Compute Express Link (CXL) is a promising technology that addresses memory and storage challenges. Despite its advantages, CXL faces performance threats from external interference when coexisting with current memory and storage systems. This interference is under-explored in existing research. To address this, we develop CXL-Interplay, systematically characterizing and analyzing interference from memory and storage systems. To the best of our knowledge, we are the first to characterize CXL interference on real CXL hardware. We also provide reverse-reasoning analysis with performance counters and kernel functions. In the end, we propose and evaluate mitigation solutions.

Machine learning platforms offer paid APIs to enable personalized inference services. However, model extraction attacks greatly threaten their intellectual property rights. Malicious users can create query samples using proxy datasets or generative models to train a clone model. Existing defense approaches usually focus on models that return soft-labels, and cannot effectively handle extracting attacks against hard-label models. In this paper, we propose an adaptive defense framework named DAV, which consists of a malicious query detector and an adaptive perturbation mechanism. Two perturbation strategies can be selected based on the detection results and the malicious query rate within the buffer queue, including accuracy-preserving perturbation and maximum-minimum probability inverse perturbation. Comprehensive experimental results show that DAV can significantly reduce the accuracy of the clone model with little impact on the performance of the victim model and benign queries, no matter whether the returned probabilities are for soft-label or hard-label.

ABSTRACT With the increasingly frequent attacks on software systems, software security is an issue that must be addressed. Within software security, automated detection of software vulnerabilities is an important subject. Most existing vulnerability detectors rely on the features of a single code type (e.g., source code or intermediate representation [IR]), which may lead to both the global features of the code slices and the memory operation information not being captured or considered. In particular, vulnerability detection based on source‐code features cannot usually include some macro or type definition content. In this paper, we propose a vulnerability‐detection method that combines the semantic features of source code and the low level virtual machine (LLVM) IR. Our proposed approach starts by slicing (C/C++) source files using improved slicing techniques to cover more comprehensive code information. It then extracts semantic information from the LLVM IR based on the executable source code. This can enrich the features fed to the artificial neural network (ANN) model for learning. We conducted an experimental evaluation using a publicly‐available dataset of 11,381 C/C++ programs. The experimental results show the vulnerability‐detection accuracy of our proposed method to reach over 96% for code slices generated according to four different slicing criteria. This outperforms most other compared detection methods.

Source code vulnerability detection is of great significance in securing software as well as addressing novel threats, and neural network-based methods have made significant progress in the field of vulnerability detection. However, the widely used neural network-based vulnerability detection methods suffer from the loss of complex structural and semantic information in the source code. To solve this problem, this paper proposes a dual-attention composite pooling-based vulnerability detection method called DA-CPVD for source code, it enhances the feature representation through fully considering the overall contextual information and complex dependencies of source code. The key of DA-CPVD is the use of a dual-attention composite pooling approach to emphasizes the key features more flexibly, thereby forming a comprehensive pooled feature representation. In specific, DA-CPVD utilizes a self-attention mechanism to adaptively assign the weights to features, as well as uses a composite pooling based on attention mechanism aggregation to dynamically adjust the weights of pooling results. The DA-CPVD method is evaluated on three publicly available and widely used datasets, and the experimental result shows that DA-CPVD improves Accuracy, Precision and F1-measure by an average of 11.52%, 28.7%, and 10.45%, and reduces False Positive Rate by an average of 19.15% compared to other existing methods.

As the paradigm of cloud computing, a datacenter accommodates many co-running applications sharing system resources. Although highly concurrent applications improve resource utilization, the resulting resource contention can increase the uncertainty of quality of services (QoS). Previous studies have shown that achieving high resource utilization and high QoS simultaneously is challenging. Moreover, quantifying the intensity of interference across multiple concurrent applications in a datacenter, where applications can be either latency-critical (LC) or best-effort (BE), poses a significant challenge. To address these issues, we propose Ah-Q, which comprises a series of theorems, a quantification theory and a scheduling strategy. Firstly, we present the necessary and sufficient conditions to precisely test whether a datacenter is both QoS guaranteed and high-throughput. We also present and prove a theorem that reveals the relationship between tail latency and throughput. Our theoretical results are insightful and useful for building datacenters that have desirable performance. By applying our theoretical results, datacenter architects can more effectively balance the trade-off between resource utilization and QoS, leading to improved performance for co-running applications. Secondly, we propose the “System Entropy” (E$\rm {_{S}}$) theory to quantitatively and analytically measure interference in a datacenter. Interference arises due to resource scarcity or irrational scheduling, and effective scheduling can alleviate resource scarcity. To assess the effectiveness of a resource scheduling strategy, we introduce the concept of “resource equivalence”. We evaluate various resource scheduling strategies to demonstrate the correctness and effectiveness of the proposed theory. Thirdly, we introduce a new resource scheduling strategy, ARQ, that leverages both isolation and sharing of resources. Our evaluations show that ARQ significantly outperforms state-of-the-art strategies PARTIES and CLITE in reducing the tail latency of LC applications and increasing the IPC of BE applications.

