Beyond a Fistful of Tumblers: Toward a Taxonomy of Ethereum-based Mixers

2023 | International Conference on Information Systems | Citations: 0

Authors: Barbereau, Tom; Ermolaev, Egor; Brennecke, Martin; Hartwich, Eduard; Sedlmeir, Johannes

Abstract: The role played by decentralised services in the obfuscation of crypto-asset tr ... Expand

Abstract: The role played by decentralised services in the obfuscation of crypto-asset transactions performed on transparent blockchains has increasingly captured the attention of regulators. This is exemplified by the headlines about the U.S. Treasury's sanctions on the Ethereum-based mixer Tornado Cash. Yet, despite the existing controversies on the use of mixers, the different functionalities of these information systems with an inherent dark side remain to be explored by the literature. So far, contributions primarily encompass technical works and studies that focus on the Bitcoin ecosystem. This paper puts forward a multi-layer taxonomy of the smart-contract-based -and, therefore, functionally richer -family of mixers on Ethereum. Our proposed taxonomy is grounded on (1) a review of existing literature, (2) an analysis of mixers' project documentation, (3) their corresponding smart contracts, and (4) expert interviews. Our evaluation included the application of the taxonomy to two mixers -RAILGUN and zkBob. The taxonomy represents a valuable tool for law enforcement, regulators, and other stakeholders to explore critical properties affecting compliance and use of Ethereum-based mixers. Collapse

Topics: Ethereum smart contract cryptoasset blockchain user experience

Methods: qualitative interview theoretical contribution mixed method

A Wake-Up Call for the Utility industry: Extreme Weather and Fundamental Lessons from 2021

2023 | HICSS | Citations: 0

Authors: Tabors, R.

Abstract: We have examined the critical extreme weather events of 2021 that resulted in d ... Expand

Abstract: We have examined the critical extreme weather events of 2021 that resulted in disruptions of normal power system operations, the loss of life, and multibillion dollar losses to the US economy. These impacts occurred due to extreme cold, extreme heat, drought, slower post-landfall dissipation of hurricanes, and more intense large-scale thunderstorm systems. We point to the causes but also argue for the changes in planning and operations required to be prepared for and have responses to these events. Specifically, we focus on recognizing the reality of extreme events and planning for their increasing frequency, intensity, duration, and geographic scope; modifying resource planning and adequacy metrics to incorporate common mode events; enabling the power system to depend on reliable natural gas fuel supplies; redesigning power markets to better compensate resources and flexible demand for reducing the probability of outages; and developing resilient systems. Collapse

Topics: tornado price management

Identifying Competitive Attributes Based on an Ensemble of Explainable Artificial Intelligence

2022 | Business & Information Systems Engineering | Citations: 0

Authors: Lee, Younghoon

Abstract: Competitor analysis is a fundamental requirement in both strategic and operation ... Expand

Abstract: Competitor analysis is a fundamental requirement in both strategic and operational management, and the competitive attributes of reviewer comments are a crucial determinant of competitor analysis approaches. Most studies have focused on identifying competitors or detecting comparative sentences, not competitive attributes. Thus, the authors propose a method based on explainable artificial intelligence (XAI) that can detect competitive attributes from consumers’ perspectives. They construct a model to classify the reviewer comments for each competitive product and calculate the importance of each keyword in the reviewer comments during the classification process. This is based on the assumption that keywords significantly influence product classification. The authors also propose an additional novel methodology that combines various XAI techniques such as local interpretable model-agnostic explanations, Shapley additive explanations, logistic regression, gradient-based class activation map, and layer-wise relevance propagation to build a robust model for calculating the importance of competitive attributes for various data sources. Collapse

Topics: explainable artificial intelligence local interpretable model agnostic explanation shapley additive explanation social network YouTube

Methods: experiment survey computational algorithm logistic regression ablation analysis

Seeing Is Believing? How Including a Video in Fake News Influences Users’ Reporting of Fake News to Social Media Platforms

2022 | Management Information Systems Quarterly | Citations: 0

Authors: Wang, Shuting (Ada); Pang, Min-Seok; Pavlou, Paul

Abstract: Social media platforms, such as Facebook, Instagram, and Twitter, are combating ... Expand

