Reviewerly/exHarmony huggingface dataset:
Privacy Preserving Federated Learning Framework Based on Multi-chain Aggregation

Federated Learning is a promising machine learning paradigm for collaborative learning while preserving data privacy. However, attackers can derive the original sensitive data from the model parameters in Federated Learning with the central server because model parameters might leak once the server is attacked. To solve the above server attack challenge, in this paper, we propose a novel server-free Federated Learning framework named MChain-SFFL which performs multi-chain parallel communication in a fully distributed way to update the model to achieve more secure privacy protection. Specifically, MChain-SFFL first randomly selects multiple participants as the chain heads to initiate the model parameter aggregation process. Then MChain-SFFL leverages the single-masking and chained-communication mechanisms to transfer the masked information between participants within each serial chain. In this way, the masked local model parameters are gradually aggregated along the chain nodes. Finally, each chain head broadcasts the aggregated local model to the other nodes and this propagation process stops until convergence. The experimental results demonstrate that for Non-IID data, MChain-SFFL outperforms the compared methods in model accuracy and convergence speed. For IID data, the accuracy and convergence speed of MChain-SFFL are close to Chain-PPFL and FedAVG.
A FINITE PRODUCTION SCHEDULING SYSTEM BASED ON FUZZY LOGIC

This paper proposes a model for a Finite Production Scheduling System based on fuzzy logic. Initially, some Production Planning and Scheduling are discussed, followed by a description of the production environment where the system was implemented and tested. Later the proposed model is presented, characterizing the backward and forward simulation processes, the used criterion for decision making and the fuzzy knowledge base. Finally the comparison test parameters are presented, the results are shown and the conclusions discussed.
A Predictive Coding Approach to Multivariate Time Series Anomaly Detection

This paper proposes LPC-AD, a fast and accurate multivariate time series (MTS) anomaly detection method. LPC-AD is motivated by the ever-increasing needs for fast and accurate MTS anomaly detection methods to support fast troubleshooting in cloud computing, micro-service systems, etc. LPC-AD is fast in the sense that it reduces the training time by as high as 38.2% compared to the state-of-the-art (SOTA) deep learning methods that focus on training speed. LPC-AD is accurate in the sense that it improves the detection accuracy by as high as 18.9% compared to SOTA sophisticated deep learning methods that focus on enhancing detection accuracy. Methodologically, LPC-AD contributes a generic architecture LPC-Reconstruct for one to attain different trade-offs between training speed and detection accuracy. More specifically, LPC-Reconstruct is built on ideas from autoencoder for reducing redundancy in time series, latent predictive coding for capturing temporal dependence in MTS, and randomized perturbation for avoiding overfitting of anomalous dependence in the training data. We present simple instantiations of LPC-Reconstruct to attain fast training speed, where we propose a simple randomized perturbation method. The superior performance of LPC-AD over SOTA methods is validated by extensive experiments on four large real-world datasets. Experiment results also show the necessity and benefit of each component of the LPC-Reconstruct architecture and that LPC-AD is robust to hyper parameters.
Accurate and Efficient Trajectory-Based Contact Tracing with Secure Computation and Geo-Indistinguishability

Contact tracing has been considered as an effective measure to limit the transmission of infectious disease such as COVID-19. Trajectory-based contact tracing compares the trajectories of users with the patients, and allows the tracing of both direct contacts and indirect contacts. Although trajectory data is widely considered as sensitive and personal data, there is limited research on how to securely compare trajectories of users and patients to conduct contact tracing with excellent accuracy, high efficiency, and strong privacy guarantee. Traditional Secure Multiparty Computation (MPC) techniques suffer from prohibitive running time, which prevents their adoption in large cities with millions of users. In this work, we propose a technical framework called ContactGuard to achieve accurate, efficient, and privacy-preserving trajectory-based contact tracing. It improves the efficiency of the MPC-based baseline by selecting only a small subset of locations of users to compare against the locations of the patients, with the assist of Geo-Indistinguishability, a differential privacy notion for Location-based services (LBS) systems. Extensive experiments demonstrate that ContactGuard runs up to 2.6 $$\times $$ faster than the MPC baseline, with no sacrifice in terms of the accuracy of contact tracing.
SNN-AAD: Active Anomaly Detection Method for Multivariate Time Series with Sparse Neural Network