Reconstructing dynamic 3D garment surfaces with open boundaries from monocular videos is an important problem as it provides a practical and low-cost solution for clothes digitization. Recent neural rendering methods achieve high-quality dynamic clothed human reconstruction results from monocular video, but these methods cannot separate the garment surface from the body. Moreover, despite existing garment reconstruction methods based on feature curve representation demonstrating impressive results for garment reconstruction from a single image, they struggle to generate temporally consistent surfaces for the video input. To address the above limitations, in this paper, we formulate this task as an optimization problem of 3D garment feature curves and surface reconstruction from monocular video. We introduce a novel approach, called REC-MV, to jointly optimize the explicit feature curves and the implicit signed distance field (SDF) of the garments. Then the open garment meshes can be extracted via garment template registration in the canonical space. Experiments on multiple casually captured datasets show that our approach outperforms existing methods and can produce high-quality dynamic garment surfaces. The source code is available at https://github.com/GAP-LAB-CUHK-SZ/REC-MV.

Interference among applications frequently occurs in a datacenter and significantly influences the cost-efficiency and the user experience. However, it is challenging for us to quantify the exact intensity of the interference that occurred in the overall system of a datacenter, because there are many concurrent applications in a datacenter, and their type can be either latency-critical (LC) and best-effort (BE). To address this issue, we present the Ah-Q which includes a theory and a strategy.First, we propose the "system entropy" (ES) theory to holistically and analytically quantify the interference in a datacenter to address this vital issue. The interference is caused by the scarcity of resources or/and the irrationality of scheduling. As more appropriate scheduling can compensate for resource scarcity, we derive the concept of "resource equivalence" to quantify the effectiveness of a resource scheduling strategy. We evaluate different resource scheduling strategies to validate the correctness and effectiveness of the proposed theory.Moreover, using the theory to eliminate interference, we propose a new resource scheduling strategy; i.e., ARQ, which dynamically allocates the isolated resources and the shared resources to simultaneously harvest the benefits of isolation and sharing. Our results show that compared to the state-of-the-art strategies (PARTIES and CLITE), ARQ is more effective to reduce the tail latency of the LC applications and to increase the IPC of the BE applications. Compared with PARTIES and CLITE, ARQ increases the yield (the ratio of satisfactory LC applications) by 25% and 20%, respectively; when the load is low, ARQ increases IPC of BE applications by 63.8% and 37.1%, respectively; ARQ reduces ESby 36.4% and 33.3%, respectively. The effectiveness of ARQ has saved resources significantly to achieve the same satisfactory overall user experience.

BACKGROUND: Dystrophinopathy is one of the most common human monogenic diseases which results in Duchenne muscular dystrophy (DMD) and Becker muscular dystrophy (BMD). Mutations in the dystrophin gene are responsible for both DMD and BMD. However, the clinical phenotypes and treatments are quite different in these two muscular dystrophies. Since early diagnosis and treatment results in better clinical outcome in DMD it is essential to establish accurate early diagnosis of DMD to allow efficient management. Previously, the reading-frame rule was used to predict DMD versus BMD. However, there are limitations using this traditional tool. Here, we report a novel molecular method to improve the accuracy of predicting clinical phenotypes in dystrophinopathy. We utilized several additional molecular genetic rules or patterns such as "ambush hypothesis", "hidden stop codons" and "exonic splicing enhancer (ESE)" to predict the expressed clinical phenotypes as DMD versus BMD. RESULTS: A computer software "DMDtoolkit" was developed to visualize the structure and to predict the functional changes of mutated dystrophin protein. It also assists statistical prediction for clinical phenotypes. Using the DMDtoolkit we showed that the accuracy of predicting DMD versus BMD raised about 3% in all types of dystrophin mutations when compared with previous methods. We performed statistical analyses using correlation coefficients, regression coefficients, pedigree graphs, histograms, scatter plots with trend lines, and stem and leaf plots. CONCLUSIONS: We present a novel DMDtoolkit, to improve the accuracy of clinical diagnosis for DMD/BMD. This computer program allows automatic and comprehensive identification of clinical risk and allowing them the benefit of early medication treatments. DMDtoolkit is implemented in Perl and R under the GNU license. This resource is freely available at http://github.com/zhoujp111/DMDtoolkit , and http://www.dmd-registry.com .