Reviewerly/exHarmony huggingface dataset:
Dynamic Adaptive Checkpoint Mechanism for Streaming Applications Based on Reinforcement Learning

For a stream processing system that uses checkpoints as a fault-tolerant method, selecting the appropriate checkpoint period is the key to ensuring the efficient operation of streaming applications. State-of-art stream processing systems currently only support fixed-cycle checkpoints, which is difficult to make a good trade-off between fault-tolerant processing and the cost of failure recovery in dynamically changing streaming application scenarios. Moreover, in a complex distributed streaming application environment, the dynamic environmental indicators (e.g., the values of workloads and failure rates) are not in coincidence with the model assumptions, such as the dynamics of Twitter’s hot events data changing quickly. In this paper, we consider the dynamic changes of environmental indicators and adaptively optimize the processing delay and fault recovery time. Then, we propose a dynamic adjustment method for the checkpoint interval by reinforcement learning, which is named DACM. DACM adaptively optimizes the processing delay and fault recovery time, while avoiding the overall environment modeling of streaming applications. The experiments conducted on the Flink platform show that DACM reduces the processing delay by 10% and the failure recovery time by 37% compared with the existing checkpoint interval optimization models.
Knowledge-Enhanced Learning for KG Embedding

Knowledge graphs (KGs) have been widely used in recent years and proved to be useful in many applications. However, large scale KGs that are collected by crowdsourcing or constructed by automated algorithms, are usually incomplete. Most existing works focus on how to complete the KG based on embedding learning, but ignore the semantic properties of the relations in KGs. In this paper, we propose a knowledge-enhanced approach for KG embedding learning that leverages the explicit knowledge about the relation properties. The knowledge is used to enhance the training data and guide the model on data selection during each training iteration. We conduct a series of experiments on datasets WN18RR and FB15k-237. The experiment results show that our method outperforms the previous state-of-the-art models.
Maxwell’s Demon in Tail-tolerant, Resource-efficient Serverless Computing

Computing systems always face a “resource allocation dilemma” that shows the great difficulties in trading off resource efficiency for tail latency, due to the internal uncertainty of cluster status and execution behavior. Inspired by the imaginary “Maxwell’s demon” in thermodynamics who can reduce the uncertainty through a per-gas molecule-level control policy, we consider the “one-to-one mapping” feature of serverless computing and build a novel resource allocator, named Maxwell, that can achieve low tail latency and high resource efficiency in serverless simultaneously. Like the “Maxwell’s demon Maxwell is able to optimize the resource allocation for every request. It observes the state of each request and makes decisions about the minimum resource allocation through a reinforcement learning predictor. As the per-request-grained control incurs significant overhead, we further design a pipeline for avoiding the accumulated effect on a workflow. Experimental results show that Maxwell not only saves up to 31% CPU resources but also reduces the standard deviation of latency by 1.9×. Its time overhead is negligible and the resource overhead is also limited when the query per second $\leq$500.
HeavyTracker: An Efficient Algorithm for Heavy-Hitter Detection in High-Speed Networks

Detecting heavy hitters that constitute the majority of network traffic is a critical task in network measurement. However, the highly skewed network traffic and the size-limited on-chip memory pose great challenges for heavy-hitter detection. The prior arts either use sketches to track all flows or maintain a fix-sized tracking list for recording elephant flows, resulting in limited detection precision or time-consuming tracking list exchanges. This paper presents HeavyTracker, an efficient algorithm that detects heavy hitters based on a count-with-threshold strategy. A two-dimensional tracker unit array is employed in our design to capture the heavy hitters, where each flow is randomly mapped to one unit in all rows. The tracker unit is designed to accept many flows and precisely report the largest two with frequencies that reach a predefined threshold. This design eliminates the frequent and time-consuming exchanges for maintaining the tracking list, allowing us to use a hash table to track the reported elephant flows. Experimental results based on real Internet traces show that HeavyTracker achieves a high $\mathbf{F}_{\beta}$-Score of threshold-t detection (0.97) and over 99% precision of top-k detection under a tight memory size. Besides, it reduces the frequency estimation error by 97.4% compared to the state-of-the-art.
ERP: An Efficient Rewrite Scheme to Improve the Inline Deduplication Restore Performance in Backup Systems

