OpenRaiser/Intern-Atlas huggingface dataset:
Bridging Screen Readers and Voice Assistants for Enhanced Eyes-Free Web Search
Authors: Alexandra Vtyurina, Adam Fourney, Meredith Ringel Morris, Leah Findlater, Ryen W. White
Venue: Conference WWW 2019
Year: 2019

People with visual impairments often rely on screen readers when interacting with computer systems. Increasingly, these individuals also make extensive use of voice-based virtual assistants (VAs). We conducted a survey of 53 people who are legally blind to identify the strengths and weaknesses of both technologies, as well as the unmet opportunities at their intersection. We learned that virtual assistants are convenient and accessible, but lack the ability to deeply engage with content (e.g., read beyond the first few sentences of Wikipedia), and the ability to get a quick overview of the landscape (list alternative search results & suggestions). In contrast, screen readers allow for deep engagement with content (when content is accessible), and provide fine-grained navigation & control, but at the cost of increased complexity, and reduced walk-up-and-use convenience. In this demonstration, we showcase VERSE, a system that combines the positive aspects of VAs and screen readers, and allows other devices (e.g., smart watches) to serve as optional input accelerators. Together, these features allow people with visual impairments to deeply engage with web content through voice interaction.
Pose estimation of jointed structures
Authors: Takeshi Shakunaga
Venue: Conference CVPR 1991
Year: 1991

A framework is proposed for model-based monocular vision covering not only conventional 3-D rigid models, but also flexible structures made up of 3-D rigid bodies connected by rotational joints. Pose-estimation problems from a single view are defined and discussed according to this object model composed of rigid bodies and rotational axes, respectively represented by sets of unit vectors and by single unit vectors. The authors define primitive problems as those which are solvable, but which would be unsolvable if any vector in the problem were invisible. A theorem is derived to extract a primitive problem family, members of which correspond to models containing rigid bodies and invisible rotational axes. Two generic rotation-estimation algorithms applicable to this problem family are also constructed. Experimental results from several primitive problems show the effectiveness of the proposed framework.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Calibration-free Visual Control Using Projective Invariance
Authors: Gregory D. Hager
Venue: Conference ICCV 1995
Year: 1995

Much of the previous work on hand-eye coordination has emphasized the reconstructive aspects of vision. Recently, techniques that avoid explicit reconstruction by placing visual feedback into a control loop have been developed. When properly defined, these methods lead to calibration insensitive hand-eye coordination. Recent work on projective geometry as applied to vision is used to extend this paradigm in two ways. First, it is shown how results from projective geometry can be used to perform online calibration. Second, results on projective invariance are used to define setpoints for visual control that are independent of viewing location. These ideas are illustrated through a number of examples and have been tested on an implemented system.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Estimation of omnidirectional camera model from epipolar geometry
Authors: Branislav Micusík, Tomás Pajdla
Venue: Conference CVPR 2003
Year: 2003

We generalize the method of simultaneous linear estimation of multiple view geometry and lens distortion, introduced by Fitzgibbon at CVPR 2001, to an omnidirectional (angle of view larger than 180/spl deg/) camera. The perspective camera is replaced by a linear camera with a spherical retina and a nonlinear mapping of the sphere into the image plane. Unlike the previous distortion-based models, the new camera model is capable to describe a camera with an angle of view larger than 180/spl deg/ at the cost of introducing only one extra parameter. A suitable linearization of the camera model and of the epipolar constraint is developed in order to arrive at a quadratic eigenvalue problem for which efficient algorithms are known. The lens calibration is done from automatically established image correspondences only. Besides rigidity, no assumptions about the scene are made (e.g. presence of a calibration object). We demonstrate the method in experiments with Nikon FC-E8 fish-eye converter for COOLPIX digital camera. In practical situations, the proposed method allows to incorporate the new omnidirectional camera model into RANSAC - a robust estimation technique.
Scene understanding by statistical modeling of motion patterns
Authors: Imran Saleemi, Lance Hartung, Mubarak Shah
Venue: Conference CVPR 2010
Year: 2010

