snu-aidas/HVMap-dataset huggingface dataset:
Capturing Evolving Visit Behavior in Clickstream Data

Many online sites, both retailers and content providers, routinely monitor visitor traffic as a useful measure of their overall success. However, simple summaries such as the total number of visits per month provide little insight about individual-level site-visit patterns, especially in a changing environment such as the Internet. This article develops an individual-level model for evolving visiting behavior based on Internet clickstream data. We capture cross-sectional variation in site-visit behavior as well as changes over time as visitors gain experience with the site. In addition, we examine the relationship between visiting frequency and purchasing propensity at an e-commerce site. We find evidence supporting the notion that people who visit a retail site more frequently have a greater propensity to buy. We also show that changes (i.e., evolution) in an individual's visit frequency over time provides further information regarding which customer segments are likely to have higher purchasing conversion rates. Disciplines Business | Business Analytics | E-Commerce | Management Sciences and Quantitative Methods | Marketing | Technology and Innovation This technical report is available at ScholarlyCommons: https://repository.upenn.edu/marketing_papers/275 Capturing Evolving Visit Behavior in Clickstream Data Wendy W. Moe and Peter S. Fader The Wharton School University of Pennsylvania Marketing Department 1400 Steinberg Hall-Dietrich Hall Philadelphia, PA 19104-6371 telephone: (215) 898-7603 fax: (215) 898-2534 e-mail: wendy@marketing.wharton.upenn.edu
World of Warcraft and the impact of game culture and play in an undergraduate game design course

During the past two decades, digital games have become an increasingly popular source of study for academics, educational researchers and instructional designers. Much has been written about the potential of games for teaching and learning, both in the design of educational/serious games and the implementation of off-the-shelf games for learning. Yet relatively little research has been conducted about how game culture and the enmeshed practice of play may impact classroom dynamics. The purpose of this study is to present a case study about how the use of World of Warcraft (WoW) as a teaching tool and medium of play impacted class dynamics in an undergraduate university-level course for game design. Specifically, this study will address how WoW’s game culture and the practice of play impacted (a) student-to-student dynamics and (b) class dynamics. The goal of this study is to explore some of the dynamics of play as a component of learning. 2010 Elsevier Ltd. All rights reserved.
Automatic Color Calibration for Large Camera Arrays

We present a color calibration pipeline for large camera arrays. We assume static lighting conditions for each camera, such as studio lighting or a stationary array outdoors. We also assume we can place a planar calibration target so it is visible from every camera. Our goal is uniform camera color responses, not absolute color accuracy, so we match the cameras to each other instead of to a color standard. We first iteratively adjust the color channel gains and offsets for each camera to make their responses as similar as possible. This step white balances the cameras, and for studio applications, ensures that the range of intensities in the scene are mapped to the usable output range of the cameras. Residual errors are then calibrated in post-processing. We present results calibrating an array of 100 CMOS image sensors in different physical configurations, including closely or widely spaced cameras with overlapping fields of views, and tightly packed cameras with non-overlapping fields of view. The process is entirely automatic, and the camera configuration runs in less than five minutes on the 100 camera array. CR Categories: I.4.1 [Image Processing and Computer Vision]: Digitization and Image Capture.Camera Calibration;
Active sampling for entity matching

In entity matching, a fundamental issue while training a classifier to label pairs of entities as either duplicates or non-duplicates is the one of selecting informative training examples. Although active learning presents an attractive solution to this problem, previous approaches minimize the misclassification rate (0-1 loss) of the classifier, which is an unsuitable metric for entity matching due to class imbalance (i.e., many more non-duplicate pairs than duplicate pairs). To address this, a recent paper [1] proposes to maximize recall of the classifier under the constraint that its precision should be greater than a specified threshold. However, the proposed technique requires the labels of all n input pairs in the worst-case.
 Our main result is an active learning algorithm that approximately maximizes recall of the classifier while respecting a precision constraint with provably sub-linear label complexity (under certain distributional assumptions). Our algorithm uses as a black-box any active learning module that minimizes 0-1 loss. We show that label complexity of our algorithm is at most log n times the label complexity of the black-box, and also bound the difference in the recall of classifier learnt by our algorithm and the recall of the optimal classifier satisfying the precision constraint. We provide an empirical evaluation of our algorithm on several real-world matching data sets that demonstrates the effectiveness of our approach.
Predictors of Computer Use in Community-Dwelling, Ethnically Diverse Older Adults

OBJECTIVE
In this study, we analyzed self-reported computer use, demographic variables, psychosocial variables, and health and well-being variables collected from 460 ethnically diverse, community-dwelling elders to investigate the relationship computer use has with demographics, well-being, and other key psychosocial variables in older adults.


