (which recovers training data) similarity from 0.50 to 0.03, and limits model-extraction–based surrogates to about 1% accuracy, comparable to random guessing.

The vulnerability of neural networks to adversarial perturbations has necessitated formal verification techniques that can rigorously certify the quality of neural networks. As the state-of-the-art, branch-and-bound (BaB) is a "divide-and-conquer" strategy that applies off-the-shelf verifiers to sub-problems for which they perform better. While BaB can identify the sub-problems that are necessary to be split, it explores the space of these sub-problems in a naive "first-come-first-served" manner, thereby suffering from an issue of inefficiency to reach a verification conclusion. To bridge this gap, we introduce an order over different sub-problems produced by BaB, concerning with their different likelihoods of containing counterexamples. Based on this order, we propose a novel verification framework Oliva that explores the sub-problem space by prioritizing those sub-problems that are more likely to find counterexamples, in order to efficiently reach the conclusion of the verification. Even if no counterexample can be found in any sub-problem, it only changes the order of visiting different sub-problems and so will not lead to a performance degradation. Specifically, Oliva has two variants, including Oliva^GR, a greedy strategy that always prioritizes the sub-problems that are more likely to find counterexamples, and Oliva^SA, a balanced strategy inspired by simulated annealing that gradually shifts from exploration to exploitation to locate the globally optimal sub-problems. We experimentally evaluate the performance of Oliva on 690 verification problems spanning over 5 models with datasets MNIST and CIFAR-10. Compared to the state-of-the-art approaches, we demonstrate the speedup of Oliva for up to 25× in MNIST, and up to 80× in CIFAR-10.

Credit card fraud detection (CCFD) is a critical application of Machine Learning (ML) in the financial sector, where accurately identifying fraudulent transactions is essential for mitigating financial losses. ML models have demonstrated their effectiveness in fraud detection task, in particular with the tabular dataset. While adversarial attacks have been extensively studied in computer vision and deep learning, their impacts on the ML models, particularly those trained on CCFD tabular datasets, remains largely unexplored. These latent vulnerabilities pose significant threats to the security and stability of the financial industry, especially in high-value transactions where losses could be substantial. To address this gap, in this paper, we present a holistic framework that investigate the robustness of CCFD ML model against adversarial perturbations under different circumstances. Specifically, the gradient-based attack methods are incorporated into the tabular credit card transaction data in both black- and white-box adversarial attacks settings. Our findings confirm that tabular data is also susceptible to subtle perturbations, highlighting the need for heightened awareness among financial technology practitioners regarding ML model security and trustworthiness. Furthermore, the experiments by transferring adversarial samples from gradient-based attack method to non-gradient-based models also verify our findings. Our results demonstrate that such attacks remain effective, emphasizing the necessity of developing robust defenses for CCFD algorithms.

With the advancement of edge computing, federated learning (FL) displays a bright promise as a privacy-preserving collaborative learning paradigm. However, one major challenge for FL is the data heterogeneity issue, which refers to the biased labeling preferences among multiple clients, negatively impacting convergence and model performance. Most previous FL methods attempt to tackle the data heterogeneity issue locally or globally, neglecting underlying class-wise structure information contained in each client. In this paper, we first study how data heterogeneity affects the divergence of the model and decompose it into local, global, and sampling drift sub-problems. To explore the potential of using intra-client class-wise structural knowledge in handling these drifts, we thus propose Federated Learning with Structural Knowledge Collaboration (FedSKC). The key idea of FedSKC is to extract and transfer domain preferences from inter-client data distributions, offering diverse class-relevant knowledge and a fair convergent signal. FedSKC comprises three components: i) local contrastive learning, to prevent weight divergence resulting from local training; ii) global discrepancy aggregation, which addresses the parameter deviation between the server and clients; iii) global period review, correcting for the sampling drift introduced by the server randomly selecting devices. We have theoretically analyzed FedSKC under non-convex objectives and empirically validated its superiority through extensive experimental results. Our code is at https://github.com/hwang52/FedSKC.

Trust management systems (TMS) are crucial for managing trust in distributed environments. The rise of decentralized systems and blockchain has sparked interest in credential-based decentralized trust management systems (DTMS). This paper bridges the gap between theory and practice through a systematic review of credential-based DTMS. We analyze existing DTMS solutions through multiple dimensions, including their architectural designs, credential mechanisms, and trust evaluation models. Our survey provides a detailed taxonomy of credential-based DTMS approaches and establishes comprehensive evaluation criteria for assessing DTMS implementations. Through extensive analysis of current systems and implementations, we identify critical challenges and promising research directions in the field. Our examination offers valuable insights for researchers and practitioners working on DTMS, particularly in areas such as access control, reputation systems, and blockchain-based trust frameworks.

Unmanned Aerial Vehicles (UAVs), play a significant role in the advancement of pervasive systems by providing efficient, scalable, and innovative solutions in various sectors, such as smart cities or location-based services. However, the current UAV delivery scenario presents various challenges for recipients, including lengthy identity verification processes, privacy concerns, and risks of fraud and theft. In response to these issues, this paper proposes an innovative system that leverages Blockchain technology and Adversarial Machine Learning (AML) to tackle these problems effectively. The proposed system streamlines the verification process, enhances privacy safeguards, and reduces fraud risks. The integration of AML is crucial as it enables users to have greater control over their personal data, boosting privacy and security. AML also plays a critical role in this system by creating test scenarios that reinforce the machine learning model against adversarial threats, ensuring its precision and dependability in the face of malicious manipulations. The paper also provides details on the practical implementation and evaluation of this system in real-life adversarial situations. The evaluation results demonstrate superior performance on selected metrics, highlighting the potential of this system as an effective solution for verifying recipients in UAV delivery.

Unmanned aerial vehicles (UAVs) have witnessed significant growth in various domains, such as agriculture, disaster management, and remote health management systems. However, the use of UAVs necessitates secure and efficient solutions that uphold privacy during task distribution. To address this challenge, this article introduces a novel architecture for privacy-aware task distribution in UAV communication systems. Our approach leverages the benefits of blockchain and smart token-based identification within the proposed architecture, ensuring decentralized, transparent, and tamper-proof operations. By adopting a crowdsourced task distribution model, our approach further optimizes task assignment among UAVs while prioritizing data privacy, user access control, and scalability. The architecture is designed to enhance fault tolerance, enabling seamless operation under dynamic and unpredictable conditions. We present a comprehensive implementation details of a proof-of-concept prototype of our proposed architecture, detailing its design and functionality. The experimental results demonstrate the feasibility, efficiency, and adaptability of our approach in diverse real-world scenarios, highlighting its potential for broader adoption across UAV applications.

Incremental verification is an emerging neural network verification approach that aims to accelerate the verification of a neural network N* by reusing the existing verification result (called a template ) of a similar neural network N . To date, the state‐of‐the‐art incremental verification approach leverages the problem splitting history produced by branch and bound ( BaB ) in verification of N , to select only a part of the sub‐problems for verification of N* , thus more efficient than verifying N* from scratch. While this approach identifies whether each sub‐problem should be re‐assessed, it neglects the information of how necessary each sub‐problem should be re‐assessed, in the sense that the sub‐problems that are more likely to contain counterexamples should be prioritized, in order to terminate the verification process as soon as a counterexample is detected. To bridge this gap, we first define a counterexample potentiality order over different sub‐problems based on the template, and then we propose Olive, an incremental verification approach that explores the sub‐problems of verifying N* orderly guided by counterexample potentiality. Specifically, Olive has two variants, including Olive g , a greedy strategy that always prefers to exploit the sub‐problems that are more likely to contain counterexamples, and Olive b , a balanced strategy that also explores the sub‐problems that are less likely, in case the template is not sufficiently precise. We experimentally evaluate the efficiency of Olive on 1445 verification problem instances derived from 15 neural networks spanning over two datasets MNIST and CIFAR‐10 . Our evaluation demonstrates significant performance advantages of Olive over state‐of‐the‐art classic verification and incremental approaches. In particular, Olive shows evident superiority on the problem instances that contain counterexamples, and performs as well as Ivan on the certified problem instances.

Bitcoin Request for Comment 20 (BRC-20) token mania was a key storyline in the middle of 2023. Setting it apart from conventional Ethereum request for comments (ERC)-20 token standards on Ethereum, BRC-20 introduces nonfungibility to Bitcoin through an editable field in each satoshi (0.00000001 Bitcoin, the smallest unit), making them unique. In this article, we pioneer the exploration of this concept, covering its intricate mechanisms, features, and state-of-the-art applications. By analyzing the multidimensional data spanning over months with factual investigations, we conservatively comment that while BRC-20 expands Bitcoin’s functionality and applicability, it may still not match Ethereum’s abundance of decentralized applications and similar ecosystems.

As a typical instance of human–computer interaction, the notion of decentralized autonomous organization (DAO) represents an organization constructed by automatically executed rules, such as via smart contracts, incorporating features of the permissionless committee, transparent proposals, and fair contributions by stakeholders. As of May 2023, DAO has impacted over $24.3B market caps. However, there are limited studies focused on this emerging field. To fill the gap, we start from the ground truth by empirically studying the breadth and depth of the DAO markets in mainstream public chain ecosystems in this article. We dive into the most widely adoptable DAO launchpad,Snapshot, which covers 95% of the wild DAO projects for data collection and analysis. By integrating extensively enrolled DAOs and corresponding data measurements, we explore statistical resources from Snapshot and analyze data from 581 DAO projects, encompassing 16 246 proposals over the course of 3+ years. Our empirical research has uncovered a multitude of previously unknown facts about DAOs, spanning topics such as their status, features, performance, threats, and ways of improvement. We have distilled these findings into a series of key insights and takeaway messages, emphasizing their significance. Notably, our study is the first of its kind to comprehensively examine the DAO ecosystem with a focus on scale and scope of data, real-time relevance, practical implementations, and comprehensive metrics, addressing critical gaps in the current literature.

As Artificial Intelligence (AI) integrates into diverse areas, particularly in content generation, ensuring rightful ownership and ethical use becomes paramount, AI service providers are expected to prioritize responsibly sourcing training data and obtaining licenses from data owners. However, existing studies primarily center on safeguarding static copyrights, which simply treat metadata/datasets as non-fungible items with transferable/trading capabilities, neglecting the dynamic nature of training procedures that can shape an ongoing trajectory. In this paper, we presentIBis, a blockchain-based framework tailored for AI model training workflows. Our design can dynamically manage copyright compliance and data provenance in decentralized AI model training processes, ensuring that intellectual property rights are respected throughout iterative model enhancements and licensing updates. Technically,IBisintegrates on-chain registries for datasets, licenses and models, alongside off-chain signing services to facilitate collaboration among multiple participants. Further,IBisprovides APIs designed for seamless integration with existing contract management software, minimizing disruptions to established model training processes. We implementIBisusing Daml on the Canton blockchain. Evaluation results showcase the feasibility and scalability ofIBisacross varying numbers of users, datasets, models, and licenses.