We present a novel method for the discovery and statistical representation of motion patterns in a scene observed by a static camera. Related methods involving learning of patterns of activity rely on trajectories obtained from object detection and tracking systems, which are unreliable in complex scenes of crowded motion. We propose a mixture model representation of salient patterns of optical flow, and present an algorithm for learning these patterns from dense optical flow in a hierarchical, unsupervised fashion. Using low level cues of noisy optical flow, K-means is employed to initialize a Gaussian mixture model for temporally segmented clips of video. The components of this mixture are then filtered and instances of motion patterns are computed using a simple motion model, by linking components across space and time. Motion patterns are then initialized and membership of instances in different motion patterns is established by using KL divergence between mixture distributions of pattern instances. Finally, a pixel level representation of motion patterns is proposed by deriving conditional expectation of optical flow. Results of extensive experiments are presented for multiple surveillance sequences containing numerous patterns involving both pedestrian and vehicular traffic.
Safevchat: Detecting obscene content and misbehaving users in online video chat services
Authors: Xinyu Xing, Yu-Li Liang, Hanqiang Cheng, Jianxun Dang, Sui Huang, Richard Han, Xue Liu, Qin Lv, Shivakant Mishra
Venue: Conference WWW 2011
Year: 2011

Online video chat services such as Chatroulette, Omegle, and vChatter that randomly match pairs of users in video chat sessions are fast becoming very popular, with over a million users per month in the case of Chatroulette. A key problem encountered in such systems is the presence of flashers and obscene content. This problem is especially acute given the presence of underage minors in such systems. This paper presents SafeVchat, a novel solution to the problem of flasher detection that employs an array of image detection algorithms. A key contribution of the paper concerns how the results of the individual detectors are fused together into an overall decision classifying the user as misbehaving or not, based on Dempster-Shafer Theory. The paper introduces a novel, motion-based skin detection method that achieves significantly higher recall and better precision. The proposed methods have been evaluated over real-world data and image traces obtained from Chatroulette.com.
Interactive Exploration of Coherent Patterns in Time-Series Gene Expression Data
Authors: Daxin Jiang, Jian Pei, Aidong Zhang
Venue: Conference KDD 2003
Year: 2003

Discovering coherent gene expression patterns in time-series gene expression data is an important task in bioinformatics research and biomedical applications. In this paper, we propose an interactive exploration framework for mining coherent expression patterns in time-series gene expression data. We develop a novel tool, coherent pattern index graph, to give users highly confident indications of the existences of coherent patterns. To derive a coherent pattern index graph, we devise an attraction tree structure to record the genes in the data set and summarize the information needed for the interactive exploration. We present fast and scalable algorithms to construct attraction trees and coherent pattern index graphs from gene expression data sets. We conduct an extensive performance study on some real data sets to verify our design. The experimental results strongly show that our approach is more effective than the state-of-the-art methods in mining real gene expression data, and is scalable in mining large data sets.
Mining phenotypes and informative genes from gene expression data
Authors: Chun Tang, Aidong Zhang, Jian Pei
Venue: Conference KDD 2003
Year: 2003

Mining microarray gene expression data is an important research topic in bioinformatics with broad applications. While most of the previous studies focus on clustering either genes or samples, it is interesting to ask whether we can partition the complete set of samples into exclusive groups (called phenotypes) and find a set of informative genes that can manifest the phenotype structure. In this paper, we propose a new problem of simultaneously mining phenotypes and informative genes from gene expression data. Some statistics-based metrics are proposed to measure the quality of the mining results. Two interesting algorithms are developed: the heuristic search and the mutual reinforcing adjustment method. We present an extensive performance study on both real-world data sets and synthetic data sets. The mining results from the two proposed methods are clearly better than those from the previous methods. They are ready for the real-world applications. Between the two methods, the mutual reinforcing adjustment method is in general more scalable, more effective and with better quality of the mining results.
Density-based clustering using graphics processors
Authors: Christian Böhm, Robert Noll, Claudia Plant, Bianca Wackersreuther
Venue: Conference CIKM 2009
Year: 2009

During the last few years, GPUs have evolved from simple devices for the display signal preparation into powerful coprocessors that do not only support typical computer graphics tasks but can also be used for general numeric and symbolic computation tasks. As major advantage GPUs provide extremely high parallelism combined with a high bandwidth in memory transfer at low cost. We want to exploit these dvantages in density-based clustering, an important paradigm in clustering since typical algorithms of this category are noise and outlier robust and search for clusters of an arbitrary shape in metric and vector spaces. Moreover, with a time complexity ranging from O(n log n) to O(n2) these algorithms are scalable to large data sets in a database system. In this paper, we propose CUDA-DClust, a massively parallel algorithm for density-based clustering for the use of a Graphics Processing Unit (GPU). While the result of this algorithm is guaranteed to be equivalent to that of DBSCAN, we demonstrate a high speed-up, particularly in combination with a novel index structure for use in GPUs.
Visual Recommendation Use Case for an Online Marketplace Platform: allegro.pl
Authors: Anna Wróblewska, Lukasz Raczkowski
Venue: Conference SIGIR 2016
Year: 2016

