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In real-world recommendation scenarios, users engage with items through various types of behaviors. Leveraging diversified user behavior information for learning can enhance the recommendation of target behaviors (e.g., buy), as demonstrated by recent multi-behavior methods. The mainstream multi-behavior recommendation framework consists of two steps: fusion and prediction. Recent approaches utilize graph neural networks for multi-behavior fusion and employ multi-task learning paradigms for joint optimization in the prediction step, achieving significant success. However, these methods have limited perspectives on multi-behavior fusion, which leads to inaccurate capture of user behavior patterns in the fusion step. Moreover, when using multi-task learning for prediction, the relationship between the target task and auxiliary tasks is not sufficiently coordinated, resulting in negative information transfer. To address these problems, we propose a novel multi-behavior recommendation framework based on the combinatorial optimization perspective, named COPF. Specifically, we treat multi-behavior fusion as a combinatorial optimization problem, imposing different constraints at various stages of each behavior to restrict the solution space, thus significantly enhancing fusion efficiency (COGCN). In the prediction step, we improve both forward and backward propagation during the generation and aggregation of multiple experts to mitigate negative transfer caused by differences in both feature and label distributions (DFME). Comprehensive experiments on three real-world datasets indicate the superiority of COPF. Further analyses also validate the effectiveness of the COGCN and DFME modules. Our code is available at https://github.com/1918190/COPF., Comment: Accepted by KDD 2025 Research Track

Approximate computing is a new computing paradigm. One important area of it is designing approximate circuits for FPGA. Modern FPGAs support dual-output LUT, which can significantly reduce the area of FPGA designs. Several existing works explored the use of dual-output in approximate computing. However, they are limited to small-scale arithmetic circuits. To address the problem, this work proposes QUADOL, a quality-driven ALS method by exploiting dual-output LUTs for modern FPGAs. We propose a technique to approximately merge two single-output LUTs (i.e., a LUT pair) into a dual-output LUT. In addition, we transform the problem of selecting multiple LUT pairs for simultaneous approximate merging into a maximum matching problem to maximize the area reduction. Since QUADOL exploits a new dimension, i.e., approximately merging a LUT pair into a dual-output LUT, it can be integrated with any existing ALS methods to strengthen them. Therefore, we also introduce QUADOL+, which is a generic framework to integrate QUADOL into existing ALS methods. The experimental results showed that QUADOL+ can reduce the LUT count by up to 18% compared to the state-of-the-art ALS methods for FPGA. Moreover, the approximate multipliers optimized by QUADOL+ dominate most prior FPGA-based approximate multipliers in the area-error plane.

With the increasing sophistication of Advanced Persistent Threats (APTs), the demand for effective detection and mitigation strategies and methods has escalated. Program execution leaves traces in the system audit log, which can be analyzed to detect malicious activities. However, collecting and analyzing large volumes of audit logs over extended periods is challenging, further compounded by insufficient labeling that hinders their usability. Addressing these challenges, this paper introduces SAGA (Synthetic Audit log Generation for APT campaigns), a novel approach for generating find-grained labeled synthetic audit logs that mimic real-world system logs while embedding stealthy APT attacks. SAGA generates configurable audit logs for arbitrary duration, blending benign logs from normal operations with malicious logs based on the definitions the MITRE ATT\&CK framework. Malicious audit logs follow an APT lifecycle, incorporating various attack techniques at each stage. These synthetic logs can serve as benchmark datasets for training machine learning models and assessing diverse APT detection methods. To demonstrate the usefulness of synthetic audit logs, we ran established baselines of event-based technique hunting and APT campaign detection using various synthetic audit logs. In addition, we show that a deep learning model trained on synthetic audit logs can detect previously unseen techniques within audit logs.

As Advanced Persistent Threats (APTs) grow increasingly sophisticated, the demand for effective detection methods has intensified. This study addresses the challenge of identifying APT campaign attacks through system event logs. A cascading approach, name SFM, combines Technique hunting and APT campaign attribution. Our approach assumes that real-world system event logs contain a vast majority of normal events interspersed with few suspiciously malicious ones and that these logs are annotated with Techniques of MITRE ATT&CK framework for attack pattern recognition. Then, we attribute APT campaign attacks by aligning detected Techniques with known attack sequences to determine the most likely APT campaign. Evaluations on five real-world APT campaigns indicate that the proposed approach demonstrates reliable performance.

With the rise of short video platforms, video recommendation technology faces more complex challenges. Currently, there are multiple non-personalized modules in the video recommendation pipeline that urgently need personalized modeling techniques for improvement. Inspired by the success of uplift modeling in online marketing, we attempt to implement uplift modeling in the video recommendation scenario. However, we face two main challenges: 1) Design and utilization of treatments, and 2) Capture of user real-time interest. To address them, we design adjusting the distribution of videos with varying durations as the treatment and propose Coarse-to-fine Dynamic Uplift Modeling (CDUM) for real-time video recommendation. CDUM consists of two modules, CPM and FIC. The former module fully utilizes the offline features of users to model their long-term preferences, while the latter module leverages online real-time contextual features and request-level candidates to model users' real-time interests. These two modules work together to dynamically identify and targeting specific user groups and applying treatments effectively. Further, we conduct comprehensive experiments on the offline public and industrial datasets and online A/B test, demonstrating the superiority and effectiveness of our proposed CDUM. Our proposed CDUM is eventually fully deployed on the Kuaishou platform, serving hundreds of millions of users every day. The source code will be provided after the paper is accepted., Comment: 9 pages, 4 figures, 5 tables