In this paper, we design, implement, and (partially-) evaluate a lightweight bridge (as a type of middleware) to connect the Bitcoin and Ethereum networks that were heterogeneously uncontactable before. Inspired by the recently introduced Bitcoin Request Comment (BRC-20) standard, we leverage the flexibility of Bitcoin inscriptions by embedding editable operations within each satoshi and mapping them to programmable Ethereum smart contracts. A user can initialize his/her requests from the Bitcoin network, subsequently triggering corresponding actions on the Ethereum network. We validate the lightweight nature of our solution and its ability to facilitate secure and seamless interactions between two heterogeneous ecosystems.

This paper presents a primary exploration of Bitcoin inscriptions. We dive into the technological underpinnings and offer a detailed comparative analysis between Bitcoin inscriptions and NFTs on other blockchains. Further, we explore a wide range of use cases and significant opportunities for future innovation, including inscription derivative protocols, Bitcoin Layer2 solutions, and interoperability techniques.

In recent years, the utilization of Ethereum has significantly increased, positioning it as a favored platform among criminal entities. A recently proposed blacklisting method offers a compelling approach; however, its implementation faces numerous challenges. For instance, criminals may circumvent the blacklisting mechanism by creating new addresses and there are several ambiguities in their explanation. This paper explores the increasing use of Ethereum for criminal activities, focusing on the challenges of enforcing blacklisting to curb illegal transactions. We analyse blacklisting within cryptocurrency networks, particularly Ethereum, and develop features to detect illegal patterns. The study identifies unique issues in transaction networks that require specialised solutions beyond general cryptocurrency techniques. We propose a detection model based on these features and validate its effectiveness using real Ethereum datasets. The paper also reviews regulatory guidelines, highlighting ambiguities in their interpretation. Experiments on real-world data underscore the need to integrate technical methods and consider Shapley-value-based frameworks in designing effective solutions. The novelty of the method lies in its development of a feature-based detection model, leveraging Shapley-value frameworks to enhance explanation, address Ethereum’s unique challenges, and empirically validate its effectiveness using real Ethereum data, offering a more robust solution than traditional blacklisting approaches.

Abstract The health of mobile software ecosystems is closely related to the interests of software developers, end‐users, and stakeholders. Therefore, it is crucial to maintain the mobile software ecosystem healthy and functioning. Researchers have done considerable research on mobile software ecosystems like Android and iOS. However, the evolution laws implicit in mobile software ecosystems have not attracted widespread attention. This paper proposes a research framework for investigating the evolution process and influencing factors of mobile software ecosystems based on community mining. Firstly, we mine the evolving ecosystem from many mobile software projects based on a community detection algorithm. Then we analyze the evolution process of the ecosystem by identifying evolution events in different periods. Furthermore, we utilize the multinomial logistics regression model to analyze the relevant indicators and summarize the crucial factors affecting the evolution. Meanwhile, by training the long short term memory (LSTM) model to predict evolution events, our prediction accuracy can reach 75%. This work can be used to maintain and improve the healthy operations of mobile software ecosystems.

In this article, we explore the aftermath of the Silicon Valley Bank (SVB) collapse, with a particular focus on its impact on crypto markets. We conduct a multidimensional investigation, which includes a factual summary, analysis of user sentiment, and examination of market performance. We uncover a somewhat counterintuitive finding: the SVB collapse did not lead to the destruction of cryptocurrencies; instead, they displayed resilience.

Bitcoin is a cryptocurrency designed based on the concept of “decentralization,” and its price fluctuation is much larger than those of traditional financial assets, which has raised concerns about intentioned Bitcoin price manipulation. Most of the existing research on Bitcoin price manipulation is limited to the analysis of manipulation behaviors, lacking effective identification methods and performance metrics of identification methods. In this article, we use MtGox real trading data to build a trading network and analyze the diverse price manipulation patterns in the trading network. Based on this, we improve the classical graph representation learning method to effectively identify the price manipulation accounts. Specifically, considering that Bitcoin has special financial investment properties, and its price condenses important information from the financial market, we propose a novel identification algorithm, called Finan2vec, by feeding the time-series information of the price into the transfer strategy of the Node2vec algorithm. This algorithm makes it easier to detect nodes with large trades or significant price-raising behavior. We then use two classification algorithms to complete the identification of abnormal nodes and finally draw on the knowledge of financial market experts to construct a hit rate indicator to measure the effectiveness of the proposed method. The experimental results show that our method can effectively identify price manipulation accounts for Bitcoin and other public blockchain systems.

In recent years, the development of machine learning-based solutions for software services, particularly for source code, has grown rapidly. It is witnessed that many machine learning models for software services require the input of source code snippets in a desired form of abstract syntax tree (AST), which is mostly generated from an external tool. However, such data pre-processing tasks could be done by different engineering tools, and the impact of these tools towards final models is often neglected. In this work, we aim to investigate the source code engineering impacts towards machine learning-based software services. Three different types of parsing tools are identified, which are parser generator, parsing library and parser developed for a certain purpose. They are thoroughly evaluated towards the impacts on the prediction model of Code2Vec for the prediction task of the method name in Java language. The collective result on the Java-small dataset shows that the generated ASTs differ a lot in terms of source code structures and contents when using different parsing tools. The difference could influence the performance of the trained model significantly. Our result suggests that when machine learning models are implemented for software services, especially for code-related tasks, the selection of parsing tools should be thoroughly considered during the data pre-processing stage. While there are some interesting findings on Java-med and Java-small, we anticipate this work could provide some insights for better ML-based software service solutions from the perspective of source code engineering.

Algorithmic stablecoins (AS) are one special type of stablecoins that are not backed by any asset. They stand to revolutionize the way a sovereign flat operates. As implemented, AS are poorly stabilized in most cases; their prices easily deviating from the target or even falling into a catastrophic collapse, and are as a result often dismissed as a Ponzi scheme. However, what is the essence of Ponzi? In this paper, we try to clarify such a deceptive concept and reveal how AS work from a higher level. We find that Ponzi is basically a financial protocol that pays existing investors with funds collected from new ones. Running a Ponzi, however, does not necessarily imply that any participant is in any sense losing out, as long as the game can be perpetually rolled over. Economists call such realization as a rational Ponzi game. We thereby propose a rational model in the context of AS and draw its holding conditions. We apply the model to examine: whether or not the algorithmic stablecoin is a rational Ponzi game. Accordingly, we discuss two types of algorithmic stablecoins (Rebase & Seigniorage Shares) and dig into the historical market performance of a number of impactful projects to demonstrate the effectiveness of our model.

Across industries, there is an ever-increasing rate of data sharing for collaboration and innovation between organizations and their customers, partners, suppliers, and internal teams. However, many enterprises are restricted from freely sharing data due to regulatory restrictions across different regions, performance issues in moving large volume data, or requirements to maintain autonomy. In such situations, the enterprise can benefit from the concept of federated learning, in which machine learning models are constructed at various geographic sites. In this paper, we introduce a general framework, namely BDSP, to share data among enterprises based on Blockchain and federated learning techniques. Specifically, we propose a transparency contribution accounting mechanism to estimate the valuation of data and implement a proof-of-concept for further evaluation. The extensive experimental results show that the proposed BDSP has a competitive performance with higher training accuracy, an increase of over 5%, and lower communication overhead, reducing 3 times, compared to baseline approaches.

Existing NFTs confront restrictions of one-time incentive and product isolation. Creators cannot obtain benefits once having sold their NFT products due to the lack of relationships across different NFTs, which results in controversial profit sharing. This paper proposes a referable NFT solution to extend the incentive sustainability of NFTs. We construct the referable NFT (rNFT) network to increase exposure and enhance the referring relationship of inclusive items. We introduce the DAG topology to generate directed edges between each pair of NFTs with corresponding weights and labels for advanced usage. We accordingly implement and propose the scheme under Ethereum Improvement Proposal (EIP) standards, indexed in EIP-5521. Further, we provide the mathematical formation to analyze the utility for each rNFT participant. The discussion gives general guidance among multi-dimensional parameters. The solution, as a result, shape the recognition of potential values hidden in isolated NFTs and raise the interest of communities toward the discovery of NFT derivatives. To our knowledge, this is the first study to build a referable NFT network, explicitly showing the virtual connections among NFTs.

Decentralized autonomous organizations (DAOs) are critical to the blockchain ecosystem as they enable decentralized decision-making and governance, and facilitate the creation of decentralized applications (DApps) and organizations. However, despite significant importance, there is currently a lack of a comprehensive overview and detailed understanding of DAOs. To address the gap, this work presents a primary investigation of DAOs (35+). We category, examine and evaluate existing DAOs regarding their operational features, (non-)functionalities and real-world performance. In addition, we provide a consolidated exploration of DAOs by conducting a literature review [1] and an empirical study on mainstream projects, particularly Snapshot [2]. Our research contributes to a better understanding of DAOs and their potential impact on the blockchain ecosystem.

The development of effective recommender systems for Web3 assets, such as the Non-Fungible Token (NFT), requires concentration along with the growth of popularity and heterogeneity in many potential applications such as Web3 gaming and NFT rental markets, the requirements of predicting rNFT classification desire a practical solution. In this paper, we make use of the referable NFT (rNFT11In this work, rNFT mainly refers to the EIP-5521 protocol and corresponding formed network/topology [1], while NFT is used in the context of a single node, node sets, or products that align with the EIP-5521 protocol.) standard [2], indexed EIP-5521, to construct an rNFT classification framework leveraging Graph Neural Network (GNN), an emerging branch of Deep Learning (DL), which learns on the inherent topology of graph-based data. In particular, we first transform the rNFT backward and onward reference relationship to a Direct Acyclic Graph (DAG) and model appropriate node and edge features from rNFT metadata and associated token transactions. Next, a multi-layer GraphSage model is designed to include the collected features for the learning process. In this way, the model takes into account graph topology together with features to classify both the existing and incoming NFT nodes in a supervised way. We also give comprehensive elaboration on the architecture of the new GNN-based recommender system with discussions in regard to its characteristics and challenges. Furthermore, we expect to conduct extensive experiments, by presenting an initial plan, to show the feasibility and efficacy of our system.

