Using a machine learning (ML) model as a core component in database knob tuning has demonstrated remarkable advancements in recent years. However, a model that optimizes both categorical and continuous values in the same way may not guarantee efficiency and effectiveness in knob tuning. This is due to the fact that the usual assumption of a differentiable input space for efficient exploration of continuous spaces does not hold true in categorical spaces. Moreover, the inherent complexity of interdependences among knobs and the high-dimensionality of the configuration space compound the challenges of tuning. In this paper, we propose TopTune, which employs tailored optimization for continuous and categorical knobs, to achieve accelerated tuning efficiency and improved tuning performance. Specifically, we decompose the configuration space into two orthogonal subspaces: categorical and continuous spaces. Subsequently, we employ Bayesian optimization models, i.e., SMAC and GP to explore the categorical and continuous subspaces, respectively. These two models will alternately explore the two spaces with the proposed communication mechanism to ensure TopTune can capture the dependence between continuous and categorical knobs. Furthermore, to balance efficiency and accuracy, we utilize a knob-dimensional projection strategy to reduce the exploration domain by embedding the high-dimension configuration space into a lower-dimensional proxy space. In addition, we implement batch Bayesian optimization technology, which enables parallel knob evaluation while balancing exploration and exploitation. We evaluate TopTune under different benchmarks (SYSBENCH, TPC-C, and JOB), metrics (throughput and latency), and DBMSs (MySQL and Dameng). Extensive experiments demonstrate that TopTune identifies better configurations in up to approximately 12.2× less time while achieving a 10.7% improvement in throughput compared to state-of-the-art methods.

Abstract Hashing-based unsupervised cross-modal retrieval has gained significant attention in the big data management community due to its low storage overhead and rapid retrieval speed. However, current methods often lack effective alignment strategies to reduce the modality gap. They also fail to explore the latent structural information of the training data for accurate relationship learning, resulting in sub-optimal cross-modal retrieval performance. To tackle these challenges, we propose a novel unsupervised cross-modal hashing method called G raph C ontrastive-and- R econstructive H ashing ( GCRH ). Specifically, GCRH first performs global graph contrastive learning , which involves both intra-modal and inter-modal pairs. This facilitates the learning of more discriminative hash codes through intra-modal discrimination and inter-modal alignment objectives. To further bridge the modality gap, GCRH conducts local graph reconstruction using GCN-based decoders to reconstruct the original features of one modality from the hash codes of another. The integration of contrastive-and-reconstructive learning with graph structural information enables GCRH to generate high-quality hash codes that are both well-aligned and discriminative. Extensive experiments on three benchmark datasets substantiate the superior cross-modal retrieval performance of GCRH .

Electroencephalography (EEG) data is easily contaminated by various sources, significantly affecting subsequent analyses in neuroscience and clinical applications. Therefore, effective artifact removal is a key step in EEG preprocessing. While current deep learning methods have demonstrated notable efficacy in EEG denoising, single-channel approaches primarily focus on temporal features and neglect inter-channel correlations. Meanwhile, multi-channel methods mainly prioritize spatial features but often overlook the unique temporal dependencies of individual channels. A common limitation of both single-channel and multi-channel methods is their strict requirements on the input channel setting, which restricts their practical applicability. To address these issues, we design a flexible architecture named Artifact removal Spatio-Temporal Integration Network (ASTI-Net), a dual-branch denoising model capable of handling arbitrary EEG channel settings. ASTI-Net utilizes spatio-temporal attention weighting with dual branches that capture inter-channel spatial characteristics and intra-channel temporal dependencies. Its architecture incorporates deformable convolutional operations and channel-wise temporal processing, accommodating varying numbers of EEG channels and enhancing applicability across diverse clinical and research settings. By integrating features from both branches through a fusion reconstruction module, ASTI-Net effectively restores clean multi-channel EEG. Extensive evaluation on two semi-simulated datasets, along with qualitative assessment on real task-state EEG data, validates that ASTI-Net outperforms existing artifact removal methods.

Implementing cross-modal hashing between 2D images and 3D point-cloud data is a growing concern in real-world retrieval systems. Simply applying existing cross-modal approaches to this new task fails to adequately capture latent multi-modal semantics and effectively bridge the modality gap between 2D and 3D. To address these issues without relying on hand-crafted labels, we propose contrastive masked autoencoders based self-supervised hashing (CMAH) for retrieval between images and point-cloud data. We start by contrasting 2D-3D pairs and explicitly constraining them into a joint Hamming space. This contrastive learning process ensures robust discriminability for the generated hash codes and effectively reduces the modality gap. Moreover, we utilize multi-modal auto-encoders to enhance the model’s understanding of multi-modal semantics. By completing the masked image/point-cloud data modeling task, the model is encouraged to capture more localized clues. In addition, the proposed multi-modal fusion block facilitates fine-grained interactions among different modalities. Extensive experiments on three public datasets demonstrate that the proposed CMAH significantly outperforms all baseline methods.

