A comparison of Jiazzi and AspectJ for feature-wise decomposition

technical reportFeature-wise decomposition is an important approach to building configurable software systems. Although there has been research on the usefulness of particular tools for featurewise decomposition, there are not many informative comparisons on the relative effectiveness of different tools. In this paper, we compare AspectJ and Jiazzi, which are two different systems for decomposing Java programs. AspectJ is an aspect-oriented extension to Java, whereas Jiazzi is a component system for Java. To compare these systems, we reimplemented an AspectJ implementation of a highly configurable CORBA Event Service using Jiazzi. Our experience is that Jiazzi provides better support for structuring the system and manipulating features, while AspectJ is more suitable for manipulating existing Java code in non-invasive and unanticipated ways

The Use of Analytical Platform to Identify Valuable Interventions in Retail Pharmacies

As BI products and services increasing, the cost for collecting data and storing data is reclined. Enterprises could get the data source from customer transactions, website logs and product reviews. BI technology is used in many fields, such as in manufacturing for order shipment and customer support, in financial services for claims analysis and fraud detection, in transportation for fleet management, in retail for user profiling to target grocery coupons during checkout, in utilities for power usage analysis, and health care for outcomes analysis. The profession of pharmacy will be more reliant upon the information that can be extracted from data analytics and BI tools. There is a great opportunity in pharmacy to develop an analytical platform to suit both business needs in retail and the delivery of patient care in pharmacy. This paper proposes a analytical platform to identify valuable interventions in retail pharmacies

Between Attention and Portfolio Adjustment: Insights from Machine Learning-based Risk Preference Assessment

Financial firms recommend products to customers, intending to gain their attention and change their portfolios. Based on behavioral decision-making theory, we argue attention’s effect on portfolio adjustment is through the risk deviation between portfolio risk and their risk preference. Thus, to fully understand the adjustment process, it is necessary to assess customers’ risk preferences. In this study, we use machine learning methods to measure customers’ risk preferences. Then, we build a dynamic adjustment model and find that attention’s impact on portfolio adjustment speed is stronger when customers’ risk preference is higher than portfolio risk (which needs an upward adjustment) and when customers’ risk preference is within historical portfolio risk experience. We conducted a field experiment and found that directing customers’ attention to products addressing the risk deviation would lead to more portfolio adjustment activities. Our study illustrates the role of machine learning in enhancing our understanding of financial decision-making

How Do Social Media Shape the Information Environment in the Financial Market?

Internet users create social media that enable information to be transferred more efficiently. In this work we focus on a typical social media platform Wikipedia and examine how management’s voluntary disclosure reacts to information arrivals on Wikipedia. In doing so, we seek to answer the question of whether social media indeed improve the information environment for investors in the financial market. Our analysis is based on a unique dataset collected from the modification history of firm entries on Wikipedia, and thus we are able to identify information arrivals on Wikipedia. We find that information arrivals on Wikipedia affect the timing of management disclosure of bad news, and the effect is in sharp contrast to the way in which traditional media affect management disclosure. Further, we find consistent evidence that information arrivals on Wikipedia preempt the negative reaction of the market to bad news. In contrast, more news coverage in traditional media exacerbates the problem of optimistic analyst forecasts. Together these findings emphasize that social media have an identifiable effect on both the management side and the investor side in the financial market

Understanding the high activity of mildly reduced graphene oxide electrocatalysts in oxygen reduction to hydrogen peroxide

The direct electrochemical synthesis of hydrogen peroxide (H2O2) would provide an attractive alternative to the traditional anthraquinone oxidation process for continuous on-site applications. Its industrial viability depends greatly on developing cost-effective catalysts with high activity and selectivity. Recent experiments have demonstrated that mildly reduced graphene oxide (mrGO) electrocatalysts exhibit highly selective and stable H2O2 formation activity [e.g., H. W. Kim, M. B. Ross, N. Kornienko, L. Zhang, J. Guo, P. Yang and B. D. McCloskey, Nat. Catal., 2018, 1, 282-290]. However, the identification of active site structures for this catalytic process on mrGO is doubtful. Herein, by means of first-principles calculations, we examine the H2O2 formation activities of the active site structures proposed in experiments and find that their activities are actually very low. Then, we systematically investigate the H2O2 formation activities of different oxygen functional group structures on mrGO based on experimental observations, and discover two types of oxygen functional group structures (2EP and 1ET + 1EP) that have comparable or even lower overpotentials (<0.10 V) for H2O2 formation compared with the state-of-the-art PtHg4 electrocatalyst. Our theoretical results reveal that the graphene edge and the synergetic effects between different oxygen functional groups are essential for the superior performance of mrGO for H2O2 production. This work not only provides a feasible explanation of the cause of high H2O2 formation activity of mrGO but also offers a guide for the design, synthesis, and mechanistic investigation of advanced carbon-based electrocatalysts for effective H2O2 production.This research was undertaken with the assistance of resources provided by the National Computational Infrastructure (NCI) facility at the Australian National University; allocated through both the National Computational Merit Allocation Scheme supported by the Australian Government and the Australian Research Council grant LE160100051 (Maintaining and enhancing merit-based access to the NCI National Facility, 2016–2018). The study was financed by an ARC Discovery Grant (DP170104853)

