BenchCouncil Transactions on Benchmarks, Standards and Evaluations最新文献

This study presents a high-accuracy deep learning-based decision support system for kidney cancer detection. The research utilizes a relatively large dataset of 10,000 CT images, including both healthy and tumour-detected kidney scans. After data preprocessing and optimization, various deep learning models were evaluated, with DenseNet-201 emerging as the top performer, achieving an accuracy of 99.75 %. The study compares multiple deep learning architectures, including AlexNet, EfficientNet, Darknet-53, Xception, and DenseNet-201, across different learning rates. Performance metrics such as accuracy, precision, sensitivity, F1-score, and specificity are analysed using confusion matrices. The proposed system outperforms different deep learning networks, demonstrating superior accuracy in kidney cancer detection. The improvement is attributed to effective data engineering and hyperparameter optimization of the deep learning networks. This research contributes to the field of medical image analysis by providing a robust decision support tool for early and rapid diagnosis of kidney cancer. The high accuracy and efficiency of the proposed system make it a promising aid for healthcare professionals in clinical settings.

The study investigates the impact of opportunistic maintenance (OM) optimization on manufacturing industries, especially in Bangladesh, to reduce maintenance costs. To that end, OM strategies have been proposed and optimized for multi-unit manufacturing systems, whereas most of the existing research is for single- or two-unit systems. OM strategies in this research cover one of the three policies: preventive replacement, preventive repair, and a two-level maintenance approach. The proposed two-level maintenance approach is a combination of lower-level maintenance, known as preventive repair, and higher-level maintenance, known as preventive replacement. Simulation optimization (SO) techniques using Python were utilized to evaluate the strategies. Historical data from two of Bangladesh's most promising and significant sectors, the footwear and railway industries, was used as the case study. Compared to the currently utilized corrective maintenance approach, the two-level maintenance approach is the most effective for both case studies, demonstrating cost savings of 16.9 % and 22.4 % for the footwear and railway industries, respectively. This study reveals that manufacturing industries can achieve significant cost savings by implementing the proposed OM strategies, a concept that has yet to be explored in developing countries like Bangladesh. However, the study considered the proposed approaches for major components of the system, and more significant benefits can be achieved if it is possible to apply them to all critical components of the system.

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 has been widely used in multiple scenarios including software vulnerability search, security patch analysis, malware detection, code clone detection, etc. With the help of deep learning, the BCSD techniques have achieved high accuracy in their evaluation. However, on the one hand, their high accuracy has become indistinguishable due to the lack of a standard dataset, thus being unable to reveal their abilities. On the other hand, since binary code can be easily changed, it is essential to gain a holistic understanding of the underlying transformations including default optimization options, non-default optimization options, and commonly used code obfuscations, thus assessing their impact on the accuracy and adaptability of the BCSD technique. This paper presents our observations regarding the diversity of BCSD datasets and proposes a comprehensive dataset for the BCSD technique. We employ and present detailed evaluation results of various BCSD works, applying different classifications for different types of BCSD tasks, including pure function pairing and vulnerable code detection. Our results show that most BCSD works are capable of adopting default compiler options but are unsatisfactory when facing non-default compiler options and code obfuscation. We take a layered perspective on the BCSD task and point to opportunities for future optimizations in the technologies we consider.

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 effectively, failing to ensure optimal end-to-end performance due to the cost of LA operators and data conversion. This underscores the demand for a system capable of seamlessly integrating RA and LA while delivering robust end-to-end performance. This paper proposes TensorTable, a tensor system that extends PyTorch to enable mixed RA and LA pipelines. We propose TensorTable as the unified data representation, storing data in a tensor format to prioritize the performance of LA operators and reduce data conversion costs. Relational tables from RA, as well as vectors, matrices, and tensors from LA, can be seamlessly converted into TensorTables. Additionally, we provide TensorTable-based implementations for RA operators and build a system that supports mixed LA and RA pipelines. We implement TensorTable on top of PyTorch, achieving comparable performance for both RA and LA operators, particularly on small datasets. TensorTable achieves a 1.15x-5.63x speedup for mixed pipelines, compared with state-of-the-art frameworks—AIDA and RMA.

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, theories, and methodologies. This lack of agreement has significant consequences. This article aims to formally introduce the discipline of evaluatology, which encompasses the science and engineering of evaluation. We propose a universal framework for evaluation, encompassing concepts, terminologies, theories, and methodologies that can be applied across various disciplines, if not all disciplines.

Our research reveals that the essence of evaluation lies in conducting experiments that intentionally apply a well-defined evaluation condition to individuals or systems under scrutiny, which we refer to as the subjects. This process allows for the creation of an evaluation system or model. By measuring and/or testing this evaluation system or model, we can infer the impact of different subjects. Derived from the essence of evaluation, we propose five axioms focusing on key aspects of evaluation outcomes as the foundational evaluation theory. These axioms serve as the bedrock upon which we build universal evaluation theories and methodologies. When evaluating a single subject, it is crucial to create evaluation conditions with different levels of equivalency. By applying these conditions to diverse subjects, we can establish reference evaluation models. These models allow us to alter a single independent variable at a time while keeping all other variables as controls. When evaluating complex scenarios, the key lies in establishing a series of evaluation models that maintain transitivity. Building upon the science of evaluation, we propose a formal definition of a benchmark as a simplified and sampled evaluation condition that guarantees different levels of equivalency. This concept serves as the cornerstone for a universal benchmark-based engineering approach to evaluation across various disciplines, which we refer to as benchmarkology.

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 usage patterns of real-world systems or applications, plays essential role for efficient resource management in these centers. Data center traces, rich in information about workload execution and resource utilization, are thus ideal data for workload generation. Traditional traces provide detailed temporal resource usage data to enable fine-grained workload generation. However, modern data centers tend to favor tracing statistical metrics to reduce overhead. Therefore the accurate reconstruction of temporal resource consumption without detailed, temporized trace information become a major challenge for trace-based workload generation. To address this challenge, we propose STWGEN, a novel method that leverages statistical trace data for workload generation. STWGEN is specifically designed to generate the batch task workloads based on Alibaba trace. STWGEN contains two key components: a suite of C program-based flexible workload building blocks and a heuristic strategy to assemble building blocks for workload generation. Both components are carefully designed to reproduce synthetic batch tasks that closely replicate the observed resource usage patterns in a representative data center. Experimental results demonstrate that STWGEN outperforms state-of-the-art workload generation methods as it emulates workload-level and machine-level resource usage in much higher accuracy.