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Articles 1 - 30 of 64
Full-Text Articles in Theory and Algorithms
Learning Optimal Inter-Class Margin Adaptively For Few-Shot Class-Incremental Learning Via Neural Collapse-Based Meta-Learning, Hang Ran, Weijun Li, Lusi Li, Songsong Tian, Xin Ning, Prayag Tiwari
Learning Optimal Inter-Class Margin Adaptively For Few-Shot Class-Incremental Learning Via Neural Collapse-Based Meta-Learning, Hang Ran, Weijun Li, Lusi Li, Songsong Tian, Xin Ning, Prayag Tiwari
Computer Science Faculty Publications
Few-Shot Class-Incremental Learning (FSCIL) aims to learn new classes incrementally with a limited number of samples per class. It faces issues of forgetting previously learned classes and overfitting on few-shot classes. An efficient strategy is to learn features that are discriminative in both base and incremental sessions. Current methods improve discriminability by manually designing inter-class margins based on empirical observations, which can be suboptimal. The emerging Neural Collapse (NC) theory provides a theoretically optimal inter-class margin for classification, serving as a basis for adaptively computing the margin. Yet, it is designed for closed, balanced data, not for sequential or few-shot …
A Chinese Power Text Classification Algorithm Based On Deep Active Learning, Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu
A Chinese Power Text Classification Algorithm Based On Deep Active Learning, Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu
Computer Science Faculty Publications
The construction of knowledge graph is beneficial for grid production, electrical safety protection, fault diagnosis and traceability in an observable and controllable way. Highly-precision text classification algorithm is crucial to build a professional knowledge graph in power system. Unfortunately, there are a large number of poorly described and specialized texts in the power business system, and the amount of data containing valid labels in these texts is low. This will bring great challenges to improve the precision of text classification models. To offset the gap, we propose a classification algorithm for Chinese text in the power system based on deep …
Optimization Of Ported Cfd Kernels On Intel Data Center Gpu Max 1550 Using Oneapi Esimd, Mohammad Zubair, Aaron Walden, Gabriel Nastac, Eric Nielsen, Christoph Bauinger, Xiao Zhu
Optimization Of Ported Cfd Kernels On Intel Data Center Gpu Max 1550 Using Oneapi Esimd, Mohammad Zubair, Aaron Walden, Gabriel Nastac, Eric Nielsen, Christoph Bauinger, Xiao Zhu
Computer Science Faculty Publications
We describe our experience porting FUN3D’s CUDA-optimized kernels to Intel oneAPI SYCL.We faced several challenges, including foremost the suboptimal performance of the oneAPI code on Intel’s new data center GPU. Suboptimal performance of the oneAPI code was due primarily to high register spills, memory latency, and poor vectorization. We addressed these issues by implementing the kernels using Intel oneAPI’s Explicit SIMD SYCL extension (ESIMD) API. The ESIMD API enables the writing of explicitly vectorized kernel code, gives more precise control over register usage and prefetching, and better handles thread divergence compared to SYCL. The ESIMD code outperforms the optimized SYCL …
A Structure-Aware Generative Adversarial Network For Bilingual Lexicon Induction, Bocheng Han, Qian Tao, Lusi Li, Zhihao Xiong
A Structure-Aware Generative Adversarial Network For Bilingual Lexicon Induction, Bocheng Han, Qian Tao, Lusi Li, Zhihao Xiong
Computer Science Faculty Publications
Bilingual lexicon induction (BLI) is the task of inducing word translations with a learned mapping function that aligns monolingual word embedding spaces in two different languages. However, most previous methods treat word embeddings as isolated entities and fail to jointly consider both the intra-space and inter-space topological relations between words. This limitation makes it challenging to align words from embedding spaces with distinct topological structures, especially when the assumption of isomorphism may not hold. To this end, we propose a novel approach called the Structure-Aware Generative Adversarial Network (SA-GAN) model to explicitly capture multiple topological structure information to achieve accurate …