Abstract: Social media platforms, such as Facebook, Instagram, and Twitter, are combating the spread of fake news by developing systems that allow their users to report fake news. However, it remains unclear whether these reporting systems that harness the "wisdom of the crowd" are effective. Notably, concerns have been raised that the popularity of videos may hamper users' reporting of fake news. The persuasive power of videos may render fake news more deceptive and less likely to be reported in practice. However, this is neither theoretically nor empirically straightforward, as videos not only affect users' ability to detect fake news, but also impact their willingness to report and their engagement (i.e., likes, shares, and comments) which would further spread fake news. Using a unique dataset from a leading social media platform, we empirically examine how including a video in a fake news post affects the number of users reporting the post to the platform. Our results indicate that including a video significantly increases the number of users reporting the fake news post to the social media platform. Additionally, we find that the sentiment intensity of the fake news text content, especially when the sentiment is positive, attenuates the effect of including a video. Randomized experiments and a set of mediation analyses are included to uncover the underlying mechanisms. We contribute to the information systems literature by examining how social media platforms can leverage their users to report fake news, and how different formats (e.g., videos and text) of fake news interact to influence users' reporting behavior. Social media platforms that seek to leverage the "wisdom of the crowd" to combat the proliferation of fake news should consider both the popularity of videos and the role of text sentiment in fake news to adjust their strategies. Collapse

Topics: fake news social media Twitter Python Facebook

Methods: experiment survey sentiment analysis mediation analysis instrumental variables estimation

The role of GIS-enabled mobile applications in disaster management: A case analysis of cyclone Gaja in India

2020 | International Journal of Information Management | Citations: 0

Authors: Sharma, Sujeet Kumar; Misra, Santosh K.; Singh, Jang Bahadur

Abstract: Geographic information systems (GIS) have generated great interest in the field ... Expand

Abstract: Geographic information systems (GIS) have generated great interest in the field of disaster management and have stimulated rich discussions in the domain. This case study describes how we selected and adapted a GISenabled mobile application to be used by field workers in the aftermath of cyclone Gaja in Tamil Nadu in 2018. It also analyses how the app was used by field workers to ensure faster electricity restoration in the cyclone-hit site. The app helped decision-makers, middle managers/supervisors, and field workers achieve faster restoration and deliver a more efficient post-disaster emergency response. The findings of this study could serve as a guide for emergency restoration teams in similar disaster management scenarios in the future. Collapse

Topics: mobile application tornado emergency management information management mobile system

Methods: case study qualitative interview

Novel Data Analytics Meets Conventional Container Shipping: Predicting Delays by Comparing Various Machine Learning Algorithms

2020 | HICSS | Citations: 0

Authors: Viellechner, Adrian; Spinler, Stefan

Abstract: Supply chain disruptions are expected to significantly increase over the next dec ... Expand

Abstract: Supply chain disruptions are expected to significantly increase over the next decades. In particular, delay of container vessels is likely to escalate due to rising congestion from continued growth of container shipping and higher frequency of extreme weather events. Predicting these delays could result in significant cost savings from optimizing operations. Both academic research and container shipping industry, however, lack analytical solutions to predict delay. To increase transparency on delay, we develop a prediction model based on 315 explanatory variables, 10 regression models, and 7 classification models. Using machine learning algorithms, we obtain best results for neural network and support vector machine with a prediction accuracy of 77 percent compared to only 59 percent of a naive baseline model. Various shipping players including sender, carrier, terminal operator, and receiver benefit from the easy-to-use prediction model to optimize operations such as buffers in schedules and the selection of ports and routes. Collapse

Topics: strategic management supply chain management natural hazard data quality logistics management

Methods: support vector machine regression analysis method computational algorithm literature study qualitative interview

Moderators for Partition Dependence in Prediction Markets

2019 | Group Decision and Negotiation | Citations: 0

Authors: Kloker, Simon; Straub, Tim; Weinhardt, Christof

Abstract: Every investment decision by organizations is an implicit expression of a predic ... Expand