Multimodal Data Augmentation for Image Captioning using Diffusion Models

Image captioning, an important vision-language task, often requires a tremendous number of finely labeled image-caption pairs for learning the underlying alignment between images and texts. In this paper, we proposed a multimodal data augmentation method, leveraging a recent text-to-image model called Stable Diffusion, to expand the training set via high-quality generation of image-caption pairs. Extensive experiments on the MS COCO dataset demonstrate the advantages of our approach over several benchmark methods, and particularly a significant boost when having fewer training instances. In addition, models trained on our augmented datasets also outperform prior unpaired image captioning methods by a large margin. Finally, further improvement regarding the training efficiency and effectiveness can be obtained after intentionally filtering the generated data based on quality assessment

Electrocatalytic Reduction of Carbon Dioxide to Methane on Single Transition Metal Atoms Supported on a Defective Boron Nitride Monolayer: First Principle Study

The electrochemical conversion of carbon dioxide (CO2) and water into useful multi‐electron transfer products, such as methanol (CH3OH) and methane (CH4), is a major challenge in facilitating a closed carbon cycle. Here, a systematic first principle study of the potential of single transition metal atoms (Sc to Zn, Mo, Rh, Ru, Pd, Ag, Pt, and Au) supported on experimentally available defective boron nitride monolayers with a boron monovacancy (TM/defective BN) to achieve highly efficient electrocatalytic CO2 reduction (ECR) to CH4 is carried out. Our computations reveal that Fe/defective BN, Co/defective BN, and Pt/defective BN nanosheets possess outstanding ECR activities with quite low (less negative) onset potentials of −0.52, −0.68, and −0.60 V, respectively. Given that Fe and Co are nonprecious metals, Fe/defective BN and Co/defective BN may provide cost‐effective electrocatalysts. The high ECR activities of these TM/defective BN catalyst systems stem from the moderate electrocatalysts’ affinities for C and O, which modulate the free energies of ECR intermediates in the reaction pathways. Moreover, it is found that Fe/defective BN and Pt/defective BN show high selectivity of ECR to CH4. This finding highlights a strategy to design highly active and selective single‐atom electrocatalysts for ECR to CH4.S.S. and H.A. acknowledge the financial support by the Australian Research Council under Discovery Project (DP170104853). This research was undertaken with the assistance of resources provided by the National Computing Infrastructure facility at the Australian National University, allocated through both the National Computational Merit Allocation Scheme supported by the Australian Government and the Australian Research Council grant LE120100181 (Enhanced merit-based access and support at the new NCI petascale supercomputing facility, 2012–2015)

Extracting Business Value from IT: A Sensemaking Perspective of Post-Adoptive Use

How can firms extract value from already-implemented information technologies (IT) that support the work processes of employees? One approach is to stimulate employees to engage in post-adoptive extended use, i.e.,to learn and apply more of the available functions of the implemented technologies to support their work. Such learning behavior of extending functions in use is ingrained in a process by which users make sense of the technologies in the context of their work system.This study draws on sensemaking theory to develop a model to understand the antecedents, contingencies, and consequences of customer service employees’ extended use of customer relationship management (CRM)technologies. The model is tested using multi-source longitudinal data collected through a field study of one of the world’s largest telecommunications service providers. Our results suggest that employees engage in post-adoptive sensemaking at two levels: technology and work system. We found that sensemaking at both of these levels impacts the extended use of CRM technologies. Employees’ sensemaking at the technology level is influenced by employees’ assessment of technology quality,while employees’ sensemaking at the work system level is influenced by customers’ assessment of servicequality. Moreover, in the case of low technology quality and low service quality, specific mechanisms for employee feedback should be conceptualized and aligned at two levels: through employee participation at the technology level and through work system coordination at the work system level. Such alignment can mitigate the undesirable effect of low technology quality and low service quality,thereby facilitating extended use. Importantly, we found that extended use amplifies employees’ service capacity, leading to better objective performance. Put together, our findings highlight the critical role of employees’ sensemaking about the implemented technologies in promoting their extended use of IT and improving their work performance