Unttangling Irregular Actin Cytoskeleton Architectures In Tomograms Of The Cell With Struwwel Tracer, Salim Sazzed, Peter Scheible, Jing He, Willy Wriggers
Unttangling Irregular Actin Cytoskeleton Architectures In Tomograms Of The Cell With Struwwel Tracer, Salim Sazzed, Peter Scheible, Jing He, Willy Wriggers
Computer Science Faculty Publications
In this work, we established, validated, and optimized a novel computational framework for tracing arbitrarily oriented actin filaments in cryo-electron tomography maps. Our approach was designed for highly complex intracellular architectures in which a long-range cytoskeleton network extends throughout the cell bodies and protrusions. The irregular organization of the actin network, as well as cryo-electron-tomography-specific noise, missing wedge artifacts, and map dimensions call for a specialized implementation that is both robust and efficient. Our proposed solution, Struwwel Tracer, accumulates densities along paths of a specific length in various directions, starting from locally determined seed points. The highest-density paths originating …
A Synthetic Prediction Market For Estimating Confidence In Published Work, Sarah Rajtmajer, Christopher Griffin, Jian Wu, Robert Fraleigh, Laxmann Balaji, Anna Squicciarini, Anthony Kwasnica, David Pennock, Michael Mclaughlin, Timothy Fritton, Nishanth Nakshatri, Arjun Menon, Sai Ajay Modukuri, Rajal Nivargi, Xin Wei, Lee Giles
A Synthetic Prediction Market For Estimating Confidence In Published Work, Sarah Rajtmajer, Christopher Griffin, Jian Wu, Robert Fraleigh, Laxmann Balaji, Anna Squicciarini, Anthony Kwasnica, David Pennock, Michael Mclaughlin, Timothy Fritton, Nishanth Nakshatri, Arjun Menon, Sai Ajay Modukuri, Rajal Nivargi, Xin Wei, Lee Giles
Computer Science Faculty Publications
[First paragraph] Concerns about the replicability, robustness and reproducibility of findings in scientific literature have gained widespread attention over the last decade in the social sciences and beyond. This attention has been catalyzed by and has likewise motivated a number of large-scale replication projects which have reported successful replication rates between 36% and 78%. Given the challenges and resources required to run high-powered replication studies, researchers have sought other approaches to assess confidence in published claims. Initial evidence has supported the promise of prediction markets in this context. However, they require the coordinated, sustained effort of collections of human experts …
Spaghetti Tracer: A Framework For Tracing Semiregular Filamentous Densities In 3d Tomograms, Salim Sazzed, Peter Scheible, Jing He, Willy Wriggers
Spaghetti Tracer: A Framework For Tracing Semiregular Filamentous Densities In 3d Tomograms, Salim Sazzed, Peter Scheible, Jing He, Willy Wriggers
Computer Science Faculty Publications
Within cells, cytoskeletal filaments are often arranged into loosely aligned bundles. These fibrous bundles are dense enough to exhibit a certain regularity and mean direction, however, their packing is not sufficient to impose a symmetry between—or specific shape on—individual filaments. This intermediate regularity is computationally difficult to handle because individual filaments have a certain directional freedom, however, the filament densities are not well segmented from each other (especially in the presence of noise, such as in cryo-electron tomography). In this paper, we develop a dynamic programming-based framework, Spaghetti Tracer, to characterizing the structural arrangement of filaments in the challenging 3D …
Systematizing Confidence In Open Research And Evidence (Score), Nazanin Alipourfard, Beatrix Arendt, Daniel M. Benjamin, Noam Benkler, Michael Bishop, Mark Burstein, Martin Bush, James Caverlee, Yiling Chen, Chae Clark, Anna Dreber Almenberg, Timothy M. Errington, Fiona Fidler, Nicholas Fox, Aaron Frank, Hannah Fraser, Scott Friedman, Ben Gelman, James Gentile, Jian Wu, Et Al., Score Collaboration
