Abstract Microservice has become a dominant approach for building large-scale Internet applications. The microservice-based system (MS) consists of thousands of services, and its complex interactions make it highly susceptible to unforeseen cascading failures. Cascading failure models are commonly used to analyze the system’s tolerance, while the existing models overlook MS’s features and fail to incorporate real-world events, leading to bias in simulation results. To address these, we proposed a comprehensive tolerance analysis framework of MS named the MSTAF. Specifically, we extracted the real-world failure-triggering scenarios and constructed the Workload-based Cascading Failure Model (WL-CFM) to model the load initialization and redistribution. Then, we implemented the Business Loss Assessment Method (BLAM) to quantify the impact by calculating the workload loss. To validate our MSTAF, we conducted experiments on the WL-CFM and BMAL and performed an analysis on the TrainTicket (TT). The results confirm the MSTAF’s superiority. Specifically, the WL-CFM outperforms baselines, reducing simulation error by 10– 48%. The BMAL demonstrates greater accuracy, with deviations from the ground truth ranging from $$-45$$ - 45 % to + 7%. Overall, the MSTAF offers valuable insights for enhancing tolerance and provides an effective solution for developers and researchers.

With the widespread use of modern browser extensions, user experience has been significantly enhanced via embedding ancillary functionality into the original webpage. The rapid development of Web tracking technology has raised privacy and security concerns, as it generates a unique identifier for users according to the diversity of installed extensions and further prompts the profiling of users. However, due to the ignorance of potential privacy risks, there is no effective method to detect browser extension fingerprinting. In this paper, we propose ExtFPDet, a CNN-based detection framework to recognize browser extension fingerprinting in websites, which fills the gap in this area. Based on the preliminary investigation, the approaches to fingerprint browser extensions can be summarized into 2 categories according to the distinctive behaviors, including resource traversing and side-channel exploring. In order to extract effective features to reflect extensions fingerprinting, ExtFPDet focuses on the structure and content in the program dependency graph of Javascript files. The generated feature vector assists the CNN-based classification model to detect the extension fingerprinting, for which we perform a systematic detection on Tranco top 10K websites. Eventually, the result is evaluated by randomly sampling and manually checking, which shows superior detection capabilities of ExtFPDet.

The proliferation of Android multimedia applications highlights the critical role of mobile sensors. Inherent manufacturing defects enable implicit device identification without consent, facilitating covert tracking. This bolsters security through reliable malicious actor tracking, unlike spoof-vulnerable explicit methods. However, prior sensor-based identification suffers from signal noise and device degradation, compromising robustness.In this paper, we propose AMSensorFP, a novel Android implicit device tracking framework. We develop an application to collect device information and multi-sensor data to construct device fingerprints. Subsequently, we build a dataset by performing pairwise difference calculations on the collected fingerprints. And then enhance the dataset with Gaussian noise to improve data diversity and robustness. An autoencoder reduces feature dimensionality, and the processed features are fed into a BiLSTM model with a multi-head attention mechanism, enabling effective fingerprint recognition. Experimental results show that AMSensorFP achieves 99.88% accuracy and 97.34% true positive rate(TPR), significantly outperforming existing methods. Ablation analysis further highlights the contributions of each module and feature in the framework. AMSensorFP delivers a reliable solution for device tracking and security enhancement.

Abstract In recent years, the evolving browser fingerprinting technology has posed significant challenges and constant demands on detection methods. Research related to malicious code shows that cross-file techniques, which disperse code into multiple files, can resist current detection methods. To address this challenge, we introduce cross-file tracking technology into browser fingerprinting, constructing cross-file browser fingerprinting (XFP). The dispersion of files and features in XFP effectively circumvents detection methods that primarily focus on single-file tracking. In this paper, we propose XFP-Recognizer, a Random Forest-based detection method for identifying XFP behaviors. XFP-Recognizer aggregates code files and dynamic APIs by constructing function call relationship graphs (FCRgraphs). It extracts dynamic and static features to train random forest models for detecting and classifying the aggregated files, and then backtracks based on FCRgraphs to mark original scripts. To validate our method, we implement a code-splitting algorithm and constructed a cross-file tracking dataset to address the lack of XFP in real-world scenarios. We combine this dataset with the dataset of Alexa Top-10K websites in different proportions to verify the effectiveness of XFP-Recognizer. The results show that XFP-Recognizer achieved an Accuracy of 92.25%, a Precision of 97.01% and an AUC of 0.9152 in recognizing browser fingerprinting, demonstrating superior performance in both single-file and cross-file tracking. XFP-Recognizer complements existing detection methods, and the constructed split dataset also serves as a foundational resource for future research.

