Applied Science and Engineering

The goal of the SunPy project is to facilitate and promote the use and development of community-led, free, and open source data analysis software for solar physics based on the scientific Python environment. The project achieves this goal by developing and maintaining the sunpy core package and supporting an ecosystem of affiliated packages. This paper describes the first official stable release (version 1.0) of the core package, as well as the project organization and infrastructure. This paper concludes with a discussion of the future of the SunPy project.

The goal of the SunPy project is to facilitate and promote the use and development of community-led, free, and open source data analysis software for solar physics based on the scientific Python environment. The project achieves this goal by developing and maintaining the sunpy core package and supporting an ecosystem of affiliated packages. This paper describes the first official stable release (version 1.0) of the core package, as well as the project organization and infrastructure. This paper concludes with a discussion of the future of the SunPy project.

Detecting anomalous subgraphs in a dynamic graph in an online or streaming fashion is an important requirement in industrial settings for intrusion detection or denial of service attacks. While only detecting anomalousness in the system by edge frequencies is an optimal approach, many latent information can get unnoticed in the process, since as a characteristic of the network only edge frequencies are considered. We propose a game theoretic approach whereby using the modularity function we try to estimate in a streaming graph whether addition of a new edge in the current time tick results in a dense subgraph creation, thus indicating possible anomalous score. Our contributions are as follows: (a) We propose a novel game-theoretic framework for detecting dense subcommunities in an online streaming environment; (b) We detect such subcommunities using constant memory storage. Our results are corroborated with empirical evaluation on real datasets.

Often logs hosted in large data centers represent network traffic data over a long period of time. For instance, such network traffic data logged via a TCP dump packet sniffer (as considered in the 1998 DARPA intrusion attack) included network packets being transmitted between computers. While an online framework is necessary for detecting any anomalous or suspicious network activities like denial of service attacks or unauthorized usage in real time, often such large data centers log data over long periods of time (e.g., TCP dump) and hence an offline framework is much more suitable in such scenarios. Given a network log history of edges from a dynamic graph, how can we assign anomaly scores to individual edges indicating suspicious events with high accuracy using only constant memory and within limited time than state-of-the-art methods? We propose MDistrib and its variants which provides (a) faster detection of anomalous events via distributed processing with GPU support compared to other approaches, (b) better false positive guarantees than state of the art methods considering fixed space and (c) with collision aware based anomaly scoring for better accuracy results than state-of-theart approaches. We describe experiments confirming that MDistrib is more efficient than prior work.

Detection of relevant and meaningful communities from any social network is always a constant research challenge. Considering large social networks existing approaches often fail to capture perfect community partition or fail to converge fast. Inspired by nonmyopic reinforcement learning domain, in this paper, we have proposed a novel game theory based community detection algorithm which considers community retention based on temporal information as part of a node’s strategy (i.e. for a node whether their current assigned community is more profitable based upon future possibilities, or should they switch at the moment), thereby minimizing cross-community false node switches. The proposed method has the following properties (a) considers community retention for a node in the network when it considers switching to another community, (b) achieves significantly better/comparable performance w.r.t baselines in terms of metrics such as quality of partition on various real world datasets,(...

Our planet is in crisis. Humanity can only avoid disaster if we pursue goals that promote prosperity while protecting the planet. The promise of human computation and crowdsourcing was to use technology to liberate the latent potential across the globe to accomplish feats that were once the province of a specialized few. This vision paper discusses the prospects and challenges of leveraging human computation and crowdsourcing to assist, measure, and accelerate progress toward the Sustainable Development Goals (SDGs) outlined by the United Nations (UN).