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A Study of Reddit-User's Response to Rape

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A Study of Reddit-User's Response to Rape

International Conference on Advances in Social Network Analysis and Mining, ASONAM

作者： Nur Shazwani Kamarudin Vineeth Rakesh Ghazaleh Beigi Lydia Manikouda Huan Liu School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe Arizona

ISBN: (纸本)9781538660522

The growth of social media has created an open web where people freely share their opinion and even discuss sensitive subjects in online forums. Forums such as Reddit help support seekers by serving as a portal for open discussions for various stigmatized subjects such as rape. This paper investigates the potential roles of online forums and if such forums provide intended resources to the people who seek support. Specifically, the open nature of forums allows us to study how online users respond to seeker's queries or needs; through their response, we attempt to assess the range of topics covered by responders in regards to the issues, concerns and, obstacles faced by the victims of rape and sexual abuse, using rape-related posts from Reddit. We employ natural language processing techniques to extract topics of responses, examine how diverse these topics are to answer research questions such as whether responses are limited to emotional support; if not, what other topics are; what the diversity of topics manifests; and how online response differs from traditional response found in a physical world.

关键词： Medical treatment Social network services Linguistics Semantics Insurance Law

Build a compact binary neural network through bit-level sensitivity and data pruning

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arXiv 2018年

作者： Li, Yixing Ren, Fengbo School of Computing Informatics Decision Systems Engineering Arizona State University United States

Convolutional neural network (CNN) has been widely used for vision-based tasks. Due to the high computational complexity and memory storage requirement, it is hard to directly deploy a full-precision CNN on embedded devices. The hardware-friendly designs are needed for resource-limited and energy-constrained embedded devices. Emerging solutions are adopted for the neural network compression, e.g., binary/ternary weight network, pruned network and quantized network. Among them, Binarized Neural Network (BNN) is believed to be the most hardware-friendly framework due to its small network size and low computational complexity. No existing work has further shrunk the size of BNN. In this work, we explore the redundancy in BNN and build a compact BNN (CBNN) based on the bit-level sensitivity analysis and bit-level data pruning. The input data is converted to a high dimensional bit-sliced format. In post-training stage, we analyze the impact of different bit slices to the accuracy. By pruning the redundant input bit slices and shrinking the network size, we are able to build a more compact BNN. Our result shows that we can further scale down the network size of the BNN up to 3.9x with no more than 1% accuracy drop. The actual runtime can be reduced up to 2x and 9.9x compared with the baseline BNN and its full-precision counterpart, respectively. Copyright © 2018, The Authors. All rights reserved.

关键词： Neural networks

Approaching Peak Ground Truth

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Approaching Peak Ground Truth

IEEE International Symposium on Biomedical Imaging

作者： Florian Kofler Johannes Wahle Ivan Ezhov Sophia J. Wagner Rami Al-Maskari Emilia Gryska Mihail Todorov Christina Bukas Felix Meissen Tingying Peng Ali Ertürk Daniel Rueckert Rolf Heckemann Jan Kirschke Claus Zimmer Benedikt Wiestler Bjoern Menze Marie Piraud Helmholtz AI Helmholtz Zentrum München Germany Department of Informatics Technical University Munich (TUM) Germany TranslaTUM - Central Institute for Translational Cancer Research TUM Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum Rechts der Isar TUM Germany German Center for Neurodegenerative Diseases (DZNE) Bonn Germany Institute for Tissue Engineering and Regenerative Medicine (iTERM) Helmholtz Munich Germany Department of Radiology Institute of Clinical Sciences Sahlgrenska Academy University of Gothenburg Gothenburg Sweden Institute for Stroke and Dementia Research (ISD) University Hospital LMU Munich Germany Chair for AI in Medicine Klinikum Rechts der Isar Munich Germany Graduate School of Neuroscience (GSN) Munich Germany Munich Cluster for Systems Neurology (Synergy) Munich Germany Department of Computing Imperial College London United Kingdom Department of Medical Radiation Sciences University of Gothenburg Gothenburg Sweden Department of Quantitative Biomedicine University of Zurich Switzerland