Architectural design contexts contain a set of factors that greatly influence software application development. Among them, organizational design contexts consist of high-level company concerns and how it is structured, for example, stakeholders and development schedules heavily impacting design considerations. The Decentralized Autonomous Organization (DAO), as a vital concept in the Web3 space, represents an organization constructed by automatically executed rules, such as via smart contracts, holding features of the permissionless committee, transparent proposals, and fair contribution by participated stakeholders. In this work, we conduct a systematic literature review of existing DAO literature to summarize its structural features, benefits and challenges, and potential development directions in the context of Web3 applications.

NEO is one of the top public chains worldwide. It adopts a new consensus algorithm calleddelegated Byzantine Fault Tolerance(dBFT). In this article, we formalize dBFT via the state machine replication model and point out its potential issues. Our theoretical analysis indicates that dBFT could guarantee neitherlivenessnorsafety, even if the number of Byzantine nodes is no more than the threshold, which has contradicted the established security claim. Then, we identify two attacks and successfully simulate them. Finally, we provide recommendations. Notably, NEO official team has accepted our suggested fixes.

Garg et al. (TCC 2018) defined the notion of registration-based encryption (RBE) where the private key generator (PKG) is decoupled from key management and replaced by a key curator (KC). KC does not possess any cryptographic secrets and only plays the role of aggregating the public keys of all the registered users and updating the public parameters whenever a new user joins the system, which solves the key escrow issue. Notwithstanding, RBE still places a significant amount of trust in KC, whose actions are not accountable, e.g., it could secretly register multiple keys for already registered users. In this article, we propose a blockchain-based RBE framework, which provides total transparency and decentralization of KC by leveraging smart contracts. Our framework transfers the right of key management from KC to individual participants and keeps publicly upgradable parameters on-chain. We provide a basic construction that calculates the public parameter on-chain and an extended construction with better efficiency, which merely calculates the roots of trees on-chain. Our basic version is theoretically feasible, while the extended version is practically feasible. In particular, the enhanced scheme reduces computing complexity to a constant level. Our prototype implementation and evaluation results demonstrate that our extended construction is satisfactorily efficient.

Multifocus image fusion (MFIF), as an efficient way to improve the visual effect of images with partial focus defects, is of great significance in the field of image enhancement. According to the imaging principle of the lens, we summarize the visual salience priors (VSP) from the daily photo scene and two relationships from MFIF. Thereby, an edge-sensitive model for MFIF is presented in this study. Supported by VSP, we consider the correlation between salience object detection (SOD) and MFIF, and select the former as a pre-training task. SOD provides the network with realistic depth of field and bokeh effects to learn, and enhances the network’s ability to extract and express the edges of focused objects. Meanwhile, given the scarcity of real multifocus training sets, we propose a randomized approach to generate massive training sets and pseudo-labels based on limited unlabeled data. Besides, two attention modules are designed based on isometric domain transformation (IDT) in the traditional edge-preservation field. IDT removes interference information from feature maps in a low-cost manner, thereby facilitating channel-wise and spatial-wise weight assignments. Experimental results on four datasets show that the performance of our model is superior to that of many supervised models, without the need of any real MFIF training set.

Automatic generation control (AGC) is paramount in maintaining the stability and operation of power grids. Its dependence on communication systems makes it vulnerable to various cyberphysical attacks. False data injection attacks (FDIA) are particularly difficult to detect and represent a major threat to AGC systems. This article proposes a novel spatio-temporal learning algorithm that can learn the normal dynamics of the power grid with AGC system to deal with this problem. The algorithm first uses a long short-term memory autoencoder to learn the normal dynamics. It then utilizes this unsupervised learned model in detecting the various possibilities of FDIA affecting the AGC system by evaluating the reconstruction residual of each measurements sample. The proposed algorithm is data-driven which makes it resilient against AGC's parameters uncertainties and modeling nonlinearities. The effectiveness of the developed algorithm is evaluated through test cases with various basic and stealth FDIAs.

It is important to minimize the energy consumption of large-scale, geographically distributed edge data centers (EDCs). While modern processing units (PUs) have energy-saving features like Dynamic Voltage and Frequency Scaling (DVFS) and Per-Core Power Gating (PCPG), optimization is still complex and requires a holistic approach. This article presents a new decentralized, three-timescale, online optimization approach that enables multicore micro data centers (MDCs) to optimize their per-PU power states, per-enabled-PU voltage-frequency levels and offloading schedules at three different timescales. The key idea is that we employ multi-timescale Lyapunov optimization to decouple the energy minimization between workload scheduling and result delivery at a small timescale and PU configuration at large timescales. Another important aspect is that we apply the primal decomposition to decouple the PU configuration between a per-enabled-PU voltage-frequency level at an intermediate timescale and a per-PU power state at a large timescale. Experiments demonstrate that the proposed approach improves energy efficiency significantly by up to 4.5 times in our considered lightly loaded situations where DVFS alone does not work effectively, compared to existing benchmarks.

Proof of Authority (PoA) is a type of permissioned consensus algorithm with a fixed committee. PoA has been widely adopted by communities and industries due to its better performance and faster finality. In this paper, we explore the unfairness issue existing in the current PoA implementations. We have investigated 2,500+ in the wild projects and selected 10+ as our main focus (covering Ethereum, Binance smart chain, etc.). We have identified two types of order manipulation attacks to separately break the transaction-level (a.k.a. transaction ordering) and the block-level (sealer position ordering) fairness. Both of them merely rely on honest-but-profitable sealer assumption without modifying original settings. We launch these attacks on the forked branches under an isolated environment and carefully evaluate the attacking scope towards different implementations. To date (as of Nov 2021), the potentially affected PoA market cap can reach up to 681,087 million USD. Besides, we further dive into the source code of selected projects, and accordingly, propose our recommendation for the fix. To the best of knowledge, this work provides the first exploration of the unfairness issue in PoA algorithms.

Ensuring the integrity of product manufacture information and securely verifying the authenticity of a component is critical for both manufacturers and customers. However, traditional product identifying solutions offer little protection over counterfeit and cyber-attack. We propose a Blockchain-based product identification and certification system called Universal Identifier of Things (UIT) that enables fast product authenticity verification using low-cost devices. We leverage additive manufacturing technologies to embed a unique identifier into a product. The identifier is then digitalized by generating a digital certificate which is stored on Blockchain during the whole product life cycle for various applications/services (provenance, traceability, product warranty and call-back, etc.). We prove this concept by integrating 3D printing and Hyperledger Blockchain technologies to demonstrate that we can ensure the integrity of products with UIT by bridging the cyber and physical worlds.

With the growing proliferation of intelligent end devices and data analytics techniques, real momentum towards the development of smart healthcare networks (SHN) has already been evident. Multiple parties in SHNs continuously exchange medical data in order to achieve a precise diagnosis and process optimization. Privacy issue emerges since medical data are susceptible, while the combination of a series of medical data may lead to further privacy leakage. Adversaries launch unceasingly launch poisoning attacks, a dominant attack to maliciously manipulate data, severely impact the authenticity of the data transmitting over the SHNs, leading to misdiagnosing or even physical damage. In this paper, we propose a personalized differential privacy model built upon blockchain, in which the community density is exploited to customize the degree of privacy protection and inject corresponding noise data. Besides using blockchain as the underlying network architecture to defeat poisoning attacks. The proposed model can guarantee the authentication of the differentially private data, traceability of data, and single-point failure avoidance in SHN. Evaluation and extensive results using real-world data sets demonstrate the superiority of the proposed model.

The ubiquity of sensor technology through the Internet of Things and mobile devices has led to a surge in the generation of personal data. The use of personal data to provide personalized services and optimize business decision-making has recently become a popular trend. The data marketplace is one of the most critical places where companies can source data to organize the variety of data source formats and improve individuals’ data self-management (e.g., data access control and usage). This paper proposes a Databox-based delivery service via blockchain that provides data consumers with secure and controlled access to requested data sources of interest. It consists of a databox-based data delivery module and decentralized activities service module. The former is responsible for data delivery and the latter is to provide a one-stop data sharing service on blockchain. We implement a preliminary prototype and conduct an evaluation of system activities and smart contracts utility.

Federated Learning (FL) presents a privacy-compliant approach by sharing model parameters instead of raw data. However, how to motivate data owners to participate in and stay within an FL ecosystem by continuously contributing their data to the FL model remains a challenge. In this article, we propose a hybrid incentive mechanism based on blockchain to address the above challenge. The proposed mechanism comprises two primary smart contract-based modules, namely the reputation module and the reverse auction module. The former is used to dynamically calculate the reputation score of each FL participant. It employs a trust-jointed reputation scheme to balance the weights between trust values of parameters and bid prices. The latter is responsible for initiating FL auction tasks, calculating price rankings, and assigning corresponding token rewards. Experiments are conducted to evaluate the feasibility and performance of the proposed mechanism against the three typical threats. Experimental results indicate that our mechanism can successfully reduce incentive costs while preventing participants from colluding and over-bidding in the data sharing auction.

As one of the most frequently used Internet-of-Things (IoT) devices, energy smart meter has been widely adopted to facilitate the measures of residential energy use. Residents pay for the bills from energy suppliers according to their monthly/seasonal usage. Practically, there is a demand from residents/governments to check whether the bills are in line with their real consumptions. However, it is challenging to realize this demand due to two critical problems. The first problem refers to the nonrepudiated privacy issue caused by access to residents’ energy consumption history (e.g., data integrity may be questioned, and residents’ daily timetables may be exposed). The second problem comes from the efficiency requirement for bulk auditing requests on residents’ bills and consumptions, usually risen by governments. So far, we have not found any solutions that can be directly used in this case. In this article, we propose using homomorphic encryption cooperated with the blockchain technique to leverage the data auditing and privacy-preserving requirements. We also employe a certificateless signature to resolve the efficiency bottleneck in batch auditing. This framework, calledpAuditChain, not only accepts personal requests from residents for consumption checking but also handles bulk auditing requests issued by governments. To validate the correctness of the framework functions, we carried out a series of theoretical analysis, especially on the privacy preserving and auditing processes. To the best of our knowledge, the proposed framework is among the first solutions to improve the security and privacy of bills without losing the auditing function. Our approach concerns with IoT smart meters in energy supply industries and could be further extended to other forms of IoT devices with the bill demands.

Since the outbreak of COVID-19, an alternative way to keep students on the track, meanwhile, prevent them from being at the risk of infection is in highly demand. Many education providers had made a move in trial of delivering knowledge and learning materials remotely. Along with this trend, learning management systems, open educational resources (OERs) and OER platforms, mini applications in social media and video-conference software were combined in a rush to create a multi-channel delivery mode to make learning resources openly available round-the-clock. Learning activities in this fast migration to online were regularly found to be carried out in gradual and fragmented time spans. Due to the little-known learner information along with the continuously released new OERs, the cold start problem still hinders the innovative mode of delivery and adaptive micro learning. To overcome the data sparsity, an online computation is proposed to benefit OER providers and instructors. A lightweight learner-micro-OER profile and two algorithmic solutions are provided to tackle the new user and new item cold start problem, respectively. Learning paths are generated and optimized in terms of heuristic rules to form the initial recommendation list. By adopting the same set of rules, newly released micro OERs are inserted into established learning paths to increase their discoverability.

