Medication recommendation systems aim to provide personalized and safe medication options based on individual patient records. However, existing approaches often face challenges related to inadequate modeling of complex relationships within Electronic Health Records (EHRs), data sparsity, and a lack of explainability for recommendations. In this paper, we present a Knowledge-enhanced Explainable HyperGraph Convolution Network (KEHGCN) that constructs a hierarchical hypergraph structure to capture the multi-level relationships within EHR data. By incorporating external knowledge graphs, our approach introduces additional positive relations that help alleviate the impact of data sparsity on model learning. Furthermore, by performing generalized metapath construction and selection on the knowledge graph, our approach achieves effective knowledge filtering and extracts semantically meaningful metapaths, thereby further enhancing the explainability of the recommendation results. We also explicitly introduce negative relations present in the domain knowledge to improve the safety of medication recommendation. Extensive experiments on different hospital departments of MIMIC-III and MIMIC-IV datasets demonstrate that KEHGCN outperforms other state-of-the-art baselines.

Zhiyin Yu, Yuchen Mou, Juncheng Yan, Junyu Luo, Chunchun Chen, Xing Wei, Yunhui Liu, Hongru Sun, Yuxing Zhang, Jun Xu, Yatao Bian, Ming Zhang, Wei Ye, Tieke He, Jie Yang, Guanjie Zheng, Zhonghai Wu, Bo Zhang, Lei Bai, Xiao Luo. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Click-through Rate (CTR) prediction is a crucial task in online advertising and recommender systems. Theoretically, proper ensemble of multiple different CTR prediction models can improve the prediction effectiveness. Unfortunately, most of the existing ensemble learning methods for CTR prediction are designed for cross-sectional data and neglect user historical behavior sequence which is important for predicting user's future click behavior. To address the above issues, we propose a Hypergraph Diffusion-based Sequential Ensemble framework for CTR prediction (HDSE). Specifically, considering the inherent capability of diffusion models in space exploration, we design a generalized conditional diffusion model, which adaptively identifies critical information from diverse sequential models, to capture user's dynamic interest evolution across diverse contexts. To avoid corrupting the item dependencies caused by isotropic Gaussian noise used in traditional diffusion models, we construct a behavior hypergraph and design an anisotropic hypergraph-based smoothing operator to inject structure-aware noise for better exploring the user's interest space. For large-scale application scenarios, we propose a computationally efficient approximation method for estimating hypergraph propagation matrix in the smoothing operator. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed model.

The current paradigm of training large language models (LLMs) on public available Web data is becoming unsustainable as high-quality data sources in specialized domains near exhaustion. Federated Learning (FL) emerges as a practical solution for the next generation of AI on a decentralized Web, enabling privacy-preserving collaborative fine-tuning on decentralized private data. While Low-Rank Adaptation (LoRA) is standard for efficient fine-tuning, its federated application faces a critical bottleneck: communication overhead under heterogeneous network conditions. Structural redundancy in LoRA parameters increases communication costs and causes aggregation conflicts. To address this, we propose FedSRD, a Sparsify-Reconstruct-Decompose framework for communication-efficient federated LLM fine-tuning. We introduce importance-aware sparsification to reduce the upload parameter count while preserving the structural integrity of LoRA updates. The server aggregates updates in full-rank space to mitigate conflicts, then decomposes the global update into a sparse low-rank format for broadcast, ensuring a symmetrically efficient cycle. We also propose an efficient variant, FedSRD-e, to reduce computational overhead. Experiments on 10 benchmarks show our framework significantly reduces communication costs by up to 90% while improving performance on heterogeneous client data.

FPGA cloud acceleration, or “FPGA as a Service” (FaaS), offered by AWS, Microsoft Azure, Alibaba Cloud, and Huawei Cloud, has become a promising solution for tackling complex, compute-intensive workloads. It targets applications such as genomics, image and video processing, electronic design automation, compression, and big data analytics. While multi-tenant FPGAs significantly enhances resource utilization efficiency, it faces security threats from power side channels, where attackers craft a malicious circuit to detect voltage fluctuations from victim circuits. Observing that all the crafted circuits exploit either Carry Chain or Look-up Table to sense voltage fluctuations, existing defenses have focused on detecting the malicious use of the two basic FPGA computing resources. However, it remains unclear whether such countermeasures are sufficient to address the growing threat of power side channels in multi-tenant FPGAs. In this paper, we reveal MUXLeak, a novel on-chip sensor that exploitsMultiplexer (MUX)to craft a stealthy power side channel, which bypasses existing countermeasures. Particularly, we perform a thorough analysis of basic resources within an FPGA unit and unveil thatMUX, another basic resource,has never been exploited before. More importantly, it can be directly initialized on Xilinx FPGAs and its incurred signal propagation delay demonstrates an inverse correlation with changes in voltage, making itself exploitable for a new power side channel leakage. In our evaluation, we test MUXLeak on three Xilinx FPGA products and use TDC [18] (i.e., the most sensitive on-chip sensor until now) to benchmark the sensitivity of MUXLeak. Our results show that MUXLeak has achieved the same level of sensitivity as TDC to voltage fluctuations. Further, we apply MUXLeak to mount two attacks, i.e., extracting AES keys within 2.54 hours and stealing DNN model architectures with an accuracy of over 90%.

The development of artificial intelligence and medical informatics has empowered the medication recommendation systems with enhanced capabilities. However, existing methods struggle with the data imbalance problem in Electronic Health Records (EHRs), where the majority of records are concentrated on a limited subset of common diagnoses, procedures, and medications. It hampers the models’ ability to recommend appropriate medications when dealing with uncommon or multifaceted cases. In addition, existing approaches often fail to adequately model the complex relationships inherent in heterogeneous medical data sources, especially medication molecular structure information. This gap restricts the potential for uncovering meaningful associations among diverse clinical entities. To address these issues, we design a hierarchical attention-based pretraining strategy, leveraging the semantic hierarchies of medical entity codes to facilitate knowledge transfer, so as to alleviate the challenge of data imbalance. Furthermore, we design a novel bilateral hierarchical heterogeneous hypergraph convolution network for medication recommendation. Specifically, we construct specialized hypergraphs for both EHR data and medication molecular structure data, enabling hypergraph convolution to capture high-order relationships while promoting bilateral knowledge enhancement between these heterogeneous data sources. This comprehensive integration allows the model to effectively capture the relationships among clinical and molecular information. Experimental results on different hospital departments of MIMIC-III and MIMIC-IV datasets demonstrate the superior performance of our model compared to state-of-the-art methods. Our source code is released at: https://github.com/LusiaZ/BH3-MedRec .

Root cause Localization methods play a crucial role in ensuring the stability of large-scale microservice systems. However, existing methods either rely on unsupervised approaches with limited localization accuracy or on supervised learning that requires large volumes of historical anomaly data. Such data is often unavailable in newly deployed systems. To address this limitation, we attempt to use the labeled data from mature systems to help new systems build root cause localization models. Specifically, we propose UDA-RCL, an unsupervised domain adaptation root cause localization method using multimodal data (log, metric and trace data). UDA-RCL first incorporates an aggregation based event extraction module to standardize the format of multimodal data from different systems. Then, it utilizes a multimodal event encoder and multimodal domain adversarial adaptation module to narrow the feature distribution gap between different systems. Furthermore, taking into account the situation of sparse anomaly samples, existing methods' classifiers are either hard to transfer to other systems or struggle to capture the process of anomaly propagation, we propose a PageRank classifier module. This module employs a neural network embedded with anomaly propagation rules to output the final root cause ranking results, alleviating the issue of sparse anomaly samples. Extensive experiments have proven that our method achieves the best results in both supervised and transfer learning scenarios.

Federated learning has become a promising solution for collaboration among medical institutions. However, data owned by each institution would be highly heterogeneous and the distribution is always non-independent and identical distribution (non-IID), resulting in client drift and unsatisfactory performance. Despite existing federated learning methods attempting to solve the non-IID problems, they still show marginal advantages but rely on frequent communication which would incur high costs and privacy concerns. In this paper, we propose a novel federated learning method: Federated learning via Valuable Condensed Knowledge (FedVCK). We enhance the quality of condensed knowledge and select the most necessary knowledge guided by models, to tackle the non-IID problem within limited communication budgets effectively. Specifically, on the client side, we condense the knowledge of each client into a small dataset and further enhance the condensation procedure with latent distribution constraints, facilitating the effective capture of high-quality knowledge. During each round, we specifically target and condense knowledge that has not been assimilated by the current model, thereby preventing unnecessary repetition of homogeneous knowledge and minimizing the frequency of communications required. On the server side, we propose relational supervised contrastive learning to provide more supervision signals to aid the global model updating. Comprehensive experiments across various medical tasks show that FedVCK can outperform state-of-the-art methods, demonstrating that it's non-IID robust and communication-efficient.

Drug repositioning offers a cost-effective strategy for identifying new therapeutic uses for existing drugs. However, existing methods struggle to fully utilize domain knowledge and structural information to capture complex network and entity features. In this work, we propose a Heterogeneous Knowledge and Global-Local Structure Integration Framework (HKGLS) for drug repositioning. HKGLS leverages graph neural networks to represent multi-source knowledge from proteins, diseases, and drugs, then utilizes a heterogeneous graph structure for knowledge fusion. Specifically, we constructed protein structure graphs based on binding pocket residues to achieve fine-grained protein representation. To comprehensively capture complex network structures, HKGLS also integrates global semantic and pharmacological information as well as local structural features through graph neural networks. Extensive experiments on three public drug repositioning datasets show that HKGLS outperforms eight state-of-the-art baseline methods across various evaluation metrics, validating its effectiveness and superiority.

Predicting herb-disease association plays a crucial role in accelerating herb repositioning and expanding clinical applications of Traditional Chinese Medicine (TCM). However, most of the existing methods neglect the fine-grained biomedical information, such as the molecular structures, which leads to inaccurate and incomplete representations of herbs and diseases. Since TCM involves multiple components and modulates various targets, existing methods lack an effective approach to represent these relations, resulting in the omission of critical information. In addition, the lack of interpretability in existing methods limits their credibility and acceptance. To tackle the above problems, we propose a herb-disease association prediction framework, named MVGP, which incorporates Multi-View and multi-Granularity representation learning as well as meta-Path-based relational modeling to accurately capture the relationships between herbs and diseases. Specifically, we design a multi-view learning frame-work that integrates latent information from both TCM and western medicine views. We incorporate fine-grained molecular structures of herbal compounds and target proteins to better characterize features. We design a hypergraph convolutional network to model high-order relations among herbs and compounds, diseases and target proteins. Moreover, we employ meta-path-based relational modeling to make the results interpretable. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed model.

Federated learning has wide applications in the medical field. It enables knowledge sharing among different healthcare institutes while protecting patients’ privacy. However, existing federated learning systems are typically centralized, requiring clients to upload client-specific knowledge to a central server for aggregation. This centralized approach would integrate the knowledge from each client into a centralized server, and the knowledge would be already undermined during the centralized integration before it reaches back to each client. Besides, the centralized approach also creates a dependency on the central server, which may affect training stability if the server malfunctions or connections are unstable. To address these issues, we propose a decentralized federated learning framework named dFLMoE. In our framework, clients directly exchange lightweight head models with each other. After exchanging, each client treats both local and received head models as individual experts, and utilizes a client-specific Mixture of Experts (MoE) approach to make collective decisions. This design not only reduces the knowledge damage with client-specific aggregations but also removes the dependency on the central server to enhance the robustness of the framework. We validate our framework on multiple medical tasks, demonstrating that our method evidently outperforms state-of-the-art approaches under both model homogeneity and heterogeneity settings.

Zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARK) schemes have been a promising technique in verified computation. Zk-SNARK schemes were designed to be mathematically secure against cryptographic attacks and it remains unclear whether they are vulnerable to fault injection attacks. In this work, we provide a positive answer by presenting ZK-Hammer, which leaks secrets from zk-SNARK schemes via Rowhammer. We incur faults in the exponentiate variables in the Quadratic Arithmetic Program (QAP) problem. Then we analyze the faulty proof using the bilinear pairing technique and manage to recover the secret. We employ a Rowhammer fault evaluation in libsnark and identify 3 CVEs.

FPGAs offer superior energy efficiency and performance in parallel computing but are vulnerable to remote power side-channel attacks. Existing attacks rely on assumptions of coresident crafted circuits and shared power delivery networks, limiting their practicality in real-world scenarios. In this paper, we present AmpereBleed, a novel current-based side-channel attack that exploits widely available INA226 sensors in ARMFPGA SoCs, bypassing the aforementioned two assumptions. AmpereBleed achieves $261 \times$ greater variations to victim activities compared to the popular ring oscillator (RO) circuit, fingerprints DNN models on the Xilinx Deep Learning Processor Unit (DPU) with $\mathbf{9 9. 7 \%}$ accuracy, and distinguishes the Hamming weights of RSA-1024 keys.

In recent years, cloud providers are dedicated to enabling FPGA multi-tenancy to improve resource utilization, but this new sharing model introduces power side-channel threats, where attackers detect voltage fluctuations from colocated circuits. This paper proposes LeakyDSP, a novel onchip sensor that maliciously configures DSP blocks to sense fine-grained voltage fluctuations but is overlooked by existing studies. Our experimental results show that LeakyDSP achieves high sensitivity to voltage fluctuations and strong robustness to different placements. Besides, we apply LeakyDSP to extract full AES keys with $25 \mathrm{k}-78 \mathrm{k}$ traces and build covert channels with a high transmission rate of 247.94 bit/s.