BACKGROUND
Although younger elders with more education, those who employ active coping strategies, or those who are low in anxiety levels are thought to use computers at higher rates than do others, previous research has produced mixed or inconclusive results regarding ethnic, gender, and psychological factors or has concentrated on computer-specific psychological factors only (e.g., computer anxiety). Few such studies have employed large sample sizes or have focused on ethnically diverse populations of community-dwelling elders.


METHOD
With a large number of overlapping predictors, zero-order analysis alone is poorly equipped to identify variables that are independently associated with computer use. Accordingly, both zero-order and stepwise logistic regression analyses were conducted to determine the correlates of two types of computer use: e-mail and general computer use.


RESULTS
Results indicate that younger age, greater level of education, non-Hispanic ethnicity, behaviorally active coping style, general physical health, and role-related emotional health each independently predicted computer usage.


CONCLUSION
Study findings highlight differences in computer usage, especially in regard to Hispanic ethnicity and specific health and well-being factors.


APPLICATION
Potential applications of this research include future intervention studies, individualized computer-based activity programming, or customizable software and user interface design for older adults responsive to a variety of personal characteristics and capabilities.
We're doing it live: A multi-method empirical study on continuous experimentation

Context: Continuous experimentation guides development activities based on data collected on a subset of online users on a new experimental version of the software. It includes practices such as canary releases, gradual rollouts, dark launches, or A/B testing. Objective: Unfortunately, our knowledge of continuous experimentation is currently primarily based on well-known and outspoken industrial leaders. To assess the actual state of practice in continuous experimentation, we conducted a mixed-method empirical study. Method: In our empirical study consisting of four steps, we interviewed 31 developers or release engineers, and performed a survey that attracted 187 complete responses. We analyzed the resulting data using statistical analysis and open coding. Results: Our results lead to several conclusions: (1) from a software architecture perspective, continuous experimentation is especially enabled by architectures that foster independently deployable services, such as microservices-based architectures; (2) from a developer perspective, experiments require extensive monitoring and analytics to discover runtime problems, consequently leading to developer on call policies and influencing the role and skill sets required by developers; and (3) from a process perspective, many organizations conduct experiments based on intuition rather than clear guidelines and robust statistics. Conclusion: Our findings show that more principled and structured approaches for release decision making are needed, striving for highly automated, systematic, and dataand hypothesis-driven deployment and experimentation.
Multiple Emitter Location and Signal Parameter-- Estimation
Multi-Prediction Deep Boltzmann Machines

We introduce the multi-prediction deep Boltzmann machine (MP-DBM). The MPDBM can be seen as a single probabilistic model trained to maximize a variational approximation to the generalized pseudolikelihood, or as a family of recurrent nets that share parameters and approximately solve different inference problems. Prior methods of training DBMs either do not perform well on classification tasks or require an initial learning pass that trains the DBM greedily, one layer at a time. The MP-DBM does not require greedy layerwise pretraining, and outperforms the standard DBM at classification, classification with missing inputs, and mean field prediction tasks.1
Data Structures for Traveling Salesmen

The choice of data structure for tour representation plays a critical role in the efficiency of local improvement heuristics for the Traveling Salesman Problem. The tour data structure must permit queries about the relative order of cities in the current tour and must allow sections of the tour to be reversed. The traditional array-based representation of a tour permits the relative order of cities to be determined in small constant time, but requires worst-case Ω(N) time (where N is the number of cities) to implement a reversal, which renders it impractical for large instances. This paper considers alternative tour data structures, examining them from both a theoretical and experimental point of view. The first alternative we consider is a data structure based on splay trees, where all queries and updates take amortized time O( logN). We show that this is close to the best possible, because in the cell probe model of computation any data structure must take worst-case amortized time Ω( logN /log logN) per operation. Empirically (for random Euclidean instances), splay trees overcome their large constant-factor overhead and catch up to arrays by N = 10 , 000, pulling ahead by a factor of 4-10 (depending on machine) when N = 100 , 000. Two alternative tree-based data structures do even better in this range, however. Although both are asymptotically inferior to the splay tree representation, the latter does not appear to pull even with them until N ∼ 1 , 000 , 000. ________________ 1 Rutgers University, New Brunswick, NJ 08903, and University of California at San Diego, La Jolla, CA 92093 2 Room 2D-150, AT&T Bell Laboratories, Murray Hill, NJ 07974 3 Department of Mathematics and Computer Science, Amherst College, Amherst, MA 01002 4 Department of Mathematics, Rutgers University, New Brunswick, NJ 08903 * A preliminary version of this paper appeared under the same title in Proceedings 4th Ann. ACM-SIAM Symp. on Discrete Algorithms (1993), 145-154.
Covariate Shift in Hilbert Space: A Solution via Sorrogate Kernels