Systematizing Confidence In Open Research And Evidence (Score), Nazanin Alipourfard, Beatrix Arendt, Daniel M. Benjamin, Noam Benkler, Michael Bishop, Mark Burstein, Martin Bush, James Caverlee, Yiling Chen, Chae Clark, Anna Dreber Almenberg, Timothy M. Errington, Fiona Fidler, Nicholas Fox, Aaron Frank, Hannah Fraser, Scott Friedman, Ben Gelman, James Gentile, Jian Wu, Et Al., Score Collaboration
Computer Science Faculty Publications
Assessing the credibility of research claims is a central, continuous, and laborious part of the scientific process. Credibility assessment strategies range from expert judgment to aggregating existing evidence to systematic replication efforts. Such assessments can require substantial time and effort. Research progress could be accelerated if there were rapid, scalable, accurate credibility indicators to guide attention and resource allocation for further assessment. The SCORE program is creating and validating algorithms to provide confidence scores for research claims at scale. To investigate the viability of scalable tools, teams are creating: a database of claims from papers in the social and behavioral …
Ssentiaa: A Self-Supervised Sentiment Analyzer For Classification From Unlabeled Data, Salim Sazzed, Sampath Jayarathna
Ssentiaa: A Self-Supervised Sentiment Analyzer For Classification From Unlabeled Data, Salim Sazzed, Sampath Jayarathna
Computer Science Faculty Publications
In recent years, supervised machine learning (ML) methods have realized remarkable performance gains for sentiment classification utilizing labeled data. However, labeled data are usually expensive to obtain, thus, not always achievable. When annotated data are unavailable, the unsupervised tools are exercised, which still lag behind the performance of supervised ML methods by a large margin. Therefore, in this work, we focus on improving the performance of sentiment classification from unlabeled data. We present a self-supervised hybrid methodology SSentiA (Self-supervised Sentiment Analyzer) that couples an ML classifier with a lexicon-based method for sentiment classification from unlabeled data. We first introduce LRSentiA …
An Outlier Detection Algorithm Based On Cross-Correlation Analysis For Time Series Dataset, Hui Lu, Yaxian Liu, Zongming Fei, Chongchong Guan
An Outlier Detection Algorithm Based On Cross-Correlation Analysis For Time Series Dataset, Hui Lu, Yaxian Liu, Zongming Fei, Chongchong Guan
Computer Science Faculty Publications
Outlier detection is a very essential problem in a variety of application areas. Many detection methods are deficient for high-dimensional time series data sets containing both isolated and assembled outliers. In this paper, we propose an Outlier Detection method based on Cross-correlation Analysis (ODCA). ODCA consists of three key parts. They are data preprocessing, outlier analysis, and outlier rank. First, we investigate a linear interpolation method to convert assembled outliers into isolated ones. Second, a detection mechanism based on the cross-correlation analysis is proposed for translating the high-dimensional data sets into 1-D cross-correlation function, according to which the isolated outlier …
A Survey Of Matrix Completion Methods For Recommendation Systems, Andy Ramlatchan, Mengyun Yang, Quan Liu, Min Li, Jianxin Wang, Yaohang Li
A Survey Of Matrix Completion Methods For Recommendation Systems, Andy Ramlatchan, Mengyun Yang, Quan Liu, Min Li, Jianxin Wang, Yaohang Li
Computer Science Faculty Publications
In recent years, the recommendation systems have become increasingly popular and have been used in a broad variety of applications. Here, we investigate the matrix completion techniques for the recommendation systems that are based on collaborative filtering. The collaborative filtering problem can be viewed as predicting the favorability of a user with respect to new items of commodities. When a rating matrix is constructed with users as rows, items as columns, and entries as ratings, the collaborative filtering problem can then be modeled as a matrix completion problem by filling out the unknown elements in the rating matrix. This article …
