Path Smoothing With Support Vector Regression

One of moving object problems is the incomplete data that acquired by Geo-tracking technology. This phenomenon can be found in aircraft ground-based tracking with data loss come near to 5 minutes. It needs path smoothing process to complete the data. One solution of path smoothing is using physics of motion, while this research performs path smoothing process using machine learning algorithm that is Support Vector Regression (SVR). This study will optimize the SVR configuration parameters such as kernel, common, gamma, epsilon and degree. Support Vector Regression will predict value of the data lost from aircraft tracking data. We use combination of mean absolute error (MAE) and mean absolute percentage error (MAPE) to get more accuracy. MAE will explain the average value of error that occurs, while MAPE will explain the error percentage to the data. In the experiment, the best error value MAE 0.52 and MAPE 2.07, which means error data ± 0.52, this is equal to 2.07% of the overall data value.Keywords: Moving Object, Path Smoothing, Support Vector Regression, MA

Transformer-Based Multi-Task Learning for Crisis Actionability Extraction

Social media has become a valuable information source for crisis informatics. While various methods were proposed to extract relevant information during a crisis, their adoption by field practitioners remains low. In recent fieldwork, actionable information was identified as the primary information need for crisis responders and a key component in bridging the significant gap in existing crisis management tools. In this paper, we proposed a Crisis Actionability Extraction System for filtering, classification, phrase extraction, severity estimation, localization, and aggregation of actionable information altogether. We examined the effectiveness of transformer-based LSTM-CRF architecture in Twitter-related sequence tagging tasks and simultaneously extracted actionable information such as situational details and crisis impact via Multi-Task Learning. We demonstrated the system’s practical value in a case study of a real-world crisis and showed its effectiveness in aiding crisis responders with making well-informed decisions, mitigating risks, and navigating the complexities of the crisis

Application of Big Data Analysis to Agricultural Production, Agricultural Product Marketing, and Influencing Factors in Intelligent Agriculture

Agricultural Internet of things (AIoT) promotes the modernization of traditional agricultural production and marketing model. However, the existing time series prediction methods for agricultural production and agricultural product (AP) marketing cannot adapt well to most real-world scenarios, failing to realize multistep forecast of production and AP marketing data. To solve the problem, this paper explores the big data analysis of agricultural production, AP marketing, and influencing factors in intelligent agriculture. To realize long-, and short-term predictions, a small-sample time series model was set up for AIoT production, and a big-sample time series model was constructed for AP marketing. The data fusion algorithm based on Kalman filter (KF) was adopted to fuse the massive multi-source AP marketing data. The proposed strategy was proved valid through experiments

Review of Research on Human Trust in Artificial Intelligence

Artificial Intelligence (AI) represents today\u27s most advanced technologies that aim to imitate human intelligence. Whether AI can successfully be integrated into society depends on whether it can gain users’ trust. We conduct a comprehensive review of recent research on human trust in AI and uncover the significant role of AI’s transparency, reliability, performance, and anthropomorphism in developing trust. We also review how trust is diversely built and calibrated, and how human and environmental factors affect human trust in AI. Based on the review, the most promising future research directions are proposed

CITIZEN PARTICIPATION IN INCREASINGLY DIGITALIZED GOVERNMENTAL ENVIRONMENTS – A SYSTEMATIC LITERATURE REVIEW

Citizen participation in increasingly digitalized governmental environments can introduce fruitful capabilities to encourage citizens to engage in municipal affairs and through this take actively part in fostering smart cities’ effectiveness. However, the practical exploitation of recent knowledge is still not sufficiently operationalized, whilst research in this field yields various approaches focusing on diverse emphases. Therefore, the necessity of systematically collecting and afterwards analysing the existing literature towards this topic is obvious. This paper depicts a proceeding to systematically review the available literature towards the relevant research units on citizen participation. Overall, 48 topic-based papers were identified out of leading journals and conference papers about information systems. The main findings of the relevant papers were assessed to a proposed analytical framework consisting of increasing participation stages and two distinct focus groups namely government and citizens. Accordingly, the covered recent focus areas of research are identified to reveal where state-of-the-art research falls short. Consequently, the imperative of emphasising investigation regarding concepts for ICT-enabled services focusing the empowerment of citizens arises as being our contribution for guiding future research, whilst governments can practically benefit from the composed framework by using it for classifying, planning and implementing proposed participation activities

Ambidexterity Through the Lens of Conventions? A Qualitative Study on Personal Virtual Assistants

Personal virtual assistants (PVAs) are demanded to effectively fulfil and support employee’s tasks in organizations. Today, PVAs are mainly trusted to take over simple administrative tasks, thus, limiting their potential long-term impact on employees and entire organizations. To overcome this shortcoming, we introduce the pragmatic perspective of the Economics of Conventions (EC) to analyze and understand employees’ plural motives and behaviors that may explain sustained or fragmented potential PVA use in organizations, especially taking the organizational challenge of ambidexterity into account. In doing so, we provide a deepened understanding of PVAs’ capabilities and give propositions for their organizational implementation and use. We also offer new avenues for future research by calling for a more holistic theoretical foundation of organizational artificial intelligence solutions that consider and represent organizations and their employees in their complexity, respectively their plural orders of worth

Are Employees Following the Rules? On the Effectiveness of IT Consumerization Policies

In most organizations, employees commonly use mobile technologies including smartphones and tablets to complete their tasks. Therefore, many organizations have started to implement policies that govern the use of mobile devices such as Bring-Your-Own-Device (BYOD) policies, that allow employees to use private devices for work-related purposes, or Company Owned PrivatelyEnabled (COPE) policies, which allow the use of organizational technologies for private purposes. Despite its relevance, there is only little empirical research that provides evidence on the effectiveness of specific policies, i.e., policies in favor of BYOD/COPE, policies that prohibit it, and no implemented policies. Based on survey data (N = 381), we provide initial insights in terms of the effectiveness of these policies. Our results indicate that policies indeed influence the degree of technology use. Policies in favor of BYOD/COPE are particularly effective. We conclude this paper by discussing our findings and derive several implications for theory and practice

The Low-Code Phenomenon: Mapping the Intellectual Structure of Research

The term low-code has been closely associated with simplifying and accelerating software development. Driven by the idea that low-code can help to meet the increased digitalization demands, the low-code phenomenon is rising in academia and industry. This resulted in an immense increase in publications on low-code, posing the question of what research streams characterize the low-code literature. Conducting bibliometric analysis on 725 articles, we unpack the intellectual structure of low-code literature and uncover how it relates to other research fields. Our contribution is to clarify the conceptual understanding of low-code by identifying six research streams, namely, origins of low-code within software engineering (SE), low-code as an enabler for emerging SE trends, workplace transformation, establishing low-code methodologies, understanding low-code adoption and leveraging low-code for digital transformation. We conclude with future research directions that still need to be explored within the low-code literature