Guidelines Aimed at Reducing the Risks of User Acceptance Delay in the Context of an IT Service Project Management Plan

Delays in the user acceptance of information technology (IT) service projects in Korea have occurred frequently due to various risk factors. User acceptance delays may hinder the achievement of the client’s project objectives and cause schedule delays or cost overruns. Furthermore, the client may impose a delay charge and claim for additional damages, causing serious disputes between buyer and supplier. The main causes of user acceptance delays are unclear user requirements, changes in user requirements, poor-quality development outputs, excessive functional and non-functional errors, lack of user involvement, unclear user roles and responsibilities, and unclear criteria of user acceptance test.We help foster the timely completion of user acceptance by proposing a method of identifying the risk factors in user acceptance delay and creating a project management plan to weed out the identified risks. We propose a guideline for an IT service management plan that weeds out or lowers the risk factors well in advance. To validate the guideline’s utility, we apply it to IT service projects. The results show that the guideline is effective in identifying and removing risk factors affecting delays in the user acceptance of IT service projects

AN ANALYSIS OF BANK CONSOLIDATION TRENDS IN RURAL PENNSYLVANIA

U.S. banking markets have undergone important structural and institutional changes. Overall, the sector has experienced steady consolidation through mergers and acquisitions that have resulted in fewer banks holding a greater value of the total assets. Despite consolidation, new branch offices and the growth of alternative providers has increased the access to banking-type services. This paper documents and describes trends in the banking industry in Pennsylvania, with special emphasis on rural areas. The first section shows that while the number of "bricks and mortar" offices in the state's rural counties has grown, the distribution of the growth has been quite uneven. As a result, access has potentially declined for some of the state's rural residents. In the second section the analysis shows that consolidation is dramatically reducing the number of banks headquartered in Pennsylvania. The analysis shows that, should current trends continuethe loss of 1.25 banks per quarterthen there will be no banks headquartered in rural Pennsylvania in 2025. Consolidation appears to be having an effect on the competitiveness of rural banking markets. While the analysis suggests that urban county banking markets remain fairly competitive, it also suggests that the state's rural banking markets may have less competition.Financial Economics,

Doubly Contrastive End-to-End Semantic Segmentation for Autonomous Driving under Adverse Weather

Road scene understanding tasks have recently become crucial for self-driving vehicles. In particular, real-time semantic segmentation is indispensable for intelligent self-driving agents to recognize roadside objects in the driving area. As prior research works have primarily sought to improve the segmentation performance with computationally heavy operations, they require far significant hardware resources for both training and deployment, and thus are not suitable for real-time applications. As such, we propose a doubly contrastive approach to improve the performance of a more practical lightweight model for self-driving, specifically under adverse weather conditions such as fog, nighttime, rain and snow. Our proposed approach exploits both image- and pixel-level contrasts in an end-to-end supervised learning scheme without requiring a memory bank for global consistency or the pretraining step used in conventional contrastive methods. We validate the effectiveness of our method using SwiftNet on the ACDC dataset, where it achieves up to 1.34%p improvement in mIoU (ResNet-18 backbone) at 66.7 FPS (2048x1024 resolution) on a single RTX 3080 Mobile GPU at inference. Furthermore, we demonstrate that replacing image-level supervision with self-supervision achieves comparable performance when pre-trained with clear weather images.Comment: Accepted for publication at BMVC 202

Stock Market Liquidity And Dividend Policy In Korean Corporations

The liquidity hypothesis predicts a negative relationship between stock liquidity and dividend payout propensity, i.e., a firm will decide to pay dividends to compensate for the liquidity demand of investors. This study comprehensively examines whether the liquidity hypothesis applies to the sample of Korean firms listed in the KOSPI and KOSDAQ markets. The main results of this paper are as follows. First, the dividend policy in Korean firms does not support the liquidity hypothesis, contradictory to the existing empirical studies. Next, the explanatory power of the liquidity hypothesis is even weaker for the KOSDAQ market, inconsistent with international evidence. Finally, even when we focus on the firm-year observations with non-negligible dividend payments, the liquidity hypothesis does not explain the dividend policy of Korean firms either. Our findings significantly contribute to the literature by robustly confirming the very limited role of the liquidity hypothesis for Korean financial markets. 

Heavy Rain Face Image Restoration: Integrating Physical Degradation Model and Facial Component Guided Adversarial Learning

With the recent increase in intelligent CCTVs for visual surveillance, a new image degradation that integrates resolution conversion and synthetic rain models is required. For example, in heavy rain, face images captured by CCTV from a distance have significant deterioration in both visibility and resolution. Unlike traditional image degradation models (IDM), such as rain removal and superresolution, this study addresses a new IDM referred to as a scale-aware heavy rain model and proposes a method for restoring high-resolution face images (HR-FIs) from low-resolution heavy rain face images (LRHR-FI). To this end, a 2-stage network is presented. The first stage generates low-resolution face images (LR-FIs), from which heavy rain has been removed from the LRHR-FIs to improve visibility. To realize this, an interpretable IDM-based network is constructed to predict physical parameters, such as rain streaks, transmission maps, and atmospheric light. In addition, the image reconstruction loss is evaluated to enhance the estimates of the physical parameters. For the second stage, which aims to reconstruct the HR-FIs from the LR-FIs outputted in the first stage, facial component guided adversarial learning (FCGAL) is applied to boost facial structure expressions. To focus on informative facial features and reinforce the authenticity of facial components, such as the eyes and nose, a face-parsing-guided generator and facial local discriminators are designed for FCGAL. The experimental results verify that the proposed approach based on physical-based network design and FCGAL can remove heavy rain and increase the resolution and visibility simultaneously. Moreover, the proposed heavy-rain face image restoration outperforms state-of-the-art models of heavy rain removal, image-to-image translation, and superresolution