Connected and autonomous vehicles (CAVs) operate in open and evolving environments, which require timely and adaptive permission restriction to address dynamic risks that arise from changes in environmental situations (hereinafter referred to as situations), such as emergency situations due to vehicle crashes. Enforcing situation-aware access control is an effective approach to support adaptive permission restriction. Current works mainly implement situation-aware access control in the permission framework and API monitoring in user space. They are vulnerable to being bypassed and are coarse-grained. Autonomous systems have widely adopted mandatory access control (MAC) to configure and enforce system-wide and fine-grained access control policies. However, the MA$C$supported by Linux security modules (LSM) relies on predefined security contexts (e.g., type) and relatively fixed permission transition conditions (e.g., exec syscall), which lacks consideration of environmental factors. To address these issues, we propose a Situation-aware Access Control framework in the Kernel (SACK), which enforces adaptive permission restriction based on environmental factors for CAVs. Incorporating environmental situations into the LSM framework is not straightforward. SACK introduces situation states as a new security context for abstracting environmental factors in the kernel. Subsequently, SACK utilizes a situation state machine to implement new adaptive permission transitions triggered by situation events. In addition, SACK provides a novel situation-aware policy language that links specific user space permissions to MAC rules while maintaining compatibility with other LSMs such as AppArmor. We develop two prototypes: an independent SACK with its own policies and a SACK-enhanced AppArmor that adaptively updates the corresponding policies of AppArmor. The experimental results demonstrate that SACK can efficiently enforce situation-adaptive permissions with negliaible runtime overhead.

Federated Learning (FL) is a distributed framework that enables multi-participant collaborative model training without the need for data sharing. Despite its advantages, FL is vulnerable to poisoning and inference attacks, which compromise model accuracy and data privacy. Trusted execution environments (TEEs) offer a potential solution by providing a secure and isolated execution space to address these security and privacy concerns in FL. However, existing TEE-based FL schemes often suffer from reduced training speed and compromised model accuracy. To mitigate these issues, we propose a robust and privacy-preserving framework for federated learning (RPPFL) that leverages TEE and pseudorandom masking. In our approach, a trusted local model is trained on a secure subset of local data within the client-side TEE, which is then used for anomaly detection to resist poisoning attacks. Additionally, we employ pseudorandom masking to obfuscate local updates and global parameters. Experimental results indicate that RPPFL effectively counters both poisoning and inference attacks, with only a minimal decrease in training speed and no adverse impact on model accuracy. Compared to full-TEE approaches, our method improved local training efficiency by 10× , with less than a 9% loss in model performance under poisoning attacks.

Anomaly detection in multivariate time series (MTS) plays a crucial role in various domains, particularly in multimedia. While significant progress has been made in modeling normal data patterns and detecting anomalies based on deviations, existing methods face challenges in capturing complex inter-variable relationships, particularly in the presence of asynchronous dependencies. To address the challenges of detecting anomalies in MTS with complex inter-variable relationships, we propose an asynchronous inter-variable relationship-aware anomaly detection method. This approach simultaneously extracts both synchronous and asynchronous feature pairs between variables and leverages attention mechanisms to automatically learn unified inter-variable relationships across these dependencies. Additionally, we utilize a memory network to store and dynamically update the normal patterns of inter-variable relationships, computing anomaly scores based on deviations in these relationships. Extensive experiments demonstrate that our method outperforms existing baseline approaches, achieving an average F1 score of 97.05% across five benchmark datasets. Ablation studies further validate the effectiveness of each component of our method.

This paper addresses the role engineering problem for RESTful applications and proposes a role engineering method based on multi-head attention and Retrieval Augmented Generation called MA-RAG. The method first performs fine-grained control flow analysis on the system source code to extract permission information of API handlers. Then, using basic blocks as units, it employs pre-trained code models to convert the source code into semantic vectors, which are stored in the retrieval augmented generation model. On this basis, a call chain structure tree is constructed with permissions as the center, utilizing the multi-head attention mechanism to aggregate semantic information of different code granularities from bottom to top, with each attention head corresponding to a role engineering objective. Finally, the root vectors of each permission tree are subjected to self-supervised clustering to adaptively determine the number of roles and perform division. We evaluated MA-RAG on 284 real-world software systems, and the results show that compared with other methods, MA-RAG can significantly save time overhead, reduce the number of generated roles, lower the role permission overlap rate, and improve the interpretability score.

The abuse of LLM Chatbot interfaces by web robots leads to a significant waste of GPU and server resources, posing a serious security challenge. To address this issue, we propose LPDetective, an unsupervised method for detecting robot prompt templates. This method is based on the assumption that robot-generated text repeatedly uses the same or highly similar phrases and sentence structures across multiple sessions, differing from human natural conversations. We design a multi-stage workflow, including message grouping, text similarity measurement, hierarchical clustering analysis, and regular expression extraction, to automatically extract potential robot behavior patterns from chat logs. LPDetective does not require predefined templates or rely on training data, enabling it to adaptively discover new, unknown patterns. We conduct systematic experiments on three large-scale real-world datasets: Bing Copilot, Wildchat, and ChatLog. The results show that LPDetective can efficiently and accurately detect robot prompt templates in various scenarios, achieving a 7.5% improvement in F1 score compared to the state-of-the-art XLNet method and reducing detection latency by 178 times on the Bing Copilot dataset.

Matching suitable jobs with qualified candidates is crucial for online recruitment. Typically, users (i.e., candidates and employers) have specific expectations in the recruitment market, making them prefer similar jobs or candidates. Metric learning technologies provide a promising way to capture the similarity propagation between candidates and jobs. However, they rely on symmetric distance measures, failing to model users' asymmetric relationships in two-way selection. Additionally, users' behaviors (e.g., candidates) are highly affected by the feedback from their counterparts (e.g., employers), which can hardly be captured by the existing person-job fit methods that primarily explore homogeneous and undirected graphs. To address these problems, we propose a quasi-metric learning framework to capture the similarity propagation between candidates and jobs while modeling their asymmetric relations for bilateral person-job fit. Specifically, we propose a quasi-metric space that not only satisfies the triangle inequality to capture the fine-grained similarity between candidates and jobs, but also incorporates a tailored asymmetric measure to model users. two-way selection process in online recruitment. More importantly, the proposed quasi-metric learning framework can theoretically model recruitment rules from similarity and competitiveness perspectives, making it seamlessly align with bilateral person-job fit scenarios. To explore the mutual effects of two-sided users, we first organize candidates, employers, and their different-typed interactions into a heterogeneous relation graph, and then propose a relation-aware graph convolution network to capture users. mutual effects through their bilateral behaviors. Extensive experiments on several real-world datasets demonstrate the effectiveness of the proposed methods.

Recently, redactable blockchains have attracted attention owing to enabling the contents of blocks to be re-written. The existing redactable blockchain solutions can be classified as two categories, the centralized one and decentralized one. In centralized solutions, a single blockchain node possessing the trapdoor conducts redaction operations. However, they suffer from the issue of single point of failure. In decentralized solutions, redaction operations are performed by numerous blockchain nodes cooperatively. But there also exists the issue of inefficiency or requiring a trusted party in these solutions. Subsequently, Jia et al. proposed a redactable blockchain solution from a decentralized chameleon hash function (DCH) they designed, which supports the threshold redaction, traceability and consistency check. Nevertheless, after carefully analyzing their solution, we find that it fails to achieve the security they claimed by presenting a concrete attack. To resolve this security issue, we propose a novel chameleon hash function scheme that achieves strong collision-resistant security while maintaining simple and efficient as the building block. Based on it, we then present an improved DCH scheme with sufficient security, so that the redactable blockchain from it can resist the presented attack. Theoretical and experimental analyses demonstrate that improved DCH achieves efficiency comparable to DCH.

Machine learning techniques can help us deal with many difficult problems in the real world. Proper ensemble of multiple learners can improve the predictive performance. Each base learner usually has different predictive ability on different instances or in different instance regions. However, existing ensemble methods often assume that base learners have the same predictive ability for all instances without consideration of the specificity of different instances or categories. To address these issues, we propose an adaptive ensemble learning framework with category-aware attention and local contrastive loss, which can adaptively adjust the ensemble weight of each base classifier according to the characteristics of each instance. Specifically, we design a category-aware attention mechanism to learn the predictive ability of each classifier on different categories. Furthermore, we design a local contrastive loss to capture local similarities between instances and further enhance the model’s ability to discern fine-grained patterns in the data. Extensive experiments on 20 public datasets demonstrate the effectiveness of the proposed model.

Online/offline identity-based signature (OO-IBS) is a versatile cryptographic tool to provide the message authentication and integrity in mobile devices, since it lightens the computational burden after the signer receiving the message and eliminates the overhead of certificate management. It has several valuable applications, for instance, wireless sensor networks. Identity-based chameleon hash (IB-CH), as an alternative building block to construct OO-IBS, has been explored in numerous literatures. Nevertheless, there still exist two major issues. 1) Nearly all of the previous IB-CH schemes with weak collision-resistance (W-CollRes) are with random oracles, which may lead to security risks in practicality. The only IB-CH scheme in the standard model suffers from the large size of public parameters and inefficient setup process. 2) The only IB-CH scheme without key exposure also relies on random oracles. In this paper, we propose two novel IB-CH schemes in the standard model. The first scheme is adaptive identity, W-CollRes secure and efficient, significantly reducing the computation costs of all algorithms and the size of public parameters compared with the existing scheme in the standard model. The second scheme is the first IB-CH achieving key exposure freeness without random oracles. Both theoretical and experimental analyses demonstrate the good performance of our proposed schemes. Furthermore, we apply our schemes to optimizing the existing generic OO-IBS construction. The optimized generic constructions reduce computational overhead by 50.0% in the online phase and enable the hash value/signature tuple generated in the offline phase to be reusable, respectively.

Authorization is a key component of cloud security. However, the differences in access control mechanisms in heterogeneous cloud environments bring many challenges to cloud users, such as the need to learn multiple policy languages and the difficulty in implementing unified access control across clouds. To address these issues, this paper proposes a new access control policy language called PERM, which achieves flexible support for various fine-grained access control models by separating authorization logic from specific policy rules, and significantly reduces the complexity of policy definition. In addition, we also design a distributed PERM enforcement framework named List-Leafed Decision Tree (L2DT), which leverages a list-tree structure and distributed key-value storage to achieve efficient policy storage and execution. We implement prototypes of PERM and L2DT based on Java and Python, and conduct comprehensive evaluations using OpenStack and XACML datasets. Experimental results show that L2DT can achieve scalable policy execution with small latency overhead (an average of 8.63% in the OpenStack scenario and 5.45% in the XACML scenario). The research in this paper provides new ideas for building flexible, efficient, and scalable access control mechanisms in cloud environments.

While interrupts play a critical role in modern OSes, they have been exploited as a wide range of side channel attacks to break system confidentiality, such as keystroke interrupts, graphic interrupts and network interrupts. However, as previous attacks mainly focus on the exploitation of movable interrupts, they are required to determine which core is handling the target interrupts before their attack, which is non-trivial. The exploitability of non-movable interrupts, which cannot be reassigned by privileged softwares at will, remains unclear. In this paper, we conduct an empirical study on exploitable non-movable interrupts and their contribution to interrupt-based side-channel leakages in x86-based systems. We propose a dynamic analysis technique to investigate how various types of non-movable interrupts are influenced by different workloads. We then conduct a model fingerprinting attack as the benchmark to show that 7 types of non-movable interrupts are exploitable. To demonstrate the viability of these non-movable interrupts, we have created two concrete side channels, called ThermalScope and TimerScope. Specifically, ThermalScope exploits the thermal event interrupts that are triggered only when the CPU temperature exceeds a pre-determined threshold, and TimerScope exploits timer interrupts that are activated regularly to enable the process schedule. Both techniques are adaptable to different attack scenarios, functioning regardless of whether the attacker and victim share the same core or reside on separate cores. Last, we successfully apply them to mount realistic case studies, ranging from constructing cross-core covert channels to breaking kernel address space layout randomization. We also demonstrate successful DNN model fingerprinting attacks under browser scenarios when the frequency scaling is disabled and attacker core is isolated from movable interrupts, where previous HertzBleed, ThermalBleed, and movable interrupt-based attacks are ineffective.

Distributed storage systems are fundamental infrastructures of today’s large-scale software systems such as cloud systems. Diagnosing anomalies in distributed storage systems is essential for maintaining software availability. Existing anomaly diagnosis approaches mainly rely on the run-time data including monitoring data and application logs. However, collecting and analyzing the run-time data requires huge computing, storage, and management costs. Typically, more fine-grained run-time data can reveal more symptoms of anomalies, but on the contrary, requires more computing, storage, and management costs. As a result, solving the anomaly diagnosis problem is a balancing between the quality of run-time data and system overhead or cost. In this paper, we take into account both data quality and system overhead or cost by introducing a new type of run-time data-Raft logs. Raft logs are naturally produced by distributed storage systems and collecting raft logs will not bring any extra system overhead. To verify the ability of Raft logs in reflecting anomalies, we conduct a comprehensive study on the interconnection between the anomalies and Raft logs. Based on the study, we propose an effectiveRaft-BasedAnomalyDiagnosis approach namedRBAD. For evaluation, we expose the first open-sourced comprehensive dataset with multiple runtime data containing both Raft logs, application logs and monitoring data. Experiments based on this dataset demonstrate RBAD’s superiority, outperforming monitoring-based methods by 15.38% and log-based methods by 53.10%.