Efficient Randomized Algorithms For The Fixed Precision Low Rank Matrix Approximation, Wenjian Yu, Yu Gu, Yaohang Li
Efficient Randomized Algorithms For The Fixed Precision Low Rank Matrix Approximation, Wenjian Yu, Yu Gu, Yaohang Li
Computer Science Faculty Publications
Randomized algorithms for low-rank matrix approximation are investigated, with the emphasis on the fixed-precision problem and computational efficiency for handling large matrices. The algorithms are based on the so-called QB factorization, where Q is an orthonormal matrix. First, a mechanism for calculating the approximation error in the Frobenius norm is proposed, which enables efficient adaptive rank determination for a large and/or sparse matrix. It can be combined with any QB-form factorization algorithm in which B's rows are incrementally generated. Based on the blocked randQB algorithm by Martinsson and Voronin, this results in an algorithm called randQB_EI. Then, we further revise …
Marim: Mobile Augmented Reality For Interactive Manuals, Tam Nguyen, Dorothy Tan, Bilal Mirza, Jose Sepulveda
Marim: Mobile Augmented Reality For Interactive Manuals, Tam Nguyen, Dorothy Tan, Bilal Mirza, Jose Sepulveda
Computer Science Faculty Publications
In this work, we present a practical system which uses mobile devices for interactive manuals. In particular, there are two modes provided in the system, namely, expert/trainer and trainee modes. Given the expert/trainer editor, experts design the step-by-step interactive manuals. For each step, the experts capture the images by using phones/tablets and provide visual instructions such as interest regions, text, and action animations. In the trainee mode, the system utilizes the existing object detection and tracking algorithms to identify the step scene and retrieve the respective instruction to be displayed on the mobile device. The trainee then follows the displayed …
An Immersive Telepresence System Using Rgb-D Sensors And Head-Mounted Display, Xinzhong Lu, Ju Shen, Saverio Perugini, Jianjun Yang
An Immersive Telepresence System Using Rgb-D Sensors And Head-Mounted Display, Xinzhong Lu, Ju Shen, Saverio Perugini, Jianjun Yang
Computer Science Faculty Publications
We present a tele-immersive system that enables people to interact with each other in a virtual world using body gestures in addition to verbal communication. Beyond the obvious applications, including general online conversations and gaming, we hypothesize that our proposed system would be particularly beneficial to education by offering rich visual contents and interactivity. One distinct feature is the integration of egocentric pose recognition that allows participants to use their gestures to demonstrate and manipulate virtual objects simultaneously. This functionality enables the instructor to effectively and efficiently explain and illustrate complex concepts or sophisticated problems in an intuitive manner. The …
Automatic Video Self Modeling For Voice Disorder, Ju Shen, Changpeng Ti, Anusha Raghunathan, Sen-Ching S. Cheung, Rita Patel
Automatic Video Self Modeling For Voice Disorder, Ju Shen, Changpeng Ti, Anusha Raghunathan, Sen-Ching S. Cheung, Rita Patel
Computer Science Faculty Publications
Video self modeling (VSM) is a behavioral intervention technique in which a learner models a target behavior by watching a video of him- or herself. In the field of speech language pathology, the approach of VSM has been successfully used for treatment of language in children with Autism and in individuals with fluency disorder of stuttering. Technical challenges remain in creating VSM contents that depict previously unseen behaviors. In this paper, we propose a novel system that synthesizes new video sequences for VSM treatment of patients with voice disorders. Starting with a video recording of a voice-disorder patient, the proposed …
Compression Of Video Tracking And Bandwidth Balancing Routing In Wireless Multimedia Sensor Networks, Yin Wang, Jianjun Yang, Ju Shen, Bryson Payne, Juan Guo, Kun Hua