Entangled graph states can be used for quantum sensing and computing applications. Error correction in measurement-based quantum computing schemes will require the construction of cluster states in at least 3 dimensions. Here we generate 1-, 2-, 3-, and 4-dimensional optical frequency-mode cluster states by sending broadband 2-mode vacuum-squeezed light through an electro-optical modulator (EOM) driven with multiple frequencies. We create the squeezed light using 4-wave mixing in Rb atomic vapor and mix the sideband frequencies (qumodes) using an EOM, as proposed by Zhu et al. (1), producing a pattern of entanglement correlations that constitute continuous-variable graph states containing up to several hundred qumodes. We verify the entanglement structure by using homodyne measurements to construct the covariance matrices and evaluate the nullifiers. This technique enables scaling of optical cluster states to multiple dimensions without increasing loss.

Click-Through Rate (CTR) prediction, crucial in applications like recommender systems and online advertising, involves ranking items based on the likelihood of user clicks. User behavior sequence modeling has marked progress in CTR prediction, which extracts users' latent interests from their historical behavior sequences to facilitate accurate CTR prediction. Recent research explores using implicit feedback sequences, like unclicked records, to extract diverse user interests. However, these methods encounter key challenges: 1) temporal misalignment due to disparate sequence time ranges and 2) the lack of fine-grained interaction among feedback sequences. To address these challenges, we propose a novel framework called TEM4CTR, which ensures temporal alignment among sequences while leveraging auxiliary feedback information to enhance click behavior at the item level through a representation projection mechanism. Moreover, this projection-based information transfer module can effectively alleviate the negative impact of irrelevant or even potentially detrimental components of the auxiliary feedback information on the learning process of click behavior. Comprehensive experiments on public and industrial datasets confirm the superiority and effectiveness of TEM4CTR, showcasing the significance of temporal alignment in multi-feedback modeling., Comment: 11 pages, 5 figures

Multi-behavior recommendation algorithms aim to leverage the multiplex interactions between users and items to learn users' latent preferences. Recent multi-behavior recommendation frameworks contain two steps: fusion and prediction. In the fusion step, advanced neural networks are used to model the hierarchical correlations between user behaviors. In the prediction step, multiple signals are utilized to jointly optimize the model with a multi-task learning (MTL) paradigm. However, recent approaches have not addressed the issue caused by imbalanced data distribution in the fusion step, resulting in the learned relationships being dominated by high-frequency behaviors. In the prediction step, the existing methods use a gate mechanism to directly aggregate expert information generated by coupling input, leading to negative information transfer. To tackle these issues, we propose a Parallel Knowledge Enhancement Framework (PKEF) for multi-behavior recommendation. Specifically, we enhance the hierarchical information propagation in the fusion step using parallel knowledge (PKF). Meanwhile, in the prediction step, we decouple the representations to generate expert information and introduce a projection mechanism during aggregation to eliminate gradient conflicts and alleviate negative transfer (PME). We conduct comprehensive experiments on three real-world datasets to validate the effectiveness of our model. The results further demonstrate the rationality and effectiveness of the designed PKF and PME modules. The source code and datasets are available at https://github.com/MC-CV/PKEF., Comment: Accepted by CIKM 2023

Multi-types of user behavior data (e.g., clicking, adding to cart, and purchasing) are recorded in most real-world recommendation scenarios, which can help to learn users' multi-faceted preferences. However, it is challenging to explore multi-behavior data due to the unbalanced data distribution and sparse target behavior, which lead to the inadequate modeling of high-order relations when treating multi-behavior data ''as features'' and gradient conflict in multitask learning when treating multi-behavior data ''as labels''. In this paper, we propose CIGF, a Compressed Interaction Graph based Framework, to overcome the above limitations. Specifically, we design a novel Compressed Interaction Graph Convolution Network (CIGCN) to model instance-level high-order relations explicitly. To alleviate the potential gradient conflict when treating multi-behavior data ''as labels'', we propose a Multi-Expert with Separate Input (MESI) network with separate input on the top of CIGCN for multi-task learning. Comprehensive experiments on three large-scale real-world datasets demonstrate the superiority of CIGF. Ablation studies and in-depth analysis further validate the effectiveness of our proposed model in capturing high-order relations and alleviating gradient conflict. The source code and datasets are available at https://github.com/MC-CV/CIGF., Comment: Wei Guo and Chang Meng are co-first authors and contributed equally to this research. Chang Meng is supervised by Wei Guo when he was a research intern at Huawei Noah's Ark Lab