Database Management Systems (DBMS) form the backbone of modern large-scale software systems, where reliable anomaly detection and diagnosis are essential for ensuring system availability. However, existing log-based methods often impose significant performance overhead by collecting large volumes of logs, which is impractical for DBMS requiring high read/write throughput. This paper addresses a critical yet underexplored challenge: how to balance logging granularity with runtime efficiency for effective anomaly management in databases. We presentE-Log, a novel fine-grained elastic log-based framework for anomaly detection and diagnosis. E-Log intelligently adjusts the amount and detail of logging based on system state—maintaining lightweight logging during normal operation for efficient anomaly detection, and triggering rich, informative logging only upon anomaly suspicion for accurate diagnosis. This adaptive strategy significantly reduces runtime overhead while preserving diagnostic precision. We implement E-Log on Apache IoTDB and evaluate it using benchmarks including TSBS, TPCx-IoT, and IoT-Bench. Experimental results show that E-Log improves anomaly detection accuracy by 3.15% and diagnosis performance by 9.32% compared to state-of-the-art methods. Moreover, it reduces log storage size by 43.53% and increases average write throughput by 26.22%. These results highlight E-Log's potential to enable efficient, accurate, and scalable anomaly management in high-performance database systems.

While interrupts play a critical role in modern OSes, they have been exploited as a wide range of side channel attacks to break system confidentiality, such as keystroke interrupts, graphic interrupts and network interrupts. In this paper, we propose ThermalScope, a new side channel that exploits thermal event interrupts, which is adaptable for both native and browser scenarios and incorporates two heat amplifying techniques. The thermal event interrupts are activated only when the CPU package temperature reaches a fixed threshold that is determined by manufacturers. Our key observation is that workloads running on CPUs inevitably generates their distinct heat, which can be correlated with the thermal event interrupts. To demonstrate the viability of ThermalScope, we conduct a comprehensive evaluation on multiple Ubuntu OSes with different Intel-based CPUs. First, we show that the activation of thermal event interrupts correlates with the level of CPU temperature. We then apply ThermalScope to mount different side channel attacks, i.e., building covert channels with a transmission rate of 0.1 b/s, fingerprinting DNN model architectures with an accuracy of over 90% and breaking KASLR within 8.2 hours.

Federated learning (FL) allows different participants to collaborate on model training without transmitting raw data, thereby protecting user data privacy. However, FL faces a series of security and privacy issues (e.g. the leakage of raw data from publicly shared parameters). Several privacy protection technologies, such as homomorphic encryption, differential privacy and functional encryption, are introduced for privacy enhancement in FL. Among them, the FL frameworks based on functional encryption better balance security and performance, thus receiving increasing attention. The previous FL frameworks based on functional encryption suffer from several security issues, including attacks by combining multiple rounds of ciphertexts and keys, and leakage of global parameters to the central server. To tackle these issues, we propose a novel multi-input functional proxy re-encryption (MI-FPRE) scheme and further design a new FL framework with better privacy based on MI-FPRE. Our framework allows a semi-trusted central server to aggregate the parameters without knowing the intermediate parameters and the result of aggregation, thus achieves better privacy in FL training. The experimental results indicate that our framework achieves less communication overhead and higher computational efficiency without losing accuracy.

Federated learning (FL) has emerged as a promising paradigm for decentralized machine learning while preserving data privacy. However, under communication constraints, the standard FL protocol faces the risk of client dropout. Although some research has focused on the risk from the perspectives of communication optimization and privacy protection, it is still challenging to deal with the client dropout issue in dynamic networks, where clients may join or drop the training process at any time. In this paper, we systematically investigate and measure the impact of client dropout on FL by considering the offline duration, frequency, and pattern. Our work allows researchers to gain valuable insights into potential vulnerabilities. First, we assume an attacker can control a limited subset of clients and manipulate these clients to persistent dropout (PD) or random dropout (RD) in some iterative round during the training process. Then, we simulate a Shapley value-based dropout (SVD) attack to preferentially drop the local model of these controlled clients with highly valuable data per iterative round. Extensive experiments show that our SVD attack causes the model accuracy degradation by up to 10.2%, and the PD attack lengthens the training time by up to 4.1×.

Representation learning frameworks in unlabeled time series have been proposed for medical signal processing. Despite the numerous excellent progresses have been made in previous works, we observe the representation extracted for the time series still does not generalize well. In this paper, we present a Time series (medical signal) Representation Learning framework via Spectrogram (TRLS) to get more informative representations. We transform the input time-domain medical signals into spectrograms and design a time-frequency encoder named Time Frequency RNN (TFRNN) to capture more robust multi-scale representations from the augmented spectrograms. Our TRLS takes spectrogram as input with two types of different data augmentations and maximizes the similarity between positive ones, which effectively circumvents the problem of designing negative samples. Our evaluation of four real-world medical signal datasets focusing on medical signal classification shows that TRLS is superior to the existing frameworks. We will open-source our code when the paper is accepted.

Due to the rapid growth of mobile internet, massive multimedia data (e.g., movies, photos, notes, etc.) on mobile devices is synchronized and shared through the cloud. During this process, public key encryption plays an important role in ensuring the confidentiality of data. However, due to the bottleneck of computing and storage resources in mobile devices, it is difficult to execute complex cryptographic algorithms on them. In this paper, we present a novel Hybrid Proxy Reencryption (HyPRE) scheme for the sharing of multimedia data on mobile devices, which empowers a semi-trusted proxy to convert a ciphertext under an identity to a new one under an expressive policy without revealing the underlying plaintext. Our scheme allows mobile devices with limited resources to encrypt data efficiently, and then to share the encrypted data to multiple entities securely. We define the HRA security for our HyPRE scheme to improve the incompleteness of the security under chosen plaintext attacks (CPA) in traditional proxy re-encryption schemes and prove it selectively secure under HRA. Experimental analysis indicates that HyPRE achieves 2× to 3× improvement in terms of re-encryption performance compared with the state-of-the-art ones.

Federated learning is widely used in medical applications for training global models without needing local data access, but varying computational capabilities and network architectures (system heterogeneity) across clients pose significant challenges in effectively aggregating information from non-independently and identically distributed (non-IID) data (statistic heterogeneity). Current federated learning methods using knowledge distillation require public datasets, raising privacy and data collection issues. Additionally, these datasets require additional local computing and storage resources, which is a burden for medical institutions with limited hardware conditions. In this paper, we introduce a novel federated learning paradigm, named Model Heterogeneous personalized Federated Learning via Injection and Distillation (MH-pFLID). Our framework leverages a lightweight messenger model, eliminating the need for public datasets and reducing the training cost for each client. We also develops receiver and transmitter modules for each client to separate local biases from generalizable information, reducing biased data collection and mitigating client drift. Our experiments on various medical tasks including image classification, image segmentation, and time-series classification, show MH-pFLID outperforms state-of-the-art methods in all these areas and has good generalizability.

Distributed databases are fundamental infrastructures of today's large-scale software systems such as cloud systems. Detecting anomalies in distributed databases is essential for maintaining software availability. Existing approaches, predominantly developed using Loghub-a comprehensive collection of log datasets from various systems-lack datasets specifically tailored to distributed databases, which exhibit unique anomalies. Additionally, there's a notable absence of datasets encompassing multi-anomaly, multi-node logs. Consequently, models built upon these datasets, primarily designed for standalone systems, are inadequate for distributed databases, and the prevalent method of deeming an entire cluster anomalous based on irregularities in a single node leads to a high false-positive rate. This paper addresses the unique anomalies and multivariate nature of logs in distributed databases. We expose the first open-sourced, comprehensive dataset with multivariate logs from distributed databases. Utilizing this dataset, we conduct an extensive study to identify multiple database anomalies and to assess the effectiveness of state-of-the-art anomaly detection using multivariate log data. Our findings reveal that relying solely on logs from a single node is insufficient for accurate anomaly detection on distributed database. Leveraging these insights, we propose MultiLog, an innovative multivariate log-based anomaly detection approach tailored for distributed databases. Our experiments, based on this novel dataset, demonstrate MultiLog's superiority, outperforming existing state-of-the-art methods by approximately 12%.

Session-based recommendation (SBR) aims to predict user behaviors based on anonymous sessions. Compared with traditional user-based recommendation, SBR has a wider range of applications, but also suffers from more severe data sparsity problems because of the absence of user-profiles and limited short-term interactions. Furthermore, both users and items in the real world have a multifaceted nature. Users may exhibit multiple intents within a session, while items may have different semantics in different contexts. Unfortunately, existing approaches often overlook or only consider one aspect of them. To address these issues, we propose a novel hypergraph-based framework for session-based recommendation, called Hyperedge Interactional Convolution Network (HICN). Each session is represented as a sequential hyperedge, and multiple modules are designed to model and make use of the multifaceted nature of items and users. In addition, two inter-hyperedge modeling modules are designed to leverage related auxiliary information from other sessions with consideration of the existence of noise, which can help alleviate the data sparsity problem. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed model HICN.

Microservices architecture has garnered extensive attention for its stability and scalability. However, in the complex and dynamic landscape of microservices systems, a incident in one service can propagate to others, resulting in significant economic losses and degraded user experiences. Therefore, the effective and precise localization of incidents in microservices systems becomes a critical concern. Previous research has leveraged runtime data (logs, metrics, call traces) and historical incident data to assist in root cause localization. However, due to the scarcity of business incidents (those causing severe impacts on business operations) and the fact that many incidents are reported by users, relevant run-time data and sufficient historical data are often unavailable, rendering previous methods impractical. In response to this challenge, we propose an online contrastive learning-based method for root cause localization of business incidents(OCRCL). We fully exploit incident tickets and the static dependency graph of services, integrating both textual semantic information and structural information from the dependency graph to discover root causes. Furthermore, we suggest that online contrastive learning can exhibit excellent performance with limited data and enable real-time model updates, making it better suited for industrial scenarios. Our approach demonstrates significant improvements over baseline methods across three real-world industrial datasets, highlighting its effectiveness in root cause localization.

With the widespread use of container technology, attackers may invade the kernel by maliciously executing certain system calls, causing damage to the host and other containers. In order to reduce the attack surface of the underlying system, Docker supports specifying a container's allowlist of system calls with seccomp configurations. Java is a mainstream programming language used by the container projects in the Docker Hub, but how to generate the allowlist of system calls for Java containers is still an open question. Firstly, most of previous efforts about container allowlist of system calls focused on the C/C++ binary code rather than Java bytecode. Secondly, some existing works on Java bytecode mainly paid attention to the security vulnerabilities analysis, and cannot be used to analyze system calls required by Java programs. In this paper, we propose the first bytecode-based system call analysis approach, named SWAT 4J, tailored for Java containers operating on x86_64 architecture. SWAT4J can generate the allowlist of system calls required for Java containers by combining static and dynamic analysis. For static analysis, SWAT4J can identify the indirect calling relationships between Java bytecode and system calls, and determine the system calls required for a containerized application. For dynamic analysis, SWAT4J can trace the system calls required for container startup. The seccomp configuration file is optimized through the combining set of system calls. In the end, we experimented with 5 types of popular open source Java containers projects from Docker Official Images. Compared to 323 system calls in Ubuntu 16.04, SWAT4J successfully reduce the number of system calls by 56.04%-59.44%, and reduce the probability of vulnerabilities without affecting the functionality of the container.

Abstract Federated learning (FL) as a privacy-preserving technology enables multiple clients to collaboratively train models on decentralized data. However, transmitting model parameters between local clients and the central server can potentially result in information leakage. Differentially private federated learning (DPFL) has emerged as a promising solution to enhance privacy. Nevertheless, existing DPFL schemes suffer from two issues: (i) most schemes that aim to achieve desired model accuracy may incur a high privacy budget. (ii) several schemes that consider the trade-off between privacy and accuracy by utilizing linear clipping bound may distort numerous model parameters. In this paper, we first propose FDP-FL, a flexible differential privacy approach for FL. FDP-FL introduces a novel series sum privacy budget allocation instead of uniform allocation and enables adaptive and nonlinear noise scale decay. In this way, a tight bound for cumulative privacy loss can be achieved while optimizing model accuracy. Then in order to mitigate gradient leakages caused by honest-but-curious clients and server, we further design client-level FDP-FL and record-level FDP-FL, respectively. Experimental results demonstrate that our FDP-FL improves model accuracy by $\sim $13.3% compared with the basic DP-FL under a fixed privacy budget and outperforms existing trade-off schemes with the same hyperparameter setting.

Abstract To alleviate the burden of data storage and management, there is a growing trend of outsourcing data to the cloud that enables users to remotely manage their data flexibly. However, this shift also raises concerns regarding outsourced data deletion, as users lose physical control over their outsourced data and are unable to verify its proper eradication. To address this issue, cloud service providers are required to provide a scheme that guarantees the effective deletion of outsourced data. Existing schemes, including key management-based and overwriting-based schemes, fail to ensure both the irrecoverability of deleted data and the verifiability of the deletion process. In this paper, we propose IPOD2, an irrecoverable and verifiable deletion scheme for outsourced data. Specifically, IPOD2 utilizes the overwriting-based deletion method to implement outsourced data deletion and extends the Integrity Measurement Architecture to measure the operations in the deletion process. The measurement results are protected by the Trusted Platform Module and verifiable for users. To demonstrate the viability of IPOD2, we implement a prototype of IPOD2 on the Linux kernel 5.4.120. Experimental results show that, compared with the three existing schemes, IPOD2 has the minimum overhead in both deletion and verification processes.

