Patrick Haffner

• PhD
• Researcher at Interactions LLC

In one embodiment, the present disclosure is a method and apparatus for classifying applications using the collective properties of network traffic. In one embodiment, a method for classifying traffic in a communication network includes receiving a traffic activity graph, the traffic activity graph comprising a plurality of nodes interconnected by...

In this work, we propose latent semantic rational kernels (LSRK) for topic spotting on conversational speech. Rather than mapping the input weighted finite-state transducers (WFSTs) onto a high dimensional n-gram feature space as in n-gram rational kernels, the proposed LSRK maps the WFSTs onto a latent semantic space. With the proposed LSRK, all a...

A method and apparatus for providing protection for mail servers in networks such as the packet networks are disclosed. For example, the present method detects a mail server is reaching its processing limit. The method then selectively limits connections to the mail server from a plurality of source nodes based on a spam index associated with each...

With rapid growth in smart phones and mobile data, effectively managing cellular data networks is important in meeting user performance expectations. However, the scale, complexity and dynamics of a large 3G cellular network make it a challenging task to understand the diverse factors that affect its performance. In this paper we study the RNC (Rad...

Using heterogeneous data sources collected from one of the largest 3G cellular networks in the US over three months, in this paper we investigate the usage patterns of mobile data users. We observe that data usage across mobile users are highly uneven. Most of the users access data services occasionally, while a small number of heavy users contribu...

The ability to accurately and scalably classify network traffic is of critical importance to a wide range of management tasks of large networks, such as tier-1 ISP networks and global enterprise networks. Guided by the practical constraints and requirements of traffic classification in large networks, in this article, we explore the design of an ac...

We propose a simple, yet novel, multi-layer model for the problem of phonetic classification. Our model combines a frame level transformation of the acoustic signal with a segment level phone classification. Our key contribution is the study of new temporal pooling strategies that interface these two levels, determining how frame scores are convert...

In this paper, we study the Location-based Reporting Tool (LRT), a smartphone application for collecting large-scale feedback from mobile customers. Using one-year data collected from one of the largest cellular networks in the US, we compare LRT feedback to the traditional customer feedback channel -- customer care tickets. Our analysis shows that...

TRECVID (TREC Video Retrieval Evaluation) is sponsored by NIST to encourage research in digital video indexing and retrieval. It was initiated in 2001 as a "video track" of TREC and became an independent evaluation in 2003. AT&T participated in three tasks in TRECVID 2006: shot boundary determination (SBD), search, and rushes exploitation. The prop...

Effective management of large-scale cellular data networks is critical to meet customer demands and expectations. Customer calls for technical support provide direct indication as to the problems customers encounter. In this paper, we study the customer tickets - free-text recordings and classifications by customer support agents - collected at a l...

Traditional DSL troubleshooting solutions are reactive, relying mainly on customers to report problems, and tend to be labor-intensive, time consuming, prone to incorrect resolutions and overall can contribute to increased customer dissatisfaction. In this paper, we propose a proactive approach to facilitate troubleshooting customer edge problems a...

In this paper, we propose a novel technique for inferring the distri- bution of application classes present in the aggregated tra ffic flows between endpoints, which exploits both the statistics of th e traffic flows, and the spatial distribution of those flows across the n etwork. Our method employs a two-step supervised model, where the boot- str...

Bloggers, professional reviewers, and consumers continuously create opinion–rich web reviews about products and services, with the result that textual reviews are now abundant on the web and often convey a useful overall rating (number of stars). However, an overall rating cannot express the multiple or conflicting opinions that might be contained...

Bloggers, professional reviewers, and consumers continuously create opinion--rich web reviews about products and services, with the result that textual reviews are now abundant on the web and often convey a useful overall rating (number of stars). However, an overall rating cannot express the multiple or conflicting opinions that might be contained...

Rule-based packet classification is a powerful method for identifying traffic anomalies, with network security as a key application area. While popular systems like Snort are used in many network locations, comprehensive deployment across Tier-1 service provider networks is costly due to the need for high-speed monitors at many network ingress poin...

The Internet gives us access to a wealth of information in languages we don't understand. The investigation of automated or semi-automated approaches to translation has become a thriving research field with enormous commercial potential. This volume investigates how Machine Learning techniques can improve Statistical Machine Translation, currently...

