On Counting Triangles through Edge Sampling in Large Dynamic Graphs

Traditional frameworks for dynamic graphs have relied on processing only the stream of edges added into or deleted from an evolving graph, but not any additional related information such as the degrees or neighbor lists of nodes incident to the edges. In this paper, we propose a new edge sampling framework for big-graph analytics in dynamic graphs which enhances the traditional model by enabling the use of additional related information. To demonstrate the advantages of this framework, we present a new sampling algorithm, called Edge Sample and Discard (ESD). It generates an unbiased estimate of the total number of triangles, which can be continuously updated in response to both edge additions and deletions. We provide a comparative analysis of the performance of ESD against two current state-of-the-art algorithms in terms of accuracy and complexity. The results of the experiments performed on real graphs show that, with the help of the neighborhood information of the sampled edges, the accuracy achieved by our algorithm is substantially better. We also characterize the impact of properties of the graph on the performance of our algorithm by testing on several Barabasi-Albert graphs.Comment: A short version of this article appeared in Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2017

Bias in data-driven artificial intelligence systems - An introductory survey

Artificial Intelligence (AI)-based systems are widely employed nowadays to make decisions that have far-reaching impact on individuals and society. Their decisions might affect everyone, everywhere, and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training, and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multidisciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well-grounded in a legal frame. In this survey, we focus on data-driven AI, as a large part of AI is powered nowadays by (big) data and powerful machine learning algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features such as race, sex, and so forth. This article is categorized under: Commercial, Legal, and Ethical Issues > Fairness in Data Mining Commercial, Legal, and Ethical Issues > Ethical Considerations Commercial, Legal, and Ethical Issues > Legal Issues

Bias in data-driven artificial intelligence systems—An introductory survey

Artificial Intelligence (AI)-based systems are widely employed nowadays to make decisions that have far-reaching impact on individuals and society. Their decisions might affect everyone, everywhere, and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training, and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multidisciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well-grounded in a legal frame. In this survey, we focus on data-driven AI, as a large part of AI is powered nowadays by (big) data and powerful machine learning algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features such as race, sex, and so forth. This article is categorized under: Commercial, Legal, and Ethical Issues > Fairness in Data Mining Commercial, Legal, and Ethical Issues > Ethical Considerations Commercial, Legal, and Ethical Issues > Legal Issues

Grid-based virtual laboratory experiments for a graduate course on sensor networks

This paper presents the pedagogical and technical challenges the authors faced in developing a distributed laboratory for the execution of virtual scientific experiments (VSEs) superimposed on a Grid infrastructure, for a course on sensor networks that is part of the Master's in Information Networking (MSIN) program jointly offered by Carnegie Mellon University (CMU), USA and Athens Information Technology (AIT), Athens, Greece. The MSIN program utilizes virtual classroom technologies because of its strong distance learning component. Courses taught by CMU faculty are attended in real-time by students in Athens, Greece, via video-wall teleconferencing sessions. Vice versa, visiting CMU faculty to AIT teach classes that are attended by students at CMU. Students in both institutions enjoy full interactivity with their classmates on the other side of the Atlantic Ocean. A distributed shared virtual laboratory is needed for many of the more empirical courses. This paper describes the challenges and issues the authors faced in developing such a lab

Grid-Based Interactive Virtual Scientific Experiments for Distributed Virtual Communities

E-learning technologies have matured to a point where distance learning classes are commonly offered from many leading Universities around the world. A major challenge in such distributed classrooms is the formation of virtual communities among the participating students, enhancing the overall learning experience. Shared virtual laboratories offer the possibility of forming such virtual communities as students form lab teams to run the same interactive simulation and in the course of such experiments learn to interact and understand each other better. We have designed and implemented a Virtual Scientific Experiment architectural framework on top of a Grid infrastructure for running interactive virtual laboratory experiments for such distributed student communities with visualization capabilities. The architecture is based on Web Services standard protocols such as WSDL and WS-Notification as implemented in the WSRF specification. For the first concrete instantiation of this architecture, we ported a stand-alone Wireless Sensor Network simulator written in Java in our Grid-based architecture and extended it to allow for initial collaborative parameter setup and on-the-fly visualization of the simulation execution and interaction with it, a capability not present in the original simulator. We report on results from running such simulations on a local Grid infrastructure. System evaluation results from a distributed pool of students show the added value of our system in enhancing distance-learning programs and Virtual Classes with extensible collaborative and interactive Virtual Laboratories sessions

CrowdUI:supporting web design with the crowd

Abstract Web design is a complex and challenging task. It involves making many design decisions that materialise preconceived notions of user needs that may or may not be true. In this paper, we investigate supporting the co-design of a website with visual feedback elicited from the website’s community of users. Website users can express their needs by re-arranging and modifying the website’s layout and design. To explore and validate this idea, we present CrowdUI, a web-based tool that enables members of the community of a website to visually express their design improvement ideas, frustrations and needs, and to send this feedback to the person in charge of designing or maintaining the website. CrowdUI is validated in a study with 45 users of a popular social media and networking website. Second, our qualitative evaluation with 60 experienced web developers shows that CrowdUI is able to elicit diverse and meaningful feedback. Put together, our results suggest that CrowdUI’s approach constitutes a productive setting for eliciting visual feedback from the user community as a complement to traditional ways of eliciting feedback and participatory design. Finally, based on our experiences, we discuss a design space for crowdsourced web design and provide design recommendations for similar future tools

Message from the technical program and art and demos chairs

Proceedings - 3rd International Conference on Digital Interactive Media in Entertainment and Arts, DIMEA 2008x