Honest majority is the key security assumption of Proof-of-Work (PoW) based blockchains. However, the recent 51% attacks render this assumption unrealistic in practice. In this paper, we challenge this assumption against rational miners in the PoW-based blockchains in reality. In particular, we show that the current incentive mechanism may encourage rational miners to launch 51% attacks in two cases. In the first case, we consider a miner of a stronger blockchain launches 51% attacks on a weaker blockchain, where the two blockchains share the same mining algorithm. In the second case, we consider a miner rents mining power from cloud mining services to launch 51% attacks. As 51% attacks lead to double-spending, the miner can profit from these two attacks. If such double-spending is more profitable than mining, miners are more intended to launch 51% attacks rather than mine honestly.

Flow-sensitive points-to analysis provides better precision than its flow-insensitive counterpart. Traditionally performed on the control-flow graph, it incurs heavy analysis overhead. For performance, staged flow-sensitive analysis (SFS) is conducted on a pre-computed def-use (value-flow) graph where points-to sets of variables are propagated across def-use chains sparsely rather than across control-flow in the control-flow graph. SFS makes the propagation of different objects' points-to sets sparse (multiple-object sparsity), however, it suffers from redundant propagation between instructions of the same object's points-to sets (single-object sparsity). The points-to set of an object is often duplicated, resulting in redundant propagation and storage, especially in real-world heap-intensive programs. We notice that a simple graph prelabelling extension can identify much of this redundancy in a pre-analysis. With this pre-analysis, multiple nodes (instructions) in the value-flow graph can share an individual memory object's points-to set rather than each node maintaining its own points-to set for that single object. We present object versioning for flow-sensitive points-to analysis, a finer single-object sparsity technique which maintains the same precision while allowing us to avoid much of the redundancy present in propagating and storing points-to sets. Our experiments conducted on 15 open-source programs, when compared with SFS, show that our approach runs up to 26.22× faster (5.31× on average), and reduces memory usage by up to 5.46× (2.11 × on average).

With the rise of crossover services, service providers usually cooperate with each other on different platforms to expand their service value. However, in cross-platform cooperation, insufficient understanding and malicious competition between different platforms would lead to inaccurate trust establishment and unreliable trust recommendations. In this paper, we propose a trust management framework of cross-platform based on blockchain to establish, store and recommend trust securely for cross-platform cooperation. Firstly, we take into account the contextual background information to enhance interaction and understanding between platforms to achieve accurate trust establishment. Secondly, the trust recommendation algorithm is written into the blockchain in the form of smart contracts, which can ensure the security of trust recommendation. Finally, experiments are used to demonstrate the superiority and reliability of the framework.

In classification, a decision tree is a common model due to its simple structure and easy understanding. Most of decision tree algorithms assume all instances in a dataset have the same degree of confidence, so they use the same generation and pruning strategies for all training instances. In fact, the instances with greater degree of confidence are more useful than the ones with lower degree of confidence in the same dataset. Therefore, the instances should be treated discriminately according to their corresponding confidence degrees when training classifiers. In this paper, we investigate the impact and significance of degree of confidence of instances on the classification performance of decision tree algorithms, taking the classification and regression tree (CART) algorithm as an example. First, the degree of confidence of instances is quantified from a statistical perspective. Then, a developed CART algorithm named C_CART is proposed by introducing the confidence of instances into the generation and pruning processes of CART algorithm. Finally, we conduct experiments to evaluate the performance of C_CART algorithm. The experimental results show that our C_CART algorithm can significantly improve the generalization performance as well as avoiding the over-fitting problem to a certain extend.

Edge computing is promising to solve the latency issue in the Internet of Vehicles (IoV). However, due to decentralization, traditional edge computing suffers in management, deployment, and security. Containerization relaxes resource deployment and migration problems, but current container scheduling policies are inefficient to process complicated tasks based on directed acyclic graph or DAG structures. In this article, we design a containerized edge computing platform CUTE, which provides low-latency computation services for the Internet of Vehicles. The centralized controller is empowered with resource management and orchestration, and containers are scheduled to appropriate edge servers to optimize the computation delay. CUTE is also integrated with blockchain to improve network security. We formulate the vehicle task offloading and container scheduling problems and develop a heuristic container scheduling algorithm for DAG-based computation tasks submitted by vehicles remotely. We implement and deploy CUTE into the China Mobile Network, and conduct comprehensive experiments and a case study. The experiment results show that CUTE can provide low-latency computation services for vehicular applications and that the heuristic algorithm outperforms traditional container scheduling policies.

Device failure detection is one of most essential problems in Industrial Internet of Things (IIoT). However, in conventional IIoT device failure detection, client devices need to upload raw data to the central server for model training, which might lead to disclosure of sensitive business data. Therefore, in this article, to ensure client data privacy, we propose a blockchain-based federated learning approach for device failure detection in IIoT. First, we present a platform architecture of blockchain-based federated learning systems for failure detection in IIoT, which enables verifiable integrity of client data. In the architecture, each client periodically creates a Merkle tree in which each leaf node represents a client data record, and stores the tree root on a blockchain. Furthermore, to address the data heterogeneity issue in IIoT failure detection, we propose a novel centroid distance weighted federated averaging (CDW_FedAvg) algorithm taking into account the distance between positive class and negative class of each client data set. In addition, to motivate clients to participate in federated learning, a smart contact-based incentive mechanism is designed depending on the size and the centroid distance of client data used in local model training. A prototype of the proposed architecture is implemented with our industry partner, and evaluated in terms of feasibility, accuracy, and performance. The results show that the approach is feasible, and has satisfactory accuracy and performance.

Summary The fast pace evolving of Android malware demands for highly efficient strategy. That is, for a range of malware types, a malware detection scheme needs to be resilient and with minimum computation performs efficient and precise. In this paper, we propose Mutual Information and Feature Importance Gradient Boosting (MIFIBoost) tool that uses byte n‐gram frequency. MIFIBoost consists of four steps in the model construction phase and two steps in the prediction phase. For training, first, n‐grams of both the classes.dex and AndroidManifest.xml binary files are obtained. Then, MIFIBoost uses Mutual Information (MI) to determine the top most informative items from the entire n‐gram vocabulary. In the third phase, MIFIBoost utilizes the Gradient Boosting algorithm to re‐rank these top n‐grams. For testing, MIFIBoost uses the learned vocabulary of byte n‐grams term‐frequency (tf) to feed into the classifier for prediction. Thus, MIFIBoost does not require reverse engineering. A key insight from this work is that filtering using XGBoost helps us to address the hard problem of detecting obfuscated malware better while having a negligible impact on nonobfuscated malware. We have conducted a wide range of experiments on four different datasets one of which is obfuscated, and MIFIBoost outperforms state‐of‐the‐art tools. MIFIBoost's f1‐score for Drebin, DexShare, and AMD datasets is 99.1%, 98.87%, and 99.62%, respectively, a False Positive Rate of 0.41% using AMD dataset. On average, the False Negative Rate of MIFIBoost is 2.1% for the PRAGuard dataset in which seven different obfuscation techniques are implemented. In addition to fast run‐time performance and resiliency against obfuscated malware, the experiments show that MIFIBoost performs quite efficiently for five zero‐day families with 99.78% AUC.

Nowadays, surveillance cameras have been pervasively equipped with vehicles in public transport systems. For the sake of public security, it is crucial to upload recorded surveillance videos to remote servers timely for backup and necessary video analytics. However, continuously uploading video content generated by tens of thousands of vehicles can be extremely bandwidth consuming. In this work, we investigate the video uploading problem for moving buses by proposing to deploy dedicated access points (AP) at bus stops to facilitate video uploading. We define the harmonic objective for our problem, which includes minimizing the video uploading delay and minimizing the AP deployment cost. This problem is with two fundamental challenges. Firstly, it is difficult to balance the bandwidth capacity allocated to many buses because a bus obtains bandwidth resource from a series of APs deployed at stops along its route. Secondly, due to the randomness of bus movement and the complexity of bus routes, it is hard to predict the workload of an AP. Hence, it is challenging to estimate the delay of uploading video content through an AP. To cope with these challenges, we propose a water filling placement (WFP) algorithm, aiming to balance the aggregated bandwidth allocated to each bus. A queuing model is established to analyze the uploading delay of video content. We further resort to machine learning models to factor the influence of bus routes into our queuing model. Finally, a convex problem is formulated to optimize the harmonic objective, which can be optimally solved with the gradient descent (GD) based algorithm. We validate the correctness of our theoretical analysis and demonstrate the effectiveness of our method by carrying out extensive experiments using bus traces collected in Shenzhen city of China. In comparison with benchmark algorithms, our solution can always achieve the best performance.

By respecting program control-flow, flow-sensitive pointer analysis promises more precise results than its flow-insensitive counterpart. However, existing heap abstractions for C and C++ flow-sensitive pointer analyses model the heap by creating a single abstract heap object for each memory allocation. Two runtime heap objects which originate from the same allocation site are imprecisely modelled using one abstract object, which makes them share the same imprecise points-to sets and thus reduces the benefit of analysing heap objects flow-sensitively. On the other hand, equipping flow-sensitive analysis with context-sensitivity, whereby an abstract heap object would be created (cloned) per calling context, can yield a more precise heap model, but at the cost of uncontrollable analysis overhead when analysing larger programs. This paper presents TypeClone, a new type-based heap model for flow-sensitive analysis. Our key insight is to differentiate concrete heap objects lazily using type information at use sites within the program control-flow (e.g., when accessed via pointer dereferencing) for programs which conform to the strict aliasing rules set out by the C and C++ standards. The novelty of TypeClone lies in its lazy heap cloning: an untyped abstract heap object created at an allocation site is killed and replaced with a new object (i.e. a clone), uniquely identified by the type information at its use site, for flow-sensitive points-to propagation. Thus, heap cloning can be performed within a flow-sensitive analysis without the need for context-sensitivity. Moreover, TypeClone supports new kinds of strong updates for flow-sensitive analysis where heap objects are filtered out from imprecise points-to relations at object use sites according to the strict aliasing rules. Our method is neither strictly superior nor inferior to context-sensitive heap cloning, but rather, represents a new dimension that achieves a sweet spot between precision and efficiency. We evaluate our analysis by comparing TypeClone with state-of-the-art sparse flow-sensitive points-to analysis using the 12 largest programs in GNU Coreutils. Our experimental results also confirm that TypeClone is more precise than flow-sensitive pointer analysis and is able to, on average, answer over 15% more alias queries with a no-alias result.