Traditional interactive environments limit agents' intelligence growth with fixed tasks. Recently, single-agent environments address this by generating new tasks based on agent actions, enhancing task diversity. We consider the decision-making problem in multi-agent settings, where tasks are further influenced by social connections, affecting rewards and information access. However, existing multi-agent environments lack a combination of adaptive physical surroundings and social connections, hindering the learning of intelligent behaviors.To address this, we introduce AdaSociety, a customizable multi-agent environment featuring expanding state and action spaces, alongside explicit and alterable social structures. As agents progress, the environment adaptively generates new tasks with social structures for agents to undertake. In AdaSociety, we develop three mini-games showcasing distinct social structures and tasks. Initial results demonstrate that specific social structures can promote both individual and collective benefits, though current reinforcement learning and LLM-based algorithms show limited effectiveness in leveraging social structures to enhance performance. Overall, AdaSociety serves as a valuable research platform for exploring intelligence in diverse physical and social settings. The code is available at https://github.com/bigai-ai/AdaSociety.

Third-party advertising libraries, which furnish mobile applications with ads, offer a revenue stream for Android application developers. However, the loaded ads potentially expose application users to privacy infringements and security threats. For instance, tracking scripts embedded in third-party ads monitor user behavior and can entice users into downloading malicious files. Therefore, the detection of advertising libraries in mobile applications is crucial for mobile security protection and serves as the foundation for preventing third-party ads from compromising user privacy.

The recent proliferation of Internet of Things (IoT) sensors has driven a myriad of industrial and urban applications. Through analyzing massive data collected by these sensors, the proactive maintenance management can be achieved such that the maintenance schedule of the installed equipment can be optimized. Despite recent progress in proactive maintenance management in industrial scenarios, there are few studies on proactive maintenance management in urban informatics. In this article, we present an integrated framework of IoT and cloud computing platform for the proactive maintenance management in smart city. Our framework consists of: 1) an IoT monitoring system for collecting time-series data of operating and ambient conditions of the equipment and 2) a hybrid deep learning model, namely, convolutional bidirectional long short-term memory (CBLM) model for forecasting the operating and ambient conditions based on the collected time-series data. In addition, we also develop a naïve Bayes classifier to detect abnormal operating and ambient conditions and assist management personnel in scheduling maintenance tasks. To evaluate our framework, we deployed the IoT system in a Hong Kong public toilet, which is the first application of proactive maintenance management for a public hygiene and sanitary facility to the best of our knowledge. We collected the sensed data more than 33 days (808 h) in this real system. Extensive experiments on the collected data demonstrated that our proposed CBLM outperformed six traditional machine learning algorithms.

Heterogeneous networks (HetNets) with end-to-end (E2E) network slicing are regarded as effective approaches to meet diverse service requirements from vertical industries. Due to the dense deployment of base stations (BSs) and the complicated associations between BSs and E2E network slices (NSs) in the scenario, the handoff problem faces challenges of the huge system state space and handoff action space and the considerable communication overhead. In this paper, we take these issues into account and consider a distributed E2E NS handoff decision framework in the HetNet. A decentralized Markov decision process (DEC-MDP)-based model is formulated for the distributed E2E NS handoff problem, and the jointly observable and random characteristics of the DEC-MDP are analyzed. To obtain a theoretical performance reference, the original distributed E2E NS handoff problem is simplified, and a Nash equilibrium-based performance bound is given. More practically, the multi-agent double deep Q-network-based distributed handoff (MA-DDQN-DH) algorithm with the centralized training and decentralized executing framework is proposed. Simulation results show that the Nash equilibrium-based performance bound is reasonable, and the proposed MA-DDQN-DH algorithm performs well in the comparison.

Technology mapping from logic netlists to programmable logic blocks (PLB) plays an important role in FPGA EDA flow, especially for architecture exploration of PLBs. However, technology mapping becomes time-consuming due to the booming scale and complexity of IC designs as well as the growing complexity of PLB architectures. To speed up this process, a parallelized technology mapping approach based on adaptive circuit partitioning is proposed in this paper to perform fast multi-thread technology mapping. First, We choose the best of the three candidate partitioning strategies for the given netlist by circuit analysis to partition the original netlist into several independent sub-netlists. Secondly, these sub-netlists are mapped to the given PLB architecture simultaneously in their corresponding mapping threads. Finally, the complete mapped netlist is generated by merging the mapped sub-netlists. The proposed approach is implemented in ABC, independent of the detailed mapping algorithm. 13 large circuits from the Titan23 benchmark set are used as benchmarks to evaluate the proposed approach. Experimental results show that the proposed approach leads to an average of 5.76 × speedup over the single-thread version (up to 8.21 × individually) with no delay loss and less than 0.57% average area penalty.

End-to-end network slicing, as a key technology in 5G and B5G mobile communication systems, is to enable traditional wireless networks to support different services in vertical industries. In a heterogeneous cellular network (HetNets) with network slicing functions, due to the dense deployment of base stations (BSs) and the mobility of user equipments (UEs), dynamically switching network slices (NSs) is necessary for better system performance. This paper models the handoff problem of end-to-end NS as a Markov decision process (MDP) maximizing the utility related to the UE's profit of being served, the handoff cost and the outage penalty. Both the states of the radio access network resources and the core network resources of each end-to-end NS are considered. The deep reinforcement learning (DRL) is adopted as the solution, and a double deep Q network (DQN) based NS handoff algorithm is designed. Numerical results confirm the convergence of the DQN used to make handoff decisions and show that compared with typical handoff algorithms, the algorithm we proposed performs the best from the aspect of the cumulative reward designed in this paper.