Compressing videos into binary codes can improve retrieval speed and reduce storage overhead. However, learning accurate hash codes for video retrieval can be challenging due to high local redundancy and complex global dependencies between video frames, especially in the absence of labels. Existing self-supervised video hashing methods have been effective in designing expressive temporal encoders, but have not fully utilized the temporal dynamics and spatial appearance of videos due to less challenging and unreliable learning tasks. To address these challenges, we begin by utilizing the contrastive learning task to capture global spatio-temporal information of videos for hashing. With the aid of our designed augmentation strategies, which focus on spatial and temporal variations to create positive pairs, the learning framework can generate hash codes that are invariant to motion, scale, and viewpoint. Furthermore, we incorporate two collaborative learning tasks, i.e., frame order verification and scene change regularization, to capture local spatio-temporal details within video frames, thereby enhancing the perception of temporal structure and the modeling of spatio-temporal relationships. Our proposed Contrastive Hashing with Global-Local Spatio-temporal Ibnformation (CHAIN) outperforms state-of-the-art self-supervised video hashing methods on four video benchmark datasets. Our codes will be released.

Automatic emotion recognition based on electroencephalogram (EEG) has attracted rapidly increasing interests. Due to large inter-subject variabilities, subject-independent emotion recognition faces great challenges. Recently, domain adaptation methods have been successfully applied in this field due to their ability to align features from different subjects. However, since EEG signals corresponding to some emotions have similar oscillation patterns, they are often confused and aligned to the wrong categories, which limits the generalization ability of the model across subjects. Besides, almost all methods only support offline applications, which require collecting a large number of samples of new subjects. To achieve online recognition, a simpler model is needed. In this paper, a novel Gated Recurrent Unit-Minimum Class Confusion (GRU-MCC) model is proposed. Specifically, a simple feature extractor based on gated recurrent unit (GRU) is firstly applied to model the spatial dependence of multiple electrodes and obtain high-level discriminative features. Then, during training, minimum class confusion (MCC) loss is introduced to reduce the confusion between the correct and ambiguous classes for the target subject and increase the transfer gains. We conduct both offline and online experiments on two public datasets: SEED and MPED. The results indicate that our method can obtain the superior performance.

Software-defined networking (SDN) eases network management by centralizing the control plane and separating it from the data plane. The separation of planes in SDN, however, introduces new vulnerabilities in SDN networks, since the difference in processing packets at each plane allows an adversary to fingerprint the network's packet-forwarding logic. In this paper, we study the feasibility of fingerprinting the controller-switch interactions by a remote adversary, whose aim is to acquire knowledge about specific flow rules that are installed at the switches. This knowledge empowers the adversary with a better understanding of the network's packet-forwarding logic and exposes the network to a number of threats. In this paper, we collect measurements from hosts located across the globe using a realistic SDN network comprising of OpenFlow hardware and software switches. We show that, by leveraging information from the RTT and packet-pair dispersion of the exchanged packets, fingerprinting attacks on SDN networks succeed with overwhelming probability. We additionally show that these attacks are not restricted to active adversaries, but can also be mounted by passive adversaries that only monitor traffic exchanged with the SDN network. Finally, we discuss the implications of these attacks on the security of SDN networks, and we present and evaluate an efficient countermeasure to strengthen SDN networks against fingerprinting. Our results demonstrate the effectiveness of our countermeasure in deterring fingerprinting attacks on SDN networks.

In this paper, we study the feasibility of fingerprinting of controller-switch interactions in SDN networks by a remote adversary whose aim is to acquire knowledge about specific flow rules that are installed at the switches. This knowledge empowers the adversary with a better understanding of the network's packet-forwarding logic and exposes the network to a number of threats. In our study, we collect measurements from hosts located across the globe using a realistic SDN network comprising of OpenFlow hardware switches. We show that, by leveraging information from the RTT and packet-pair dispersion of the exchanged packets, fingerprinting attacks on SDN networks succeed with overwhelming probability. We also show that these attacks are not restricted to active adversaries, but can be equally mounted by passive adversaries that only monitor traffic exchanged with the SDN network. Finally, we sketch an efficient countermeasure to strengthen SDN networks against fingerprinting.

Sharing network resources with user groups, divisions, or even other companies in software defined networking promises better network utilization. Resource sharing is effectively realized by empowering these tenants at the control plane with permissions for administrating network components. However, since the network resources at the data plane are shared and different tenants can have competing objectives, mechanisms are needed to protect the network resources from unauthorized access. In this paper, we propose mechanisms that focus on protecting the network flows, which are determined by the entries installed in the flow tables of the shared switches. To this end, we present an access control scheme, based on the OpenFlow model, for accessing the switches' flow tables and their entries. Our scheme accounts for various security requirements in multi-tenant networks, including requirements on sharing flow table entries for handling network flows, and the resolution of conflicts originating from the reconfiguration of network components.

One common way to search and access information available in the Internet is via a Web browser. When clicking on a Web page, the user expects that the page gets rendered quickly, otherwise he will lose interest and may abort the page load. The causes for a Webpage to load slowly are multiple and not easy to comprehend for an end-user. In this paper, we present FireLog, a plugin for the Firefox Web browser that relies on passive measurements during users' browsing, and helps identify why a web page loads slowly. We present details of our methodology and illustrate it in a case study with real users.