Statistical divergence is widely applied in multimedia processing, basically due to regularity and interpretable features displayed in data. However, in a broader range of data realm, these advantages may no longer be feasible, and therefore a more general approach is required. In data detection, statistical divergence can be used as a similarity measurement based on collective features. In this paper, we present a collective detection technique based on statistical divergence. The technique extracts distribution similarities among data collections, and then uses the statistical divergence to detect collective anomalies. Evaluation shows that it is applicable in the real world.

Android supports an event dispatching system that reacts to system and user actions by generating events. However, lack of synchronization between events can lead to event-based races in Android apps. Such event-based races are difficult to detect dynamically due to the challenges faced in generating the right events to satisfy the right event-dependent conditional branches, so that their guarded racy statements can be reached. As a result, existing dynamic tools, which try to find and reschedule some race-triggering events heuristically, are often ineffective.We introduce SIEVE, a tool for exposing event-based races in Android apps dynamically by leveraging a new selective branch instrumentation technique. For the conditionals potentially affecting a race (detected, say, by a static tool), SIEVE fixes the true/false outcomes of some of these conditionals based on a systematic branch analysis, which analyzes the satisfiability of all the conditionals guarding the given racy statements and their safeness for instrumentation. By instrumenting certain branches selectively this way, we can not only expose effectively event-based races but also reduce substantially the negative ramifications of instrumentation (e.g., reporting non-existent races and introducing unexpected crashes during dynamic execution). An evaluation of SIEVE with 25 Android apps shows that our tool can expose event-based races more effectively than the state of the art.

Blockchain brings opportunities and challenges for financial data sharing services. The essential properties in a distributed multi-parties system are data access control and privacy control. This paper proposes a hierarchical data access model for financial services, which contains consent management and dynamic credits management. We implement fine-grained data access control through rating accredited data recipients (ADRs). By accessing corresponding blockchain service logs, ADRs' credits will dynamically be updated. The credits evaluation algorithm is responsible for calculating ADRs' credits based on their completion rate, business ethics rate, and feedback positive rate. Moreover, through applying smart contracts, the efficiency of consent management can be improved, and privacy policies can be managed elastically. Finally, we deploy smart contracts on the Ethereum Rinkeby testnet to evaluate the model feasibility. Furthermore, the theoretical analysis and experimental results indicate that the prototype is secure and efficient.

Abstract Malware detection based on static features and without code disassembling is a challenging path of research. Obfuscation makes the static analysis of malware even more challenging. This paper extends static malware detection beyond byte level $n$-grams and detecting important strings. We propose a model (Byte2vec) with the capabilities of both binary file feature representation and feature selection for malware detection. Byte2vec embeds the semantic similarity of byte level codes into a feature vector (byte vector) and also into a context vector. The learned feature vectors of Byte2vec, using skip-gram with negative-sampling topology, are combined with byte-level term-frequency (tf) for malware detection. We also show that the distance between a feature vector and its corresponding context vector provides a useful measure to rank features. The top ranked features are successfully used for malware detection. We show that this feature selection algorithm is an unsupervised version of mutual information (MI). We test the proposed scheme on four freely available Android malware datasets including one obfuscated malware dataset. The model is trained only on clean APKs. The results show that the model outperforms MI in a low-dimensional feature space and is competitive with MI and other state-of-the-art models in higher dimensions. In particular, our tests show very promising results on a wide range of obfuscated malware with a false negative rate of only 0.3% and a false positive rate of 2.0%. The detection results on obfuscated malware show the advantage of the unsupervised feature selection algorithm compared with the MI-based method.

Summary Provenance is information about the origin and creation of data. In data science and engineering related with cloud environment, such information is useful and sometimes even critical. In data analytics, it is necessary for making data‐driven decisions to trace back history and reproduce final or intermediate results, even to tune models and adjust parameters in a real‐time fashion. Particularly, in cloud, users need to evaluate data and pipeline trustworthiness. In this paper, we propose a solution: LogProv, toward realizing these functionalities for big data provenance, which needs to renovate data pipelines or some of big data software infrastructure to generate structured logs for pipeline events, and then stores data and logs separately in cloud space. The data are explicitly linked to the logs, which implicitly record pipeline semantics. Semantic information can be retrieved from the logs easily since they are well defined and structured beforehand. We implemented and deployed LogProv in Nectar Cloud,* associated with Apache Pig, Hadoop ecosystem, and adopted Elasticsearch to provide query service. LogProv was evaluated and empirically case studied. The results show that LogProv is efficient since the performance overhead is no more than 10%; the query can be responded within 1 second; the trustworthiness is marked clearly; and there is no impact on the data processing logic of original pipelines.

Under dynamic workload, cost-efficient stream processing on the Cloud, where the cost for Cloud resources is minimized and the performance is optimized, is non-trivial to achieve. To tackle this, we make use of queueing theory to model the performance of stream processing components. We propose a stable performance and cost trade-off scaling algorithm for large-scale stream processing on the Cloud. Our approach takes into consideration typical characteristics of Cloud environments such as the machine size, dynamic cost, high machine start-up costs and large migration time of Cloud resources. We present a strategy based on a multi-step configuration to enable dynamic stream processing on the Cloud while cost-efficiently achieving user-level Quality of Service (QoS). Our proposed solution addresses the issues of resource provisioning with respect to both the performance objectives and dynamic scheduling of Cloud environments. We present an exemplar implementation, based on a Heron stream processing application that was deployed automatically and subsequently monitored to ensure its QoS.

A recent trojan attack on deep neural network (DNN) models is one insidious variant of data poisoning attacks. Trojan attacks exploit an effective backdoor created in a DNN model by leveraging the difficulty in interpretability of the learned model to misclassify any inputs signed with the attacker's chosen trojan trigger. Since the trojan trigger is a secret guarded and exploited by the attacker, detecting such trojan inputs is a challenge, especially at run-time when models are in active operation. This work builds STRong Intentional Perturbation (STRIP) based run-time trojan attack detection system and focuses on vision system. We intentionally perturb the incoming input, for instance by superimposing various image patterns, and observe the randomness of predicted classes for perturbed inputs from a given deployed model---malicious or benign. A low entropy in predicted classes violates the input-dependence property of a benign model and implies the presence of a malicious input---a characteristic of a trojaned input. The high efficacy of our method is validated through case studies on three popular and contrasting datasets: MNIST, CIFAR10 and GTSRB. We achieve an overall false acceptance rate (FAR) of less than 1%, given a preset false rejection rate (FRR) of 1%, for different types of triggers. Using CIFAR10 and GTSRB, we have empirically achieved result of 0% for both FRR and FAR. We have also evaluated STRIP robustness against a number of trojan attack variants and adaptive attacks.

Unlike Java, Android provides a rich set of APIs to support a hybrid concurrency system, which consists of both Java threads and an event queue mechanism for dispatching asynchronous events. In this model, concurrency errors often manifest themselves in the form of order violations. An order violation occurs when two events access the same shared object in an incorrect order, causing unexpected program behaviors (e.g., null pointer dereferences). This paper presents SARD, a static analysis tool for detecting both intra-and inter-thread use-after-free (UAF) order violations, when a pointer is dereferenced (used) after it no longer points to any valid object, through systematic modeling of Android's concurrency mechanism. We propose a new flow-and context-sensitive static happens-before (HB) analysis to reason about the interleavings between two events to effectively identify precise HB relations and eliminate spurious event interleavings. We have evaluated SARD by comparing with NADROID, a state-of-the-art static order violation detection tool for Android. SARD outperforms NADROID in terms of both precision (by reporting three times fewer false alarms than NADROID given the same set of apps used by NADROID) and efficiency (by running two orders of magnitude faster than NADROID).

Crossover cooperation and fusion between services is becoming very common in the modern service industry. Crossover service fusion can create value that cannot be provided by single-domain services, thus achieving the value-emergence effect of 1+1>2. However, semantic inconsistencies in business and interface make crossover service fusion difficult and time-consuming. This paper proposes an interactive crossover service fusion approach based on microservice architecture to enable smooth and rapid integration of domain services. This approach takes service fusion requirements as the driving force to detect business inconsistencies between the services to be fused, and carries out business process reengineering by human-computer interaction. Then, semantic inconsistencies in service interface matching are detected and solved by splitting and completing parameter concepts to obtain the service fusion design scheme. Finally, the implementation scheme based on microservice architecture transforms the business coupling between domain services into asynchronous data communication, which facilitates the crossover fusion of complex business services. The elderly healthcare application is used to demonstrate and validate our approach.

This article studies synchronous online distributed software update, also known as rolling upgrade in DevOps, which in clouds upgrades software versions in virtual machine instances even when various failures may occur. The goal is to minimise completion time, availability degradation, and monetary cost for entire rolling upgrade by selecting proper parameters. For this goal, we propose a stochastic model and a novel optimisation method. We validate our approach to minimise the objectives through both experiments in Amazon Web Service (AWS) and simulations.

In this paper, we present an online computation approach implemented in a cloud-based system to assist open education resource (OER) providers and instructors dealing with the sparsity of data in micro OER recommendation. An algorithmic framework is provided to realize the novel micro OER recommendation system based on heuristic rules. These rules can also optimize the approaches to blending new-coming micro OERs into established learning paths. Comparing with different widely used recommender systems, our evaluation shows the proposed heuristic algorithms for online computation performs satisfactorily in terms of precision and recall values.

Real-time data processing is often a necessity as it can provide insights that have less value if discovered off-line or after the fact. However, large-scale stream processing systems are non-trivial to build and deploy. While there are many frameworks that allow users to create large-scale distributed systems, there remains many challenges in understanding the performance, cost of deployment and considerations and impact of potential (partial) outages on real-time systems performance. Our work considers the performance of Cloud-based stream processing systems in terms of back-pressure and expected utilization. The performance of an exemplar stream application is explored using different Cloud-based virtual machine resources and where the scale of deployment and cost benefits are taken into consideration in relation to the overall performance. To achieve this, we develop an algorithm based on queueing theory to predict the throughput and latency of stream data processing while supporting system stability. Our methodology for making fundamental measurements is applicable to mainstream stream processing frameworks such as Apache Storm and Heron. The method is especially suitable for large-scale distributed stream processing where jobs can run for extended time periods. We benchmark the performance of the system on the national research cloud of Australia (Nectar), and present a performance analysis based on estimating the overall effective utilization.