Anomaly detection of time series data is an important and popular problem in both research and application fields. Kinds of solutions have been developed to uncover the anomaly instances from data. However, the labelled data is always limited and costly for real applications, which adds to the difficulty of identifying various anomalies in multivariate time series. In this paper, we propose a novel active anomaly detection method with sparse neural network (SNN-AAD) to improve the accuracy and efficiency in anomaly detection for time series with limited labels. SNN-AAD is designed for two objectives: (1) to achieve sufficient generalization capacity of the model with small-size labels, and (2) to effectively reduce the human cost in active learning process. We introduce sparse neural network in training which minimizes the detection loss caused by the sparsity of labels. We improve the active anomaly detection with the design of sample selection strategy and abnormal feature order generation algorithm, which extracts the important features of instances and reduce the cost of human intelligence. Experimental results on four real-life datasets show SNN-AAD has good detection performance with limited labels, and improves the detection accuracy by 10.2%–18.2% from benchmark methods, while it achieves 12.1% human cost reduction.
JG2Time: A Learned Time Estimator for Join Operators Based on Heterogeneous Join-Graphs

The join operator is one of the key operators in RDBMS, and estimating its evaluation time is a fundamental task in query optimization, scheduling, etc. However, it is hard to make a precise estimation, which is not only related with the physical join implementations (hash, sort, loop) but also with the corresponding parameters, like the size of the data, the number of partitions, the number of threads in a modern hash join. Existing works rely on the time complexity analysis but yield rough results, or employ machine learning techniques to build a predictive model but require many training instances. In this paper, we propose a method, named JG2Time, to estimate the running time using the join-graphs constructed from the source codes. Specifically, we construct a heterogonous join-graph by annotating parameter nodes to a call-graph generated by running time analysis tools, and propose ReGAT, a heterogonous graph neural network, to fully capture the edge weights (the number of function calls) in the join-graph. The embeddings learned from ReGAT can be used to predict the running time. In addition, we optimize JG2Time with a multi-task model that also predicts the times of function calls, and an unsupervised code learning method to enhance its generalization. The experimental results illustrate the effectiveness of JG2Time and its optimization strategies.
Unsupervised Fraud Transaction Detection on Dynamic Attributed Networks

Fraud transaction detection is a pressing need in industrial applications, aiming to detect the fraud for a transaction involving the buyer and the seller. Due to the prohibitive cost of accessing appropriate labels for the task in a supervised fashion, unsupervised anomaly detection has become an alternative solution. However, previous methods mainly handcraft some features to detect the fraud on a single entity, which neglects the dynamic and topological nature between the buyer and the seller within the transaction. In this paper, we propose a novel Temporal Structure Augmented Gaussian Mixture Model (TSAGMM) for unsupervised fraud transaction detection on dynamic attributed networks. Specifically, we propose a time-encoded graph autoencoder to utilize both the topological structure and temporal information within the dynamic transaction graph to reconstruct the node attributes and graph topology. The learned latent representations as well as reconstruction errors are combined and fed into a density-based model for unsupervised fraud detection. Experimental results on the real-world transaction dataset from Alipay show the superiority of our proposed method among the state-of-the-art methods.
Global-Aware Model-Free Self-distillation for Recommendation System

The recommendation performance in our Alipay advertising system may suffer from label noise in training data. Earlier approaches that relied on soft targets typically neglected global similarities. Here, we introduce a novel algorithm called Global-aware Model-free Self-Distillation (GMSD) to create soft targets using the information at the global scale. Specifically, we propose calculating the similarities between the target sample and cluster centers produced by clustering the training dataset. The direct calculation of global similarities requires computation across the full dataset, which is prohibitively expensive. Additionally, we develop a contrastive cluster loss (CCLoss) for limiting the distance between the data of the intra-class to be lower than that of the inter-class to promote samples to be drawn to accurate cluster clusters. Extensive experiments on public and industry datasets demonstrate that GMSD outperforms state-of-the-art self-distillation methods in efficiency and effectiveness.
TUAF: Triple-Unit-Based Graph-Level Anomaly Detection with Adaptive Fusion Readout

