Understanding and modeling dynamic inter-stock dependencies is crucial for effective investment decisions. While graph-based methods have demonstrated potential in capturing stock relationships, existing approaches based on static or discrete-time dynamic graphs fail to address the continuous evolution of market dynamics. In this work, we propose CFC-GMixer, a novel continuous-time graph framework that synergizes multi-scale temporal features with graph-based continuous-time modeling. Our architecture consists of two main components: (1) a Time Mixer module that extracts multi-scale temporal features to capture market dynamics at different time horizons, enabling effective graph relationship modeling, (2) a unified CFC-GCN module that leverages these hierarchical features to simultaneously model adaptive inter-stock relationships and their continuous temporal evolution through Graph Convolutional Networks (GCN) and Closed-Form Continuous-Time Networks (CFC). Experiments on three real-world datasets demonstrate that CFC-GMixer outperforms state-of-the-art methods in both accuracy and precision.

Stock trend prediction is a pivotal task in quantitative investment, yet the complex and dynamic temporal correlations in stock data pose significant challenges. Traditional methods often fail to account for concept drifts, limiting their adaptability to diverse and evolving data distributions. To address these limitations, we propose the Adaptive Stock Trend Prediction Framework with a Variable Structure (ASTP-VS). To handle inner-task distribution shifts, ASTP-VS introduces a Multi-Head Data Adapter Based on Cross-Attention (MDAC), enabling the model to capture latent information and reduce such differences by identifying common characteristics between the data. To handle inter-task distribution shifts, ASTP-VS introduces a Variable Predictive Model Structure (VS), enabling the model to dynamically adapt to significant changes in data distributions. Additionally, ASTP-VS leverages meta-learning by treating MDAC and VS as meta-learners, pretraining them for each incremental task to ensure effective initialization tailored to current data. Extensive experiments on two real-world datasets demonstrate that ASTP-VS outperforms six baseline models, showcasing its robustness and effectiveness in stock trend prediction.

The generalized sparse iterative covariance-based estimation ($q$-SPICE) algorithm has significant capability to enhance angular resolution in scanning radar applications. However, two drawbacks remain: one is that it focuses solely on the azimuth dimension and ignores the pitch dimension, and the other is that its computational complexity grows exponentially with increasing data size. To address these, a 2D weighted$q$-SPICE (W$q$-SPICE) is proposed in this letter, which enables rapid azimuth-pitch sparse reconstruction of the scanning radar. The proposed algorithm is an extension of$q$-SPICE within the framework of weighted SPICE (WSPICE), and it is also a generalized version of the recent 2D WSPICE algorithm. Moreover, a selection criterion for$q$is provided. Experiment results based on simulated and real radar data illustrate the superiority of the proposed method over its counterparts in the task of 2D resolution enhancement for the scanning radar.

Inner product arguments (IPA) are arguments of knowledge that two committed vectors satisfy an inner product relation. With the recursive proof technique by Bootle et al. 2016, the size of IPA proofs only grows logarithmically in the length of the vectors, without a trusted setup. The succinct proof makes IPAs well suited for blockchain applications. However, current IPA can only handle a vector with length a power of 2, which limits the application of the argument. One direct solution is to pad the vectors with zeros, which incurs additional overhead. We propose Springproofs, a new framework deriving IPAs from many existing IPA schemes. Springproofs are natively compatible with vectors of arbitrary length. With a novel recursive compression structure, Springproofs achieve the same proof size as the original IPA but with more efficient computation. In particular, we instantiate Springproofs with Bulletproofs and find the optimal recursive structure for the IPA. First, we experimentally show that Springproofs are almost twice as fast as Bulletproofs for range proof, when the vector length is slightly larger than a power of 2. Afterwards, we incorporate the Springproofs into Monero, a popular cryptocurrency supporting privacy in transactions, revealing that the Springproofs based Monero outperforms Bulletproofs based Monero both in generating and verifying transactions. Moreover, we apply the Springproofs to the general arithmetic circuit, including SHA256, Merkle tree, and typical statistics, the performances on which are better than the performances by using Bulletproofs. Interestingly, Springproofs increase the range of parameters on which the performance of Bulletproofs exceeds that of Groth16, meanwhile naturally inherit the advantages of Bulletproofs, e.g., without initial trusted setup, aggregation, and batch verification. As a result, Springproofs have many promising applications, including confidential transactions in cryptocurrency and privacy computing for specific arithmetic circuits in smart contracts.