Machine learning models are typically evaluated by computing similarity with reference annotations and trained by maximizing similarity with such. Especially in the biomedical domain, annotations are subjective and suffer from low inter-and intra-rater reliability. Since annotations only reflect one interpretation of the real world, this can lead to sub-optimal predictions even though the model achieves high similarity scores. Here, the theoretical concept of Peak Ground Truth (PGT) is introduced. PGT marks the point beyond which an increase in similarity with the reference annotation stops translating to better Real World Model Performance (RWMP). Additionally, a quantitative technique to approximate PGT by computing inter- and intra-rater reliability is proposed. Finally, four categories of PGT-aware strategies to evaluate and improve model performance are reviewed.

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Markov game modeling of moving target defense for strategic detection of threats in cloud networks

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arXiv 2018年

作者： Chowdhary, Ankur Sengupta, Sailik Huang, Dijiang Kambhampati, Subbarao Computing Informatics Decision Systems Engineering Arizona State University

The processing and storage of critical data in large-scale cloud networks necessitate the need for scalable security solutions. It has been shown that deploying all possible security measures incurs a cost on performance by using up valuable computing and networking resources which are the primary selling points for cloud service providers. Thus, there has been a recent interest in developing Moving Target Defense (MTD) mechanisms that helps one optimize the joint objective of maximizing security while ensuring that the impact on performance is minimized. Often, these techniques model the problem of multi-stage attacks by stealthy adversaries as a single-step attack detection game using graph connectivity measures as a heuristic to measure performance, thereby (1) losing out on valuable information that is inherently present in graph-theoretic models designed for large cloud networks, and (2) coming up with certain strategies that have asymmetric impacts on performance. In this work, we leverage knowledge in attack graphs of a cloud network in formulating a zero-sum Markov Game and use the Common Vulnerability Scoring System (CVSS) to come up with meaningful utility values for this game. Then, we show that the optimal strategy of placing detecting mechanisms against an adversary is equivalent to computing the mixed Min-max Equilibrium of the Markov Game. We compare the gains obtained by using our method to other techniques presently used in cloud network security, thereby showing its effectiveness. Finally, we highlight how the method was used for a small real-world cloud system. Copyright © 2018, The Authors. All rights reserved.

关键词： Network security

Discovering patterns of online popularity from time series

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arXiv 2019年

作者： Ozer, Mert Sapienza, Anna Abeliuk, Andrés Muric, Goran Ferrara, Emilio School of Computing Informatics and Decision Science Engineering Arizona State University Tempe United States Information Sciences Institute University of Southern California Marina Del Rey United States

How is popularity gained online? Is being successful strictly related to rapidly becoming viral in an online platform or is it possible to acquire popularity in a steady and disciplined fashion? What are other temporal characteristics that can unveil popularity of online content? To answer these questions, we leverage a multi-faceted temporal analysis of the evolution of popular online contents. Here, we present dipm-SC: a multi-dimensional shape-based time-series clustering algorithm with a heuristic to find the optimal number of clusters. First, we validate the ac- curacy of our algorithm on synthetic datasets generated from benchmark time series models. Second, we show that dipm-SC can uncover mean- ingful clusters of popularity behaviors in a real-world Twitter dataset. By clustering the multidimensional time-series of popularity of contents coupled with other domain-specific dimensions, we uncover two main patterns of popularity: bursty and steady temporal behaviors. Moreover, we find that the way popularity is gained over time has no significant impact on the final cumulative popularity. Copyright © 2019, The Authors. All rights reserved.

关键词： Social networking (online)

A WEB-SERVICE FRAMEWORK FOR THE WATER EVALUATION AND PLANNING SYSTEM

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A WEB-SERVICE FRAMEWORK FOR THE WATER EVALUATION AND PLANNIN...