Under severe channel mismatch conditions, such as training with far-field speech and testing with telephone data, performance of speaker identification (SID) degrades significantly, often below practical use. But for many SID tasks, it is sufficient to recognize an N-best list of speakers for further human analysis. We investigate N-best SID accura...

We demonstrate a stochastic gradient algorithm that can handle the very large number of stump features generated by considering every possible threshold over numerical, or continuous, features. Our problem is to classify data with continuous features, where small variations in the feature value can result in a different classification decision. Con...

One of the key tasks in the design of large-scale dialog systems is classification. This consists of assigning, out of a finite set, a specific category to each spoken utterance, based on the output of a speech recognizer. Classification in general is a standard machine-learning problem, but the objects to classify in this particular case are word...

Solutions for learning from large scale datasets, including kernel learning algorithms that scale linearly with the volume of the data and experiments carried out on realistically large datasets. Pervasive and networked computers have dramatically reduced the cost of collecting and distributing large datasets. In this context, machine learning algo...

E-mail has become indispensable in today's networked society. However, the huge and ever-growing volume of spam has become a serious threat to this important communication medium. It not only affects e-mail recipients, but also causes a significant overload to mail servers which handle the e-mail transmission. We perform an extensive analysis of IP...

The proposed shot boundary determination (SBD) algorithm contains a set of finite state machine (FSM) based detectors for pure cut, fast dissolve, fade in, fade out, dissolve, and wipe. Support vector machines (SVM) are applied to the cut and dissolve detectors to further boost performance. Our SBD system was highly effective when evaluated in TREC...

Machine translation of a source language sentence involves selecting appropriate tar- get language words and ordering the se- lected words to form a well-formed tar- get language sentence. Most of the pre- vious work on statistical machine transla- tion relies on (local) associations of target words/phrases with source words/phrases for lexical sel...

Task 1 of the 2006 KDD Challenge Cup required classification of pulmonary embolisms (PEs) using variables derived from computed tomography angiography. We present our approach to the challenge and justification for our choices. We used boosted trees to perform the main classification task, but modified the algorithm to address idiosyncrasies of the...

Large margin classifiers, such as SVMs and AdaBoost, have achieved state-of-the-art performance for semantic classification problems that occur in spoken language understanding or textual data mining applications. However, these computationally expensive learning algorithms cannot always handle the very large number of examples, features, and class...

Kernel-based learning algorithms, such as Support Vector Machines (SVMs) or Perceptron, often rely on sequential optimization where a few examples are added at each iteration. Updating the kernel matrix usually requires matrix-vector multiplications. We propose a new method based on transposition to speedup this computation on sparse data. Instead...

In this paper, we present our system for statistical machine translation that is based on weighted finite-state transducers. We describe the construction of the transducer, the estima-tion of the weights, acquisition of phrases (locally ordered tokens) and the mechanism we use for global reordering. We also present a novel approach to machine trans...

This paper introduces a methodology for speech data mining along with the tools that the methodology requires. We show how they increase the productivity of the analyst who seeks relationships among the contents of multiple utterances and ultimately must link some newly discovered context into testable hypotheses about new information. While, in it...

This paper discusses the application of L1-regularized maximum entropy modeling or SL1-Max [9] to multiclass categorization problems. A new modification to the SL1-Max fast sequential learning algorithm is proposed to handle conditional distributions. Furthermore, unlike most previous studies, the present research goes beyond a single type of condi...

This paper introduces a methodology for speech data mining along with the tools that the methodology requires. We show how they increase the productivity of the analyst who seeks relationships among the contents of multiple utterances and ultimately must link some newly discovered context into testable hypotheses about new information. While in its...

This paper proposes a learning approach for discovering the semantic structure of Web pages. The task includes partitioning the text on a Web page into information blocks and identifying their semantic categories. We employed two machine learning techniques, Adaboost and SVMs, to learn from a labeled Web page corpus. We evaluated our approach on ge...

An accurate mapping of traffic to applications is important for a broad range of network management and measurement tasks. Internet applications have traditionally been identified using well-known default server network-port numbers in the TCP or UDP headers. However this approach has become increasingly inaccurate. An alternate, more accurate tech...