Graph-level anomaly detection (GAD) has emerged as a significant research direction due to its practical application in diverse domains, such as toxic drug identification and compound activity assay. Existing GAD methods generally regard the node as the basic unit to learn graph representation, thus ignoring the vital information of the triple structure "node-edge-node". Intuitively, the occurrences of anomalous events in form of triples are the primary cause of an abnormal graph. Meanwhile, previous works adopt trivial readout strategies to obtain the graph-level representation without considering the different contributions of nodes. In this paper, we propose a novel GAD method named TUAF, based on triple-unit graphs with adaptive fusion readout. Specifically, we first transform the original graph into the triple-unit graph, and then learn triple representations for capturing abundant information about an edge and its corresponding nodes simultaneously. Furthermore, we design an adaptive fusion readout to obtain a high-quality graph-level representation by adaptively learning the optimal gravity coefficient for each triple. Through extensive experiments, we demonstrate the effectiveness of TUAF on 18 real-world datasets.
FAN: Fatigue-Aware Network for Click-Through Rate Prediction in E-commerce Recommendation

Since clicks usually contain heavy noise, increasing research efforts have been devoted to modeling implicit negative user behaviors (i.e., non-clicks). However, they either rely on explicit negative user behaviors (e.g., dislikes) or simply treat non-clicks as negative feedback, failing to learn negative user interests comprehensively. In such situations, users may experience fatigue because of seeing too many similar recommendations. In this paper, we propose Fatigue-Aware Network (FAN), a novel CTR model that directly perceives user fatigue from non-clicks. Specifically, we first apply Fourier Transformation to the time series generated from non-clicks, obtaining its frequency spectrum which contains comprehensive information about user fatigue. Then the frequency spectrum is modulated by category information of the target item to model the bias that both the upper bound of fatigue and users' patience is different for different categories. Moreover, a gating network is adopted to model the confidence of user fatigue and an auxiliary task is designed to guide the learning of user fatigue, so we can obtain a well-learned fatigue representation and combine it with user interests for the final CTR prediction. Experimental results on real-world datasets validate the superiority of FAN and online A/B tests also show FAN outperforms representative CTR models significantly.
A Topic-Aware Data Generation Framework for Math Word Problems

In educational practice, math word problems (MWPs) are important material to train pupils' to solve real-world math problems. In this paper, our focus is on the learning-based topic-aware MWP generation problem. To summarize, there are several challenges with respect to the problem. The first one is how to design the generation model considering the structural characteristics of math equations as well as the topic information. Second, how to generate multiple MWPs covering diverse topics is another challenge. To address the above challenges, we propose a novel framework, GenMWP, to automatically generate high-quality MWPs. First, we use the Tree-LSTM model to capture the structure of the equation. Second, we design a topic embedding model to consider the topic information, allowing the user to input any words or sentences to describe the topic. Third, given only an equation, we also support generating multiple MWPs covering different topics. The automatic evaluation result shows that GenMWP outperforms other baseline methods, and the human evaluation result shows that the MWPs generated by GenMWP have a higher rating in fluency, consistency, equation relevance, and topic relevance.
Self-Training with Label-Feature-Consistency for Domain Adaptation

Mainstream approaches for unsupervised domain adaptation (UDA) learn domain-invariant representations to address the domain shift. Recently, self-training has been used in UDA, which exploits pseudo-labels for unlabeled target domains. However, the pseudo-labels can be unreliable due to distribution shifts between domains, severely impairing the model performance. To address this problem, we propose a novel self-training framework-Self-Training with Label-Feature-Consistency (ST-LFC), which selects reliable target pseudo-labels via label-level and feature-level voting consistency principle. The former means target pseudo-labels generated by a source-trained classifier and the latter means the nearest source-class to the target in feature space. In addition, ST-LFC reduces the negative effects of unreliable predictions through entropy minimization. Empirical results indicate that ST-LFC significantly improves over the state-of-the-arts on a variety of benchmark datasets.
Process Drift Detection in Event Logs with Graph Convolutional Networks

The only constant is change. Drift detection in process mining is a family of methods to detect changes by analyzing event logs to ensure the accuracy and reliability of business processes in process-aware information systems (e.g., ERP systems). However, artificial feature selection is still a mountain to climb in existing methods, which requires high domain knowledge for users. In this paper, we propose a novel approach by using Graph convolutional networks for Drifts Detection in the event log, we name it GDD. Specifically, 1) we transform event sequences into two directed graphs by using two consecutive time windows, and construct the line graphs for the directed graphs to capture the orders between different activities; 2) we use graph convolutional networks to capture the features in these graphs, and augment the original graphs with virtual nodes to represent the latent aspects of the graphs; 3) we calculate the distances between virtual nodes, and use the K-means algorithm to find the outliers that are considered as candidate change points. Then, a filter mechanism is used to confirm the actual change points. The experiments on simulated event logs and real-life event logs confirmed the improvements of GDD compared with the baselines.
A Scalable Social Recommendation Framework with Decoupled Graph Neural Network