In this paper we describe a small content-based visual recommendation project built as part of the Allegro online marketplace platform. We extracted relevant data only from images, as they are inherently better at capturing visual attributes than textual offer descriptions. We used several image descriptors to extract color and texture information in order to find visually similar items. We tested our results against available textual offer tags and also asked human users to subjectively assess the precision. Finally, we deployed the solution to our platform.
Exploiting Human Mobility Patterns for Point-of-Interest Recommendation
Authors: Zijun Yao
Venue: Conference WSDM 2018
Year: 2018

Point-of-interest (POI) recommendation, which provides personalized recommendation of places to mobile users, is an important task in location-based social networks (LBSNs). Unlike traditional interest-oriented merchandise recommendation, POI recommendation is more complex due to the timing effects: we need to examine whether the POI fits a user»s availability. While there are some prior studies which consider temporal effects by solely using check-in timestamps for modeling, they suffer from check-in data sparsity. Recent years, the advent in positioning technology has accumulated a variety of urban data related to human mobility. There is a potential to exploit human mobility patterns from heterogeneous information sources for improving POI recommendation. To this end, we propose a novel method which incorporates the degree of temporal matching between users and POIs into personalized POI recommendations. Specifically, we profile the temporal popularity of POIs, learn the latent regularity to characterize users, and conduct comprehensive experiments with real-world data. Evaluation results demonstrate the effectiveness of the proposed method.
Skinny-dip: Clustering in a Sea of Noise
Authors: Samuel Maurus, Claudia Plant
Venue: Conference KDD 2016
Year: 2016

Can we find heterogeneous clusters hidden in data sets with 80% noise? Although such settings occur in the real-world, we struggle to find methods from the abundance of clustering techniques that perform well with noise at this level. Indeed, perhaps this is enough of a departure from classical clustering to warrant its study as a separate problem. In this paper we present SkinnyDip which, based on Hartigan's elegant dip test of unimodality, represents an intriguing approach to clustering with an attractive set of properties. Specifically, SkinnyDip is highly noise-robust, practically parameter-free and completely deterministic. SkinnyDip never performs multivariate distance calculations, but rather employs insightful recursion based on "dips" into univariate projections of the data. It is able to detect a range of cluster shapes and densities, assuming only that each cluster admits a unimodal shape. Practically, its run-time grows linearly with the data. Finally, for high-dimensional data, continuity properties of the dip enable SkinnyDip to exploit multimodal projection pursuit in order to find an appropriate basis for clustering. Although not without its limitations, SkinnyDip compares favorably to a variety of clustering approaches on synthetic and real data, particularly in high-noise settings.
Et: events from tweets
Authors: Ruchi Parikh, Kamalakar Karlapalem
Venue: Conference WWW 2013
Year: 2013

Social media sites such as Twitter and Facebook have emerged as popular tools for people to express their opinions on various topics. The large amount of data provided by these media is extremely valuable for mining trending topics and events. In this paper, we build an efficient, scalable system to detect events from tweets (ET). Our approach detects events by exploring their textual and temporal components. ET does not require any target entity or domain knowledge to be specified; it automatically detects events from a set of tweets. The key components of ET are (1) an extraction scheme for event representative keywords, (2) an efficient storage mechanism to store their appearance patterns, and (3) a hierarchical clustering technique based on the common co-occurring features of keywords. The events are determined through the hierarchical clustering process. We evaluate our system on two data-sets; one is provided by VAST challenge 2011, and the other published by US based users in January 2013. Our results show that we are able to detect events of relevance efficiently.
Visual Appearance of Display Ads and Its Effect on Click Through Rate
Authors: Javad Azimi, Ruofei Zhang, Yang Zhou, Vidhya Navalpakkam, Jianchang Mao, Xiaoli Z. Fern
Venue: Conference CIKM 2012
Year: 2012

One of the most important categories of online advertising is display advertising which provides publishers with significant revenue. Similar to other categories, the main goal in display advertising is to maximize user response rate for advertising campaigns, such as click through rates (CTR) or conversion rates. Previous studies have tried to optimize these parameters using objectives such as behavioral targeting. However, there is no published work so far to address the effect of the visual appearance of ads (creatives) on user response rate via a systematic data-driven approach. In this paper, we quantitatively study the relationship between the visual appearance and performance of creatives using large scale data in the world's largest display ads exchange system, RightMedia. We designed a set of 43 visual features, some of which are novel and others are inspired by related work. We extracted these features from real creatives served on RightMedia. We also designed and conducted a series of experiments to evaluate the effectiveness of visual features for CTR prediction, ranking and performance classification. Based on the evaluation results, we selected a subset of features that have the highest impact on CTR. We believe that the findings presented in this paper will be very useful for the online advertising industry in designing high-performance creatives. It also provides the research community with the first ever data set, initial insights into visual appearance's effect on user response propensity, and evaluation benchmarks for further study.
Road traffic prediction by incorporating online information
Authors: Tian Zhou, Lixin Gao, Daiheng Ni
Venue: Conference WWW 2014
Year: 2014