Mobile cloud computing has emerged as a pervasive paradigm to execute computing tasks for capacity- limited mobile devices. More specifically, at the network edge, the resource-rich and trusted cloudlet system is acting as a 'data center in a box' to support compute-intensive mobile applications. The mobile cloudlets can provide in-proximity services by executing the workloads for nearby devices. Nevertheless, load balancing in mobile cloudlet network is of great importance, as it has a huge impact on task response time. Existing methods for cloudlet load balancing basically rely on the strategic placement or user cooperation. However, the above solutions require the global task load information from the whole network, which is costly in both communication and computation. To achieve more efficient and low-cost load balancing, we propose 'CTOM', a Collaborative Task Offloading Mechanism for mobile cloudlet networks. Our solution is based on the balls-and-bins theory and can balance the task load only requiring limited information. Extensive simulations and evaluation based on mobility trace demonstrate that, our CTOM outperforms the conventional random and proportional allocation schemes by reducing the task gaps among mobile cloudlets by 65% and 55% respectively. Meanwhile, CTOM's performance is close to that of the greedy algorithm but with much lower computing complexity.

Recent findings show that many mission critical Internet of Things (IoT) applications are exposed to increasing security risks. The complex and dynamic nature of the IoT and its applications also bring new types of security threats. To achieve end-to-end secure communication, IoT applications need key establishment schemes with integrated security fundamentals such as identification and authentication of IoT components, as well as integrity, confidentiality, availability and authenticity of data, to prevent security attacks from weakening and disrupting the communication. In this context, we present a new lightweight key establishment scheme that comprises a novel Identity-Based Credentials (IBC) mechanism and key establishment protocol. The IBC mechanism enables an IoT component to securely disclose a single identity for security support and privacy enhancement for key establishment. In this paper, we model IoT application attributes to develop our lightweight security and privacy enhancing key establishment scheme. The formal verification and analysis show that, compared to the existing schemes, our proposed scheme is resilient against more types of security attacks, and incurs lower computational and communication costs in IoT applications.

Zero-day Use-After-Free (UAF) vulnerabilities are increasingly popular and highly dangerous, but few mitigations exist. We introduce a new pointer-analysis-based static analysis, CRed, for finding UAF bugs in multi-MLOC C source code efficiently and effectively. CRed achieves this by making three advances: (i) a spatio-temporal context reduction technique for scaling down soundly and precisely the exponential number of contexts that would otherwise be considered at a pair of free and use sites, (ii) a multi-stage analysis for filtering out false alarms efficiently, and (iii) a path-sensitive demand-driven approach for finding the points-to information required.

Mobile learning in massive open online course (MOOC) evidently differs from its traditional ways as it relies more on collaborations and becomes more fragmented. We present a cloud-based virtual learning environment (VLE) which can organize learners into a better teamwork context and customize micro learning resources in order to meet personal demands in real time. Particularly, a smart micro learning environment was built by a newly designed Software as a Service (SaaS), namely Micro Learning as a Service (MLaaS). It aims to provide adaptive micro learning contents as well as learning path identifications customized for each individual learner. To personalize the micro learning, a dynamic learner model is constructed with regards to the internal and external factors that can affect learning experience and outcomes. Educational data mining (EDM) techniques are employed as the main method to understand learners' behaviors and recognize learning resource features. A solution of learning path optimization is also proposed towards assembling a complete MOOC learning experience.

Typestate analysis relies on pointer analysis for detecting temporal memory safety errors, such as use-after-free (UAF). For large programs, scalable pointer analysis is usually imprecise in analyzing their hard "corner cases", such as infeasible paths, recursion cycles, loops, arrays, and linked lists. Due to a sound over-approximation of the points-to information, a large number of spurious aliases will be reported conservatively, causing the corresponding typestate analysis to report a large number of false alarms. Thus, the usefulness of typestate analysis for heap-intensive clients, like UAF detection, becomes rather limited, in practice.

Open learning is a rising trend in the educational sector and it attracts millions of learners to be engaged to enjoy massive latest and free open education resources (OERs). Through the use of mobile devices, open learning is often carried out in a micro learning mode, where each unit of learning activity is commonly shorter than 15 minutes. Learners are often at a loss in the process of choosing OER leading to their long term objectives and short term demands. Our pilot work, namely MLaaS, proposed a smart system to deliver personalized OER with micro learning to satisfy their real-time needs, while its decision-making process is scarcely supported due to the lack of historical data. Inspired by this, MLaaS now embeds a new solution to tackle the cold start problem, by opening up a brand new profile for each learner and delivering them the first resources in their fresh start learning journey. In this paper, we also propose an ontology-based mechanism for learning prediction and recommendation.

It is a challenge to ensure data integrity for cloud-based Internet of Things (IoT) applications because of the inherently dynamic nature of IoT data. The available frameworks of data integrity verification with public auditability cannot avoid the Third Party Auditors (TPAs). However, in a dynamic environment, such as the IoT, the reliability of the TPA-based frameworks is far from being satisfactory. In this paper, we propose a blockchain-based framework for Data Integrity Service. Under such framework, a more reliable data integrity verification can be provided for both the Data Owners and the Data Consumers, without relying on any Third Party Auditor (TPA). In this paper, the relevant protocols and a subsequent prototype system, which is implemented to evaluate the feasibility of our proposals, are presented. The performance evaluation of the implemented prototype system is conducted, and the test results are discussed. The work lays a foundation for our future work on dynamic data integrity verification in a fully decentralized environment.

With more proliferation of services and higher degree of personalization, higher accurate approaches to service recommendation are becoming more and more pivotal. Performance of existing service recommendation approaches is not satisfactory due to the sparseness of available data set or the incomplete information of the global service market, which make it difficult to identify a customer's potential preferences on available services. In this paper, we extract finegrained value features from customer reviews, and identify the personalized distribution of each value features to demonstrate the value preference of a specific customer. Then, a novel recommendation algorithm (VFDSR) is proposed. An algorithm VFMine based on text mining is presented to effectively extract value features from customer reviews. A VFDAnalysis algorithm based on sentiment analysis is employed to identify the value feature distributions. Based on it, VFDSR recommends top-satisfying services to customers. In addition, the value feature distributions are visualized in the form of "heatmaps". Comprehensive experiments are conducted on a Yelp dataset and the experimental results show the superiority of our approach.

With the overload of information on the Web, Recommender Systems (RSs) are becoming increasingly popular and have been employed to provide suggestions to meet different requirements. RSs are utilized in a variety of areas including movies, music, social tags, user group and products as Web services evoked on the Internet either as mobile Apps or PC-based applications. However, it is challenging to achieve personalized recommendations instead of offering up too many lowest common denominator recommendations. Understanding how products relate to each other is important because it has great impact on the performance. Furthermore, the personalized sequential behavior, which is closely related to a particular product, is essential for recommender systems. Most models simply integrate features from users and items without considering potential product bundle relationships between products exposed by users' personalized sequential behaviors. In this paper, a novel method based on Factorizing Personalized Markov Chain (FPMC) is proposed to comprehensively explore the latent bundle relations from users perspective, along with the hidden correlative semantics between products obtained from logic regression method, which provides a unified view to describe the user preferences, product/item features, and the user sequential patterns in timely manner. The involved semantic features are extracted using deep learning models. We evaluate our method on real-world Amazon datasets and our framework significantly outperforms other baseline models, especially on sparse datasets. The experimental results show that our approach qualitatively captures personalized behaviors with superior recommendation performance.

A wide array of clouds have been built and adopted by business and research communities. Similarly, many research communities use workflow environments and tools such as Taverna and Galaxy to model the execution of tasks to support and expedite the reenactment of complex processes, and ultimately support the repeatability of science. As a result, a number of systems that integrate clouds with these workflow systems have emerged including CloudMap, CloudMan, Galaxy cloud, BioBlend, etc. Though these systems have proven to be successful in service and data integration, they have not dealt with the data security issue inherent in cloud-based systems with the outsourced models of infrastructure provisioning. For many domains, e.g., health, this poses serious challenges regarding the adoption of cloud infrastructures. Yet such domains also have much to gain from clouds especially given the explosion of genomics data and opportunities for personalized medicine in the big data era. This paper addresses this problem by presenting a trusted storage cloud for scientific workflows, called TruXy. The paper describes the TruXy architecture, the corresponding protocols and illustrates the adoption of TruXy to support collaborative bioinformatics research in endocrine genomics. A range of experiments have been performed to measure the performance of TruXy for processing of exome data sets on individuals with a rare genetic disorders: disorders of sex development (DSD). Our results show that the performance of TruXy is comparable to that of using a standalone workflow tool and that it can handle the big data security challenges required.

Modern Internet of Things (IoT)-aware business processes include various geographically dispersed sensor devices. Large amounts of raw data acquired from sensors need to be regularly transmitted to the targeted processes in enterprise data centers, resulting in a significant increase in network load and latency. It is necessary to execute such processes in a distributed way. The existing work has proposed different algorithms to partition a given process for distributed execution; however, they cannot satisfy the decentralized nature of IoT-aware business processes. Moreover, up to now, there is few work that studies uncertain optimal deployment problems in which traffic data for guiding subsequent deployment derives from experts' empirical knowledge. This paper proposes a novel location-based fragmentation algorithm and α-optimal deployment solution to deal with the mentioned problems, where α is the given confidence level. A hardware-in-the-loop simulation platform based on NS-3 was built. Based on this platform, an integrated monitoring process was deployed that ran on different virtual computers, and process fragments communicated with each other via a simulated network. The experimental results show that the proposed approach can reduce network traffic and round-trip time.

Blockchain is an emerging technology for decentralized and transactional data sharing across a large network of untrusted participants. It enables new forms of distributed software architectures, where components can find agreements on their shared states without trusting a central integration point or any particular participating components. Considering the blockchain as a software connector helps make explicitly important architectural considerations on the resulting performance and quality attributes (for example, security, privacy, scalability and sustainability) of the system. Based on our experience in several projects using blockchain, in this paper we provide rationales to support the architectural decision on whether to employ a decentralized blockchain as opposed to other software solutions, like traditional shared data storage. Additionally, we explore specific implications of using the blockchain as a software connector including design trade-offs regarding quality attributes.

The communication systems in existing smart grids mainly take the request/reply interaction model, in which data access is under the direct control of data producers. This tightly controlled interaction model is not scalable to support complex interactions among smart grid services. On the contrary, the publish/subscribe system features a loose coupling communication infrastructure and allows indirect, anonymous and multicast interactions among smart grid services. The publish/subscribe system can thus support scalable and flexible collaboration among smart grid services. However, the access is not under the direct control of data producers, it might not be easy to implement an access control scheme for a publish/subscribe system. In this article, we propose a Data-Centric Access Control Framework (DCACF) to support secure access control in a publish/subscribe model. This framework helps to build scalable smart grid services, while keeping features of service interactions and data confidentiality at the same time. The data published in our DCACF is encrypted with a fully homomorphic encryption scheme, which allows in-grid homomorphic aggregation of the encrypted data. The encrypted data is accompanied by bloom-filter encoded control policies and access credentials to enable indirect access control. We have analyzed the correctness and security of our DCACF and evaluated its performance in a distributed environment.