Data deduplication is an effective technique to reduce the amount of redundant data, which is widely used in backup systems. To reconstruct the original backup data, restore is a typical procedure in inline deduplication, which brings read amplification when duplicate chunks are shared among various data streams. Rewrite is a cost-efficient method to improve the inline deduplication restore performance by writing the fragmented duplicate chunks repeatedly. Although several rewrite schemes are proposed to improve the restore performance, they either decrease the deduplication ratio or increase the temporal overhead of the rewrite procedure. This is because existing rewrite methods select inappropriate number of containers, or ignore the inter-container redundancy information. To address the above problems, we propose an E ffective –Region -Partitioning based rewrite scheme (ERP), which improves the restore performance in backup systems and ensures a high deduplication ratio. The key idea of ERP is to effectively narrow the selection range and choose a flexible number of containers by investigating the inter-container redundancy information. To demonstrate the effectiveness of ERP, we conduct several experiments in a deduplication backup system. Compared to the state-of-the-art rewrite schemes, the results show that ERP reduces the rewrite cost by up to 97.71%.
Secure and Efficient Cloud Ciphertext Deduplication Based on SGX

With the development of data outsourcing technology, the data stored by cloud storage servers are exploding. Secure deduplication for encrypted data helps cloud servers reduce storage overhead in the scenario that cloud users outsource their data in ciphertext. To satisfy client-side semantic security, most existing deduplication schemes for encrypted data need trusted third parties. However, trusted third parties are difficult to deploy and may cause potential risks. Therefore, we propose a secure cloud ciphertext deduplication scheme based on Intel SGX. The proposed scheme uses the Enclave security container provided by Intel SGX as the trusted execution environment on the cloud server to replace the trusted third party to perform sensitive operations. At the same time, our scheme simplifies the secure management of the file encryption keys so that the encryption key of the files with the same data can be securely distributed to other owners of the same file without the need for the original uploader online. We prove the security of the proposed scheme and the experiment shows the efficiency of the scheme.
MLPs: Efficient Training of MiniGo on Large-scale Heterogeneous Computing System

Deep Reinforcement Learning has been successfully applied in various applications and achieved impressive performance compared with previous traditional methods but suffers from high computation cost and long training time. MLPerf takes deep reinforcement learning as one of the benchmark tracks and provides a single node training version of MiniGo as a reference. A key challenge is to achieve efficient MiniGo training on a large-scale computing system. According to the training computation pattern in MiniGo and the characteristics of our large-scale heterogeneous computing system, we propose a MultiLevel Parallel strategy, MLPs, including task-level parallelism between nodes, CPU-DSP heterogeneous parallelism, and DSP multi-core parallelism. The proposed method reduces the overall execution time from 43 hours to 16 hours while scaling the node size from 1067 to 4139. The scaling efficiency is 69.1%. According to our fitting method, the scaling efficiency is 46.5% when scaling to 8235 nodes. The experimental results show that the proposed method achieves the efficient training of MiniGo on the largescale heterogeneous computing system.
An Efficient Fully Homomorphic Encryption Sorting Algorithm Using Addition Over TFHE

Fully homomorphic encryption (FHE) can effectively protect data privacy. It allows users to entrust data operations to a third party on a cloud server without revealing their own privacy. Sorting algorithms are the fundamental technique of managing and processing data. Currently, the sorting algorithms based on the traditional FHE schemes of BGV (Brakerski-Gentry-Vaikuntanathan), BFV (BrakerskiFan-Vercauteren) and CKKS (Cheon-Kim-Kim-Song) are difficult to implement and are inefficient because of their slow bootstrapping techniques. TFHE (Fast Fully Homomorphic Encryption over the Torus), a fast FHE scheme over the torus based on GSW (Gentry-Sahai-Waters), significantly decrease the time cost of bootstrapping. In this paper, we first design the bitwise FHE comparison and swap operations using the bootstrapped binary gates of the TFHE library. With the two operations, a bubble sort algorithm is proposed. By reducing the depth of sorting circuits in the proposed bubble sort, we further present AdditionSort, an efficient sorting algorithm using homomorphic addition, which can support the operations of arbitrary array length and unlimited element size. The experiments show that AdditionSort is nearly 50% faster than the bubble sort when the size of an element exceeds 32 bits.
Enhancing Security of Certificate Authorities by Blockchain-based Domain Transparency