Road traffic conditions are typically affected by events such as extreme weather or sport games. With the advance of Web, events and weather conditions can be readily retrieved in real-time. In this paper, we propose a traffic condition prediction system incorporating both online and offline information. RFID-based system has been deployed for monitoring road traffic. By incorporating data from both road traffic monitoring system and online information, we propose a hierarchical Bayesian network to predict road traffic condition. Using historical data, we establish a hierarchical Bayesian network to characterize the relationships among events and road traffic conditions. To evaluate the model, we use the traffic data collected in Western Massachusetts as well as online information about events and weather. Our proposed prediction achieves an accuracy of 93% overall.
150 Successful Machine Learning Models: 6 Lessons Learned at Booking.Com
Authors: Lucas Bernardi, Themistoklis Mavridis, Pablo Estevez
Venue: Conference KDD 2019
Year: 2019

Booking.com is the world's largest online travel agent where millions of guests find their accommodation and millions of accommodation providers list their properties including hotels, apartments, bed and breakfasts, guest houses, and more. During the last years we have applied Machine Learning to improve the experience of our customers and our business. While most of the Machine Learning literature focuses on the algorithmic or mathematical aspects of the field, not much has been published about how Machine Learning can deliver meaningful impact in an industrial environment where commercial gains are paramount. We conducted an analysis on about 150 successful customer facing applications of Machine Learning, developed by dozens of teams in Booking.com, exposed to hundreds of millions of users worldwide and validated through rigorous Randomized Controlled Trials. Following the phases of a Machine Learning project we describe our approach, the many challenges we found, and the lessons we learned while scaling up such a complex technology across our organization. Our main conclusion is that an iterative, hypothesis driven process, integrated with other disciplines was fundamental to build 150 successful products enabled by Machine Learning.
Sensitivity analysis of centralities on unweighted networks
Authors: Shogo Murai, Yuichi Yoshida
Venue: Conference WWW 2019
Year: 2019

Revealing important vertices is a fundamental task in network analysis. As such, many indicators have been proposed for doing so, which are collectively called centralities. However, the abundance of studies on centralities blurs their differences. In this work, we compare centralities based on their sensivitity to modifications in the graph. Specifically, we introduce a quantitative measure called (average-case) edge sensitivity, which measures how much the centrality value of a uniformly chosen vertex (or an edge) changes when we remove a uniformly chosen edge. Edge sensitivity is applicable to unweighted graphs, regarding which, to our knowledge, there has been no theoretical analysis of the centralities. We conducted a theoretical analysis of the edge sensitivities of six major centralities: the closeness centrality, harmonic centrality, betweenness centrality, endpoint betweenness centrality, PageRank, and spanning tree centrality. Our experimental results on synthetic and real graphs confirm the tendency predicted by the theoretical analysis. We also discuss an extension of edge sensitivity to the setting that we remove a uniformly chosen set of edges of size k for an integer k = 1.
Towards building an intelligent chatbot for customer service: Learning to respond at the appropriate time
Authors: Che Liu, Junfeng Jiang, Chao Xiong, Yi Yang, Jieping Ye
Venue: Conference KDD 2020
Year: 2020

In recent years, intelligent chatbots have been widely used in the field of customer service. One of the key challenges for chatbots to maintain fluent dialogues with customers is how to respond at the appropriate time. However, most of the state-of-the-art chatbots follow the turn-by-turn interaction scheme. Such chatbots respond after each time when a customer sends an utterance, which in some cases leads to inappropriate responses and misleads the process of the dialogues. In this paper, we propose a multi-turn response triggering model (MRTM) to address this problem. MRTM is learned from large-scale human-human dialogues between the customers and the agents with a self-supervised learning scheme. It leverages the semantic matching relationships between the context and the response to train a semantic matching model and obtains the weights of the co-occurring utterances in the context through an asymmetrical self-attention mechanism. The weights are then used to determine whether the given context should be responded to. We conduct extensive experiments on two dialogue datasets collected from the real-world online customer service systems. Results show that MRTM outperforms the baselines by a large margin. Furthermore, we incorporate MRTM into DiDi's customer service chatbot. Based on the ability to identify the appropriate time to respond, the chatbot can incrementally aggregate the information across multiple utterances and make more intelligent responses at the appropriate time.