332 research outputs found
Can we do that simpler? Simple, Efficient, High-Quality Evaluation Metrics for NLG
We explore efficient evaluation metrics for Natural Language Generation
(NLG). To implement efficient metrics, we replace (i) computation-heavy
transformers in metrics such as BERTScore, MoverScore, BARTScore, XMoverScore,
etc. with lighter versions (such as distilled ones) and (ii) cubic inference
time alignment algorithms such as Word Mover Distance with linear and quadratic
approximations. We consider six evaluation metrics (both monolingual and
multilingual), assessed on three different machine translation datasets, and 16
light-weight transformers as replacement. We find, among others, that (a)
TinyBERT shows best quality-efficiency tradeoff for semantic similarity metrics
of the BERTScore family, retaining 97\% quality and being 5x faster at
inference time on average, (b) there is a large difference in speed-ups on CPU
vs. GPU (much higher speed-ups on CPU), and (c) WMD approximations yield no
efficiency gains but lead to a substantial drop in quality on 2 out of 3
datasets we examine.Comment: Work in progres
E-commerce quality evaluation metrics: a sharia compliance approach
There is a growing concern and need for Sharia compliance e-commerce quality metrics to evaluate policies and practice that will ensure that Sharia principles are adhered and user’s desirable characteristics are provided. Therefore, extant conventional e-commerce quality metrics from the literature are critically reviewed. Furthermore, an exploratory study involving Sharia compliance experts was conducted, revealing adherence to the maqasid Sharia and the principles of Islamic law of contract as the fundamental Sharia compliance requirements for e-commerce systems. Hence, we integrated the relevant conventional e-commerce quality metrics and the Sharia compliance requirements deduced to propose a set of Sharia compliance e-commerce quality metrics based Information, systems and service quality dimensions. The Sharia compliance e-commerce information quality metrics proffered are accuracy, relevance, timeliness, understandability, completeness, and currency. System quality metrics involves being devoid of riba, devoid of gharar, devoid of haram objects, ethical advertisements, usability, reliability, functionality, customization, security, and privacy. While Service quality metrics are Sharia compliance assurance, khiyar policy, responsiveness, empathy, follow-up services, and the effectiveness of online support capabilities. Developing and evaluating Sharia compliance e-commerce quality based on the proposed metrics is envisaged to foster Muslim consumer trust, use and user satisfaction with e-commerce systems
A Non-Reference Evaluation of Underwater Image Enhancement Methods Using a New Underwater Image Dataset
The rise of vision-based environmental, marine, and oceanic exploration research highlights the need for supporting underwater image enhancement techniques to help mitigate water effects on images such as blurriness, low color contrast, and poor quality. This paper presents an evaluation of common underwater image enhancement techniques using a new underwater image dataset. The collected dataset is comprised of 100 images of aquatic plants taken at a shallow depth of up to three meters from three different locations in the Great Lake Superior, USA, via a Remotely Operated Vehicle (ROV) equipped with a high-definition RGB camera. In particular, we use our dataset to benchmark nine state-of-the-art image enhancement models at three different depths using a set of common non-reference image quality evaluation metrics. Then we provide a comparative analysis of the performance of the selected models at different depths and highlight the most prevalent ones. The obtained results show that the selected image enhancement models are capable of producing considerably better-quality images with some models performing better than others at certain depths
Load-independent characterization of trade-off fronts for operational amplifiers
Abstract—In emerging design methodologies for analog integrated circuits, the use of performance trade-off fronts, also known as Pareto fronts, is a keystone to overcome the limitations of the traditional top-down methodologies. However, most techniques reported so far to generate the front neglect the effect of the surrounding circuitry (such as the output load impedance) on the Pareto-front, thereby making it only valid for the context where the front was generated. This strongly limits its use in hierarchical analog synthesis because of the heavy dependence of key performances on the surrounding circuitry, but, more importantly, because this circuitry remains unknown until the synthesis process. We will address this problem by proposing a new technique to generate the trade-off fronts that is independent of the load that the circuit has to drive. This idea is exploited for a commonly used circuit, the operational amplifier, and experimental results show that this is a promising approach to solve the issue
Multi-modal Image Processing based on Coupled Dictionary Learning
In real-world scenarios, many data processing problems often involve
heterogeneous images associated with different imaging modalities. Since these
multimodal images originate from the same phenomenon, it is realistic to assume
that they share common attributes or characteristics. In this paper, we propose
a multi-modal image processing framework based on coupled dictionary learning
to capture similarities and disparities between different image modalities. In
particular, our framework can capture favorable structure similarities across
different image modalities such as edges, corners, and other elementary
primitives in a learned sparse transform domain, instead of the original pixel
domain, that can be used to improve a number of image processing tasks such as
denoising, inpainting, or super-resolution. Practical experiments demonstrate
that incorporating multimodal information using our framework brings notable
benefits.Comment: SPAWC 2018, 19th IEEE International Workshop On Signal Processing
Advances In Wireless Communication
Multi-Modal Medical Image Fusion using Multi-Resolution Discrete Sine Transform
Quick advancement in high innovation and current medical instrumentations, medical imaging has turned into a
fundamental part in many applications such as in diagnosis, research and treatment. Images from multimodal imaging devices
usually provide complementary and sometime conflicting information. Information from one image may not be adequate to
give exact clinical prerequisites to the specialist or doctor. Of-late, Multi-Model medical image fusion playing a challenging
role in current research areas. There are many theories and techniques developed to fuse the multimodal images by
researchers. In this paper, introducing a new algorithm called as Multi Resolution Discrete Sine Transform which is used for
Multi-Model image fusion in medical applications. Performance and evaluation of this algorithm is presented. The main
intention of this paper is to apply DST which is easy to understand and demonstrated method to process image fusion
techniques. The proposed MDST based image fusion algorithm performance is compared with that of the well-known
wavelet based image fusion algorithm. From the results it is observed that the performance of image fusion using MDST is
almost similar to that of wavelet based image fusion algorithm. The proposed MDST based image fusion techniques are
computationally very simple and it is suitable. The proposed MDST based image fusion algorithms are computationally,
exceptionally basic and it is appropriate for real time medical diagnosis applications
- …