Simulation Multi-Conference

作者： Mostafa D. Fard Hessam Sarjoughian Arizona Center for Integrative Modeling & Simulation School of Computing Informatics and Decision Systems Engineering Tempe AZ USA

For systems-of-systems, the use of different modeling methods is important not only because each system can be described more correctly, but also benefit from tools that are in use by different communities. Using an opaque or closed source-code tool with others, however, is challenging. Consequently, to facilitate the development of simulations for systems-of-systems it is useful to cast closed-source models in a flexible component-based framework. Using this concept, a RESTful web service framework is developed for the Water Evaluation And Planning (WEAP) software system. The WEAP RESTful framework has a suite of model components for all the model entities defined in the WEAP system. An example water system model shows the computational cost for the web-service framework is negligible. Casting the model entities to components can play a key role in using the WEAP system with other modeling frameworks useful for simulating the complexities of the Food-Energy-Water systems.

关键词： Component-based modeling MDA RESTful services systems-of-systems WEAP

Technologies for developing a smart city in computational thinking

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International Journal of Simulation and Process Modelling 2018年第2期13卷 91-101页

作者： Chen, Yinong De Luca, Gennaro School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85287-8809 United States

Building a smart city involves almost all the aspects of social, economic, and technological issues. This paper gives a brief survey and overview of the latest technologies for building a smart city. It covers IoT, device as a service, service-oriented computing and cloud computing as the infrastructure, big data analysis and processing for data mining, and artificial intelligence. A visual programming language for IoT and robotics application development is also introduced, which offers a tool for architects and developers to define the applications using computational thinking and workflow of existing services and components. Copyright © 201 Inderscience Enterprises Ltd.

关键词： Cloud computing

Translating lpoD and CR-Prolog2 into standard answer set programs

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arXiv 2018年

作者： Lee, Joohyung Yang, Zhun School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe United States

Logic Programs with Ordered Disjunction (LPOD) is an extension of standard answer set programs to handle preference using the construct of ordered disjunction, and CR-Prolog2 is an extension of standard answer set programs with consistency restoring rules and LPOD-like ordered disjunction. We present reductions of each of these languages into the standard ASP language, which gives us an alternative way to understand the extensions in terms of the standard ASP language. Copyright © 2018, The Authors. All rights reserved.

关键词： Logic programming

Determination of risk factors influencing binge eating behaviour among university students

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AIP Conference Proceedings 2024年第1期2895卷

作者： Shaidatul Akma Huzid Nurain Ibrahim Zalina Zahid Siti Aida Sheikh Hussin Nor Azura Md Ghani Ali Selamat FGV Prodata Systems Sdn Bhd Wisma FGV Jalan Raja Laut 50350 Kuala Lumpur Malaysia College of Computing Informatics and Media Universiti Teknologi MARA 40450 Shah Alam Selangor Malaysia Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Kompleks Al-Khawarizmi Universiti Teknologi MARA 40450 Shah Alam Selangor Malaysia Malaysia-Japan International International Institute of Technology (MJIIT) Universiti Teknologi Malaysia Kuala Lumpur 54100 Malaysia Media and Games Center of Excellence (MagicX) Faculty of Engineering School of Computing Universiti Teknologi Malaysia Johor Baharu 81310 Malaysia

In Malaysia, binge eating is a significant issue because free food is so readily available. As a result, many Malaysians tend to overeat and have binge eating disorder symptoms. Teenagers and university students are typically affected. There are limited studies on binge eating behaviour in Malaysia, especially among university students. Therefore, Fisher Exacts Test was used in this study’s initial attempt to determine the relationship between the factors and binge eating behaviour. The Ordinal Logistic regression model was then used in this study to identify factors that significantly influence student binge eating behaviours. The two-stage stratified sampling method was used for selecting the sample, involving 339 undergraduate and postgraduate students from Faculty of Computer and Mathematical Sciences (FSKM) at UiTM Shah Alam. The questionnaire was distributed and analysed using Fisher’s Exact test and Ordinal Logistic Regression. Fisher’s Exact test revealed that binge eating levels are associated with age, depression, anxiety, physical appearance, and educations among FSKM students. According to Ordinal logistic regression, only age and physical appearance are significantly affecting binge eating levels. In conclusion, the teenager students are more prone to show severe binge eating behaviours. Additionally, those who are very self-conscious about their bodies are more prone to experience severe binge eating.

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