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Learning a good similarity measure for large-scale high-dimensional data is a crucial task in machine learning applications, yet it poses a significant challenge. Distributed minibatch stochastic gradient descent (SGD) serves as an efficient optimization ...

The emergence of <italic>mobile edge computing</italic> (MEC) in distributed systems has sparked increased attention toward edge data management. A conflict arises from the disparity between limited edge resources and the continuously expanding data ...

With its advantages in ensuring low data retrieval latency and reducing backhaul network traffic, edge computing is becoming a backbone solution for many latency-sensitive applications. An increasingly large number of data is being generated at the edge, ...

In the edge computing environment, app vendors can distribute popular data from the cloud to edge servers to provide low-latency data retrieval. A key problem is how to distribute these data from the cloud to edge servers cost-effectively. Under current ...

Standing as a foundational element within blockchain systems, the <italic>Byzantine Fault Tolerant</italic> (BFT) consensus has garnered significant attention over the past decade. The introduction of a <italic>Directed Acyclic Directed</italic> (DAG) ...

Driven by the substantial profits, the evolution of Portable Executable (PE) malware has posed persistent threats. PE malware classification has been an important research field, and numerous classification methods have been proposed. With the development ...

Emerging resistive random-access memory (ReRAM) based processing-in-memory (PIM) accelerators have been increasingly explored in recent years because they can efficiently perform in-situ matrix-vector multiplication (MVM) operations involved in a wide ...

Oftentimes fault candidates produced by logic diagnosis are too many to effectively guide the follow-on failure analysis. In this work, we propose a novel two-stage fast screening method to sift through a large of candidates in the fault callout outputted ...

Protecting system observability records (logs) from compromised OSs has gained significant traction in recent times, with several note-worthy approaches proposed. Unfortunately, none of the proposed approaches achieve high performance with tiny log ...

Graph processing is pivotal in deriving insights from complex data structures but faces performance limitations due to the irregular nature of graphs. Traditional general-purpose processors often struggle with low instruction-level parallelism and energy ...

Graph neural networks (GNNs) have gained traction and have been applied to various graph-based data analysis tasks due to their high performance. However, a major concern is their robustness, particularly when faced with graph data that has been ...

Container-based virtualization is increasingly popular in cloud computing due to its efficiency and flexibility. Isolation is a fundamental property of containers and weak isolation could cause significant performance degradation and security ...

There are usually a large number of concurrent graph queries (CGQs) requirements in streaming graphs. However, existing graph processing systems mainly optimize a single graph query in streaming graphs or CGQs in static graphs. They have a large number of ...

Graph Neural Networks (GNNs) have achieved remarkable successes in various graph-based learning tasks, thanks to their ability to leverage advanced GPUs. However, GNNs currently face challenges arising from the concurrent use of advanced Tensor Cores (TCs)...

The Sparse General Matrix-Matrix multiplication (SpGEMM) is a fundamental component for many applications, such as algebraic multigrid methods (AMG), graphic processing, and deep learning. However, the unbearable latency of computing high-dimensional, ...

Graph processing has become a central concern for many real-world applications and is well-known for its low compute-to-communication ratios and poor data locality. By integrating computing logic into memory, resistive random access memory (ReRAM) tackles ...

Queries on linked data structures, such as trees and graphs, often suffer from frequent cache misses and significant performance loss due to dependent and random pointer-chasing memory accesses. In this paper, we propose a software-hardware co-designed ...

Cloud-based machine learning services offer significant advantages but also introduce the risk of tampering with cloud-deployed deep neural network (DNN) models. Black-box integrity verification (BIV) allows model owners and end-users to determine if a ...

Android applications (apps) widely use third-party libraries (TPLs) to reuse functionalities and simplify the development process. Unfortunately, these TPLs often suffer from vulnerabilities that attackers can exploit, leading to catastrophic ...

Automatic testing has garnered significant attention and success over the past few decades. Techniques such as unit testing and coverage-guided fuzzing have revealed numerous critical software bugs and vulnerabilities. However, a long-standing, ...