Establishing secure collaborations between multiple organisations, potentially who are in competitors, requires substantial careful attention to how information is exchanged during the collaboration, from the formulation of policies and agreements between the organisations that govern the collaboration at the most abstract level, through to authentication and authorisation services and down to secure network and storage infrastructure. This paper presents a high level description of the secure integrated collaboration platform for distributed groups that has been developed and deployed as a part of a pilot for the AU2EU project. This secure platform utilises advanced eAuthentication and eAuthorisation services integrated into an advanced real-time collaborative system offering high definition telepresence combined with a secure common shared workspace that gives capability based collaborative access to specialised instruments, data sets and images.

Secure cloud storage solutions such as Trust Store, Sec Cloud, HPI Secure, and Twin Cloud have primarily focused on securing persistent data while storing it in public cloud services. Though data sharing has been recognized as an important security feature, these storage solutions mostly focus on three key properties: confidentiality, integrity and availability. Modern enterprise applications demand data is able to be shared within or across organizations. The challenge is how to securely share data in public clouds without increasing data movement and computation costs. This problem has been addressed in recent times by utilizing or developing new data encryption techniques such as identity-based encryption, attribute-based encryption and proxy-re-encryption. However, these techniques suffer from scalability and flexibility problems when dealing with big data and support for dynamic access control rules. This paper presents a novel architecture and corresponding protocols to provide secure sharing of documents on public cloud services: Cloud Docs. This system uses AES for data encryption to achieve scalability and supports identity based access control rules using private-public key pairs to provide flexibility.

Although content sharing provides many benefits, content owners lose full control of their content once they are given away. Existing solutions provide limited capabilities of content access control as they are vendor-specific, non-structured and non-flexible. In this paper, we present an open and flexible software solution called SelfProtect Object (SPO). SPO bundles content and policy files in an object that can protect its contents by itself anywhere and anytime. Our policy is based on XACML, a generic policy language allowing fine-grain access with rules and conditions. We also design and implement a prototype of SPO and demonstrate its capability through examples. Our solution is flexible to express a variety of access control rules and open to integrate into different applications on different platforms.

Recently the massive open online course (MOOC) is an emerging trend that attracts many educators' and researchers' attentions. Based on our pilot study focusing on the development and operation of MOOC in Australia, we found MOOC is featured with mastery learning and blended learning, but it suffers from low completion rates. Brining micro learning into MOOC can be a feasible solution to improve current MOOC delivery and learning experience. We design a system which aims to provide adaptive micro learning contents as well as learning path identifications customized for each individual learner. To investigate how micro learning can impact learning experience and knowledge acquisitions of learners participated in MOOC, we suggest a potential scheme including hypotheses to evaluate our proposed approach.

In this paper we illustrate a proposal with regard to providing learners adaptive micro learning experiences, which can be fulfilled within fragmented time pieces. The framework of our system is demonstrated, while it aims to deliver customized micro learning contents taking into account learners' specific demands, learning styles, preference and context.

In the paradigm of service oriented science, scientific computing applications and data are all wrapped as web accessible services. Scientific workflows further integrate these services to answer complex research questions. However, our earlier study conducted on myExperiment has revealed that although the sharing of service-based capabilities opens a gateway to resource reuse, in practice, the degree of reuse is very low. This finding has motivated us to propose ServiceMap to provide navigation facility through the network of services to facilitate the design and development of scientific workflows. This paper proposes ReputationNet as an enhancement of ServiceMap, to incorporate the often-ignored reputation aspects of services/workflows and their publishers, in order to offer better service and workflow recommendations. We have developed a novel model to reflect the reputation of e-Science services/workflows, and developed heuristic algorithms to provide service recommendations based on reputations. Experiments on myExperiment have illustrated a strong positive correlation (with Pearson correlation coefficient 0.82) between the reputation scores computed and the actual performance (i.e. usage frequency) of the services/workflows, which demonstrates the effectiveness of our approach.

The trend towards Cloud computing infrastructure has increased the need for new methods that allow data owners to share their data with others securely taking into account the needs of multiple stakeholders. The data owner should be able to share confidential data while delegating much of the burden of access control management to the Cloud and trusted enterprises. The lack of such methods to enhance privacy and security may hinder the growth of cloud computing. In particular, there is a growing need to better manage security keys of data shared in the Cloud. BYOD provides a first step to enabling secure and efficient key management, however, the data owner cannot guarantee that the data consumers device itself is secure. Furthermore, in current methods the data owner cannot revoke a particular data consumer or group efficiently. In this paper, we address these issues by incorporating a hardware-based Trusted Platform Module (TPM) mechanism called the Trusted Extension Device (TED) together with our security model and protocol to allow stronger privacy of data compared to software-based security protocols. We demonstrate the concept of using TED for stronger protection and management of cryptographic keys and how our secure data sharing protocol will allow a data owner (e.g, author) to securely store data via untrusted Cloud services. Our work prevents keys to be stolen by outsiders and/or dishonest authorised consumers, thus making it particularly attractive to be implemented in a real-world scenario.

With cloud computing, a large number of Virtual Machines (VMs) can be provisioned to form high performance computing (HPC) to run computation-intensive applications using the Hardware as a Service (HaaS) model. Fault Tolerance (FT) for HPC in the cloud is increasingly a challenging issue, because any fault during the execution would result in re-running the application, which will cost time, money and energy. There has been a significant increase in energy consumption of HPC systems in cloud as a result of rerunning application and fault tolerance (e.g., redundant computing). In this paper we present energy efficient fault tolerance for HPC in the cloud. We develop a generic FT algorithm for HPC systems in the cloud. Our algorithm uses proactive processlevel migration approach, however it does not rely on a spare node or redundant computing prior to prediction of a failure. Our experimental results obtained from a real cloud execution environment show that the energy utilization for HPC in the cloud while providing fault tolerance can be reduced by as much as 30%.

Cloud computing services offer highly reliable, scalable and efficient solutions with a large pool of easily accessible, virtualized resources. They are becoming an increasingly prevalent delivery model. We have developed a cloud-based image analysis toolbox to provide a wide user base with easy access to the software tools we have developed over the last decade. The toolbox is provided as a service on an Australian national cloud infrastructure. The design and implementation of the cloud-based service are presented, including its architecture, key components and some image analysis and visualization examples showing the capabilities of the service for biomedical image analysis.

SUMMARY The globally integrated contemporary business environment has prompted new challenges to database architectures in order to enable organizations to improve database applications performance, scalability, reliability and data privacy in adapting to the evolving nature of business. Although a number of distributed database architectures are available for choice, there is a lack of an in‐depth understanding of the performance characteristics of these database architectures in a comparison way. In this paper, we report a performance study of three typical (centralized, partitioned and replicated) database architectures. We used the TPC‐C as the evaluation benchmark to simulate a contemporary business environment, and a commercially available database management system that supports the three architectures. We compared the performance of the partitioned and replicated architectures against the centralized database, which results in some interesting observations and practical experience. The findings and the practice presented in this paper provide useful information and experience for the enterprise architects and database administrators in determining the appropriate database architecture in moving from centralized to distributed environments. Copyright © 2012 John Wiley & Sons, Ltd.

In recent years, High Performance Computing (HPC) systems have been shifting from expensive massively parallel architectures to clusters of commodity PCs to take advantage of cost and performance benefits. Fault tolerance in such systems is a growing concern for long-running applications. In this paper, we briefly review the failure rates of HPC systems and also survey the fault tolerance approaches for HPC systems and issues with these approaches. Rollback-recovery techniques which are most often used for long-running applications on HPC clusters are discussed because they are widely used for long-running applications on HPC systems. Specifically, the feature requirements of rollback-recovery are discussed and a taxonomy is developed for over twenty popular checkpoint/restart solutions. The intent of this paper is to aid researchers in the domain as well as to facilitate development of new checkpointing solutions.

Cloud computing offers new capacity and flexibility solution to high performance computing (HPC) applications with provisioning of a large number of virtual machines for computational intensive applications. Fault tolerance allows HPC systems on cloud with multiple of nodes to complete execution of computational intensive applications in the present of fault. The most commonly used fault tolerance techniques for HPC is checkpoint/restart. However, checkpoint/restart increases the wall clock time of the execution of applications which increases the execution cost. In this paper we present a fault tolerance framework for high performance computing in Cloud. This framework proposes using process level redundancy (PLR) techniques to reduce the wall clock time of the execution of computational intensive applications.

Business process simulation is an effective approach to many organisations to understand the progress of processes over a period of time and predict future activities, which can then be given as a feedback to their customers and improve their overall productivity. In order to provide valuable information to both customers and system managers, the timings of business processes need to be forecast with high accuracy and efficiency. In particular, organisations require to study the process and event flows, recognize their patterns, and forecast the total time it would take for a workflow to complete. This paper proposes the architecture for business process simulation and describes its prototype implementation. It lists several prediction techniques that have been implemented as part of the prototype system. The paper also describes an artificial neural network model that could be used for predicting the completion time of business processes that are constrained by the availability of resources.

The Internet has been encouraging and enabling business collaborations via online transactions over the Web. However, managing transactions in a long-running business process across domains still remains a challenge. In this paper, we propose a novel financial-compensation-based transaction management model, fcBTxM, to address this challenge. Unlike classical transaction management, fcBTxM does not attempt to recover data consistency via rollback when a failure occurs. Instead, our model always tries to forward-roll a business process via financial compensation. We use a state machine to capture and describe the states of our transaction model and their relationships. We also develop a set of technologies and protocols for enabling the new transaction management. A real business collaboration example is used to demonstrate the concept, and preliminary testing results are provided to evaluate our technologies.

Service-centric solutions usually require rich context to fully deliver and better reflect on the underlying applications. We present a novel use of context in the form of customized user interface services with the concept of User Interface as a Service (UIaaS). UIaaS takes user profiles as input to generate context-aware interface services. Such interface services can be used as context to augment semantic services with contextual information leading to UIaaS as a Context (UIaaSaaC). The added serendipitous benefit of the proposed concept is that the composition of a customized user interface with the requested service is performed by the service composition engine, as is the case with any other services. We use a special-purpose language (called User Interface Description Language (UIDL)) to model and realize user interfaces as services. We use a real-life e-government application, human services delivery for the citizens, as a proof-of-concept. We also present a comprehensive evaluation of the proposed approach using a functional evaluation and a nonfunctional evaluation consisting of an end user usability test and expert usability reviews.