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

With the prevalent adoption of blockchain in the financial system, there has been an increase in phishing scams on cryptocurrency platforms such as Ethereum, and an effective anomaly detection method is urgently required. The latest studies have focused on anomaly identification using natural language processing techniques or constructing simple static graphs. However, the existing methods are insufficient to convey the diversity of connectivity patterns in the Ethereum transaction network concerning amount and time. To this end, we proposed a novel transaction network embedding algorithm transE based on the multi-channel random walk to model the detection of Ethereum phishing scam accounts as a multigraph node classification task. Specifically, we first model the Ethereum transaction as a time-amount directed multigraph. Then, the hybrid feature representation of network nodes is learned via transE from their local and global neighbours, which uses the attention mechanism to maximize the probability of preserving node network neighbours. Ultimately, we employ visualization techniques and machine learning models to validate the effectiveness of the algorithms, and the model with the top performance is picked for Ethereum account classification. Experimental results indicate that the embedding vector extracted by transE improves the detection accuracy of Ethereum phishing accounts in the different classification tasks.
Traffic Processing and Fingerprint Generation for Smart Home Device Event

Recent studies show that smart home devices are vulnerable to passive network observers, referred to as adversaries. An adversary can use mobile devices as a sniffer to infer device events (e.g., a door is opened/closed) even through encrypted WiFi traffic. However, to obtain high event identification accuracy, existing works heavily rely on either machine learning (ML) or large traffic feature sizes, which results in a costly retraining or pairing process. In this paper, we generate an event traffic fingerprint and propose an event identification method with good extensibility. To address the interference of network fluctuations and unrelated traffic, we filter all redundant and event-unrelated packets and extract event packet sequences based on time interval. After processing, we generate a representative packet-level fingerprint for each event and identify the event based on fingerprint matching. The precision and recall of event identification reach 96.77% and 92.31% on average. Our traffic processing method can work as an add-on to existing ML-based event inference methods, which leads to at least 12% increase on the accuracy.
Hebo System: Trusted Copyright Authorization in Computer Networks

The DRM (Digital Rights Management) systems protect owners’ copyrights by controlling consumers’ access to digital works. However, they fail to provide authorization evidence if customers use digital works on other platforms outside the DRM systems. There is no such evidence that can trustworthily be disseminated in computer networks, which results in many copyright lawsuits. To address the problem, we propose the Copyrights Authorization Model (CAM) and the Hebo system to ensure the consensus on copyright authorization in computer networks. The CAM proves that participants agree on copyright authorizations if they are traceable, integrated, and non-repudiated. Based on the CAM, we design the ledger of trusted authorization forest that keeps the three properties and independent zones that maintain the ledger. The Hebo system is composed of these zones. It can provide authorization evidence for consumers to avoid copyright disputes. Besides, it has the advantages of a beneficial locality. The TPS (transactions per second) can increase with the number of independent zones, nodes can flexibly choose ledgers according to their capability, the system reduces redundant storage, and zones allow asynchrony. Finally, we evaluate the system on five different platforms, and the average time costs of operations are less than 60 milliseconds. The TPS of a single node depends on the configuration of the hardware, which is an average of 138, 19, 24, 21, and 98 in Server, MacBook, J-Nano, Pi4B, and J-TX2, respectively.
Polynomial-Based Secret Audio Sharing Scheme with Smaller Share Dimensions and Authentication

Polynomial-based secret sharing is the art of protecting information and a tool used in areas where a secret has to be distributed among multiple parties. In this method, the secret is encrypted into noisy shares and transferred to participants in the group. Decoding of the secret is possible only when a sufficient number of authorized members in the group, stack their respective shares. In this article, polynomial-based secret sharing for audio is proposed that generates audio shares of smaller dimensions so that it has the benefit of saving storage space and reducing transmission overhead. In addition, an authentication mechanism is proposed to check the validity of shares before the reconstruction of the secret. Experimental results indicate that proposed scheme can efficiently generate noisy shares with reduced dimensions and can reconstruct secret without loss of information.
Talking Head Generation for Media Interaction System with Feature Disentanglement

The task of talking head generation for the media interaction system is to take images and audio clips of the target face as input, and generate a realistic video of the target synchronized with the audio. Most of the existing works directly take all the information in the image as input, which causes the problem of feature information redundancy. At the same time, in the stage of feature fusion, the image and audio information is directly spliced, and the relationship between the two modal information is ignored. To solve the problems, we initially implement the disentanglement of image by introducing a loss function to separate the image into identity features and content-related features. Besides, we introduce a multi-head selfattention mechanism to learn the relationship between the two modal information of image and audio, and implement the full fusion of multi-modal information. In addition, we validate the effectiveness of our model through extensive quantitative and qualitative analysis of two datasets. Extensive experiments show the superiority of the proposed model in all aspects.
DPSS: Dynamic Parameter Selection for Outlier Detection on Data Streams

