MARGIN: Uncovering Deep Neural Networks using Graph Signal Analysis

Interpretability has emerged as a crucial aspect of machine learning, aimed at providing insights into the working of complex neural networks. However, existing solutions vary vastly based on the nature of the interpretability task, with each use case requiring substantial time and effort. This paper introduces MARGIN, a simple yet general approach to address a large set of interpretability tasks ranging from identifying prototypes to explaining image predictions. MARGIN exploits ideas rooted in graph signal analysis to determine influential nodes in a graph, which are defined as those nodes that maximally describe a function defined on the graph. By carefully defining task-specific graphs and functions, we demonstrate that MARGIN outperforms existing approaches in a number of disparate interpretability challenges.Comment: Technical Repor

Intellectual Structure of Business Analytics in Information Systems

Business Analytics is arguably the most dominant topic of discussion among both academics and practitioners. As organizations scramble to derive insights from rapidly growing data, we see an exponential growth in the number of articles related to Business Analytics. The purpose of this article is to examine the conceptual foundations of the field of analytics based on an analysis of articles that have appeared in the IS senior scholars’ basket of eight journals during the last 25 years (1992 - 2016). Using a combination of citation analysis and text mining, our study: (a) reveals the disciplines that influence Business Analytics research in information systems; and (b) explicates dominant themes latent in the corpus. Concepts related to Predictive Analytics, Business Intelligence, the Web, IT Management, Firm Performance and Decision Support were found to be at the heart of analytics research conducted by IS scholars in the basket of eight journals

Energy-Efficient Video Text-Spotting

There is a lot of research done to increase the accuracy and reduce the latency of deep learning algorithms. But, there is very little research done to reduce the energy consumption of the deep learning models. For applications that require deploying the deep learning models on the edge devices that have low compute resources, it is important that these algorithms are energy-efficient. Efficient Video Text Spotting is the field that deals with developing deep learning models to be deployed on edge devices to detect, localize, and recognize text appearing in the frames of the videos. Previous methods followed a four-step pipeline: text detection in every frame, text recognition for the localized text region in every frame, tracking text streams, and post-processing. The two main problems with the above approach are high computational cost and low performance. This thesis focuses on the text spotting model design for an Efficient Video Text Spotting System. In this thesis, model design experiments are carried out keeping efficiency in mind. Two different real-time text spotting models were experimented i.e. ABCNet and FOTS. For ABCNet different backbones, normalization schemes, and feature pyramid variations are experimented with to attain the best accuracy and energy tradeoff. For the FOTS model, the two-step text spotting and two-stage text spotting model design are experimented. The influence of various factors such as bounding box to character count ratio, character count, blur level, bounding box count, bounding box area are experimented. From the experiments, it was observed that the two-step text spotting model design method performed better for all resolutions. Further, it was observed that the recognition performance improves with a higher bounding box to character count ratio and lower character count. The energy measurement of the two-step FOTS text spotting model on Raspberry Pi is also presented

J/psi+gamma production at the LHC

The associated production of J/psi + gamma at the LHC is studied within the NRQCD framework. The signal we focus on is the production of a J/psi and an isolated photon produced back-to-back, with their transverse momenta balanced. It is shown that even for very large values of transverse momentum (pT of the order of 50 GeV) the dominant contribution to this process is not fragmentation. This is because of the fact that fragmentation-type contributions to the cross-section come from only a q q(bar) initial state, which is suppressed at the LHC. We identify gg-initiated diagrams higher-order in alpha(s) which do have fragmentation-type vertices. We find, however, that the contribution of these diagrams is negligibly small.Comment: 9 pages, LaTeX, 6 ps figures, Minor changes, Version to appear in Physical Review

Medical Crowdsourcing: Harnessing the “Wisdom of the Crowd” to Solve Medical Mysteries

Medical crowdsourcing offers hope to patients who suffer from complex health conditions that are difficult to diagnose. Such crowdsourcing platforms empower patients to harness the “wisdom of the crowd” by providing access to a vast pool of diverse medical knowledge. Greater participation in crowdsourcing increases the likelihood of encountering a correct solution. However, more participation also leads to increased “noise,” which makes identifying the most likely solution from a broader pool of recommendations (i.e., diagnostic suggestions) difficult. The challenge for medical crowdsourcing platforms is to increase participation of both patients and solution providers, while simultaneously increasing the efficacy and accuracy of solutions. The primary objectives of this study are: (1) to investigate means to enhance the solution pool by increasing participation of solution providers referred to as “medical detectives” or “detectives,” and (2) to explore ways of selecting the most likely diagnosis from a set of alternative possibilities recommended by medical detectives. Our results suggest that our strategy of using multiple methods for evaluating recommendations by detectives leads to better predictions. Furthermore, cases with higher perceived quality and more negative emotional tones (e.g., sadness, fear, and anger) attract more detectives. Our findings have strong implications for research and practice

HybridGuard: A Principal-based Permission and Fine-Grained Policy Enforcement Framework for Web-based Mobile Applications

Web-based or hybrid mobile applications (apps) are widely used and supported by various modern hybrid app development frameworks. In this architecture, any JavaScript code, local or remote, can access available APIs, including JavaScript bridges provided by the hybrid framework, to access device resources. This JavaScript inclusion capability is dangerous, since there is no mechanism to determine the origin of the code to control access, and any JavaScript code running in the mobile app can access the device resources through the exposed APIs. Previous solutions are either limited to a particular platform (e.g., Android) or a specific hybrid framework (e.g., Cordova) or only protect the device resources and disregard the sensitive elements in the web environment. Moreover, most of the solutions require the modification of the base platform. In this paper, we present HybridGuard, a novel policy enforcement framework that can enforce principal-based, stateful policies, on multiple origins without modifying the hybrid frameworks or mobile platforms. In HybridGuard, hybrid app developers can specify principal-based permissions, and define fine-grained, and stateful policies that can mitigate a significant class of attacks caused by potentially malicious JavaScript code included from third-party domains, including ads running inside the app. HybridGuard also provides a mechanism and policy patterns for app developers to specify fine-grained policies for multiple principals. HybridGuard is implemented in JavaScript, therefore, it can be easily adapted for other hybrid frameworks or mobile platforms without modification of these frameworks or platforms. We present attack scenarios and report experimental results to demonstrate how HybridGuard can thwart attacks against hybrid mobile apps