In this paper we investigate the utility of three aspects of n amed entity processing: detection, localization and value extraction. We corroborate this task categorization by providing examples of practical applications for each of these subtasks. We als o suggest methods for tackling these subtasks, giving particular attention to working with spe...

We introduce a general family of kernels based on weighted transducers or rational relations, rational kernels, that can be used for analysis of variable-length sequences or more generally weighted automata, in applications such as computational biology or speech recognition. We show that rational kernels can be computed efficiently using a general...

Kernel methods are widely used in statistical learning techniques. We recently introduced a general kernel framework based on weighted transducers or rational relations, rational kernels, to extend kernel methods to the analysis of variable-length sequences or more generally weighted automata. These kernels are efficient to compute and have been su...

Large margin classifiers such as Support Vector Machines (SVM) or Adaboost are obvious choices for natural language document or call routing. However, how to combine several binary classifiers to optimize the whole routing process and how this process scales when it involves many different decisions (or classes) is a complex problem that has only r...

Kernel methods have found in recent years wide use in statistical learning techniques due to their good performance and their computational efficiency in high-dimensional feature space. However, text or speech data cannot always be represented by the fixed-length vectors that the traditional kernels handle. We recently introduced a general kernel f...

Classification is a key task in spoken-dialog systems. The response of a spoken-dialog system is often guided by the category assigned to the speaker's utterance. Unfortunately, classifiers based on the one-best transcription of the speech utterances are not satisfactory because of the high word error rate of conversational speech recognition syste...

Maximum margin classifiers such as Support Vector Machines (SVMs) critically depends upon the convex hulls of the training samples of each class, as they implicitly search for the minimum distance between the convex hulls. We propose Extrapolated Vector Machines (XVMs) which rely on extrapolations outside these convex hulls. XVMs improve SVM genera...

We describe the "DjVu" (Deja Vu) technology: an efficient document image compression methodology, a file format, and a delivery platform that together, enable instant access to high quality documents from es- sentially any platform, over any connection. Originally developed for scanned color documents, it was recently expanded to electronic docu- m...

Finding an appropriate set of features is an essential problem in the design of visual recognition

Introduction Avec l'utilisation g'en'eralis'ee de l'Internet, avec les couts d'ecroissants des num'eriseurs et des disques, l'archivage, la transmission et la manipulation des documents se fait de plus en plus sur ordinateur et de moins en moins sur papier. L"ecran de nos ordinateurs est en train de devenir le moyen privil'egi'e de consultation de...

We present a new image compression technique called "DjVu " that is specifically geared towards the compression of high-resolution, high-quality images of scanned documents in color. With DjVu , any screen connected to the Internet can access and display images of scanned pages while faithfully reproducing the font, color, drawing, pictures, and pa...

How can we turn the description of a digital (i.e. electronically produced) document into something that is efficient for multi-layer raster formats? It is first shown that a foreground/background segmentation without overlapping foreground components can be more efficient for viewing or printing. Then, a new algorithm that prevents overlaps betwee...

Image-based digital documents are composed of multiple pages, each of which may be composed of multiple components such as the test, pictures background, and annotations. We describe the image structure and software architecture that allows the DjVu system to load and render the required components on demand while minimizing the bandwidth requireme...

. Finding an appropriate set of features is an essential problem in the design of shape recognition systems. This paper attempts to show that for recognizing simple objects with high shape variability such as handwritten characters, it is possible, and even advantageous, to feed the system directly with minimally processed images and to rely on lea...

DjVu is an image compression technique specifically geared towards the compression of scanned documents in color at high resolution. Typical magazine pages in color scanned at 300dpi are compressed to between 40 and 80 KB, or 5 to 10 times smaller than with JPEG for a similar level of subjective quality. The foreground layer, which contains the tex...

Traditional classification approaches generalize poorly on image classification tasks, because of the high dimensionality of the feature space. This paper shows that Support Vector Machines (SVM) can generalize well on difficult image classification problems where the only features are high dimensional histograms. Heavy-tailed RBF kernels of the fo...

Finding an appropriate set of features is an essential problem in the design of shape recognition systems. This paper attempts to show that for recognizing simple objects with high shape variability such as handwritten characters, it is possible, and even advantageous, to feed the system directly with minimally processed images and to rely on learn...