Modern software systems, either system or application software, are increasingly being developed on top of virtualized software platforms. They may simply intend to execute on virtual machines or they may be expected to port to physical machines eventually. In either case, the devices, virtual or silicon, in the target virtual or physical machines are expected to conform to the specifications based on which the software systems have been developed. Non-conformance of these devices to the specifications can cause catastrophic failures of the software systems. In this article, we propose a mutation-based framework for effective and efficient conformance checking between virtual/silicon device implementations and their specifications. Based on our defined mutation operators, device specifications can be automatically instrumented with weak mutant-killing constraints to model potential erroneous device behaviors. To kill all feasible mutants, our approach adopts a cooperative symbolic execution mechanism that can efficiently automate the test case generation and conformance checking for virtual/silicon devices. By symbolically executing the instrumented specifications with virtual/silicon device traces obtained from the cooperative execution, our method can accurately measure whether the designs have been sufficiently validated and report the inconsistencies between device specifications and implementations. Comprehensive experiments on two industrial network adapters and their virtual devices demonstrate the effectiveness of our proposed approach in conformance checking for both virtual and silicon devices.

SQL injection attack (SQLIA) is among the most common security threats to web-based services that are deployed on cloud. By exploiting web software vulnerabilities, SQL injection attackers can run arbitrary malicious code on target databases to acquire or compromise sensitive data. Although web application firewalls (WAFs) are offered by most cloud service providers, tenants are reluctant to pay for them, since there are few approaches that can report accurate SQLIA statistics for their deployed services. Traditional WAFs focus on blocking suspicious SQL requests. Few of them can accurately decide whether an attack is really harmful and quickly answer how severe the attack is. To raise the tenants' awareness of the seriousness of SQLIAs, in this paper, we introduce a novel traffic-based SQLIA detection and vulnerability analysis framework named DIAVA, which can proactively send warnings to tenants promptly. By analyzing the bidirectional network traffic of SQL operations and applying our proposed multilevel regular expression model, DIAVA can accurately identify successful SQLIAs among all the suspects. Meanwhile, the severity of such SQLIAs and the vulnerabilities of the corresponding leaked data can be quickly evaluated by DIAVA based on its GPU-based dictionary attack analysis engine. Experimental results show that DIAVA not only outperforms state-of-the-art WAFs in detecting SQLAs from the perspectives of precision and recall, but also enables real-time vulnerability evaluation of leaked data caused by SQL injection.

In this paper, we proposed a flexible video coding scheme for content-based video storage and retrieval. The proposed flexible video coding scheme applies shot clustering on the uncompressed video in the first pass and encodes the video in multi-clusters based on different video contents in the second pass. The compressed video can be flexibly decoding to meet the different requirements such as video indexing, video summary and structure extraction without fully decoding and re-analyzing. Also the flexible encoder can achieve global coding optimization by encoding similarity video shots continuously. It is very important to reduce the process complexity at the decoder and provide the flexible video content access on the compressed videos in many off-line video storage and retrieval applications. The experiments on sport and film videos illustrate the coding performance increase and the effect of the proposed coding scheme for content-based video storage.

In this paper, a new motion-compensated interpolation method (MCI) for frame rate up-conversion is proposed. In conventional MCI algorithms using transmitted motion vectors (MVs), the block artifacts are caused by the unreliable MVs from the encoder. In our proposed method, motion vectors correction (MVC) is applied on the transmitted MVs before interpolation considering the spatial correlation. In consideration of the temporal correlation, generative model is used to generate the temporal-predicted MVs. Then position prediction method is applied on the transmitted MVs and the temporal-predicted MVs separately to predict the positions the interpolated blocks really move to, which makes the MVs used for interpolation nearer to the true motion. The final MVs are selected from the two candidates of position prediction by the minimum sum of absolute difference (SAD). Applied to the H.264 decoder, our proposed method achieves significant increase on PSNR and obvious decrease of the block artifacts.

This paper proposed a novel video shot clustering algorithm using spectral method by joint modeling of inter and intra shot. Gauss Mixture Model (GMM) is used for probabilistic space-time modeling of intra-shot pixels. The spectral clustering method is applied on the GMM parameters. The problem of automatic model selection is currently an open issue for spectral method. Here we propose a novel automatic model selection based on joint inter-intra model optimization to achieve the global optimization in both model parameters and cluster numbers. We compare the proposed method with the conventional spectral method for sports video clustering. The simulation results show more accuracy on how many clusters and clustering results.