The development of novel analgesics with improved safety profiles to combat the opioid epidemic represents a central question to G protein coupled receptor structural biology and pharmacology: What chemical features dictate G protein or β-arrestin signaling? Here we use adaptively biased molecular dynamics simulations to determine how fentanyl, a potent β-arrestin biased agonist, binds the μ-opioid receptor (μOR). The resulting fentanyl-bound pose provides rational insight into a wealth of historical structure-activity-relationship on its chemical scaffold. Following an in-silico derived hypothesis we found that fentanyl and the synthetic opioid peptide DAMGO require M153 to induce β-arrestin coupling, while M153 was dispensable for G protein coupling. We propose and validate an activation mechanism where the n-aniline ring of fentanyl mediates μOR β-arrestin through a novel M153 "microswitch" by synthesizing fentanyl-based derivatives that exhibit complete, clinically desirable, G protein biased coupling. Together, these results provide molecular insight into fentanyl mediated β-arrestin biased signaling and a rational framework for further optimization of fentanyl-based analgesics with improved safety profiles.

Resembling the rapid learning capability of human, low-shot learning empowers vision systems to understand new concepts by training with few samples. Leading approaches derived from meta-learning on images with a single visual object. Obfuscated by a complex background and multiple objects in one image, they are hard to promote the research of low-shot object detection/segmentation. In this work, we present aflexible and general methodology to achieve these tasks. Our work extends Faster /Mask R-CNN by proposing meta-learning over RoI (Region-of-Interest) features instead of a full image feature. This simple spirit disentangles multi-object information merged with the background, without bells and whistles, enabling Faster /Mask R-CNN turn into a meta-learner to achieve the tasks. Specifically, we introduce a Predictor-head Remodeling Network (PRN) that shares its main backbone with Faster /Mask R-CNN. PRN receives images containing low-shot objects with their bounding boxes or masks to infer their class attentive vectors. The vectors take channel-wise soft-attention on RoI features, remodeling those R-CNN predictor heads to detect or segment the objects consistent with the classes these vectors represent. In our experiments, Meta R-CNN yields the new state of the art in low-shot object detection and improves low-shot object segmentation byMaskR-CNN.Code: https://yanxp.github.io/metarcnn.html.

A broad range of cross-$m$-domain generation researches boil down to matching a joint distribution by deep generative models (DGMs). Hitherto algorithms excel in pairwise domains while as $m$ increases, remain struggling to scale themselves to fit a joint distribution. In this paper, we propose a domain-scalable DGM, i.e., MMI-ALI for $m$-domain joint distribution matching. As an $m$-domain ensemble model of ALIs (Dumoulin et al., 2016), MMI-ALI is adversarially trained with maximizing Multivariate Mutual Information (MMI) w.r.t. joint variables of each pair of domains and their shared feature. The negative MMIs are upper bounded by a series of feasible losses provably leading to matching $m$-domain joint distributions. MMI-ALI linearly scales as $m$ increases and thus, strikes a right balance between efficacy and scalability. We evaluate MMI-ALI in diverse challenging $m$-domain scenarios and verify its superiority.

This Demo presents G-Miner, a distributed system for graph mining. The take-aways for Demo attendees are: (1) a good understanding of the challenges of various graph mining workloads; (2) useful insights on how to design a good system for graph mining by comparing G-Miner with existing systems on performance, expressiveness and user-friendliness; and (3) how to use G-Miner for interactive graph analytics.

Forecasting short-term cycle time (CT) of wafer lots is crucial for production planning and control in the wafer manufacturing. A novel recurrent neural network called “bilateral long short-term memory (bilateral LSTM)” is proposed to model a short-term cycle time forecasting (CTF) of each re-entrant period of a wafer lot. First, a two-dimensional (2-D) architecture is designed to transmit the wafer and layer correlations by using wafer and layer connections. Subsequently, aiming to store various error signals caused by the diverse CT data, a multiply memory structure is presented to extend the capacity of constant error carousel (CEC) in the LSTM model. The experiment results indicate that the proposed model outperforms conventional models in the accuracy and stability for the short-term CTF. Further comparative experiments reveal that the 2-D architecture can enhance the prediction accuracy and the multi-CEC structure can improve the forecasting stability for the short-term CTF of wafer lots.

Low-frequency words place a major challenge for automatic speech recognition (ASR). The probabilities of these words, which are often important name entities, are generally underestimated by the language model (LM) due to their limited occurrences in the training data. Recently, we proposed a wordpair approach to deal with the problem, which borrows information of frequent words to enhance the probabilities of lowfrequency words. This paper presents an extension to the wordpair method by involving multiple ‘predicting words’ to produce better estimation for low-frequency words. We also employ this approach to deal with out-of-language words in the task of multi-lingual speech recognition.