Exploring Factors Influencing Adoption of Blockchain in Accounting Applications using Technology–Organization–Environment Framework

Blockchain is one of the most promising technological innovations of recent times, with the potential to change the very way information systems are used by the accounting function. It is however expected to be disruptive and yet to see high adoption rates. Identification of factors influencing the adoption is required to empower the accounting fraternity to harness the full potential of blockchains. This study is one of the first to inductively explore and develop an adoption model for blockchains as well as for accounting applications with theoretical groundings in the Technology-Organization-Environment (TOE) framework, which has been extended with a variable for trust. Triangulation of methods and data sources used in this study contributed to the depth of research and understanding. A comprehensive literature review was first conducted. Its results were further enhanced using the encoding methodology, based on which influencing factors were identified and a model for adoption was developed. A qualitative exploratory study was undertaken next on twelve organizations at the cusp of adoption for accounting applications. Eight significant factors influencing the adoption thus identified are: relative advantage, uncertainty, top management support, technology readiness, industry, regulatory environment, competitive pressure and trust. The study contributes to revealing the relevance of blockchain to accounting while highlighting potential disruptions to enable better evaluation of the technology for adoption. The results may have limited generalizability, which may be overcome through a quantitative study in the future

A reduced-reference perceptual image and video quality metric based on edge preservation

In image and video compression and transmission, it is important to rely on an objective image/video quality metric which accurately represents the subjective quality of processed images and video sequences. In some scenarios, it is also important to evaluate the quality of the received video sequence with minimal reference to the transmitted one. For instance, for quality improvement of video transmission through closed-loop optimisation, the video quality measure can be evaluated at the receiver and provided as feedback information to the system controller. The original image/video sequence-prior to compression and transmission-is not usually available at the receiver side, and it is important to rely at the receiver side on an objective video quality metric that does not need reference or needs minimal reference to the original video sequence. The observation that the human eye is very sensitive to edge and contour information of an image underpins the proposal of our reduced reference (RR) quality metric, which compares edge information between the distorted and the original image. Results highlight that the metric correlates well with subjective observations, also in comparison with commonly used full-reference metrics and with a state-of-the-art RR metric. © 2012 Martini et al

Ovarian leiomyoma with torsion: a case report

We report an uncommon case of primary ovarian leiomyoma with torsion. Leiomyoma of the ovary is an extremely rare benign solid neoplasm of uncertain etiology. Our patient presented with abdominal pain and imaging showed right adnexal solid mass. All tumour markers except LDH were normal. The patient underwent laparotomy proceed right salpingo-ophorectomy. Intraoperatively, it was found to be twisted right ovarian solid tumour and histopathological examination revealed leiomyoma of ovary

Quality Assessment of In-the-Wild Videos

Quality assessment of in-the-wild videos is a challenging problem because of the absence of reference videos and shooting distortions. Knowledge of the human visual system can help establish methods for objective quality assessment of in-the-wild videos. In this work, we show two eminent effects of the human visual system, namely, content-dependency and temporal-memory effects, could be used for this purpose. We propose an objective no-reference video quality assessment method by integrating both effects into a deep neural network. For content-dependency, we extract features from a pre-trained image classification neural network for its inherent content-aware property. For temporal-memory effects, long-term dependencies, especially the temporal hysteresis, are integrated into the network with a gated recurrent unit and a subjectively-inspired temporal pooling layer. To validate the performance of our method, experiments are conducted on three publicly available in-the-wild video quality assessment databases: KoNViD-1k, CVD2014, and LIVE-Qualcomm, respectively. Experimental results demonstrate that our proposed method outperforms five state-of-the-art methods by a large margin, specifically, 12.39%, 15.71%, 15.45%, and 18.09% overall performance improvements over the second-best method VBLIINDS, in terms of SROCC, KROCC, PLCC and RMSE, respectively. Moreover, the ablation study verifies the crucial role of both the content-aware features and the modeling of temporal-memory effects. The PyTorch implementation of our method is released at https://github.com/lidq92/VSFA.Comment: 9 pages, 7 figures, 4 tables. ACM Multimedia 2019 camera ready. -> Update alignment formatting of Table

Comparing apples and oranges: assessment of the relative video quality in the presence of different types of distortions

<p>Abstract</p> <p>Video quality assessment is essential for the performance analysis of visual communication applications. Objective metrics can be used for estimating the relative quality differences, but they typically give reliable results only if the compared videos contain similar types of quality distortion. However, video compression typically produces different kinds of visual artifacts than transmission errors. In this article, we focus on a novel subjective quality assessment method that is suitable for comparing different types of quality distortions. The proposed method has been used to evaluate how well different objective quality metrics estimate the relative subjective quality levels for content with different types of quality distortions. Our conclusion is that none of the studied objective metrics works reliably for assessing the co-impact of compression artifacts and transmission errors on the subjective quality. Nevertheless, we have observed that the objective metrics' tendency to either over- or underestimate the perceived impact of transmission errors has a high correlation with the spatial and temporal activity levels of the content. Therefore, our results can be useful for improving the performance of objective metrics in the presence of both source and channel distortions.</p

Objective and subjective evaluation of High Dynamic Range video compression

A number of High Dynamic Range (HDR) video compression algorithms proposed to date have either been developed in isolation or only-partially compared with each other. Previous evaluations were conducted using quality assessment error metrics, which for the most part were developed for qualitative assessment of Low Dynamic Range (LDR) videos. This paper presents a comprehensive objective and subjective evaluation conducted with six published HDR video compression algorithms. The objective evaluation was undertaken on a large set of 39 HDR video sequences using seven numerical error metrics namely: PSNR, logPSNR, puPSNR, puSSIM, Weber MSE, HDR-VDP and HDR-VQM. The subjective evaluation involved six short-listed sequences and two ranking-based subjective experiments with hidden reference at two different output bitrates with 32 participants each, who were tasked to rank distorted HDR video footage compared to an uncompressed version of the same footage. Results suggest a strong correlation between the objective and subjective evaluation. Also, non-backward compatible compression algorithms appear to perform better at lower output bit rates than backward compatible algorithms across the settings used in this evaluation

Influence of affective image content on subjective quality assessment

Image quality assessment (IQA) enables distortions introduced into an image (e.g., through lossy compression or broadcast) to be measured and evaluated for severity. It is unclear to what degree affective image content may influence this process. In this study, participants (n=25) were found to be unable to disentangle affective image content from objective image quality in a standard IQA procedure (single stimulus numerical categorical scale). We propose that this issue is worthy of consideration, particularly in single stimulus IQA techniques, in which a small number of handpicked images, not necessarily representative of the gamut of affect seen in true broadcasting, and unrated for affective content, serve as stimuli

Content-aware packet scheduling strategy for medical ultrasound videos over LTE wireless networks

In parallel to the advancements in communication technologies, telemedicine research has continually adapted to develop various healthcare applications. The latest wireless technology Long-Term Evolution(LTE) is being increasingly deployed across developed countries and rapidly adopted by developing countries. In this paper, a content-aware packet scheduling approach for medical ultrasound videos is proposed. The contribution of this work is introducing a utility function based on the temporal complexity of the video frames. The utility function is used with four schedulers to prioritise the video packets based on their temporal complexity and type of frame (e.g. I frame). The results show that the utility function improves the packet delay performance obtained in our simulation when compared with content-unaware approach. Further, gain in average PSNR and SSIM are also observed in the received video quality. Research on content-aware packet scheduling for telemedicine applications over advanced wireless networks is limited and our work contributes towards addressing this research gap