Multi-types of behaviors (e.g., clicking, adding to cart, purchasing, etc.) widely exist in most real-world recommendation scenarios, which are beneficial to learn users' multi-faceted preferences. As dependencies are explicitly exhibited by the multiple types of behaviors, effectively modeling complex behavior dependencies is crucial for multi-behavior prediction. The state-of-the-art multi-behavior models learn behavior dependencies indistinguishably with all historical interactions as input. However, different behaviors may reflect different aspects of user preference, which means that some irrelevant interactions may play as noises to the target behavior to be predicted. To address the aforementioned limitations, we introduce multi-interest learning to the multi-behavior recommendation. More specifically, we propose a novel Coarse-to-fine Knowledge-enhanced Multi-interest Learning (CKML) framework to learn shared and behavior-specific interests for different behaviors. CKML introduces two advanced modules, namely Coarse-grained Interest Extracting (CIE) and Fine-grained Behavioral Correlation (FBC), which work jointly to capture fine-grained behavioral dependencies. CIE uses knowledge-aware information to extract initial representations of each interest. FBC incorporates a dynamic routing scheme to further assign each behavior among interests. Additionally, we use the self-attention mechanism to correlate different behavioral information at the interest level. Empirical results on three real-world datasets verify the effectiveness and efficiency of our model in exploiting multi-behavior data. Further experiments demonstrate the effectiveness of each module and the robustness and superiority of the shared and specific modelling paradigm for multi-behavior data., Comment: The self-attention mechanism should be contained in FBC and SRR is a part of CIE, but we does not do this in our previous code implementation, thus we redid the experiment and updated the experimental results, and tested the significance of the ablation experiment. Besides, we corrected some of the experimental details

Developing new types of high-capacity and high-energy density rechargeable battery is important to future generations of consumer electronics, electric vehicles, and mass energy storage applications. Recently we reported ~ 3.5 V sodium/chlorine $(Na/Cl_2)$ and lithium/chlorine $(Li/Cl_2)$ batteries with up to 1200 mAh $g^{-1}$ reversible capacity, using either a Na or Li metal as the negative electrode, an amorphous carbon nanosphere (aCNS) as the positive electrode, and aluminum chloride $(AlCl_3)$ dissolved in thionyl chloride $(SOCl_2)$ with fluoride-based additives as the electrolyte. The high surface area and large pore volume of aCNS in the positive electrode facilitated NaCl or LiCl deposition and trapping of $Cl_2$ for reversible $NaCl/Cl_2$ or $LiCl/Cl_2$ redox reactions and battery discharge/charge cycling. Here we report an initially low surface area/porosity graphite (DGr) material as the positive electrode in a $Li/Cl_2$ battery, attaining high battery performance after activation in carbon dioxide $(CO_2)$ at 1000 {\deg}C (DGr_ac) with the first discharge capacity ~ 1910 mAh $g^{-1}$ and a cycling capacity up to 1200 mAh $g^{-1}$. Ex situ Raman spectroscopy and X-ray diffraction (XRD) revealed the evolution of graphite over battery cycling, including intercalation/de-intercalation and exfoliation that generated sufficient pores for hosting $LiCl/Cl_2$ redox. This work opens up widely available, low-cost graphitic materials for high-capacity alkali metal/$Cl_2$ batteries. Lastly, we employed mass spectrometry to probe the $Cl_2$ trapped in the graphitic positive electrode, shedding light into the $Li/Cl_2$ battery operation., Comment: 37 pages, 16 figures, 1 table

As technology grows faster, the news spreads through social media. In order to attract more readers and acquire additional profit, some news agencies reproduce massive news in a more appealing manner. Therefore, it is essential to accurately predict whether a news article is from official news agencies. This work develops a headline classification based on Convoluted Neural Network to determine credibility of a news article. The model primarily focuses on investigating key factors from headlines. These factors include word segmentation, part-of-speech tags, and sentiment features. With integrating these features into the proposed classification model, the demonstrated evaluation achieves 93.99% for accuracy., Comment: Accepted by The 26th Taiwan Academic Network Conference (TANET) 2020

This report summarizes the results of Learning to Understand Aerial Images (LUAI) 2021 challenge held on ICCV 2021, which focuses on object detection and semantic segmentation in aerial images. Using DOTA-v2.0 and GID-15 datasets, this challenge proposes three tasks for oriented object detection, horizontal object detection, and semantic segmentation of common categories in aerial images. This challenge received a total of 146 registrations on the three tasks. Through the challenge, we hope to draw attention from a wide range of communities and call for more efforts on the problems of learning to understand aerial images., Comment: 7 pages, 2 figures, accepted by ICCVW 2021

We investigate the effect of 2-beam coupling in different imaging geometries in generating intensity-difference squeezing from four-wave mixing (4WM) in Rb atomic vapors. A recently-introduced dual-seeding technique can cancel out the classical noise in a seeded four-wave mixing process. This dual-seeding technique, however, can introduce new complications that involve 2-beam coupling between different seeded spatial modes in the atomic vapor and can ruin squeezing at frequencies on the order of the atomic linewidth and below. This complicates some forms of quantum imaging using these systems. Here we show that seeding the 4WM process with skew rays can eliminate the excess noise caused by 2-beam coupling. To avoid 2-beam coupling in bright, seeded images, it is important to re-image the object in the gain medium, instead of focussing through it.