Covariate shift is an unconventional learning scenario in which training and testing data have different distributions. A general principle to solve the problem is to make the training data distribution similar to that of the test domain, such that classifiers computed on the former generalize well to the latter. Current approaches typically target on sample distributions in the input space, however, for kernel-based learning methods, the algorithm performance depends directly on the geometry of the kernel-induced feature space. Motivated by this, we propose to match data distributions in the Hilbert space, which, given a pre-defined empirical kernel map, can be formulated as aligning kernel matrices across domains. In particular, to evaluate similarity of kernel matrices defined on arbitrarily different samples, the novel concept of surrogate kernel is introduced based on the Mercer’s theorem. Our approach caters the model adaptation specifically to kernel-based learning mechanism, and demonstrates promising results on several real-world applications. Proceedings of the 30 th International Conference on Machine Learning, Atlanta, Georgia, USA, 2013. JMLR: W&CP volume 28. Copyright 2013 by the author(s).
Security Threats to Automotive CAN Networks - Practical Examples and Selected Short-Term Countermeasures

The IT security of automotive systems is an evolving area of research. To analyse the current situation and the potentially growing tendency of arising threats we performed several practical tests on recent automotive technology. With a focus on automotive systems based on CAN bus technology, this article summarises the results of four selected tests performed on the control systems for the window lift, warning light and airbag control system as well as the central gateway. These results are supplemented in this article by a classification of these four attack scenarios using the established CERT taxonomy and an analysis of underlying security vulnerabilities, and especially, potential safety implications. With respect to the results of these tests, in this article we further discuss two selected countermeasures to address basic weaknesses exploited in our tests. These are adaptations of intrusion detection (discussing three exemplary detection patterns) and IT-forensic measures (proposing proactive measures based on a forensic model). This article discusses both looking at the four attack scenarios introduced before, covering their capabilities and restrictions. While these reactive approaches are short-term measures, which could already be added to today’s automotive IT architecture, long-term concepts also are shortly introduced, which are mainly preventive but will require a major redesign. Beneath a short overview on respective research approaches, we discuss their individual requirements, potential and restrictions. & 2010 Elsevier Ltd. All rights reserved.
Streaming Live Media over a Peer-to-Peer Network

The high bandwidth required by live streaming video greatly limits the number of clients that can be served by a source. In this work, we discuss and evaluate an architecture, calledSpreadIt, for streaming live media over a network of clients, using the resources of the clients themselves. Using SpreadIt, we can distribute bandwidth requirements over the network. The key challenge is to allow an application level multicast tree to be easily maintained over a network of transient peers, while ensuring that quality of service does not degrade. We propose a basic peering infrastructure layer for streaming applications, which uses a redirect primitive to meet the challenge successfully. Through empirical and simulation studies, we show that SpreadIt provides a good quality of service, which degrades gracefully with increasing number of clients. Perhaps more significantly, existing applications can be made to work with SpreadIt, without any change to their code base. Keywords— peer-to-peer networks, live streaming media, peering layer, redirect primitive, transient nodes
A hybrid feature selection scheme for unsupervised learning and its application in bearing fault diagnosis

With the development of the condition-based maintenance techniques and the consequent requirement for good machine learning methods, new challenges arise in unsupervised learning. In the real-world situations, due to the relevant features that could exhibit the real machine condition are often unknown as priori, condition monitoring systems based on unimportant features, e.g. noise, might suffer high falsealarm rates, especially when the characteristics of failures are costly or difficult to learn. Therefore, it is important to select the most representative features for unsupervised learning in fault diagnostics. In this paper, a hybrid feature selection scheme (HFS) for unsupervised learning is proposed to improve the robustness and the accuracy of fault diagnostics. It provides a general framework of the feature selection based on significance evaluation and similarity measurement with respect to the multiple clustering solutions. The effectiveness of the proposed HFS method is demonstrated by a bearing fault diagnostics application and comparison with other features selection methods. 2011 Elsevier Ltd. All rights reserved.
An exact algorithm for a vehicle routing problem with time windows and multiple use of vehicles

The vehicle routing problem with multiple use of vehicles is a variant of the classical vehicle routing problem. It arises when each vehicle performs several routes during the workday due to strict time limits on route duration (e.g., when perishable goods are transported). The routes are defined over customers with a revenue, a demand and a time window. Given a fixed-size fleet of vehicles, it might not be possible to serve all customers. Thus, the customers must be chosen based on their associated revenue minus the traveling cost to reach them. We introduce a branch-and-price approach to address this problem where lower bounds are computed by solving the linear programming relaxation of a set packing formulation, using column generation. The pricing subproblems are elementary shortest path problems with resource constraints. Computational results are reported on euclidean problems derived from well-known benchmark instances for the vehicle routing problem with time windows. 2009 Elsevier B.V. All rights reserved.
Faster Algorithms for the Shortest Path Problem

