Reviewerly/exHarmony huggingface dataset:
TMANomaly: Time-Series Mutual Adversarial Networks for Industrial Anomaly Detection

Large-scale sewage treatment plants are one of the typical Industrial Internet of Things systems, where the presence of a large number of sensors generates massive dynamic time series data, and such multivariate time series data are usually time-dependent and random. Therefore, there is a certain risk when fitting the potential anomalies of real-world data, which will bring great challenges to anomaly detection. In this article, we propose a time-series mutual adversarial network (TMAN), a novel reconstruction model for anomaly detection on multivariate time series. It is based on the idea of adversarial learning and consists of two identical subnetworks. During the training process, two subnetworks can independently complete the learning of the time distribution of normal samples of industrial time series data for mutual adversarial. In the process of detecting, we obtain the residual values of TMAN reconstructed for different time series samples to discriminate anomalies. We combine TMAN and anomaly determination mechanisms to build a new industrial time series anomaly detection framework named TMANomaly. In addition, we select the dataset features with a grey correlation algorithm to achieve very high performance with a small number of features. Experimental results show that our proposed TMANomaly outperforms five popular anomaly detection methods and effectively improves the accuracy of industrial multivariate time series anomaly detection.
A Streaming Graph Partitioning Method to Achieve High Cohesion and Equilibrium via Multiplayer Repeated Game

With the advent of 5G era, Internet-of-Things will become possible, and a large amount of correlative data, namely the streaming graph (SG), is continuously generated in various application fields, which poses a great challenge to analyze and make good use of such graph data. The balanced partition of a big graph, especially a dynamic SG, has always been basic research in graph theory, and there are many classical methods. However, this article explores a novel method, that is, an SG <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> -way partitioning via a multiplayer repeated game so as to realize the equilibrium of the total number of nodes in different partitions and achieve high cohesion or low edge-cut in each partition. We regard the SG partitioning process during each time window as a multiplayer game process, treat <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> target partitions as game players, and use all possible actions that players select new nodes as the strategy set. Some effective constraints are given to reduce the strategy space and accelerate the game process. Moreover, we define equilibrium degree (ED) and modularity degree (MD), which are used to design the utility function of game players. All game players finally reach the Nash equilibrium via multiplayer repeated games. At this time, each player corresponds to a strategy. Under the guidance of these strategies, the new nodes are added to each target partition, and then the goal of optimal partitioning is achieved. The above operations are carried out in each time window. The subsequent time windows can reuse the partitioning results of the previous time window in order to realize dynamic incremental partitioning for an SG. Extensive experiments indicate that while ensuring the partition quality of an SG, our proposed algorithm also greatly improves the speed of online partitioning. Even compared with the state-of-the-art SG partitioning algorithm PLDG, our algorithm still obtains better performance.
DAG-BLOCK: A Novel Architecture for Scaling Blockchain-Enabled Cryptocurrencies

With the rapid development of blockchain technology and industries, scalability has been widely realized as one of the primary and urgent concerns for the large-scale adoption of blockchain, especially for cryptocurrencies. In this respect, directed acyclic graph (DAG) proves to be an elegant solution to scaling blockchain but suffers from weak consistency and security issues. In this article, we designed a novel DAG-BLOCK architecture for blockchain-enabled cryptocurrency markets in order to improve the scalability. In our work, DAG is used to replace the Merkel-tree-based transaction structure within the block, and a novel design of open blocks is proposed to enable user nodes to participate in verifying the transactions in blockchain systems. On this basis, we designed a new segmented market structure, in which each miner serves only a group of users instead of all users, so as to reduce miners’ workload and thus scale transaction processing capabilities. Our work can help improve the scalability of cryptocurrencies via evolving the underlying blockchain systems to graph-based distributed ledgers and is expected to shed new light on designing blockchain-based decentralized markets.
DeepInsight: Topology Changes Assisting Detection of Adversarial Samples on Graphs

With the rapid development of artificial intelligence, a number of machine learning algorithms, such as graph neural networks (GNNs), have been proposed to facilitate network analysis or graph data mining. Although effective, recent studies show that these advanced methods may suffer from adversarial attacks, i.e., they may lose effectiveness when only a small fraction of links are unexpectedly changed. This article investigates three well-known adversarial attack methods, i.e., Nettack, Meta Attack, and GradArgmax. It is found that different attack methods have their specific attack preferences on changing the target network structures. Such attack patterns are further verified by experimental results on some real-world networks, revealing that, generally, the top-4 most important network attributes on detecting adversarial samples suffice to explain the preference of an attack method. Based on these findings, the network attributes are utilized to design machine learning models for adversarial sample detection and attack method recognition with outstanding performance.
Learning Embedding for Signed Network in Social Media With Global Information