Vertical federated learning (VFL) enables collaborative machine learning on vertically partitioned data with privacy-preservation. Most VFL methods face three daunting challenges in real-world applications. First, most existing VFL methods assume that at least one party holds the complete set of labels of all data samples. However, this assumption often violates the nature of many practical scenarios, where the parties only have partial labels. Second, the heterogeneity and dynamic of computational and communication resources in participated parties may cause the straggler problem and slow down training convergence. Third, the confidential label information could be exposed through malicious parties during VFL. To address these challenges, we propose a novel VFL algorithm named Cascade Vertical Federated Learning (CVFL), in which partitioned labels can be fully utilized to train neural networks with privacy-preservation. To mitigate the straggler problem, we design a novel optimization objective to increase straggler's contribution to the trained models. To mitigate the label privacy risks, we design a novel defense approach to protect the label privacy of CVFL. We conduct comprehensive experiments and the results demonstrate the effectiveness and efficiency of CVFL. Further, the proposed defense approach can achieve a better tradeoff between label privacy and model utility than two widely-used defense approaches.

Nearly all of the previous attribute-based proxy re-encryption (ABPRE) schemes cannot support keyword search and keyword updating without the aid of private key generator (PKG) simultaneously. To resolve this problem, recently in IEEE Transactions on Dependable and Secure Computing (doi: 10.1109/TDSC.2020.2963978), Ge et al. proposed a ciphertext-policy ABPRE scheme with keyword search, dubbed CPAB-KSDS, which supports keyword updating without communicating with PKG. It also achieves indistinguishability against chosen-ciphertext attack (IND-CCA) security and indistinguishability against chosen-keyword attack (INDCKA) security in the random oracle model. In this paper, we carefully analyze the security of Ge et al.’s CPAB-KSDS scheme and find that they did not give a correct reduction from IND-CKA security of theirs to the underlying cryptographic assumption. Furthermore, we also give a concrete attack on IND-CKA security of the CPAB-KSDS scheme. Therefore, it fails to achieve IND-CKA security they claimed, which is an essential security requirement for the encryption scheme with keyword search.

Sequential recommendation seeks to predict users' next behaviors and recommend related items over time. Existing research has mainly focused on modeling users' dynamic preferences from their sequential behaviors. However, most of these studies have ignored the negative effects of noise behaviors in the given sequences, which may mislead the recommender. In addition, users' behavior data is always sparse, which makes it difficult to effectively learn users' preferences purely from their historical behaviors. Most recently, knowledge graphs (KGs) have been exploited by few researchers for sequential recommendation. However, they always assume all information in KGs or KG paths with limited length are useful for recommendation, which may bring irrelevant information from KGs into the recommender and further mislead the recommender. To address these issues, we propose a novel KG-based behavior denoising and preference learning model named KGDPL for sequential recommendation. We argue that the paths in KGs that reflect semantic relations between entities can not only help to remove noise behaviors and recommend successive items for users, but also provide relevant explanations. Therefore, we first devise a supervised knowledge path selection module to select effective paths between items from KGs for behavior prediction, which aims to filter out irrelevant information from KGs for the given recommendation task. Then, we design a knowledge-enhanced behavior denoising module to mitigate the negative effects of the noise behaviors contained in historical sequences by using the knowledge path information. After that, we propose a knowledge-enhanced preference learning module to better learn users' personalized and dynamic preferences from their historical behavior sequences and related knowledge information, which can also help tag users and provide explanations for recommendation results. Experimental results on four real-world datasets demonstrate the effectiveness and interpretability of the proposed model KGDPL.

Code writing is repetitive and predictable, inspiring us to develop various code intelligence techniques. This survey focuses on code search, that is, to retrieve code that matches a given natural language query by effectively capturing the semantic similarity between the query and code. Deep learning, being able to extract complex semantics information, has achieved great success in this field. Recently, various deep learning methods, such as graph neural networks and pretraining models, have been applied to code search with significant progress. Deep learning is now the leading paradigm for code search. In this survey, we provide a comprehensive overview of deep learning-based code search. We review the existing deep learning-based code search framework that maps query/code to vectors and measures their similarity. Furthermore, we propose a new taxonomy to illustrate the state-of-the-art deep learning-based code search in a three-step process: query semantics modeling, code semantics modeling, and matching modeling, which involves the deep learning model training. Finally, we suggest potential avenues for future research in this promising field.

To ensure the stability and reliability of microservice systems, timely and accurate anomaly detection is of utmost importance. Recently, considering the lack of labels in real-world scenarios and the collaborative and complementary relationships of multi-modal data in reflecting system anomalies, unsupervised multi-modal anomaly methods have been proposed. However, existing methods face challenges in effectively distinguishing normal hard samples (they are normal but hard to classify correctly) from anomalies. This is mainly caused by two aspects. First, the hard sample patterns are complex. Second, the convergence speed is inconsistent between hard and simple samples. To overcome these issues, we propose an unsupervised adversarial contrastive multi-modal anomaly detection method (UAC-AD). We utilize contrastive learning to help learn the complex patterns of hard samples and enlarge the distance between hard and anomaly samples. Meanwhile, the adversarial framework automatically identifies hard samples and fine-grained adjusts the training weights to each modality part of these hard samples. In this case, The hard sample problems of two aspects can be alleviated. We extensively evaluate UAC-AD on two open-source simulated datasets and a real industrial dataset from a large communication company. Extensive experimental results demonstrate the effectiveness of our approach in anomaly detection. We also release the code and dataset for replication and future research.

Computer-aided diagnosis plays an increasingly important role in modern medical activities, relying largely on the deployment of medical machine learning models. Protecting the security of model parameters is crucial for model providers. However, the current schemes for protecting model parameters are mostly interactive. This interactive nature makes it difficult to support offline deployment of models and flexible authorization of prediction results, thus hindering the widespread application of computer-aided diagnosis. To address these limitations, we propose a new computer-aided medical diagnosis framework by designing a new identity-based inner product functional proxy re-encryption (IB-IPFPRE) scheme. Our framework supports private deployment of medical diagnostic models without compromising model parameters. It also enables access control of prediction results based on user identity. Compared to existing privacy-preserving prediction techniques, our framework significantly reduces communication overhead and does not require the model owner to be online in real-time. Furthermore, our scheme enables flexible delegation of prediction results, allowing users to authorize the sharing of prediction results with other entities as needed. We conducted extensive experiments for logistic regression on three medical datasets. The experiments demonstrate that our scheme achieved 40% to 7× performance improvement in LAN environment and 13× to 15× improvement in WAN environment, and did not require any communication overhead during the privacy preserving prediction phase.

With the widely application of cloud, a series of privacy challenges arise. Generally, encryption methods are used to ensure privacy, which may result in high computation and communication overheads. Access control is another fundamental and important measure to protect resources. Usually cloud computing systems are managed through RESTful web services and users can conduct access control measures like role-based access control (RBAC) to manage the permissions to RESTful resources. By running integration test, test cases and the corresponding RESTful permissions can be parsed out automatically. We are the first to define the role engineering problem based on integration test and summarize three metrics for role engineering. Then we propose a novel role engineering method based on spectral clustering analysis which supporting more feature set such as permission weight, role hierarchy and customized number of roles. Finally, we conduct experiments using real integration test on three cloud computing systems to demonstrate the effectiveness and performance, outperforming prior works.

At present, backdoor attacks attract attention as they do great harm to deep learning models. By poisoning the training data, the adversary makes the model trained based on this dataset being injected with a backdoor. In the field of text, however, existing works do not provide sufficient defense against backdoor attacks. In this paper, we propose a Noise-augmented Contrastive Learning (NCL) framework to defend against textual backdoor attacks when training models with untrustworthy data. With the aim of mitigating the mapping between triggers and the target label, we add appropriate noise perturbing possible backdoor triggers, augment the training dataset, and then pull homology samples in the feature space utilizing contrastive learning objective. Experiments demonstrate the effectiveness of our method in defending three types of textual backdoor attacks, outperforming the prior works.

Reciprocal recommendation is the core of many social websites like online recruitment and online dating. Most recently, graph neural networks have been exploited by few researchers for reciprocal recommendation. However, they tend to oversimplify the interactions between users, treating them as simple pairwise relationships, which overlooks the multidimensional relationships among users. Additionally, these methods fail to consider users’ historical interaction sequences and feedback behaviors, which makes it challenging to effectively capture the changes of user preferences over time.To address these issues, this study proposes a novel bilateral recommendation model based on sequential hypergraphs for reciprocal scenarios. Firstly, to capture the complex multidimensional relationships between bilateral users, we design a new data structure called bilateral sequential hypergraphs to capture the diverse relationships among users and to mine collaborative signals at the sequential level. Secondly, we propose corresponding bilateral sequential hypergraph convolution structures to learn the embedded representations of bilateral users. To adequately capture the changes in user preferences, the model incorporates position modeling and feedback behavior modeling within the proposed convolution strategy. Extensive experiments on several real-world datasets demonstrate that the proposed method consistently outperforms state-of-the-art methods.

In online service systems, a majority of incidents are caused by changes, which can influence user experience and cause huge economic loss. Experiences with a real-world, large-scale online service system show that more than half of the change-induced incidents are reported by users. Identifying root-cause changes for these incidents is challenging due to the inherent gap between user-perceived functional-level incident information and component-level change details. Inadequate causal knowledge also brings challenges. In this paper, we propose a novel causal knowledge mining based approach aiming at root-cause change identification for user-reported incidents named Raccoon. To bridge the gap between incidents and changes, it utilizes the fault tree and software product line to represent incidents and changes at the user-perceived functional level. They are also used as the backbone of causal knowledge. To overcome the lack of causal knowledge, Raccoon adopts efficient knowledge extraction and inference methods. Moreover, Raccoon provides recommendations at the software product line and change granularity to meet diverse demands of incident triage and root-cause change identification scenarios in incident management. We evaluate Raccoon on a real-world dataset collected in a large-scale online service system. The result shows that Raccoon significantly outperforms the state-of-the-art baseline approaches, which proves its effectiveness.

In online service systems, software changes cause a majority of incidents (i.e., unplanned interruptions and outages). Managing change-induced incidents efficiently is crucial for ensuring the reliability and availability of online service systems. Understanding the incidents can help improve change-induced incident management. The task is challenging because the life cycle of change-induced incidents is complicated due to diverse change deployment and incident resolution procedures. Detailed records of the incidents and changes, together with a comprehensive analysis, are needed to gain an in-depth understanding. In this paper, we conduct a qualitative and quantitative study on 231 change-induced incidents in a real-world, large-scale online service system. Detailed change tickets and incident timeline in the post-mortems provides extensive information about the incident life cycle, enabling us to understand each incident in depth. Based on the data, we give a generic model of the complicated life cycle of change-induced incidents. Following the model, we systematically study the whole life cycle of the incident, including the introduction and resolution stages, and answer what affects the efficiency of resolution. We obtain 9 major findings from our study. Based on the findings, we discuss existing techniques and promising future directions for improving change-induced incident management.

Software defect prediction can automatically locate defective code modules to focus testing resources better. Traditional defect prediction methods mainly focus on manually designing features, which are input into machine learning classifiers to identify defective code. However, there are mainly two problems in prior works. First manually designing features is time consuming and unable to capture the semantic information of programs, which is an important capability for accurate defect prediction. Second the labeled data is limited along with severe class imbalance, affecting the performance of defect prediction.In response to the above problems, we first propose a new unsupervised domain adaptation method using pseudo labels for defect prediction(UDA-DP). Compared to manually designed features, it can automatically extract defective features from source programs to save time and contain more semantic information of programs. Moreover, unsupervised domain adaptation using pseudo labels is a kind of transfer learning, which is effective in leveraging rich information of limited data, alleviating the problem of insufficient data.Experiments with 10 open source projects from the PROMISE data set show that our proposed UDA-DP method outperforms the state-of-the-art methods for both within-project and cross-project defect predictions. Our code and data are available at https://github.com/xsarvin/UDA-DP.

As cloud services have become popular, and their adoption is growing, consumers are becoming more concerned about the cost of cloud services. Cloud Service Providers (CSPs) generally use a pay-per-use billing scheme in the cloud services model: consumers use resources as they needed and are billed for their resource usage. However, CSPs are untrusted and privileged; they have full control of the entire operating system (OS) and may tamper with bills to cheat consumers. So, how to provide a trusted solution that can keep track of and verify the consumers’ resource usage has been a challenging problem. In this article, we propose a T-Counter framework based on Intel SGX. The T-Counter allows applications to construct a trusted solution to measure its CPU usage by itself in cloud computing. These constructed applications are instrumented with counters in basic blocks and added three components in trusted parts to count instructions and defend against malicious CSPs’ manipulations. We propose two algorithms which selectively instrument counters in the CFG. T-Counter is implemented as an extension of the LLVM framework and integrated with the SGX SDK. Theoretical analyses and evaluations show that T-Counter can effectively measure CPU usage and defend against malicious CSPs’ manipulations.