Compression Of Video Tracking And Bandwidth Balancing Routing In Wireless Multimedia Sensor Networks, Yin Wang, Jianjun Yang, Ju Shen, Bryson Payne, Juan Guo, Kun Hua
Computer Science Faculty Publications
There has been a tremendous growth in multimedia applications over wireless networks. Wireless Multimedia Sensor Networks(WMSNs) have become the premier choice in many research communities and industry. Many state-of-art applications, such as surveillance, traffic monitoring, and remote heath care are essentially video tracking and transmission in WMSNs. The transmission speed is constrained by the big file size of video data and fixed bandwidth allocation in constant routing paths. In this paper, we present a CamShift based algorithm to compress the tracking of videos. Then we propose a bandwidth balancing strategy in which each sensor node is able to dynamically select …
Leading Undergraduate Students To Big Data Generation, Jianjun Yang, Ju Shen
Leading Undergraduate Students To Big Data Generation, Jianjun Yang, Ju Shen
Computer Science Faculty Publications
People are facing a flood of data today. Data are being collected at unprecedented scale in many areas, such as networking, image processing, virtualization, scientific computation, and algorithms. The huge data nowadays are called Big Data. Big data is an all encompassing term for any collection of data sets so large and complex that it becomes difficult to process them using traditional data processing applications. In this article, the authors present a unique way which uses network simulator and tools of image processing to train students abilities to learn, analyze, manipulate, and apply Big Data. Thus they develop students hands-on …
Hole Detection And Shape-Free Representation And Double Landmarks Based Geographic Routing In Wireless Sensor Networks, Jianjun Yang, Zongming Fei, Ju Shen
Hole Detection And Shape-Free Representation And Double Landmarks Based Geographic Routing In Wireless Sensor Networks, Jianjun Yang, Zongming Fei, Ju Shen
Computer Science Faculty Publications
In wireless sensor networks, an important issue of geographic routing is “local minimum” problem, which is caused by a “hole” that blocks the greedy forwarding process. Existing geographic routing algorithms use perimeter routing strategies to find a long detour path when such a situation occurs. To avoid the long detour path, recent research focuses on detecting the hole in advance, then the nodes located on the boundary of the hole advertise the hole information to the nodes near the hole. Hence the long detour path can be avoided in future routing. We propose a heuristic hole detecting algorithm which identifies …
A Dynamic Programming Algorithm For Finding The Optimal Placement Of A Secondary Structure Topology In Cryo-Em Data, Abhishek Biswas, Desh Ranjan, Mohammad Zubair, Jing He
A Dynamic Programming Algorithm For Finding The Optimal Placement Of A Secondary Structure Topology In Cryo-Em Data, Abhishek Biswas, Desh Ranjan, Mohammad Zubair, Jing He
Computer Science Faculty Publications
The determination of secondary structure topology is a critical step in deriving the atomic structures from the protein density maps obtained from electron cryomicroscopy technique. This step often relies on matching the secondary structure traces detected from the protein density map to the secondary structure sequence segments predicted from the amino acid sequence. Due to inaccuracies in both sources of information, a pool of possible secondary structure positions needs to be sampled. One way to approach the problem is to first derive a small number of possible topologies using existing matching algorithms, and then find the optimal placement for each …
Structure Preserving Large Imagery Reconstruction, Ju Shen, Jianjun Yang, Sami Taha Abu Sneineh, Bryson Payne, Markus Hitz
Structure Preserving Large Imagery Reconstruction, Ju Shen, Jianjun Yang, Sami Taha Abu Sneineh, Bryson Payne, Markus Hitz
Computer Science Faculty Publications