In this paper, we propose a secure cloud storage service architecture with the focus on Data Integrity as a Service (DIaaS) based on the principles of Service-Oriented Architecture and Web services. Our approach not only releases the burdens of data integrity management from a storage service by handling it through an independent third party data Integrity Management Service (IMS), but also reduces the security risk of the data stored in the storage services by checking the data integrity with the help of IMS. We define data integrity protocols for a number of different scenarios, and demonstrate the feasibility of the proposed architecture, service and protocols by implementing them on a public cloud, Amazon S3. We also study the impact of our proposed protocols on the performance of the storage service and show that the benefits of our approach outweigh the little penalty on the storage service performance.

Particle swarm optimization (PSO) is an optimization approach and has been widely used for a verity of optimization problem in both research and industrial domains. Due to the potential of PSO, several variants of the original PSO algorithms have been developed to improve PSO's efficiency and robustness. This paper proposes another variant of particle swarm optimization algorithm, called N-PωSO. This N-PωSO algorithm is based on classical feedback control theory and topological neighborhood, which offers better search efficiency and convergence stability. As a result, our N-PuωSO method features faster searching from the proportional term without steady-state error. And empirical results show that our N-PωSO algorithm is able to achieve high performance for both unimodal and multimodal optimization problems.

Business process prediction technologies are being increasingly used by organisations to provide timely feedback to their customers and improve their overall productivity. In order to provide valuable information to both customers and system managers, the timings of business processes need to be fo

In recent years, we witnessed a range of innovations in the 'service' related technologies, such as Software as a Service, Platform as a Service and Infrastructure as a Service. Along with the Service Oriented Architecture, companies can wrap their technological product as a service, to collaborate with others. Facing the ever-escalating global competition, such collaboration is crucial. The viability of this paradigm highly depends on the compliance and therefore the trustworthiness of all collaborators. However, it is challenging to achieve trustworthiness in such a dynamic cross-domain environment, as each participator may deceit for individual benefits. As a solution, we have proposed to enforce strong accountability to enhance the trustworthiness. With this accountability, incompliance can always be determined in a provable and undeniable way. In this paper, we extend our work by proposing a novel modeling of the collaborative business process. Based on this modeling, we thoroughly analyze the evidence and proving procedure needed for different types of compliance, and evaluate the extent to which those compliance can be indeed proved. We have implemented a demonstrative system to show its effectiveness in real practice.

Web services have been widely accepted as a platform-independent services-oriented technology. On the other hand, ubiquitous technologies are getting popular in a variety of domain applications. In particular, hosting web services from mobile devices became a way of extending knowledge sharing for teaching and learning purposes. This paper presents our design and implementation of a mobile web services framework for syndromic surveillance diagnosis and learning. This framework can assist farmers and veterinary students to study surveillance and diagnosis of farm animal diseases in the field. In this paper, we also present a performance study of hosting web services on mobile devices by evaluating the mobile learning application.

The enormous amount of data generated in daily operations and the increasing demands for data accessibility across organizations are pushing individuals and organizations to outsource their data storage to cloud storage services. However, the security and the privacy of the outsourced data goes beyond the data owners' control. In this paper, we propose a service composition approach to preserve privacy for data stored in untrusted storage service. A virtual file system, called Trust Store, is prototyped to demonstrate this concept. It allows users utilize untrusted storage service provider with confidentiality and integrity of the data preserved. We deployed the prototype with Amazon S3 and evaluate its performance.

As hand-held devices become popular in society, the demand for mobility is extended to teaching and learning purposes. This paper presents the design and implementation of a mobile learning system for syndromic surveillance and diagnosis. This system can assist farmers and veterinary students to study surveillance and diagnosis of farm animal diseases in the field. In this paper, we present a mobile problem-based learning method used for designing the diagnosis learning system. We also describe our solution to the limitation of storage capacity of hand-held devices. We also present how the design is implemented as portable software that can be deployed onto a large range of mobile phones and other hand-held devices.

Summary form only given. Accountability in Service Oriented Architecture (SOA) is a capability of making business processes across all participants (services, applications and people) accountable in terms of both business logic and Quality of Services (QoS). While accountability is a critical mechanism to enhance trust between collaborative services, there is the lack of standard accountability support in the current SOA infrastructure. For example, it is difficult with the existing technologies/infrastructure to resolve a dispute between two (web) services if some interactions between the two services go wrong; there is also little existing accountability support for a service consumer to collect quantity evidences to complain a service provider, who fails to meet its Service Level Agreement (SLA). As the increasing real-world activities are performed through the Internet connected services, we envision that there will be growing requirements for making the behaviors of both service providers and consumers accountable. In the business world, one may be reluctant to transact directly with a stranger. But a mutually trusted middleman can be used to facilitate transactions and resolve possible disputes. In this tutorial, we will share our observations and research results on building accountability into SOA. First, we will review related work on accountability in traditional distributed systems, ranging from Internet protocols and network file systems to outsourced database management systems. We will examine what methods embodied in these work can fit service computing in Internet scale and what cannot. Then we will present our research work on middleman-based approach to delivering accountability as a service, including our recent research results. This tutorial will focus on the major technical challenges of enabling SOA accountable and our solutions to these challenges. Finally, we will demonstrate our solutions using a collaborative services scenario deployed in Amazon EC^2 cloud. The goal of this tutorial is to provide detailed understanding of accountability issues and related technologies in SOA with in-depth related work discussions, recent research outcomes and a deployed accountability service prototype.

We propose a service-oriented approach to generate and manage mashups. The proposed approach is realized using the Mashup Services System (MSS), a novel platform to support users to create, use, and manage mashups with little or no programming effort. The proposed approach relieves users from programming-intensive, error-prone, and largely nonreusable output process for creating and maintaining mashups. We describe the overall design of MSS and discuss and evaluate its main enabling technologies.

As growing number of real-world activities are performed through Internet connected services, there are increasing needs to make the behaviors of both service consumer and provider accountable. Many efforts attempting to regulate services and to guarantee service qualities lack sufficient accountability support. This paper treats accountability as a service and proposes a novel contract-based accountability service model to tackle this problem. The model uses federated accountability services to audit interactions between service consumers and service providers so that misbehaviors can be detected with undeniable evidences. We show how Internet data management services can be made accountable using this service model. We implemented the data management service and characterized its performance.

Electronic Contract (eContract) has been recognized as a good combination of technical specification and legal documentation for establishing and regulating virtual organizations built for dynamic collaborations. This paper presents a design and implementation of an eContract service with an aim of providing a trusted collaboration platform for collaborators. The implemented service uses Web services technologies to facilitate its collaborators not only to contribute resources in an eContract, but also to negotiate and instantiate them through eContract. This paper describes the interface and protocols for the eContract service. The architecture, interface and protocols designed for the service are demonstrated using an example of providing universal connectivity service for a telepresence application in the context of eResearch domain.

Composing adaptive and self-managing Web services needs plug-and-play architecture so that the deployment of control components does not require changes made to the Web services and the host middleware platforms. This is especially challenging for Web services running on COTS middleware platforms, such as Microsoft.Net. In this paper, we propose an architectural solution that introduces a management proxy between adaptive control components and Web services. The management proxy can be customized and seamlessly integrated with a COTS middleware platform by leveraging the existing middleware mechanisms. This solution enables dynamically composing adaptive Web services on COTS middleware without stopping its services. We demonstrate this architecture by a realistic Web service application built on .Net Windows Communication Foundation (WCF). The performance overhead incurred by this architecture is measured, and the results validate that our solution is efficient in terms of performance and flexibility.

Dynamic collaborations involve contributed resources across the organisational boundaries that are subjected to different set of policies. The management of such resources for dynamic collaborations including negotiation, validation, instantiation and termination is difficult. Existing approaches for collaborations using Web Services such as WSLA are designed to deal with scenarios involving two parties: a service provider and a service consumer. These approaches do not scale well to multiparty nature of dynamic collaborations. To address this problem, we propose a framework for a language called Web Service Collaborative Context Definition Language for dynamic collaborations. The language itself has been defined using XML Schema and has been implemented in a dynamic collaboration platform.

While Web Services Security (WSS) enhances the security of web services, it may also introduce additional performance overheads to standard web services due to additional CPU processing and larger messages transferred. In this paper, we aim at clarifying this concern by conducting a quantitative performance evaluation of WSS overhead. Based on the evaluation, we extend our existing web services performance model by taking the extra WSS overheads into account. The extended performance model is validated on different environments with different messages sizes and WSS security policies.

Web services security (WSS) has been approved as a standard by OASIS and widely adopted in the industry as a solution for enhancing the security of Web services. However, the performance of WSS remains a concern due to the additional security contents added to SOAP message and the extra service time for processing these security contents. This paper aims at clarifying this concern by conducting a performance evaluation of WSS. A simple Web service is designed and used for performance testing with a variety of WSS polices and message sizes. The test results are categorized, compared and analyzed to work out the overheads for individual security setting. This work is expected to provide an overview and guidance for WSS performance overhead

While Web services have been widely accepted as a platform-independent services-oriented technology, its performance remains a concern due to the verbosity and inefficiency inherent from using text-based XML. This paper presents a study of Web services performance by evaluating the current implementations of Web services and comparing them with a number of alternative technologies. This study gives a picture of the current Web services performance behaviors and develops a simple performance model that can be used to estimate Web services latencies

In multi-tier enterprise systems, application servers are key components that implement business logic and provide application services. To support a large number of simultaneous accesses from clients over the Internet and intranet, most application servers use replication and multi-threading to handle concurrent requests. While multiple processes and multiple threads enhance the processing bandwidth of servers, they also increase the contention for resources in application servers. The paper investigates this issue empirically based on a middleware benchmark. A cost model is proposed to estimate the overall performance of application servers, including the contention overhead. This model is then used to determine the optimal degree of the concurrency of application servers for a specific client load. A case study based on CORBA is presented to validate our model and demonstrate its application.

One of the major problems in building large-scale distributed systems is to anticipate the performance of the eventual solution before it has been built. This problem is especially germane to Internet-based e-business applications, where failure to provide high performance and scalability can lead to application and business failure. The fundamental software engineering problem is compounded by many factors, including individual application diversity, software architecture trade-offs, COTS component integration requirements, and differences in performance of various software and hardware infrastructures. We describe the results of an empirical investigation into the scalability of a widely used distributed component technology, Enterprise JavaBeans (EJB). A benchmark application is developed and tested to measure the performance of a system as both the client load and component infrastructure are scaled up. A scalability metric from the literature is then applied to analyze the scalability of the EJB component infrastructure under two different architectural solutions.