The healthcare system is a distributed collaborative system and the sensitivity of the medical data is one of the most important requirements. Preventing unauthorized access to healthcare information and data sharing security in the healthcare environment are critical processes that affect the credibility of the system. To achieve this goal and to meet the requirements of the healthcare system, access control is an important measure to realize the safe sharing of resources. The attribute-based access control (ABAC) model meets the complex security requirements of large and complex systems and provides a dynamic, flexible and scalable solution. The main obstacle to deploying ABAC is the precise development of ABAC policies. Manually developing access control policies is tedious, time-consuming and error prone. Most systems have high-level requirement specifications, which are written in natural language. These natural language (NL) documents have the intended access control policies for the systems. In this paper, we propose a new approach towards extracting policies from natural language documents. By fully taking advantage of Bidirectional Encoder Representations from Transformers (BERT) and Semantic role labeling (SRL), we are able to correctly identify access control policy (ACP) sentences with an average F1 score of 85% and correctly extract rules with an average F1 score of 72%, which outperforms the state-of-the-art and leads to a performance improvement of 7% and 2% respectively over the previously reported results.

In knowledge-grounded dialogue generation, pre-trained language models (PLMs) can be expected to deepen the fusing of dialogue context and knowledge because of their superior ability of semantic understanding. Unlike adopting the plain text knowledge, it is thorny to leverage the structural commonsense knowledge when using PLMs because most PLMs can only operate plain texts. Thus, linearizing commonsense knowledge facts into plan text is a compulsory trick. However, a dialogue is always aligned to a lot of retrieved fact candidates; as a result, the linearized text is always lengthy and then significantly increases the burden of using PLMs. To address this issue, we propose a novel two-stage framework SAKDP. In the first pre-screening stage, we use a ranking network PriorRanking to estimate the relevance of a retrieved knowledge fact. Thus, facts can be clustered into three sections of different priorities. As priority decreases, the relevance decreases, and the number of included facts increases. In the next dialogue generation stage, we use section-aware strategies to encode the linearized knowledge. The powerful but expensive PLM is only used for a few facts in the higher priority sections, reaching the performance-efficiency balance. Both the automatic and human evaluation demonstrate the superior performance of this work.

Online/offline identity-based signature (OO-IBS) is an adequate cryptographic tool to provide the message authentication and integrity in mobile devices, since it lightens the computational burden after the signer receives the message and eliminates the overhead of certificate management. It has several valuable applications, such as wireless sensor networks and automatic dependent surveillance-broadcast systems. Identity-based chameleon hash (IB-CH), as an alternative building block to construct OO-IBS, has been explored in several literatures. Nevertheless, almost all of the prior IB-CH schemes are in the random oracle model, which may lead to security risks in practicality. The only IB-CH scheme in the standard model proposed by Xie et al. (ICC’21) suffers from the large size of public parameters and inefficient setup process. In this paper, we propose an efficient IB-CH scheme in the standard model, significantly reducing the computational costs of all the algorithms and the size of public parameters compared with Xie’s scheme. The security and experimental analyses demonstrate the security and good performance of our scheme. Furthermore, we applied our scheme to optimize the existing generic OO-IBS construction. Our optimized construction reduces computational overhead by 50.0% in the online phase compared with the original construction.

Unsupervised image segmentation is a challenge task, since a high-quality segmented image should perceive not only local object structures but also certain semantics without any annotations. In this paper, we propose a novel encoder-decoder pixel clustering framework with dual constraints to incorporate local structure and global semantic information for guiding pixel feature learning in a self-supervised manner. On one hand, a Local Structure Constraint (LStC) is constructed based on fine-grained superpixels, which improves the boundary perception of pixel features by keeping intra-superpixel feature consistency and largening inter-superpixel feature distance. On the other hand, a new Global Semantic Constraint (GSeC) is proposed via adapting the mutual information maximization technique to the single-image setting, and it strengthens the global semantic perception of pixel features and thus improves the segmenting integrity of objects. Finally, based on the learned pixel features, a smoothing component is employed to achieve semantically meaningful pixel clustering. The experimental evaluation on BSDS500 and PASCAL Context datasets show the superiority of our method on region and boundary qualities.

Vertical federated learning (VFL) enables collaborative machine learning on vertically partitioned data with privacy-preservation, attracting widespread attentions from academia and industry. Most existing VFL methods face two daunting challenges in real-world applications. First, most VFL methods assume at least one party holds the complete set of labels of all data samples. However, this assumption often violates the nature of many scenarios, where the parties only have partial labels. Second, the limitation of computational and communication resources in participated parties may cause the straggler problem and slow down training convergence. To address these challenges, we propose a novel VFL algorithm named Cascade Vertical Federated Learning (CVFL), in which partitioned labels can be fully utilized to train neural networks. To mitigate the straggler problem, we design a novel optimization objective to increase straggler's contribution to the trained models. We conduct comprehensive experiments and the results demonstrate the effectiveness and efficiency of CVFL.

Web Application Firewall (WAF) is widely deployed in cloud to protect web applications, whose performance becomes one of the major bottlenecks for web services. In this paper, we comprehensively analyze several root causes that downgrade WAF’s efficiency. Inspired by that, we build a caching system RuleCache to devise optimization strategies for improving WAF’s performance. Among, Rule Ordering Cache is online learning an optimal order of the ruleset for a better performance of blocking. Rule Result Cache reuses rule results of targets, saving large repetitive computations. Additionally, Rule Prepruning Cache aims to cut extra overhead by processing the static rules in the offline stage. Our evaluation demonstrates that the prototype can improve the performance by up to 3.85x, 1.57x, and 2.4x respectively with the above modules, and up to 5.5x in total.

With the increasing complexity of modern software systems, it is essential yet hard to detect anomalies and diagnose problems precisely. Existing log-based anomaly detection approaches rely on a few key assumptions on system logs and perform well in some experimental systems. However, real-world industrial systems are often with poor logging quality, in which system logs are noisy and often violate the assumptions of existing approaches. This makes these approaches inefficient. This paper first conducts a comprehensive study on the system logs of three large-scale industrial software systems. Through the study, we identify four typical anti-patterns that affect the detection results the most. Based on these patterns, we propose HiLog, an effective human-in-the-loop log-based anomaly detection approach that integrates human knowledge to augment anomaly detection models. With little human labeling effort, our approach can significantly improve the effectiveness of existing models. Experiment results on three large-scale industrial software systems show that our method improves over 50% precision rate on average.

In recent years, data security incidents caused by insider threats in distributed file systems have attracted the attention of academia and industry. The most common way to detect insider threats is based on user profiles. Through analysis, we realize that based on existing user profiles are not efficient enough, and there are many false positives when a stable user profile has not yet been formed. In this work, we propose personalized user profiles and design an insider threat detection framework, which can intelligently detect insider threats for securing distributed file systems in real-time. To generate personalized user profiles, we come up with a time window-based clustering algorithm and a weighted kernel density estimation algorithm. Compared with non-personalized user profiles, both the Recall and Precision of insider threat detection based on personalized user profiles have been improved, resulting in their harmonic mean F1 increased to 96.52%. Meanwhile, to reduce the false positives of insider threat detection, we put forward operation recommendations based on user similarity to predict new operations that users will produce in the future, which can reduce the false positive rate (FPR). The FPR is reduced to 1.54% and the false positive identification rate (FPIR) is as high as 92.62%. Furthermore, to mitigate the risks caused by inaccurate authorization for users, we present user tags based on operation content and permission. The experimental results show that our proposed framework can detect insider threats more effectively and precisely, with lower FPR and high FPIR.

Traditional conversational systems can only access the given query during the response generation, leading to meaningless responses. To this end, researchers proposed to enhance dialogue generation by integrating external knowledge. Although such methods have achieved remarkable gains, the use of only single-source knowledge often makes existing knowledge-enhanced methods degenerate into traditional models in real scenarios because of the insufficient knowledge coverage of single-source knowledge. To improve the applicability of knowledge-enhanced methods, we propose two novel frameworks to use heterogeneous knowledge from multiple sources. We first propose an MHKD-Seq2Seq framework, which can use different heterogeneous knowledge by identifying abstract-level knowledge behaviors; meanwhile, a Diffuse-Aggregate scheme is used to process multiple knowledge simultaneously and produce a unified result. The next framework MHKD-ARPLM can leverage the advantages of pretrained language models with Knowledge Linearization techniques. In experiments, we collected dialogues from previously open-released datasets and built a multi-source knowledge-aligned dataset TriKE-Weibo, which involves three knowledge sources: commonsense, texts, and infobox tables. Extensive evaluations demonstrate the performance leadership of our approaches against competitive baseline models.

Social recommendation, which leverages social connections to construct Recommender Systems (RS), plays an important role in alleviating information overload. Recently, Graph Neural Networks (GNNs) have received increasing attention due to their great capacity for graph data. Since data in RS is essentially in the structure of graphs, GNN-based RS is flourishing. However, existing works lack in-depth thinking of social recommendations. These methods contain implicit assumptions that are not well analyzed in practice. To tackle these problems, we conduct statistical analyses on widely used social recommendation datasets. We design metrics to evaluate the social information, which can provide guidance about whether and how we should use this information in the RS task. Based on these analyses, we propose a Distillation Enhanced SocIal Graph Network (DESIGN). We train a model that integrates information from the user-item interaction graph and the user-user social graph and train two auxiliary models that only use one of the above graphs respectively. These models are trained simultaneously, where the knowledge distillation technique restricts the training process and makes them learn from each other. Our extensive experiments show that our model significantly and consistently outperforms the state-of-the-art competitors on real-world datasets.

Error decomposition analysis is a key problem for ensemble learning, which indicates that proper combination of multiple models can achieve better performance than any individual one. Existing theoretical research of ensemble learning focuses on regression or classification tasks. There is limited theoretical research for ranking ensemble. In this paper, we first generalize the ambiguity decomposition theory from regression ensemble to ranking ensemble, which proves the effectiveness of ranking ensemble with consideration of list-wise ranking information. According to the generalized theory, we propose an explicit diversity measure for ranking ensemble, which can be used to enhance the diversity of ensemble and improve the performance of ensemble model. Furthermore, we adopt an adaptive learning scheme to learn query-dependent ensemble weights, which can fit into the generalized theory and help to further improve the performance of ensemble model. Extensive experiments on recommendation and information retrieval tasks demonstrate the effectiveness and theoretical advantages of the proposed method compared with several state-of-the-art methods.

Pass selection and phase ordering are two critical compiler auto-tuning problems. Traditional heuristic methods cannot effectively address these NP-hard problems especially given the increasing number of compiler passes and diverse hardware architectures. Recent research efforts have attempted to address these problems through machine learning. However, the large search space of candidate pass sequences, the large numbers of redundant and irrelevant features, and the lack of training program instances make it difficult to learn models well. Several methods have tried to use expert knowledge to simplify the problems, such as using only the compiler passes or subsequences in the standard levels (e.g., -O1, -O2, and -O3) provided by compiler designers. However, these methods ignore other useful compiler passes that are not contained in the standard levels. Principal component analysis (PCA) and exploratory factor analysis (EFA) have been utilized to reduce the redundancy of feature data. However, these unsupervised methods retain all the information irrelevant to the performance of compilation optimization, which may mislead the subsequent model learning. To solve these problems, we propose a compiler pass selection and phase ordering approach, called Iterative Compilation based on Metric learning and Collaborative filtering (ICMC) . First, we propose a data-driven method to construct pass subsequences according to the observed collaborative interactions and dependency among passes on a given program set. Therefore, we can make use of all available compiler passes and prune the search space. Then, a supervised metric learning method is utilized to retain useful feature information for compilation optimization while removing both the irrelevant and the redundant information. Based on the learned similarity metric, a neighborhood-based collaborative filtering method is employed to iteratively recommend a few superior compiler passes for each target program. Last, an iterative data enhancement method is designed to alleviate the problem of lacking training program instances and to enhance the performance of iterative pass recommendations. The experimental results using the LLVM compiler on all 32 cBench programs show the following: (1) ICMC significantly outperforms several state-of-the-art compiler phase ordering methods, (2) it performs the same or better than the standard level -O3 on all the test programs, and (3) it can reach an average performance speedup of 1.20 (up to 1.46) compared with the standard level -O3.

Dialogues systems endow machines with the ability to converse with humans using natural language. Nonetheless, previous Seq2Seq-based generative dialogue systems often generate safe but meaningless responses, such as ‘I don't know' or ‘I think so'. To this end, researchers proposed to infuse external knowledge into dialogue generation, and such knowledge-enhanced methods have achieved remarkable improvements in the open-domain dialogue systems. External knowledge is an exogenous input, where the estrangement inevitably exists between knowledge and dialogue context. Although previous knowledge-enhanced works can already use commonsense knowledge to generate informative responses, they always use knowledge in a single-channel paradigm, which is hard to accurately handle different data-flows and then tends to generate irrational dialogue responses. Thus, they tend to be confused and generate strange responses when infusing the knowledge into dialogue generation, such as ‘I just ate a basketball’, dramatically degrading the user experience. To address this problem, this paper proposes a novelChannel-Aware Knowledge FusingNetwork (CAKF). Rather than following the traditional single-channel paradigm, CAKF employs three unique channels to handle different data-flows more clearly and rationally: abasechannel serves like a vanilla Seq2Seq decoder; acontextchannel to utilize the contextual information, and aknowledgechannel to infuse commonsense knowledge into the dialogue generation. Above such three channels, aSequential Manageris built to maintain the global sequential decision state, aggregate the local data-flows, and make the final prediction. Experiments on two open-released datasets (a Chinese Weibo and an English Reddit) demonstrated the superior performance of this work against various state-of-the-art approaches.