With the explosive growth of web-based cameras and mobile devices, billions of photographs are uploaded to the internet. We can trivially collect a huge number of photo streams for various goals, such as image clustering, 3D scene reconstruction, and other big data applications. However, such tasks are not easy due to the fact the retrieved photos can have large variations in their view perspectives, resolutions, lighting, noises, and distortions. Furthermore, with the occlusion of unexpected objects like people, vehicles, it is even more challenging to find feature correspondences and reconstruct realistic scenes. In this paper, we propose a structure-based image …
Automatic Objects Removal For Scene Completion, Jianjun Yang, Yin Wang, Honggang Wang, Kun Hua, Wei Wang, Ju Shen
Automatic Objects Removal For Scene Completion, Jianjun Yang, Yin Wang, Honggang Wang, Kun Hua, Wei Wang, Ju Shen
Computer Science Faculty Publications
With the explosive growth of Web-based cameras and mobile devices, billions of photographs are uploaded to the Internet. We can trivially collect a huge number of photo streams for various goals, such as 3D scene reconstruction and other big data applications. However, this is not an easy task due to the fact the retrieved photos are neither aligned nor calibrated. Furthermore, with the occlusion of unexpected foreground objects like people, vehicles, it is even more challenging to find feature correspondences and reconstruct realistic scenes. In this paper, we propose a structure-based image completion algorithm for object removal that produces visually …
Unstructured P2p Link Lifetimes Redux, Zhongmei Yao, Daren B. H. Cline
Unstructured P2p Link Lifetimes Redux, Zhongmei Yao, Daren B. H. Cline
Computer Science Faculty Publications
We revisit link lifetimes in random P2P graphs under dynamic node failure and create a unifying stochastic model that generalizes the majority of previous efforts in this direction. We not only allow nonexponential user lifetimes and age-dependent neighbor selection, but also cover both active and passive neighbor-management strategies, model the lifetimes of incoming and outgoing links, derive churn-related message volume of the system, and obtain the distribution of transient in/out degree at each user. We then discuss the impact of design parameters on overhead and resilience of the network.
A Robust Rgbd Slam System For 3d Environment With Planar Surfaces, Po-Chang Su, Ju Shen, Sen-Ching S. Cheung
A Robust Rgbd Slam System For 3d Environment With Planar Surfaces, Po-Chang Su, Ju Shen, Sen-Ching S. Cheung
Computer Science Faculty Publications
With the increasing popularity of RGB-depth (RGB-D) sensors such as the Microsoft Kinect, there have been much research on capturing and reconstructing 3D environments using a movable RGB-D sensor. The key process behind these kinds of simultaneous location and mapping (SLAM) systems is the iterative closest point or ICP algorithm, which is an iterative algorithm that can estimate the rigid movement of the camera based on the captured 3D point clouds. While ICP is a well-studied algorithm, it is problematic when it is used in scanning large planar regions such as wall surfaces in a room. The lack of depth …
Gpu-Optimized Code For Long-Term Simulations Of Beam-Beam Effects In Colliders, Y. Roblin, V. Morozov, B. Terzić, M. Aturban, D. Ranjan, M. Zubair
Gpu-Optimized Code For Long-Term Simulations Of Beam-Beam Effects In Colliders, Y. Roblin, V. Morozov, B. Terzić, M. Aturban, D. Ranjan, M. Zubair
Computer Science Faculty Publications
We report on the development of the new code for long-term simulation of beam-beam effects in particle colliders. The underlying physical model relies on a matrix-based arbitrary-order symplectic particle tracking for beam transport and the Bassetti-Erskine approximation for beam-beam interaction. The computations are accelerated through a parallel implementation on a hybrid GPU/CPU platform. With the new code, a previously computationally prohibitive long-term simulations become tractable. We use the new code to model the proposed medium-energy electron-ion collider (MEIC) at Jefferson Lab.
