USE OF VISUALIZATION IN DIGITAL FINANCIAL REPORTING: THE EFFECT OF SPARKLINE

Information visualization (InfoViz) is an essential component of decision support systems (DSS). Sparklines is a visualization tool. This study examines if Sparklines in digital financial reports aids novice investors and if so under what circumstances? Does it enhances decision-making performance and facilitates effective decision-making experience? Additionally, does it lowers decision making effort; reduces dilution effect from non-relevant data in financial reports and mitigates recency bias in using digital financial reports? The hypothesis is guided by the theory of Proximity Compatibility Principle and the Theory of Cognitive Fit. The research methodology for this study is a repeated measure, controlled laboratory based experiment. A pilot test was conducted in with a sample of forty undergraduate students from Gatton College of Business and Economics. The sample size for this study was 275 subjects. The result revealed that there was significant effect of sparklines on decision making performance and it provides an incremental value over a tabular format. Sparklines makes an important contribution towards mitigating recency bias. The results also suggested that the irrelevant information cue in the shareholder’s report were not able to weaken the impact of relevant information in the audited financial data reported using sparklines. Sparklines increased the attention of the readers to the tables. Subjects performed the integrative tasks and spatial better when using Sparklines. For tasks such as symbolic tasks, Sparkline does not necessarily improve decision performance. It was also found out that decision makers experience greater satisfaction when using sparklines. The overall cognitive load experienced by subjects was lower using sparklines when task demands are high (such as in a bankruptcy prediction task). Interestingly, the results indicate that there is no significant effect of sparkline on decision confidence and time. In conclusion, recall of facts and pattern among subjects was found superior with use of sparkline. This study provides an empirical and justifiable basis for policy makers to make explicit recommendations about use of novel graphics such as sparkline in digital financial reports. Limitations of this study are noted

Enhancing Microbial Fuel Cell Performance Prediction and IoT Integration Using Machine Learning and Renewable Energy

This study aims to enhance Microbial Fuel Cells (MFCs) reliability for remote environmental monitoring, emphasizing unexplored facets of accurate energy prediction and the integration of renewable energy-powered Internet of Things (IoT) devices. Following comprehensive research, design, and component procurement, an innovative and cost-effective IoT system was developed, leveraging renewable energy from MFCs. Using an Arduino UNO-WiFi, data was collected and showcased on a web page while logged in a Google Firebase database, with an Android app created for intuitive smartphone visualization. Over four months, sensor data was accumulated. An Artificial Intelligence (AI) model, employing Autoregressive Integrated Moving Average (ARIMA), precisely forecasted MFC energy production (RMSE: 0.0119 and 0.0113 for trials 1 and 2). Despite the initial energy production surge, a subsequent decline occurred due to organic matter depletion. This prototype represents an affordable and sustainable solution for cloud-based IoT environmental monitoring with AI-driven energy forecasts, embodying innovation in renewable energy applications and sustainable practices

Wirelessly Sensing Open Parking Spaces : Accounting and Management of Parking Facility

Driving into a parking lot only to find that all of the parking spaces are taken is frustrating. It would be very beneficial toimplement a system that tracks the number of spaces left. Wireless sensors are well-fitted to do this task. Existing systemsperform this task in various ways, discussed at the beginning of the paper. Taking the shortcomings of these systems intoaccount, a new system based on signal strength is proposed and tested in a virtual environment

IoT Security: Problems and a Centralized Adaptive Approach as a Solution

The Internet of Things (IOT) is the interconnection, and communication of technology devices. This leads to speedy advancements in technology, but unfortunately invites in room for major security vulnerabilities. One facet of the Internet of Things, is smart home devices. Smart home devices are those that are utilized within one's home to improve quality of life. As all these devices communicate with each other, more and more security risks are developed. In this paper we review the existing security issues and solutions for IOT, and propose and approach to centralize the communications through a singular hub, acting as a central command for the smart home devices, allowing that hub to be the main secure point in the smart home network

e-Commerce and taxation: Past, present and future

This paper examines the relation between security returns, industry-wide cash flows and accruals, and firm-specific cash flows and accruals during the period 1991-2001. We first replicate Ayers & Freeman’s (1997) findings that returns are significantly associated with industry earnings and this association begins and ends earlier than returns associated with firm earnings. We hypothesize the components of industry earnings - cash flows and accruals - are significantly associated with stock returns. Consistent with this hypothesis we find that both industry-wide cash flows and industry-wide accruals are significantly associated with returns and this association begins and ends earlier than returns associated with firm-specific cash flows and accruals. Contrary to expectations, we find no significant difference between industry-wide cash flows and industry-wide accruals. Finally, we hypothesize and find that industry-wide accruals are more value relevant than firm-specific accruals in years t and t+1. Collectively, these findings suggest industry-wide cash flows and accruals are value relevant; however, disaggregating industry earnings into its cash flow and accrual components is unnecessary as it does not provide incremental explanatory power beyond that of industry earnings. All data are publicly available from the sources identified in the paper

Assurance on the reliability of mobile payment system and its effects on its\u27 use: An empirical examination

In this paper, we decompose the CAPM equity beta for Coca-Cola and Pepsi (KOPEP) to show the industry component and the operating leverage and the financial leverage components for the period from 2004 to 2012. We compute the CAPM equity betas using a standard five year, sixty month, regression between returns for KOPEP using the S&P500 as the market index. We adjust for financial leverage using the Hamada (1969) methodology and we adjust for operating leverage using the degree of operating leverage (DOL). The average business beta for Coca-Cola is 0.1882 and the average business beta for Pepsico is 0.1369. Over the period of this analysis, Coca-Cola has had a business beta slightly higher than the business beta for Pepsico

Hybrid data mining to reduce false positive and false negative prediction in intrusion detection system

This paper proposes an approach of data mining machine learning methods for reducing the false positive and false negative predictions in existing Intrusion Detection Systems (IDS). It describes our proposal for building a confidential strong intelligent intrusion detection system which can save data and networks from potential attacks, having recognized movement or infringement regularly reported ahead or gathered midway. We have addressed different data mining methodologies and presented some recommended approaches which can be built together to enhance security of the system. The approach will reduce the overhead of administrators, who can be less concerned about the alerts as they have been already classified and filtered with less false positive and false negative alerts. Here we have made use of KDD-99 IDS dataset for details analysis of the procedures and algorithms which can be implemented

Data Mining Based Crime Analysis Mapping and Intrusion Detection

Data Mining plays a key role in Crime Analysis. There are many different algorithms mentioned in previous research papers, among them are the virtual identifier, pruning strategy, support vector machines, and apriori algorithms. VID is to find relation between record and vid. The apriori algorithm helps the fuzzy association rules algorithm and it takes around six hundred seconds to detect a mail bomb attack. In this research paper, we identified Crime mapping analysis based on KNN (K – Nearest Neighbor) and ANN (Artificial Neural Network) algorithms to simplify this process. Crime Mapping is conducted and Funded by the Office of Community Oriented Policing Services (COPS). Evidence based research helps in analyzing the crimes. We calculate the crime rate based on the previous data using data mining techniques. Crime Analysis uses quantitative and qualitative data in combination with analytic techniques in resolving the cases. For public safety purposes, the crime mapping is an essential research area to concentrate on. We can identity the most frequently crime occurring zones with the help of data mining techniques. In Crime Analysis Mapping, we follow the following steps in order to reduce the crime rate: Collect crime data  Group data Clustering Forecasting the data. Crime Analysis with crime mapping helps in understanding the concepts and practice of Crime Analysis in assisting police and helps in reduction and prevention of crimes and crime disorders