Entity alignment which aims at linking entities with the same meaning from different knowledge graphs (KGs) is a vital step for knowledge fusion. Existing research focused on learning embeddings of entities by utilizing structural information of KGs for entity alignment. These methods can aggregate information from neighboring nodes but may also bring noise from neighbors. Most recently, several researchers attempted to compare neighboring nodes in pairs to enhance the entity alignment. However, they ignored the relations between entities which are also important for neighborhood matching. In addition, existing methods paid less attention to the positive interactions between the entity alignment and the relation alignment. To deal with these issues, we propose a novel Relation-aware Neighborhood Matching model named RNM for entity alignment. Specifically, we propose to utilize the neighborhood matching to enhance the entity alignment. Besides comparing neighbor nodes when matching neighborhood, we also try to explore useful information from the connected relations. Moreover, an iterative framework is designed to leverage the positive interactions between the entity alignment and the relation alignment in a semi-supervised manner. Experimental results on three real-world datasets demonstrate that the proposed model RNM performs better than state-of-the-art methods.

Deep neural networks (DNNs) have progressed rapidly during the past decade and have been deployed in various real-world applications. Meanwhile, DNN models have been shown to be vulnerable to security and privacy attacks. One such attack that has attracted a great deal of attention recently is the backdoor attack. Specifically, the adversary poisons the target model’s training set to mislead any input with an added secret trigger to a target class.

Despite achieving remarkable performance, previous knowledge-enhanced works usually only use a single-source homogeneous knowledge base of limited knowledge coverage.Thus, they often degenerate into traditional methods because not all dialogues can be linked with knowledge entries.This paper proposes a novel dialogue generation model, MSKE-Dialog, to solve this issue with three unique advantages: ( 1) Rather than only one, MSKE-Dialog can simultaneously leverage multiple heterogeneous knowledge sources (it includes but is not limited to commonsense knowledge facts, text knowledge, infobox knowledge) to improve the knowledge coverage; (2) To avoid the topic conflict among the context and different knowledge sources, we propose a Multi-Reference Selection to better select context/knowledge; (3) We propose a Multi-Reference Generation to generate informative responses by referring to multiple generation references at the same time.Extensive evaluations on a Chinese dataset show the superior performance of this work against various state-of-the-art approaches.To our best knowledge, this work is the first to use the multi-source heterogeneous knowledge in the open-domain knowledge-enhanced dialogue generation.

The availability of enormous event data with timestamps has aroused the study of Dynamic Knowledge Graphs (KGs). In dynamic KGs, temporal link prediction is an important task, which predicts future interactions between entities. Compared with conventional statistic link prediction tasks, temporal link prediction has three main challenges: i) How to deal with new entities that we have not observed before. ii) How to model the temporal evolutionary patterns. iii) How to adapt to the changes in KGs without re-training the model. To deal with these challenges, we present a novel reinforcement learning approach with an update mechanism to integrate temporal information. To predict future events, we train a time-aware agent to navigate the graph conditioned on the input query to find predictive paths. The experimental results indicate a clear improvement over the state-of-the-art methods.

Nowadays, web data is often used to train a dialogue system. However, noises in web data can disturb the training process, as well as can impact the performance. Consequently, dialogue models tend to be brittle when receiving noisy inputs during the inference. This paper proposes a novel framework, Multi-Path Training (MPT), for training a robust dialogue response generation system. MPT improves the robustness to the noisy training data and the noisy inference queries using three paths. Experimental results show MPT can outperform baselines using the same backbone model, and also prove MPT can improve the robustness to the noise in both the training and inference stage.

Graph Neural Networks (GNNs) have become the predominant technique for learning over graphs. However, there is little understanding of why GNNs are successful in practice. In this paper, we revisit GNNs for node classification tasks and find that GNNs mainly gain from iterative message aggregation, performing embedding transformation during aggregation leads to over-fitting and poor performance. Furthermore, the over-fitting problem is more severe in heterogeneous GNNs because of more complex operations. Driven by these findings, we propose a highway heterogeneous graph attention network that decouples message aggregation and embedding transformation to alleviate the over-fitting problem. To model heterogeneous structures, we design a hierarchical attention mechanism and a node-type dependent gate mechanism. Extensive experimental results show that our model has superior performance over the state-of-the-art methods.

Due to limited knowledge carried by queries, traditional dialogue systems often face the dilemma of generating boring responses, leading to poor user experience. To alleviate this issue, this paper proposes a novel infobox knowledge-aware dialogue generation approach, HITA-Graph, with three unique features. First, open-domain infobox tables that describe entities with relevant attributes are adopted as the knowledge source. An order-irrelevance Hierarchical Infobox Table Encoder is proposed to represent an infobox table at three levels of granularity. In addition, an Infobox-Dialogue Interaction Graph Network is built to effectively integrate the infobox context and the dialogue context into a unified infobox representation. Second, a Hierarchical Infobox Attribute Attention mechanism is developed to access the encoded infobox knowledge at different levels of granularity. Last but not least, a Dynamic Mode Fusion strategy is designed to allow the Decoder to select a vocabulary word or copy a word from the given infobox/query. We extract infobox tables from Chinese Wikipedia and construct an infobox knowledge base. Extensive evaluation on an open-released Chinese corpus demonstrates the superior performance of our approach against several representative methods.

Online reviews, which contain the quality information and user experience about products, always affect the consumption decisions of customers. Unfortunately, quite a number of spammers attempt to mislead consumers by writing fake reviews for some intents. Existing methods for detecting spam reviews mainly focus on constructing discriminative features, which heavily depend on experts and may miss some complex but effective features. Recently, some models attempt to learn the latent representations of reviews, users, and items. However, the learned embeddings usually lack interpretability. Moreover, most of existing methods are based on single classification model while ignoring the complementarity of different classification models.

Abstract Attribute-based encryption with equality test (ABEET) simultaneously supports fine-grained access control on the encrypted data and plaintext message equality comparison without decrypting the ciphertexts. Recently, there have been several literatures about ABEET proposed. Nevertheless, most of them explore the ABEET schemes in the random oracle model, which has been pointed out to have many defects in practicality. The only existing ABEET scheme in the standard model, proposed by Wang et al., merely achieves the indistinguishable against chosen-plaintext attack security. Considering the aforementioned problems, in this paper, we propose the first direct adaptive chosen-ciphertext security ciphertext-policy ABEET scheme in the standard model. Our method only adopts a chameleon hash function and adds one dummy attribute to the access structure. Compared with the previous works, our scheme achieves the security improvement, ciphertext validity check and large universe. Besides, we further optimize our scheme to support the outsourced decryption. Finally, we first give the detailed theoretical analysis of our constructions in computation and storage costs, then we implement our constructions and carry out a series of experiments. Both results indicate that our constructions are more efficient in Setup and Trapdoor and have the shorter public parameters than the existing ABEET ones do.

Anonymous Communication (AC) hides traffic patterns and protects message metadata from being leaked during message transmission. Many practical AC systems have been proposed aiming to reduce communication latency and support a large number of users. However, how to design AC systems which possess strong security property and at the same time achieve optimal performance (i.e., the lowest latency or highest horizontal scalability) has been a challenging problem. In this paper, we propose an ObliComm framework, which consists of six modular AC subroutines. We also present a strong security definition for AC, named oblivious communication, encompassing confidentiality, unobservability, and a new requirement sending-and-receiving operation hiding. The AC subroutines in ObliComm allow for modular construction of oblivious communication systems in different network topologies. All constructed systems satisfy oblivious communication definition and can be provably secure in the universal composability (UC) framework. Additionally, we model the relationship between the network topology and communication measurements by queuing theory, which enables the system's efficiency can be optimized and estimated by quantitative analysis and calculation. Through theoretical analyses and empirical experiments, we demonstrate the efficiency of our scheme and soundness of the queuing model.

Generative dialogue systems tend to produce generic responses, which often leads to boring conversations.For alleviating this issue, Recent studies proposed to retrieve and introduce knowledge facts from knowledge graphs.While this paradigm works to a certain extent, it usually retrieves knowledge facts only based on the entity word itself, without considering the specific dialogue context.Thus, the introduction of the context-irrelevant knowledge facts can impact the quality of generations.To this end, this paper proposes a novel commonsense knowledge-aware dialogue generation model, ConKADI.We design a Felicitous Fact mechanism to help the model focus on the knowledge facts that are highly relevant to the context; furthermore, two techniques, Context-Knowledge Fusion and Flexible Mode Fusion are proposed to facilitate the integration of the knowledge in the ConKADI.We collect and build a large-scale Chinese dataset aligned with the commonsense knowledge for dialogue generation.Extensive evaluations over both an open-released English dataset and our Chinese dataset demonstrate that our approach ConKADI outperforms the state-of-the-art approach CCM, in most experiments.

Insufficient semantic understanding of dialogue always leads to the appearance of generic responses, in generative dialogue systems. Recently, high-quality knowledge bases have been introduced to enhance dialogue understanding, as well as to reduce the prevalence of boring responses. Although such knowledge-aware approaches have shown tremendous potential, they always utilize the knowledge in a black-box fashion. As a result, the generation process is somewhat uncontrollable, and it is also not interpretable. In this paper, we introduce a topic fact-based commonsense knowledge-aware approach, TopicKA. Different from previous works, TopicKA generates responses conditioned not only on the query message but also on a topic fact with an explicit semantic meaning, which also controls the direction of generation. Topic facts are recommended by a recommendation network trained under the Teacher-Student framework. To integrate the recommendation network and the generation network, this paper designs four schemes, which include two non-sampling schemes and two sampling methods. We collected and constructed a large-scale Chinese commonsense knowledge graph. Experimental results on an open Chinese benchmark dataset indicate that our model outperforms baselines in terms of both the objective and the subjective metrics.

Data privacy is becoming one of the most critical concerns in cloud computing. Several proposals based on Intel SGX such as VC3 [1] and M2R [2] have been introduced in the literature to protect data privacy during job execution in the cloud. However, a comprehensive formal proof of their security guarantees is still lacking. In this paper, we propose ObliDC, a general UC-secure SGX-based oblivious distributed computing framework. First, we model the life-cycle of a distributed computing job as data-flow graphs. Under the assumption of malicious, adaptive adversaries in the cloud, we then formally define data privacy of a distributed computing job by introducing a notion named ODC-privacy, which encompasses both semantic security (to protect data confidentiality during computation and transmission) and oblivious traffic (to prevent data leakage from traffic analysis). ObliDC is composed of four two-party protocols -- job deployment, job initialization, job execution, and results return, which allow for modular construction of concrete privacy-preserving job protocols in different distributed computing frameworks. Finally, inspired by a formal abstraction for trusted processors proposed by R. Pass et al. [3], we formally prove the security of ObliDC under the universal composability (UC) framework.

Yao Zhu, Hongzhi Liu, Zhonghai Wu, Yang Song, Tao Zhang. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Most of heterogeneous information network (HIN) based recommendation models are based on the user and item modeling with meta-paths. However, they always model users and items in isolation under each meta-path, which may lead to information extraction misled. In addition, they only consider structural features of HINs when modeling users and items during exploring HINs, which may lead to useful information for recommendation lost irreversibly. To address these problems, we propose a HIN based unified embedding model for recommendation, called HueRec. We assume there exist some common characteristics under different meta-paths for each user or item, and use data from all meta-paths to learn unified users’ and items’ representations. So the interrelation between meta-paths are utilized to alleviate the problems of data sparsity and noises on one meta-path. Different from existing models which first explore HINs then make recommendations, we combine these two parts into an end-to-end model to avoid useful information lost in initial phases. In addition, we embed all users, items and meta-paths into related latent spaces. Therefore, we can measure users’ preferences on meta-paths to improve the performances of personalized recommendation. Extensive experiments show HueRec consistently outperforms state-of-the-art methods.

Bugs severely damage the reliability of open source software. In order to improve the reliability of open source software, bug tracking system is built to collect and manage bugs reported from users all over the world. When system failures occur, users investigate whether failures are induced by software bugs and then report bugs. However, it is usually difficult and time consuming to identify bugs from system failures. To accelerate bug reporting and reduce the time users spend on identifying bugs, we present BugIdentifier, an automatic bug identifying approach based on log mining. BugIdentifier combines Doc2Vec with Deep Neural Network (DNN) and treats bug identifying as a binary classification problem. Doc2Vec is adopted to train a log sequence embedding model that transforms log sequences into feature vectors, and then DNN is used to identify whether the log sequence is bug-induced or not. The results of our empirical evaluation show that our approach can automatically identify real-world bugs of Hadoop and OpenStack with the F1-score higher than 75%, specifically, old-version bugs of OpenStack can be identified with 97% F1-score, as a result, bug reporting can be accelerated correspondingly.

The goal of this paper is to automatically generate synthetic data to enable data analyzers to cope with the problem of insufficient data. Taking the most typical machine learning tasks, classification and regression, as an example, limited and insufficient samples cause low generalization of machine learning models, which cannot provide reasonable predictions. Data are insufficient either because of sample rarity or because data are impeded to be accessed for privacy concerns or confidential protection. To overcome this, we present a Synthetic Data Approach for Classification and Regression, adopting probability distribution and k-nearest neighbor model to generate synthetic data. We first estimate the probability distribution of each feature and construct a k-nearest neighbor model for all original data samples. Then we generate random samples based on probability distributions, adopt the k-nearest neighbor model to validate these random samples, and output the synthetic samples. We use proposed synthetic approaches to generate synthetic data of five publicly available datasets for classification and regression, respectively, and evaluate the performance of machine learning models to evaluate the resemblance between synthetic data and original data. The experimental results show that the synthetic data can resemble the original data, which indicates it is an effective approach for data analyzer to overcome the problem of insufficient data.