Social relationships are usually used to improve recommendation quality, especially when users' behavior is very sparse in recommender systems. Most existing social recommendation methods apply Graph Neural Networks (GNN) to capture users' social structure information and user-item interaction information. However, the GNNs need to conduct expensive neighborhood propagation, leading to scalability issues. Some recent works pointed out that the GNNs can be simplified via decoupling. Therefore, we propose a scalable framework for social recommendation to decouple the model into two stages, Gumbel-based feature propagation and self-supervised multi-representation fusion. In the first stage, since the similarity between friends will not change frequently, we pre-train a Gumbel sampling-based attention model offline to learn the importance of each social relation and use the importance as the weight to aggregate the feature during pre-computation. Due to the diversity of user interests, the features are propagated upon different propagation layers to capture information with different aspects. In the second stage, we use the aggregated representations as inputs and fuse the inputs by an attention mechanism to obtain comprehensive embeddings on the online mode to update daily. Besides, we use a contrastive learning way to enrich users' information. Moreover, extensive experimental results demonstrate the scalability and effectiveness of our framework over state-of-the-art algorithms.
Chinese Medical Nested Named Entity Recognition Model Based on Feature Fusion and Bidirectional Lattice Embedding Graph

Medical named entity recognition can assist doctors to quickly identifying key content and improving clinical work efficiency. Chinese named entity recognition methods based on pre-trained language models have achieved remarkable performance. However, most of these models have the following problems for medical named entity recognition: these models are designed for flat named entity recognition tasks but not for nested entities. Furthermore, the medical entities are hard to be recognized due to the lack of medical domain knowledge. To tackle these problems, we propose a Chinese medical nested named entity recognition model based on feature fusion and a bidirectional lattice embedding graph. The problem of poor recognition of medical entities due to the lack of medical domain knowledge is solved by introducing a medical lexicon. The problem of Chinese polyphonic characters with different meanings in the same form is solved by introducing pinyin information. The model considers the similarity between different entity types to improve the model's effectiveness. The results on a Chinese medical nested named entity dataset CBLUE-CMeEE demonstrate the outperform performance and effectiveness of the model.
PMJEE: A Prototype Matching Framework for Joint Event Extraction

Events are vital parts of natural language, reflecting the state changes of entities. The Event Extraction (EE) task aims to extract event triggers (the most representative words or phrases) and their arguments (participants in the event) from the given text. Most current works use sequence tagging models to solve the EE task. However, those methods only treat event and argument types as different class numbers, ignoring the semantics of those labels. However, label semantics are critical in the EE task. For example, the trigger word "fight" is semantically closer to the event type "Conflict:Attack" rather than "Life:Marriage". To emphasize the label semantics in events, we formulate EE as a prototype matching task and propose a Prototype Matching framework for Joint Event Extraction (PMJEE). Specifically, prototypical embeddings for both event trigger and argument types are introduced to encode their label semantics and correlations. Then a dual-channel attention layer and extraction modules are used to jointly extract event triggers and arguments. Prototypical embeddings will be optimized during training to improve the event extraction performance. Extensive experiments indicate our method achieves better performance than strong baselines, especially in data-scarce scenarios. In the detailed analysis, we verify the effectiveness of each part of the model and explore the impact of different label semantic materials.
Fine-Grained Software Vulnerability Detection via Neural Architecture Search

Vulnerability detection methods based on the deep learning have achieved remarkable performance improvements compared to traditional methods. Current deep learning-based detectors mostly use a single RNN or its variants (i.e., LSTM or GRU) to detect vulnerabilities. However, vulnerability detection is a multi-domain problem. Different types of vulnerabilities have different characteristics. Using a single neural network cannot perform well in all types of vulnerability detection tasks. Manually designing a matching neural network for each type of vulnerability detection task not only requires a lot of trials and computational resources but also highly relies on the knowledge and design experience of experts. To address this issue, in this paper, we propose a novel fine-grained vulnerability detection framework named A-DARTS, which is capable of searching for well-performed neural network architectures for different vulnerability detection tasks by introducing neural network architecture search (NAS) techniques. Specifically, we design a more efficient search space to ensure superior neural network architectures can be found by the search algorithm. Besides, we propose an adaptive differentiable search algorithm to search for superior neural network architectures. Experimental results show that searched models consistently outperform all baseline models and achieve significant performance improvements.