Abstract: Every investment decision by organizations is an implicit expression of a prediction on the future state of companies, demand trends, future challenges, or politics. Usually, decision makers can chose between different options to invest and also tend to diversify investments in order to reduce risk. Research on cognitive processing, however, showed that unconscious heuristics triggered by “heuristic cues” influence such decisions in an irrational manner. Partition dependence, the partition of the state space, is such a heuristic cue that leads to an irrational bias in the assessment of probabilities towards a uniform distribution. This work demonstrates that this bias is also persistent in group-based forecasting. We explain and predict the occurrence of the partition dependence bias by applying the Dual-Process Theory of cognitive processing. The results of our three consecutive experiments suggest that besides task complexity, individual expertise, and motivation are moderators for the occurrence of partition dependence. We contribute to the research of biases in forecasting by applying approaches of the Dual-process Theories to explain the occurrence of partition dependence for the first time and shed light on future pathways in this research field. Collapse

Topics: Apple iPhone tornado personality user interface decision making

Methods: experiment survey longitudinal research within subject experiment statistical power analysis

Theories: dual-system theory

CrisMap: a Big Data Crisis Mapping System Based on Damage Detection and Geoparsing

2018 | Information Systems Frontiers | Citations: 9

Authors: Avvenuti, Marco; Cresci, Stefano; Del Vigna, Fabio; Fagni, Tiziano; Tesconi, Maurizio

Abstract: Natural disasters, as well as human-made disasters, can have a deep impact on wi ... Expand

Abstract: Natural disasters, as well as human-made disasters, can have a deep impact on wide geographic areas, and emergency responders can benefit from the early estimation of emergency consequences. This work presents CrisMap, a Big Data crisis mapping system capable of quickly collecting and analyzing social media data. CrisMap extracts potential crisis-related actionable information from tweets by adopting a classification technique based on word embeddings and by exploiting a combination of readily-available semantic annotators to geoparse tweets. The enriched tweets are then visualized in customizable, Web-based dashboards, also leveraging ad-hoc quantitative visualizations like choropleth maps. The maps produced by our system help to estimate the impact of the emergency in its early phases, to identify areas that have been severely struck, and to acquire a greater situational awareness. We extensively benchmark the performance of our system on two Italian natural disasters by validating our maps against authoritative data. Finally, we perform a qualitative case-study on a recent devastating earthquake occurred in Central Italy. Collapse

Topics: Twitter earthquake Wikipedia natural hazard big data

Methods: natural language processing word embedding case study support vector machine experiment

Emergency Vocabulary

2018 | Information Systems Frontiers | Citations: 3

Authors: Nemeskey, Dávid Márk; Kornai, András

Abstract: For disaster preparedness, a key aspect of the work is the identification, ahead ... Expand

Abstract: For disaster preparedness, a key aspect of the work is the identification, ahead of time, of the vocabulary of emergency messages. Here we describe how static repositories of traditional news reports can be rapidly exploited to yield disaster- or accident-implicated words and named entities. Collapse

Topics: tornado tsunami earthquake armed conflict terrorist incident

Methods: experiment CoreNLP pilot experiment computational algorithm Okapi BM25

Weakly Supervised and Online Learning of Word Models for Classification to Detect Disaster Reporting Tweets

2018 | Information Systems Frontiers | Citations: 7

Authors: Palshikar, Girish Keshav; Apte, Manoj; Pandita, Deepak

Abstract: Social media has quickly established itself as an important means that people, N ... Expand

Abstract: Social media has quickly established itself as an important means that people, NGOs and governments use to spread information during natural or man-made disasters, mass emergencies and crisis situations. Given this important role, real-time analysis of social media contents to locate, organize and use valuable information for disaster management is crucial. In this paper, we propose self-learning algorithms that, with minimal supervision, construct a simple bag-of-words model of information expressed in the news about various natural disasters. Such a model is human-understandable, human-modifiable and usable in a real-time scenario. Since tweets are a different category of documents than news, we next propose a model transfer algorithm, which essentially refines the model learned from news by analyzing a large unlabeled corpus of tweets. We show empirically that model transfer improves the predictive accuracy of the model. We demonstrate empirically that our model learning algorithm is better than several state of the art semi-supervised learning algorithms. Finally, we present an online algorithm that learns the weights for words in the model and demonstrate the efficacy of the model with word weights. Collapse

Topics: Twitter criminality knowledge representation natural hazard earthquake

Methods: computational algorithm WordNet semi-supervised learning support vector machine part of speech tagging