Recent years have witnessed a surge of interest on response generation for neural conversation systems. Most existing models are implemented by following the Encoder-Decoder framework and operate sentences of conversations at word-level. The word-level model is suffering from the Unknown Words Issue and the Preference Issue, which seriously impact the quality of generated responses, for example, generated responses may become irrelevant or too general (i.e. safe responses). To address these issues, this paper proposes a hybrid-level Encoder-Decoder model (HL-EncDec), which not only utilizes the word-level features but also character-level features. We conduct several experiments to evaluate HL-EncDec on a Chinese corpus, experimental results show our model significantly outperforms other non-word-level models in automatic metrics and human annotations and is able to generate more informative responses. We also conduct experiments with a small-scale English dataset to show the generalization ability.

Recent years have witnessed a surge of interest in building conversation systems such as smart agents or chatbots. The most existing generation-based neural responding conversation systems are implemented by RNN Encoder-Decoder framework relying on word-level modeling with explicit segmentation. A word-level model typically maintains a fixed vocabulary, which correspondingly encounters the unknown words and segmentation issues. In this paper, we proposed a fully character-level Encoder-Decoder model DLCEncDec without explicit segmentation for neural responding conversation. DLCEncDec utilizes both of fine-grained character embedding features and coarse-grained n-gram features. Coarse-grained n-gram features are captured by constructing a convolutional layer and a four-layer highway network on the top of the character embeddings. The appearance of out-of-vocabulary words (i.e. unknown words) can be addressed due to the fully character-level operating. We evaluate the DLCEncDec on a Chinese corpus consisting of 4.44 million message-response pairs from Sina Weibo. Experimental results show that our fully character-level model DLCEncDec significantly outperforms baseline models in terms of BLEU and ROUGE.

For massive data processing platforms such as Spark, configuration tuning is a necessary step since it is closely related to task parallelism, resource allocation and fault tolerance, which has a great influence on performance. However, to tune more than 190 interrelated configuration parameters of Spark for performance optimization is a challenging job. In this paper, a neural network based configuration tuning approach is proposed. In this approach, a neural network model is trained to predict the increase or decrease of configurations which determines the next search space. And a performance model based on random forest is used to improve search efficiency by predicting running time of jobs instead of running jobs actually. We evaluated the approach with four typical Spark applications. Experiment results show that compared to the default configuration, on average 42.8% of execution time of Spark applications are reduced by our approach. Moreover, the proposed approach outperforms over related approaches with optimal configuration and less search time.

In order to better serve users, several location-based services rely on the real-time spatio-temporal information. Existing location privacy- preserving methods traverse the whole dataset to anonymize k locations together, and do not utilize parallel computing technology. The anonymization for big volume of location data may result in huge computing cost. We propose a new method called Never Wait for Long (NW4L), which protects the privacy of big-volume location data in parallel and real-time. Instead of linear structure, a k-d tree structure is adopted for the nearest location search to speed up computation. To further improve efficiency, locations are pre- classified in several groups, so that each group can be anonymized in parallel with support from the distributed stream computation framework. In this paper, we implemented NW4L based on Spark and used real-world dataset for performance evaluation. Experimental results show that 100,000 location samples can be processed in 2 minutes, which is feasible for real-time computation.

Precise prediction of users' behavior is critical for users' satisfaction and platforms' benefit. A user's behavior heavily depends on the user's general preference and contextual information (current location, weather etc.). In this paper, we propose a succinct hierarchical framework named Hierarchical Hybrid Feature Model (HHFM). It combines users' general taste and diverse contextual information into a hybrid feature representation to profile users' dynamic preference w.r.t context. Meanwhile, we propose an n-way concatenation pooling strategy to capture the non-linear and complex inherent structures of real-world data, which were ignored by most existing methods like Factorization Machines. Conceptually, our model subsumes several existing methods when choosing proper concatenation and pooling strategies. Extensive experiments show our model consistently outperforms state-of-the-art methods on three real-world data sets.

Apache Spark uses Resilient Distributed Datasets (RDDs) as primitives for data sharing. The in-memory feature of RDD makes Spark faster but it also brings a volatile problem where a failure or a missing RDD causes Spark to recompute all the missing RDD in the lineage. A checkpoint cuts off the lineage by saving the data which is required in the coming computing, thus becoming an essential fault-tolerance mechanism. In this paper, we find that as for Spark Streaming jobs with checkpoint, user authentication is not performed while doing checkpoint during job execution. We present two typical attack scenarios where attackers exploit this vulnerability to interfere with normal users job, causing data loss or even incorrect results. And we put forward a solution which focuses on the administration of checkpoint directory permissions. The experimental results show that our scheme can effectively monitor and resist this attack.

Recommendation based on heterogeneous information network(HIN) is attracting more and more attention due to its ability to emulate collaborative filtering, content-based filtering, context-aware recommendation and combinations of any of these recommendation semantics. Random walk based methods are usually used to mine the paths, weigh the paths, and compute the closeness or relevance between two nodes in a HIN. A key for the success of these methods is how to properly set the weights of links in a HIN. In existing methods, the weights of links are mostly set heuristically. In this paper, we propose a Bayesian Personalized Ranking(BPR) based machine learning method, called HeteLearn, to learn the weights of links in a HIN. In order to model user preferences for personalized recommendation, we also propose a generalized random walk with restart model on HINs. We evaluate the proposed method in a personalized recommendation task and a tag recommendation task. Experimental results show that our method performs significantly better than both the traditional collaborative filtering and the state-of-the-art HIN-based recommendation methods.

To improve the efficiency of elderly assessments, an influence-based fast preceding questionnaire model (FPQM) is proposed. Compared with traditional assessments, the FPQM optimizes questionnaires by reordering their attributes. The values of low-ranking attributes can be predicted by the values of the high-ranking attributes. Therefore, the number of attributes can be reduced without redesigning the questionnaires. A new function for calculating the influence of the attributes is proposed based on probability theory. Reordering and reducing algorithms are given based on the attributes’ influences. The model is verified through a practical application. The practice in an elderly-care company shows that the FPQM can reduce the number of attributes by 90.56% with a prediction accuracy of 98.39%. Compared with other methods, such as the Expert Knowledge, Rough Set and C4.5 methods, the FPQM achieves the best performance. In addition, the FPQM can also be applied to other questionnaires.

Error decomposition analysis is a key problem for ensemble learning. Two commonly used error decomposition schemes, the classic Ambiguity Decomposition and Bias-Variance-Covariance decomposition, are only suitable for regression tasks with square loss. We generalized the classic Ambiguity Decomposition from regression problems with square loss to classification problems with any loss functions that are twice differentiable, including the logistic loss in Logistic Regression, the exponential loss in Boosting methods, and the 0-1 loss in many other classification tasks. We further proved several important properties of the Ambiguity term, armed with which the Ambiguity terms of logistic loss, exponential loss and 0-1 loss can be explicitly computed and optimized. We further discussed the relationship between margin theory, "good'' and "bad'' diversity theory and our theoretical results, and provided some new insights for ensemble learning. We demonstrated the applications of our theoretical results in active learning and unsupervised ensemble pruning, and the experimental results confirmed the effectiveness of our methods.

As the complex workload scheduling and resource allocating mechanism in big data system, programmers' configuration error is one of the most typical root causes of unsuccessful termination of jobs, which can result in performance deterioration, availability degradation, resource inefficiency and user unsatisfactory. In this paper, we propose an approach called SD-Predictor, to predict misconfiguration-induced unsuccessful executions of jobs combining static job configurations and dynamic runtime system state before scheduling and execution, so as to save computing resource and scheduling overheads in big data system. We implement and incorporate SD-Predictor with a popular scheduling framework YARN to optimize job scheduling so as to avoid negative impacts by misconfigured jobs. Moreover, we explore correlations between configurations and termination status of jobs and provide some recommendations for configuration optimization. The experiment results show that our approach performs at 78% of precision, 52% of recall and 2% of false positive rate in unsuccessful job prediction, with significantly better recall and false positive rate than related works.

Failing to promote the least privilege principle in administration can lead to substantial vulnerabilities in cloud computing. A malicious insider like a compromised cloud administrator can affect security of data and workloads belonging to cloud customers. Enforcing the least privilege principle in cloud administration can fairly restrict the permissions of administrators and reduce the attack surface. However, writing a least privilege policy can be hard and error prone for cloud service providers. In this paper, we propose a framework called Least Privilege for Cloud (LPCloud) to address these concerns. LPCloud automatically produces policies for minimization of administrators' privileges at the granularity of representational state transfer (REST) application program interfaces (API), and enforces the policies without affecting current systems. Specifically, we introduce a novel algorithm to partition privileges based on dependencies between API calls. This paper presents design of LPCloud, including a service called Policy Generator which produces partitioned policies and a component named Policy Enforcer to enforce the policies. We implement a prototype of our framework in OpenStack Mitaka. Experiments indicate that LPCloud can produce proper policies to enforce the least privilege principle. Meantime, the average performance overhead is 10.1% which is in acceptable level.

Apache Spark is an increasingly popular distributed computation framework based on in-memory computations, which enables iterative or interactive applications to run faster. In Spark, memory management is the key to performance enhancement to avoid memory bloat problems. Compared with previous static memory manager, in Spark 1.6 and later versions, unified memory manager is implemented as the default memory management model, targeting to achieve optimal memory utilization by borrowing between storage and execution memory. However, the storage memory borrowed from execution memory may frequently be evicted when memory pressure arises. It is because of frequently re- computation during cache evicted and frequent garbage collection during shuffle in iterative applications. This situation will produce runtime overhead caused by garbage collection including cache eviction and cache re-computation. We propose a memory constraint strategy for unified memory manager in Spark to reduce runtime overhead caused by garbage collection by reducing the cache eviction size. We implement the strategy in Spark 1.6.1 using SparkPageRank, WordCount and GroupByTest to compare three different memory managers. Experimental results reveal that compared unified memory manager, memory constraint strategy can achieve better performance improvement with lower job runtime and garbage collection time when the dataset sizes or the iterations are increasing.

At present, a growing number of web applications especially cloud computing systems employ representational state transfer (REST) API as the interface to expose their services for simplicity and clarity. For security purposes, service providers prefer to control the access to the provided interface based on the principle of least privilege. However, how to divide the administrative privileges remains a difficulty in practice. In this work, we simplify the privilege partitioning problem into a classification problem of RESTful functions, so the permission to call a category of functions can be granted to a specific administrator. We propose a RESTful API classification approach called RestSep based on genetic algorithm. A classification is represented as a 2-dimensional matrix, which is used as the chromosome. Customized operators of selection, mutation and crossover are designed. The fitness function is designed to balance parameters such as number of categories, test case coverage, function overlapping, etc. Experiments on popular clouds like OpenStack and Kubernetes indicate RestSep can generate a self-explanatory classification result, which can serve as a guideline for privilege partitioning. The overhead of test generation is at most 13.1% and the overhead of genetic algorithm is at most 183.29s, which are acceptable for practical use.

Cloud storage is a service provided by cloud service provider (CSP). More and more individuals and organizations accept the scheme and outsource their data to CSP seeking to reduce local storage burden. Since the outsourced files are no longer under the data owners' direct control and CSP is not trustworthy, it is necessary for data owners to check the integrity of their files outsourced to CSP. To address this crucial problem, some provable data possession (PDP) protocols have been presented. In order to obtain higher availability, storing multiple replicas is a common strategy in cloud storage. Most recent researches on multiple-replica provable data possession (MR-PDP) use homomorphic linear authenticators (HLAs) to generate aggregated tags for the blocks at the same indices in each replica, but it cannot verify single replica to identify the corrupted replicas. In this paper, we proposed a novel multiple replica provable data possession scheme, named FMR-PDP. In FMR-PDP, we compute tags based on vector dot product instead of expensive group exponentiation operations, and only generate one tag for all blocks at the same indices in each replica. Our FMR-PDP scheme supports a flexible data possession verification, which means the data owner can check any number of replicas in a verification. In addition, we make a comprehensive performance analysis, which shows that our proposed scheme is flexible and efficient.

Data outsourcing has become quite popular in recent years. While the rapid development of cloud storage service, it brings about many new security challenges. One of the biggest concerns with cloud data storage is that of data integrity at untrusted cloud servers. For example, when individual users store important data files in cloud servers, the data files may be damaged or corrupted because of cloud server failures or malicious attackers. Our work focuses on the problem of ensuring the integrity of data storage in cloud computing. The prior approaches mainly solved static data and provide simple recovery method like PoRs or dealt with public verifiability of data integrity without recovery method like PDP. Our scheme proposed a novel Cross-encoding recovery(Cer) method based erasure code for reducing unnecessary network bandwidth cost and provided a new data structure named Dynamic Storage Table(DST) that supports dynamic data operations for cloud data storage applications. It is worth mentioning that we consider Counting Bloom Filter(CBF) in our solution which can verify the data integrity practically and efficiently. Meanwhile, we implemented a security system and performance analysis showing that the proposed scheme is highly efficient and provably secure.