Outlier detection on data streams identifies unusual states to sense and alarm potential risks and faults of the target systems in both the cyber and physical world. As different parameter settings of machine learning algorithms can result in dramatically different performance, automatic parameter selection is also of great importance in deploying outlier detection algorithms in data streams. However, current canonical parameter selection methods suffer from two key challenges: (i) Data streams generally evolve over time, but these existing methods use a fixed training set, which fails to handle this evolving environment and often results in suboptimal parameter recommendations; (ii) The stream is infinite, and thus any parameter selection method taking the entire stream as input is infeasible. In light of these limitations, this paper introduces a Dynamic Parameter Selection method for outlier detection on data Streams (DPSS for short). DPSS uses Gaussian process regression to model the relationship between parameters and detecting performance and uses Bayesian optimization to explore the optimal parameter setting. For each new subsequence, DPSS updates the recommended parameter setting to suit the evolving characteristics. Besides, DPSS only uses historical calculations to guide the parameter setting sampling and adjust the Gaussian process regression results. DPSS can be employed as an auxiliary plug-in tool to improve the detection performance of outlier detection methods. Extensive experiments show that our method can significantly improve the F-score of outlier detectors in data streams compared to its counterparts and obtains more superior parameter selection performance than other state-of the-art parameter selection approaches. DPSS also achieves better time and memory efficiency compared to competitors.
Service Level Objective Adaptive Energy Efficiency Management

Cloud applications are transforming to microservice based deployment. Microservice comes with many advantages-scalability, agility, availability – and it suits the complicated cloud applications. For microservice deployment, service quality metrics play important role to ensure reliable user experiences for those applications built upon many small services, and the service level objective (SLO) is adopted to govern resource provision for optimal operation cost. Amid growing awareness on data center carbon emission, more organizations are paying attention to energy-efficient practices in cloud deployment to reduce energy consumption and operational carbon footprint. Modern processors provide fine-grained power scaling capabilities, e.g., Intel Xeon PCPS (PerCore P-States), this paper presented a dynamic energy efficiency management framework to adaptively scale processor core frequency as well as implicated energy consumption in accordance with service quality requirements. In this paper, we discussed hurdles and solutions for applying platform energy intelligence to microservices that are often with intrinsic platform-agnostic. The experiments proved the proposed method can effectively reduce energy consumption with competitive cost benefits without compromising service quality goals.
Fused Local Pattern (FLP): A Novel Local Descriptor in Pose Variations

In this paper a new local descriptor is launched for Face Recognition (FR) named as Fused Local Pattern (FLP). FLP feature size is characterized from MRELBP-NI, RD-LBP and 6 × 6 MB-LBP. MRELBP-NI and 6 × 6 MB-LBP captures microstructure and macrostructure information from the higher and lower patches and RD-LBP captures useful information from radial pixels. Taking merits of all three descriptors their feature size are integrated and called as FLP. Compression and matching is done by PCA and SVMs. For experiments evaluation ORL dataset used. Experiments proves the effectiveness of FLP descriptor than singly implemented ones. FLP also beats various literature work.
Spatio-Temporal Data Anomaly Detection Using 3G-Net in IoT

In Internet of Things (IoT), smart devices continuously collect various spatio-temporal data via different kinds of sensors, whose anomaly detection is crucial for the smooth operation of the whole system and the decisionmaking afterwards. Recently, traditional Deep-Learning based methods (i.e., CNN) for anomaly detection gain significant progress in the Euclidean space, but they fail to capture the spatial dependencies of spatio-temporal data from non-Euclidean domain. Besides, there might be few or even no abnormal data available for training when dealing with anomaly detection issue. In this paper, an end-to-end deep learning framework 3G-Net is proposed to address the problem above. The core idea of 3G-Net is to learn the normal pattern of IoT data considering both the long-short-term temporal dependencies and non-Euclidean spatial dependencies, and the more an observation differs from normal pattern, the more likely it is anomalous. Besides, an offline anomaly detection workflow based on Correcting Mechanism and Anomaly Score is also put forward in 3G-Net. Experiments conducted on different real-world and public datasets demonstrate that, major techniques in 3G-Net are effective in IoT scenes and it outperforms other state-of-theart approaches overall.