Efficient implementations of Dijkstra's shortest path algorithm are investigated. A new data structure, called the <italic>radix heap</italic>, is proposed for use in this algorithm. On a network with <italic>n</italic> vertices, <italic>m</italic> edges, and nonnegative integer arc costs bounded by <italic>C</italic>, a one-level form of radix heap gives a time bound for Dijkstra's algorithm of <italic>O</italic>(<italic>m</italic> + <italic>n</italic> log <italic>C</italic>). A two-level form of radix heap gives a bound of <italic>O</italic>(<italic>m</italic> + <italic>n</italic> log <italic>C</italic>/log log <italic>C</italic>). A combination of a radix heap and a previously known data structure called a <italic>Fibonacci heap</italic> gives a bound of <italic>O</italic>(<italic>m</italic> + <italic>n</italic>a @@@@log <italic>C</italic>). The best previously known bounds are <italic>O</italic>(<italic>m</italic> + <italic>n</italic> log <italic>n</italic>) using Fibonacci heaps alone and <italic>O</italic>(<italic>m</italic> log log <italic>C</italic>) using the priority queue structure of Van Emde Boas et al. [ 17].
Project Success : A Multidimensional Strategic Concept

This article presents projects as powerful strategic weapons, initiated to create economic value and competitive advantage. It suggests that project managers are the new strategic leaders, who must take on total responsibility for project business results. Defining and assessing project success is therefore a strategic management concept, which should help align project efforts with the shortand long-term goals of the organization. While this concept seems simple and intuitive, there is very little agreement in previous studies as to what really constitutes project success. Traditionally, projects were perceived as successful when they met time, budget, and performance goals. However, many would agree that there is more to project success than meeting time and budget. The object of this study was to develop a multidimensional framework for assessing project success, showing how different dimensions mean different things to different stakeholders at different times and for different projects. Given the complexity of this question, a combination of qualitative and quantitative methods and two data sets were used. The analysis identified four major distinct success dimensions: (1) project efficiency, (2) impact on the customer, (3) direct business and organizational success, and (4) preparing for the future. The importance of the dimensions varies according to time and the level of technological uncertainty involved in the project. The article demonstrates how these dimensions should be addressed during the project’s definition, planning, and execution phases, and provides a set of guidelines for project managers and senior managers, as well as suggestions for further research. c 2002 Elsevier Science Ltd. All rights reserved.
Better ϵ-Dependencies for Offline Approximate Nearest Neighbor Search, Euclidean Minimum Spanning Trees, and ϵ-Kernels

Recently, Arya, da Fonseca, and Mount [STOC 2011, SODA 2012] made notable progress in improving the &epsis;-dependencies in the space/query-time tradeoffs for (1 + &epsis;)-factor approximate nearest neighbor search in fixed-dimensional Euclidean spaces. However, &epsis;-dependencies in the preprocessing time were not considered, and so their data structures cannot be used to derive faster algorithms for offline proximity problems. Known algorithms for many such problems, including approximate bichromatic closest pair (BCP) and approximate Euclidean minimum spanning trees (EMST), typically have factors near (1/&epsis;)d/2±O(1) in the running time when the dimension d is a constant.
 We describe a technique that breaks the (1/&epsis;)d/2 barrier and yields new results for many well-known proximity problems, including:
 • an O((1/&epsis;)d/3+O(1) n)-time randomized algorithm for approximate BCP,
 • an O((1/&epsis;)d/3+O(1) n log n)-time algorithm for approximate EMST, and
 • an O(n log n + (1/&epsis;)d/3+O(1) n)-time algorithm to answer n approximate nearest neighbor queries on n points.
 Using additional bit-packing tricks, we can shave off the log n factor for EMST, and even move most of the &epsis;-factors to a sublinear term.
 The improvement arises from a new time bound for exact "discrete Voronoi diagrams", which were previously used in the construction of &epsis;-kernels (or extent-based coresets), a well-known tool for another class of fundamental problems. This connection leads to more results, including:
 • a streaming algorithm to maintain an approximate diameter in O((1/&epsis;)d/3+O(1)) time per point using O((1/&epsis;)d/2+O(1)) space, and
 • a streaming algorithm to maintain an &epsis;-kernel in O((1/&epsis;)d/4+O(1)) time per point using O((1/&epsis;)d/2+O(1)) space.