Signed networks contain nodes connected by positive and negative signed links. Signed network representation learning concentrates on learning the low-dimensional representations of these nodes, which facilitates downstream tasks such as link prediction using general data mining framework. However, most signed network embedding (SiNE) approaches neglect global information in the networks, limiting the capacity to learn genuine signed graph topology. To overcome this limitation, a deep graph neural network (GNN) framework SiG to learn SiNE with global information is proposed. To be more explicit, a hierarchical pooling mechanism is developed to encode the high-level features of the networks. Moreover, a graph convolution layer is introduced to aggregate both positive and negative information from neighbor nodes, and the concatenation of two parts generates the final embedding of nodes. Extensive experiments on four large real-world signed network datasets demonstrate the effectiveness and excellence of the proposed method.
Distributed Differentially Private Ranking Aggregation

A key step in cooperative decision-making is for all participants to achieve a consensus that avoids individual favoritism. To reach a consensus, a quantitative systematic mechanism is sometimes preferred. An example of such a mechanism is ranking aggregation, where the task is to rank elements in a certain order. While participating in ranking activities, it is also critical under certain circumstances to protect each decision maker’s preference. A promising privacy-preserving technique that is suitable for such a need is differential privacy (DP), which ensures plausible deniability of the protected information with rigorous mathematical guarantee and adjustable privacy level. A concern of the standard DP model is its assumption of letting a curator collect and analyze sensitive information, where in practical situations such a trusted independent curator may not exist. This article proposed a mechanism to solve the above issue using the distributed DP (DDP) framework. The proposed mechanism collects locally differential private rankings from individuals, and then randomly permutes pairwise rankings using a shuffle model to further amplify privacy protection. The final representative is produced by hierarchical rank aggregation (HRA). The mechanism was theoretically analyzed and experimentally compared against the existing methods and demonstrated competitive results in both output accuracy and privacy protection.
Sparse Graph Transformer With Contrastive Learning

Information aggregation and propagation over networks via graph neural networks (GNNs) plays an important role in node or graph representation learning, which currently depend on the calculation with a fixed adjacency matrix, facing over-smoothing problem, and difficulty to stack multiple layers for high-level representations. In contrast, Transformer calculates an importance score for each node to learn its embedding via the attention mechanism and has achieved great successes in many natural language processing (NLP) and computer vision (CV) tasks. However, Transformer is inflexible to extend to graphs, as its input and output must have the same dimension. It will also become intractable to allocate attention over a large-scale graph due to distractions. Moreover, most graph Transformers are trained in supervised ways, which consume additional resources to annotate samples with potentially wrong labels and have limited generalization of representations. Therefore, this article attempts to build a new Sparse Graph Transformer with Contrastive learning for graph representation learning, called SGTC. Specifically, we first employ centrality measures to remove the redundant topological information from input graph according to the influences of nodes and edges, then disturb the pruned graph to get two different augmentation views, and learn node representations in a contrastive manner. Besides, a novel sparse attention mechanism is also proposed to capture structural features of graphs, which effectively save memory and training time. SGTC can produce low-dimensional and high-order node representations, which have better generalization for multiple tasks. The proposed model is evaluated on three downstream tasks over six networks, and experimental results confirm its superior performance against the state-of-the-art baselines.
Context-Tree Weighting and Bayesian Context Trees: Asymptotic and Non-Asymptotic Justifications

The Bayesian Context Trees (BCT) framework is a recently introduced, general collection of statistical and algorithmic tools for modelling, analysis and inference with discrete-valued time series. The foundation of this development is built in part on some well-known information-theoretic ideas and techniques, including Rissanen’s tree sources and Willems et al.’s context-tree weighting algorithm. This paper presents a collection of theoretical results that provide mathematical justifications and further insight into the BCT modelling framework and the associated practical tools. It is shown that the BCT prior predictive likelihood (the probability of a time series of observations averaged over all models and parameters) is both pointwise and minimax optimal, in agreement with the MDL principle and the BIC criterion. The posterior distribution is shown to be asymptotically consistent with probability one (over both models and parameters), and asymptotically Gaussian (over the parameters). And the posterior predictive distribution is also shown to be asymptotically consistent with probability one.
Defect enhancement method for ZrO2-bearing spherical surface representation based on complete defect stitching and self-defined background balancing algorithm