Stochastic Analysis Of Horizontal Ip Scanning, Derek Leonard, Zhongmei Yao, Xiaoming Wang, Dmitri Loguinov
Stochastic Analysis Of Horizontal Ip Scanning, Derek Leonard, Zhongmei Yao, Xiaoming Wang, Dmitri Loguinov
Computer Science Faculty Publications
Intrusion Detection Systems (IDS) have become ubiquitous in the defense against virus outbreaks, malicious exploits of OS vulnerabilities, and botnet proliferation. As attackers frequently rely on host scanning for reconnaissance leading to penetration, IDS is often tasked with detecting scans and preventing them. However, it is currently unknown how likely an IDS is to detect a given Internet-wide scan pattern and whether there exist sufficiently fast scan techniques that can remain virtually undetectable at large-scale. To address these questions, we propose a simple analytical model for the window-expiration rules of popular IDS tools (i.e., Snort and Bro) and utilize a …
On Superposition Of Heterogeneous Edge Processes In Dynamic Random Graphs, Zhongmei Yao, Daren B. H. Cline, Dmitri Loguinov
On Superposition Of Heterogeneous Edge Processes In Dynamic Random Graphs, Zhongmei Yao, Daren B. H. Cline, Dmitri Loguinov
Computer Science Faculty Publications
This paper builds a generic modeling framework for analyzing the edge-creation process in dynamic random graphs in which nodes continuously alternate between active and inactive states, which represent churn behavior of modern distributed systems. We prove that despite heterogeneity of node lifetimes, different initial out-degree, non-Poisson arrival/failure dynamics, and complex spatial and temporal dependency among creation of both initial and replacement edges, a superposition of edge-arrival processes to a live node under uniform selection converges to a Poisson process when system size becomes sufficiently large. Due to the convoluted dependency and non-renewal nature of various point processes, this result significantly …
Automatic Content Generation For Video Self Modeling, Ju Shen, Anusha Raghunathan, Sen-Ching S. Cheung, Ravi R. Patel
Automatic Content Generation For Video Self Modeling, Ju Shen, Anusha Raghunathan, Sen-Ching S. Cheung, Ravi R. Patel
Computer Science Faculty Publications
Video self modeling (VSM) is a behavioral intervention technique in which a learner models a target behavior by watching a video of him or herself. Its effectiveness in rehabilitation and education has been repeatedly demonstrated but technical challenges remain in creating video contents that depict previously unseen behaviors. In this paper, we propose a novel system that re-renders new talking-head sequences suitable to be used for VSM treatment of patients with voice disorder. After the raw footage is captured, a new speech track is either synthesized using text-to-speech or selected based on voice similarity from a database of clean speeches. …
In-Degree Dynamics Of Large-Scale P2p Systems, Zhongmei Yao, Daren B. H. Cline, Dmitri Loguinov
In-Degree Dynamics Of Large-Scale P2p Systems, Zhongmei Yao, Daren B. H. Cline, Dmitri Loguinov
Computer Science Faculty Publications
This paper builds a complete modeling framework for understanding user churn and in-degree dynamics in unstructured P2P systems in which each user can be viewed as a stationary alternating renewal process. While the classical Poisson result on the superposition of n stationary renewal processes for n→∞ requires that each point process become sparser as n increases, it is often difficult to rigorously show this condition in practice. In this paper, we first prove that despite user heterogeneity and non-Poisson arrival dynamics, a superposition of edge-arrival processes to a live user under uniform selection converges to a Poisson process when …
Program Transformations For Information Personalization, Saverio Perugini, Naren Ramakrishnan
Program Transformations For Information Personalization, Saverio Perugini, Naren Ramakrishnan
Computer Science Faculty Publications
Personalization constitutes the mechanisms necessary to automatically customize information content, structure, and presentation to the end user to reduce information overload. Unlike traditional approaches to personalization, the central theme of our approach is to model a website as a program and conduct website transformation for personalization by program transformation (e.g., partial evaluation, program slicing). The goal of this paper is study personalization through a program transformation lens and develop a formal model, based on program transformations, for personalized interaction with hierarchical hypermedia. The specific research issues addressed involve identifying and developing program representations and transformations suitable for classes of hierarchical …
Personalization By Website Transformation: Theory And Practice, Saverio Perugini
Personalization By Website Transformation: Theory And Practice, Saverio Perugini
Computer Science Faculty Publications
We present an analysis of a progressive series of out-of-turn transformations on a hierarchical website to personalize a user’s interaction with the site. We formalize the transformation in graph-theoretic terms and describe a toolkit we built that enumerates all of the traversals enabled by every possible complete series of these transformations in any site and computes a variety of metrics while simulating each traversal therein to qualify the relationship between a site’s structure and the cumulative effect of support for the transformation in a site. We employed this toolkit in two websites. The results indicate that the transformation enables users …