Summary We present a practical large universe hierarchical attribute‐based encryption (LU‐HABE) scheme, which supports monotone access structures. In our system, key generation centers (KGCs), any one in which is labeled by a unique identity, are organized as a hierarchical structure. Thus, all secret keys issued by the KGC contain 2 parts: the identity‐related one and the attribute‐related one. Once the data owner wants to encrypt his/her data, he/she needs to specify certain numbers of pairs according to his/her demand. The pair consists of an identity of a KGC and a policy of attributes managed by the corresponding KGC, eg, IDi and (Mi, ρi). If and only if an identity associated with user's secret key is equal to or is an ancestor of one of the identities appearing in ciphertext, and simultaneously a set of attributes belonging to the user satisfies the policy, the user can decrypt it successfully. Our scheme is proved to be selectively secure in the standard model under the modified “q‐type” assumption similar to the ones used in former works and is extended to support online/offline encryption. To show the efficiency of our construction, we implement our original scheme and the extended one in Charm. Analyses show that both of them are very practical.

Software bugs have been one of the dominantcauses of system failures, especially in cloud systems basedon open source platforms. One big challenge fortroubleshooting these cloud systems is to pinpoint thesoftware bug-induced failure in large and complex log fileswhich is a nightmare for administrators. So far, there hasbeen little study on how to identity bug-induced failuresbased on log analysis. In this paper, we analyze and describefeatures of bug-induced failure logs from bug repository and Q&A websites, and then propose a general automaticapproach to pinpoint logs of bug-induced failure from logfiles of open cloud platform. In the approach, two algorithmscalled MPIN and SPIN are presented for log classification. We evaluate our approach by applying logs collected frombug repositories of OpenStack and Hadoop, and five Q&A websites. The experimental result shows that the proposedapproach can identify logs of bug-induced failure inOpenStack logs with 83.9% precision, and for Hadoop logswith 82.52% precision.

The access control mechanisms of existing cloud systems, mainly OpenStack, fail to provide two key factors: i) centralized access mediation and ii) flexible policy customization. This situation prevents cloud administrators and end customers from enhancing their security. Furthermore, a variety of clouds have implemented their access control systems and policies in separated ways. This might confuse the customers whose businesses are built on multiple clouds, as they have to take efforts to accommodate their policies for different platforms. The OpenStack Security Modules (OSM) project has developed a least-invasive access control framework for OpenStack to enable different access control models to be implemented as loadable modules. This framework can be a good replacement of the existing permission checks in OpenStack and other platforms. We also propose an integration mechanism for multiple policies to form a single decision. This paper presents the design and implementation of OSM, including a new service called patron and an attachment module called access endpoint middleware (AEM). Experiments on the tempest benchmark indicate that OSM has improved the flexibility and security of policy management without affecting other services. Meantime, the average performance overhead remains as low as 7.3%, which is acceptable for practical use.

One of the major advantages of the cloud storage system is that it simplifies the time-consuming processes of hardware and software provisioning, deployment and distribution for users. Currently there is a tremendous increase in the scale of data generated as well as being consumed by applications on cloud storage systems, but besides availability metrics, few of cloud storage services provide data privacy guarantees in their Service Level Agreements (SLAs), which can be a major concern for the adoption of cloud storage services. The paper firstly proposes an analysis approach for the data privacy, then introduces privacy into the service quality, and defines a service quality evaluation model including privacy, availability, throughput, transfer time and operating cost. At last, a novel data distribution strategy called SLA-DO is presented. The comparison experiment results confirmed that SLA-DO strategy shows better performance in SLAs compliance, resource utilization, security guarantees and multiple cloud environmental adaptability than the data distribution policy adopted in HDFS and OpenStack.

Recently an increasing number of web applications especially cloud computing systems utilize representational state transfer (REST) API to deploy their services for simplicity and clarity. Users can employ the same interface to invoke various applications from the Internet. For security purposes, service providers would control the access to the provided interface through policy enforcement. Yet the access control of REST interfaces lacks a uniform standard regarding the policy language and corresponding enforcement implementation, which brings two limitations: i) Users have to deal with totally different types of policies to accommodate certain systems. ii) Service providers have to design their own platform-specific authorization policy language and the related enforcement mechanisms. In this paper, we propose a REST Policy Language (RestPL) to express the authorization policies especially for REST APIs. RestPL is ensured to be request-oriented, based on our definition of the standard request form. This indicates that a RestPL policy can be automatically generated from an actual request, which helps mitigate a user's pressure during policy designing. Furthermore, we also provide a reference implementation for the enforcement code of RestPL based on regular expressions and deploy it on OpenStack Liberty to demonstrate its feasibility. The experimental results indicate the enforcement overhead of RestPL can be reduced to 80.6% compared with the original policy. In addition, we show that an end-user can also benefit from RestPL for reducing the learning effort by at least 41.6%.

Motivated by frequent failures in cloud computing systems, we analyze failure frequency and failure continuity of tasks from the Google cloud cluster, and find what we call killer tasks that suffer from frequent failures and repeated rescheduling. Killer tasks cause unnecessary resource wasting and significant increase of scheduling workloads, which can be a big concern in cloud systems. We aim to recognize killer tasks at the very early stage of their occurrence so that they can be addressed proactively instead of being rescheduled repeatedly, so as to promote reliability and save resources. To recognize killer tasks from a large amount of tasks in real time is really challenging. In this paper, we first investigate characteristics and behavior patterns of killer tasks and then develop two machine learning based methods, K-HUNTER and C-HUNTER, for online recognition of killer tasks. The empirical results show that our approach performs at 97% of precision in recognizing killer tasks with an 89% timing advance and 88% of resource saving for the cloud system on average.

A combination of multiple retrieval systems can outperform its individual component systems, but it remains a challenging problem to predict whether two systems can be beneficially combined and, if so, the optimal means by which they should be merged. The performance of combined systems is affected by many factors, including the performance of individual systems, the diversity between a pair of systems, and the method for combination. In this paper, we undertake the study of these issues using combinatorial fusion algorithm (CFA) utilizing the rank-score characteristic (RSC) function and the notion of a weighted cognitive diversity. Using the selected eight TREC datasets, we demonstrated that: (a) the combination of two retrieval systems performs better than each individual system only when the individual systems have relatively good performance and they are diverse, (b) a dynamic combination method, using rank vs. score combination based on cognitive diversity which does not display a tight correlation with other statistical diversity measures, can improve the performance of the combined system, even when performance of each individual system is not known or in the context of an unsupervised learning environment. Within the TREC datasets, the proposed dynamic approach offers a potential for substantial improvement with no significant risk. Our results provide a new paradigm of dynamic fusion to the study of the combination of multiple retrieval systems.

Availability is one of the most essential attributes of qualities of cloud services. Most popular public cloud services claim availability commitments with corresponding penalties in their SLAs. To gain the maximal profits, cloud providers should choose an optimal penalty strategy in the competitive cloud market. In this paper, we firstly survey the penalty calculation methods of cloud providers. Based on the survey, we propose a competitive penalty model and a corresponding penalty based profit maximization algorithm for cloud providers. According to the model, each cloud provider would choose the best fit penalty strategy to gain the maximal expected profit during the game procedure. The proposed model is evaluated with real data of popular cloud providers with sensitive analysis, and is valuable for cloud providers to define their penalty strategy.

Availability is the most essential attribute of qualities of cloud services. Most popular public cloud services claim availability commitments with corresponding penalties in their SLAs. However, lack of clarity in availability commitment and penalty make it hard for consumers to understand the SLAs well and furthermore, to compare different cloud providers under different contexts, which would even become an obstacle for enterprise consumers to embrace public clouds. We present a cloud SLA availability commitment framework including availability calculation and penalty calculation services, and compare SLAs of well-known public IaaS cloud providers with investigation of their merits and defects. We also present a business model for cloud providers to find the optimal penalty degree for their SLAs, which will help in defining availability based SLA of cloud services.

This paper proposes a comparative study of cloud dependability between two methods -- bug analysis and fault injection for assessing the impact of component failure on cloud service availability. We focus on the IaaS cloud with open source platform Open Stack. The actual bug data are analyzed to show numerical examples of dependability assessment. A fault injection tool has also been developed to create failures of components and then observe their effects on services. The comparison analysis between two methods shows that bug analysis method has richer features for analyzing but not as precise as fault injection.

The emergence of Web services has changed the Internet a lot, and greatly facilitated the development of service based software systems. How to select appropriate services and compose them according to given context to satisfy a user's requirement is a big challenge. This paper proposes a novel Genetic Algorithm (GA) method to synthesis web services in a context-aware environment. We first present a context space model to illustrate both contexts and services in a formal way, we utilize GA to compose context-aware services according to users' preference. We transform the problem of service composition to a multi-objective optimization problem. To resolve the conflict and dependencies among services in GA process, we propose a service similarity tree (SST) model to measure the similarity between services. Finally, we design a simulation experiment to evaluate our method. The experiment result shows that our method is a promising one to solve service composition problem in a context-aware environment.

In a context-aware pervasive environment, it is crucial to provide an efficient adaption mechanism to rapidly discover appropriate services and compose them to achieve user desired situation when context change. In this paper, we propose a GA(Genetic Algorithm) based novel service composition method in a context-aware environment. We first use Context Space model to represent context, situation, user preference and services in a unified formal way. Then we transform the service composition problem into a multi-objective optimization problem and utilize GA to find the optimal service composition based on user's preference. To affiliate the connection between context and services, we use a vector to represent each service to reflect which context types a service can change so as to utilize GA to find the best service composition. Confliction of services may happen during the GA process. In order to resolve the conflicts, we propose a SST(Service Similarity Tree) method to measure the similarity among services to find out the best alternative. Finally, we design and implement a simulation experiment to verify our method. The results shows that our method can leverage GA to synthesis appropriate services to achieve user desired goal in an efficient way.

Combining multiple retrieval systems is a commonly used method to improve the retrieval performance. However, it is still a challenging problem to figure out when and how the combined system can perform better than its individual systems. In this paper, we study these issues by using an information fusion paradigm: Combinatorial Fusion Analysis (CFA). TREC datasets are used as our experiment data. We measure the cognitive diversity between different individual systems by using a rank-score characteristic (RSC) function. Our results demonstrate that: 1) The performance of combination of p systems does not always increase with p, 2) Rank combination is better than score combination in particular when RSC diversity between two individual systems is large enough, and 3) combination of two systems can improve performance only if the two individual systems have relative good performance and are diverse.

Cloud Storage has been turned into a common platform shared among varied organizations, even market competitors, thus has raised many security concerns. Most of the current researches focus on data encryption and decryption, in this paper, however, we take an alternative perspective-access control, to design and implement a secure solution for cloud storage, aiming to solve both the data isolation problem, which ensures that data in storage cloud owned by one company wouldn't be crossly accessed by other ones, and data collaboration problem, which makes data sharing between different organizations through storage cloud possible while still under the restriction of company data isolation. Besides, we have presented a pretty flexible security policy which could be easily customized to fit the variant security requirements in different cooperation. Finally, a prototype has been implemented based on HDFS by this policy, and the time cost is given and evaluated.

With the development of cloud computing, cloud security issues have recently gained traction in the research community. Although much of the efforts are focused on securing the operation system and virtual machine, or securing data storage inside a cloud system, this paper takes an alternative perspective to cloud security-the security of data migration between different clouds. First, we describe some threats when we are doing data migration. Second, we propose a security mechanism to deal with the security issues on data migration from one cloud to another. Third, we design a prototype to give the mechanism a brief implementation based on HDFS (Hadoop Distributed File System) and we do a series of tests to evaluate our prototype. Here, the solutions to securing data migration between clouds mainly involve in SSL negotiation, migration ticket design and block encryption in distributed file system and cluster parallel computing.

Context-aware service is a new service mode that can provide appropriate service automatically to improve service level based on context information. In context-aware service, service is provided based on the current scene of customer. The scene is identified by context, and scene transition is caused by the change of context. Current research focuses on modeling service with context directly. However, the fact is that the vast majority of context change does not cause the change of scene, and monitoring context to determine the scene directly is inefficient, especially of the multi-context application. We use event which is defined by context as the motivation of scene change and propose an event driven model of context-aware service: EDM. A case of smart home service is discussed to show how to use EDM to do the requirement analysis, design, and implementation of context-aware service. Experimental results show that this approach can effectively reduce the number of scene determination in multi-context application.

This paper presents a middleware for building context-aware applications. One of the main components, Device Information Access (DIA), is discussed in detail. Since many kinds of devices (e.g., RFID, GPS, Bluetooth, etc.) can be used to collect the context information, the middleware defines the Device Information Access component to communicate with different devices. A set of interfaces are devised in DIA, and the common functions such as getting and setting a data element are defined in the interfaces. For each device, we shall provide an implementation of the interfaces to communicate with the corresponding servers or software agents. DIA can communicate with the software agents or servers using various protocols such as RMI, Web Services, and REST. In this way the access to the hardware are encapsulated by the middleware and virtualized to the end-point applications. The architecture of the middleware and the functions of DIA are discussed, and an empirical application is also developed to validate our design.

In the past few years, software as a service (SaaS) has had a tremendous impact on the software industry. More and more enterprises, especially small and medium businesses (SMBs), are willing to adopt SaaS to construct their enterprise information applications. This paper designs and implements a credit bank information system (CBIS) based on SaaS Level 3 maturity model. Credit bank is a service provided by authorized education organization so that customers can save, manage and exchange education credits. In the CBIS, each authorized education organization is a tenant and each organization has multiple students (end users). Firstly the SaaS maturity model is discussed in details. Secondly a SaaS solution in level 3 is proposed together with the multi-tenant database design and configuration policy. Finally the functions of the CBIS are given. The CBIS is built with service oriented architecture, in which business functions are mapped into web services. In this way the CBIS can integrate with other systems easily.