Fundamental concepts and methodologies in evaluatology
September 2024
While I have authored three articles introducing Evaluatology, a novel discipline that encompasses the science and engineering of evaluation across various domains, I have struggled to fully depict...
MultiPoint: Enabling scalable pre-silicon performance evaluation for multi-task workloads
September 2024
With the core numbers integrated within single processors growing and the fast development of cloud computing, performance evaluation for multi-core systems is increasingly crucial. It is typically...
BinCodex: A comprehensive and multi-level dataset for evaluating binary code similarity detection techniques
June 2024
The binary code similarity detection (BCSD) technique can quantitatively measure the differences between two given binaries and give matching results at predefined granularity (e.g., function), and...
Analyzing the impact of opportunistic maintenance optimization on manufacturing industries in Bangladesh: An empirical study
June 2024
•The study investigates the impact of opportunistic maintenance (OM) optimization on manufacturing industries to reduce maintenance costs.•Three OM strategies: preventive replacement, preventive repair,...
Enhanced deep learning based decision support system for kidney tumour detection
June 2024
•The study introduces a deep learning-based decision support system for kidney cancer detection that achieved an accuracy of 99.75 % using DenseNet-201 and utilizes a dataset of 10,000 CT images, including...
A short summary of evaluatology: The science and engineering of evaluation
June 2024
Evaluation is a crucial aspect of human existence and plays a vital role in each field. However, it is often approached in an empirical and ad-hoc manner, lacking consensus on universal concepts, terminologies,...
Evaluatology: The science and engineering of evaluation
March 2024
Evaluation is a crucial aspect of human existence and plays a vital role in each field. However, it is often approached in an empirical and ad-hoc manner, lacking consensus on universal concepts, terminologies,...
An approach to workload generation for modern data centers: A view from Alibaba trace
March 2024
Modern data centers provide the foundational infrastructure of cloud computing. Workload generation, which involves simulating or constructing tasks and transactions to replicate the actual resource...
TensorTable: Extending PyTorch for mixed relational and linear algebra pipelines
March 2024
The mixed relational algebra (RA) and linear algebra (LA) pipelines have become increasingly common in recent years. However, contemporary widely used frameworks struggle to support both RA and LA operators...
AIGCBench: Comprehensive evaluation of image-to-video content generated by AI
December 2023
The burgeoning field of Artificial Intelligence Generated Content (AIGC) is witnessing rapid advancements, particularly in video generation. This paper introduces AIGCBench, a pioneering comprehensive...
CloudAISim: A toolkit for modelling and simulation of modern applications in AI-driven cloud computing environments
December 2023
There is a very significant knowledge gap between Artificial Intelligence (AI) and a multitude of industries that exist in today’s modern world. This is primarily attributable to the limited availability...
Characterizing and understanding deep neural network batching systems on GPUs
December 2023
As neural network inference demands are ever-increasing in intelligent applications, the performance optimization of model serving becomes a challenging problem. Dynamic batching is an important feature...
Benchmarking ChatGPT for prototyping theories: Experimental studies using the technology acceptance model
December 2023
•We explore the paradigm of leveraging ChatGPT as a bench tool for theory prototyping in business research.•We conducted two experimental studies using the classical technology acceptance model (TAM)...
A pluggable single-image super-resolution algorithm based on second-order gradient loss
December 2023
Convolutional neural networks for single-image super-resolution have been widely used with great success. However, most of these methods use L1 loss to guide network optimization, resulting in blurry...