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Recommender systems take inputs from user history, use an internal ranking algorithm to generate results and possibly optimize this ranking based on feedback. However, often the recommender system is unaware of the actual intent of the user and simply provides recommendations dynamically without properly understanding the thought process of the user. An intelligent recommender system is not only useful...
Network centrality reflects node importance in networks, which is a challenging problem in social network analysis. Based on Fuzzy Set and MYCIN theory, this paper proposes a novel node centrality measuring method and models n-monkeys dataset, where n is 20. Initially, we created monkeys relationship graph and generated relationship matrix based on the monkeys' encountering times in a specific time...
In this work, we report an ongoing study that aims to apply cluster validation measures for analyzing email communications at an organizational level of a company. This analysis can be used to evaluate the company structure and to produce further recommendations for structural improvements. Our initial evaluations, based on data in the forms of emails logs and organizational structure for a large...
Analyzing job hopping behavior is important for the understanding of job preference and career progression of working individuals. When analyzed at the workforce population level, job hop analysis helps to gain insights of talent flow and organization competition. Traditionally, surveys are conducted on job seekers and employers to study job behavior. While surveys are good at getting direct user...
Change point analysis is a statistical tool to identify homogeneity within time series data. We propose a pruning approach for approximate nonparametric estimation of multiple change points. This general purpose change point detection procedure 'cp3o' applies a pruning routine within a dynamic program to greatly reduce the search space and computational costs. Existing goodness-of-fit change point...
Adapted from biological sequence alignment, trace alignment is a process mining technique used to visualize and analyze workflow data. Any analysis done with this method, however, is affected by the alignment quality. The best existing trace alignment techniques use progressive guide-trees to heuristically approximate the optimal alignment in O(N2L2) time. These algorithms are heavily dependent on...
Comparing images to recommend items from an image-inventory is a subject of continued interest. Added with the scalability of deep-learning architectures the once 'manual' job of hand-crafting features have been largely alleviated, and images can be compared according to features generated from a deep convolutional neural network. In this paper, we compare distance metrics (and divergences) to rank...
The large adoption of Twitter during electioneering has created a valuable opportunity to monitor political deliberation nationwide. Recent work has analyzed online attention to forecast elections results addressing some limitations of opinion polling. However, the reproducibility of such methods remains a challenge given that most of them rely on the number of political parties or candidates mentions...
Social surveys have been used by researchers and policy makers as an essential tool for understanding social and political activities in society. Social media has introduced a new way of capturing data from large numbers of people. Unlike surveys, social media deliver data more rapidly and cheaply. In this paper, we aim to rapidly identify socio-political activity in South Africa using proxy data...
Cloud users have little visibility into the performance characteristics and utilization of the physical machines underpinning the virtualized cloud resources they use. This uncertainty forces users and researchers to reverse engineer the inner workings of cloud systems in order to understand and optimize the conditions their applications operate. At Massachusetts Open Cloud (MOC), as a public cloud...
Even while engaged in an attention-consuming activity such as watching TV, social media users often end up paying attention to one or more social media. This is an example of a behavioral phenomenon called Continuous Partial Attention (CPA). Quantification of user attention can be a valuable metric in understanding user behavior under scenarios where their attention is divided. In this study, we propose...
In this paper, we propose an online spatiotemporal data-driven methodology to detect malicious cyber attacks that target power system balancing and frequency control. The anomaly detection, which spots abnormal generator behavioral patterns in real time, is achieved locally at a power plant with peer to peer communication capability. We mainly consider the data integrity attack targeting Automatic...
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