Traditional classification approaches generalize poorly on image classification tasks, because of the high dimensionality of the feature space. This paper shows that support vector machines (SVM's) can generalize well on difficult image classification problems where the only features are high dimensional histograms. Heavy-tailed RBF kernels of the...

We present a new image compression technique called “DjVu” that is specifically geared towards the compression of scanned documents in color at high resolution. With DjVu, a magazine page in color at 300 dpi typically occupies between 40 KB and 80 KB, approximately 5 to 10 times better than JPEG for a similar level of readability. Using a combinati...

We present a new image compression technique called "DjVu" that is specifically geared towards the compression of scanned documents in color at high revolution. DjVu enable any screen connected to the Internet to access and display images of scanned pages while faithfully reproducing the font, color, drawings, pictures, and paper texture. With DjVu...

Signal processing and pattern recognition algorithms make extensive use of convolution. In many cases, computational accuracy is not as important as computational speed. In feature extraction, for instance, the features of interest in a signal are usually quite distorted. This form of noise justifies some level of quantization in order to achieve f...

Finding an appropriate set of features is an essential problem in the design of shape recognition systems. This paper attempts to show that for recognizing simple objects with high shape variability such as handwritten characters, it is possible, and even advantageous, to feed the system directly with minimally processed images and to rely on learn...

Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten char...

Discusses coding standards for still images and motion video. We first briefly discuss standards already in use, including: Group 3 and Group 4 for bilevel fax images; JPEG for still color images; and H.261, H.263, MPEG-1, and MPEG-2 for motion video. We then cover newly emerging standards such as JBIG1 and JBIG2 for bilevel fax images, JPEG-2000 f...

We present a new image compression technique called \DjVu " that is speci cally geared towards the compression of high-resolution, high-quality images of scanned documents in color. This enables fast transmission of document images over low-speed connections, while faithfully reproducing the visual aspect of the document, including color, fonts, pi...

We present a new image compression technique called "Dj Vu" that is specifically geared towards the compression of scanned documents in color at high revolution. Dj Vu enable any screen connected to the Internet to access and display images of scanned pages while faithfully reproducing the font, color, drawings, pictures, and paper texture. With Dj...

Presents a new image compression technique called “DjVu” that is specifically geared towards the compression of high-resolution, high-quality images of scanned documents in color. With DjVu, any screen connected to the Internet can access and display images of scanned pages while faithfully reproducing the font, color, drawings, pictures and paper...

SVCnet, a system for modeling speaker variability, is presented. Encoder neural networks specialized for each speech sound produce low-dimensionality models of acoustical variation, and these models are further combined into an overall model of voice variability. A training procedure is described which minimizes the dependence of this model on whic...

MS-TDNN (multistate time delay neural networks), a connectionist architecture with embedded time alignment, was proposed recently. It makes word level classification possible and efficient on speech recognition tasks. Connectionist classification at the word level (rather than the usual maximum likelihood estimation) has not been commonly used in s...

The authors describe two systems in which neural network classifiers are merged with dynamic programming (DP) time alignment methods to produce high-performance continuous speech recognizers. One system uses the connectionist Viterbi-training (CVT) procedure, in which a neural network with frame-level outputs is trained using guidance from a time a...

To extend the performance of TDNNs (time-delay neural networks) to all phoneme recognition and word/continuous speech recognition, the authors present several techniques. First, they show that it is possible to scale up the TDNN to a large phonemic TDNN aimed at discriminating all phonemes without loss of recognition performance and without excessi...

AT&T participated in two tasks at TRECVID 2007: shot boundary detection (SBD) and rushes summarization. The SBD system developed for TRECVID 2006 was enhanced for robustness and efficiency. New visual features are extracted for cut, dissolve, and fast dissolve detectors, and SVM based verification method is used to boost the accuracy. The speed is...

In this paper we study the interaction patterns among traffic from different application classes, namely, how they collab-oratively form a mixed traffic activity graph (mixed TAG). Utilizing real traffic traces from a major ISP and a large uni-versity network, we show that densely connected subgraphs or clusters are the building blocks for a mixed...