To address the problems of difficulty in extracting complete defects and high interference of surface background in the process of ZrO2 bearing ball defect detection. A method of ZrO2 bearing ball surface representation defect enhancement based on complete defect stitching and self-defined background balance algorithm is proposed. To determine the location of complete defects, we propose a scanning and acquisition scheme for sub-aperture images and quickly stitch the edges of sub-aperture images. The image feature point gradient definition is optimized, and a less error Euclidean distance finding method is used for feature point matching. The stitched complete defect image is smooth and has high pixel accuracy. The background feature layer of the complete defect image is extracted for background gray leveling, and this process is introduced into the γ-value calculation process. The γ setting allows the image to be self-defined enhancement, outputting a fully defective image with a balanced background and outstanding defects. The average gradient is reduced by more than 90% after complete defect image enhancement, and the information entropy is preserved by about 40%. The defect edges of the image are intact, and the fine texture of the defect features is preserved.
Differentially Private Federated Clustering Over Non-IID Data

In this paper, we investigate federated clustering (FedC) problem, that aims to accurately partition unlabeled data samples distributed over massive clients into finite clusters under the orchestration of a parameter server, meanwhile considering data privacy.Though it is an NP-hard optimization problem involving real variables denoting cluster centroids and binary variables denoting the cluster membership of each data sample, we judiciously reformulate the FedC problem into a non-convex optimization problem with only one convex constraint, accordingly yielding a soft clustering solution.Then a novel FedC algorithm using differential privacy (DP) technique, referred to as DP-FedC, is proposed in which partial clients participation and multiple local model updating steps are also considered.Furthermore, various attributes of the proposed DP-FedC are obtained through theoretical analyses of privacy protection and convergence rate, especially for the case of non-identically and independently distributed (non-i.i.d.) data, that ideally serve as the guidelines for the design of the proposed DP-FedC.Then some experimental results on two real datasets are provided to demonstrate the efficacy of the proposed DP-FedC together with its much superior performance over some state-of-the-art FedC algorithms, and the consistency with all the presented analytical results.
Performance- and Energy-Aware Gait-Based User Authentication With Intermittent Computation for IoT Devices

Internet-of-Things (IoT) makes it possible to utilize a multitude of small and medium-scale devices allowing for increased functionality in flexible networks. However, user authentication on IoT devices is as important as it can be challenging. Due to limitations on the available energy, interface, and processing power, IoT devices can be the weakest link in their networks. Multiple authentication techniques have been developed to address these challenges. However, the existing techniques are limited in terms of performance, overheads, and efficiency. In contrast, our proposed authentication method uses a user’s walking gait as an input, because gait is unique to every user and can be collected using low-power inertial sensors found on all handheld devices. Our authentication method uses a lightweight Neural Network (NN) which is further complimented with early exits to further optimize computational cost(s). We also propose reinforcement learning that considers the energy consumption to dynamically determine which of the exits should be chosen to strike a balance between performance and computational and energy cost. Though effective, one of the challenges with IoT devices is their power supply such as dependence on batteries. Discharge of the battery or any other interruptions can lead to re-computations, which are expensive on already limited batteryoperated IoT devices. To address such challenges, especially for user authorization, we introduce intermittent computation to our proposed authentication framework. Intermittent computation can store the state of the NNs at checkpoints. In the case of power disruption, the execution will be resumed from the saved checkpoints instead of performing the whole execution. Most implementations of intermittent computation take place at the compiler level, which makes for a very efficient design, however, that also makes them hardware specific. Our method is implemented on software and is hardware-agnostic, allowing us to create checkpoints to save and retrieve the authentication framework state, in case of power interruption. The proposed authentication framework can authenticate users with up to more than 85% accuracy and can save up to 34% of computations due to the proposed intermittent computing.
Nighttime vehicle detection algorithm based on image translation technology1

In order to address the problem of decreased accuracy in vehicle object detection models when facing low-light conditions in nighttime environments, this paper proposes a method to enhance the accuracy and precision of object detection by using the image translation technology based on the Generative Adversarial Network (GAN) in the field of computer vision, specifically the CycleGAN, from the perspective of improving the training set of object detection models. This is achieved by transforming the existing well-established daytime vehicle dataset into a nighttime vehicle dataset. The proposed method adopts a comparative experimental approach to obtain translation models with different degrees of fitting by changing the training set capacity, and selects the optimal model based on the evaluation of the effect. The translated dataset is then used to train the YOLO-v5-based object detection model, and the quality of the nighttime dataset is evaluated through the evaluation of annotation confidence and effectiveness. The research results indicate that utilizing the translated nighttime vehicle dataset for training the object detection model can increase the area under the PR curve and the peak F1 score by 10.4% and 9% respectively. This approach improves the annotation accuracy and precision of vehicle object detection models in nighttime environments without requiring additional labeling of vehicles in monitoring videos.
An efficient and robust gradient reinforcement learning: Deep comparative policy

Recently, actor-critic architectures such as deep deterministic policy gradient (DDPG) are able to understand higher-level concepts for searching rich reward, and generate complex actions in continuous action space, and widely used in practical applications. However, when action space is limited and has dynamic hard margins, training DDPG can be problematic and inefficiency. Since real-world actuators always have margins and interferences, after initialization, the actor network is likely to be stuck at a local optimal point on action space margin: actor gradient orients to the outside of action space but actuators stop at the margin. If the hard margins are complex, dynamic and unknown to the DDPG agent, it is unable to use penalty functions to recover from local optimum. If we enlarge the random process for local exploration, the training could be in potential risk of failure. Therefore, simply relying on gradient of critic network to train the actor network is not a robust method in real environment. To solve this problem, in this paper we modify DDPG to deep comparative policy (DCP). Rather than leveraging critic-to-actor gradient, the core training process of DCP is regulated by a T-fold compare among random proposed adjacent actions. The performance of DDPG, DCP and related algorithms are tested and compared in two experiments. Our results show that, DCP is effective, efficient and qualified to perform all tasks that DDPG can perform. More importantly, DCP is less likely to be influenced by the action space margins, DCP can provide more safety in avoiding training failure and local optimum, and gain more robustness in applications with dynamic hard margins in the action space. Another advantage is that, complex penalty for margin touching detection is not required, the reward function can always be brief and short.
A Cross-Lingual Summarization method based on cross-lingual Fact-relationship Graph Generation

The aim of cross-lingual summarization (CLS) is to condense the content of a document in one language into a summary in another language. In essence, a CLS model requires both translation and summarization capabilities, which presents a unique challenge, as the model must effectively tackle the difficulties associated with both tasks simultaneously (e.g., semantic alignment, information compression and factual inconsistency). Graph-based semantic representation can model important text information in a structured manner, which may alleviate these challenges. Therefore, in this paper, we propose a Cross-Lingual Summarization method based on cross-lingual Fact-relationship Graph Generation (FGGCLS). Specifically, we first construct fact-relationship graphs for source language documents and target language summaries. Then, we introduce a cross-lingual fact-relationship graph generation method, which converts the CLS problem into a cross-lingual fact-relationship graph generation problem. This approach simplifies semantic alignment and information compression through the generation of graphs and leads to improved fact consistency. Finally, the generated fact-relationship graph of the target language summary serves as a draft for generating the summary, which enhances the quality of the generated summary. We conduct systematic experiments on the Zh2EnSum and En2ZhSum datasets, and the results demonstrate that our method can effectively improve the performance of CLS and alleviate factual inconsistency.
Secure and Real-Time Traceable Data Sharing in Cloud-Assisted IoT

Cloud-assisted Internet of Things (IoT) has become an increasingly popular paradigm to greatly improve the performance of IoT applications by delegating the cloud to manage the massive IoT data. How to achieve secure and real time traceable data sharing is crucial in this paradigm, especially, a large amount of sensitive data produced by IoT devices need to be stored or accessed to/from the clouds. This paper proposes a secure and traceable data sharing (STDS) scheme, which leverages the decentralized information flow control model to allow data owners to not only securely and efficiently share their data produced by IoT devices with data users, but also have the capability of tracking the data users’ identity with non-repudiation based on the hash chain technique. Subsequently, the high-level Petri net, satisfiability modulo theories library, and Z3 solver are used to formally analyze and verify STDS based on bounded model checking technique to prove the correctness and security STDS. The formal analysis results show that STDS fulfills its intended security goals. Finally, the performance evaluation results have demonstrated the efficiency of STDS.