OpenRaiser/Intern-Atlas huggingface dataset:
FluxMarker: Enhancing Tactile Graphics with Dynamic Tactile Markers
Venue: arXiv cs.HC 2017
Year: 2017

For people with visual impairments, tactile graphics are an important means to learn and explore information. However, raised line tactile graphics created with traditional materials such as embossing are static. While available refreshable displays can dynamically change the content, they are still too expensive for many users, and are limited in size. These factors limit wide-spread adoption and the representation of large graphics or data sets. In this paper, we present FluxMaker, an inexpensive scalable system that renders dynamic information on top of static tactile graphics with movable tactile markers. These dynamic tactile markers can be easily reconfigured and used to annotate static raised line tactile graphics, including maps, graphs, and diagrams. We developed a hardware prototype that actuates magnetic tactile markers driven by low-cost and scalable electromagnetic coil arrays, which can be fabricated with standard printed circuit board manufacturing. We evaluate our prototype with six participants with visual impairments and found positive results across four application areas: location finding or navigating on tactile maps, data analysis, and physicalization, feature identification for tactile graphics, and drawing support. The user study confirms advantages in application domains such as education and data exploration.
TraceDiff: Debugging Unexpected Code Behavior Using Trace Divergences
Venue: arXiv cs.HC 2017
Year: 2017

Recent advances in program synthesis offer means to automatically debug student submissions and generate personalized feedback in massive programming classrooms. When automatically generating feedback for programming assignments, a key challenge is designing pedagogically useful hints that are as effective as the manual feedback given by teachers. Through an analysis of teachers' hint-giving practices in 132 online Q&A posts, we establish three design guidelines that an effective feedback design should follow. Based on these guidelines, we develop a feedback system that leverages both program synthesis and visualization techniques. Our system compares the dynamic code execution of both incorrect and fixed code and highlights how the error leads to a difference in behavior and where the incorrect code trace diverges from the expected solution. Results from our study suggest that our system enables students to detect and fix bugs that are not caught by students using another existing visual debugging tool.
Revisiting the Effectiveness of Off-the-shelf Temporal Modeling Approaches for Large-scale Video Classification
Venue: arXiv cs.CV 2017
Year: 2017

This paper describes our solution for the video recognition task of ActivityNet Kinetics challenge that ranked the 1st place. Most of existing state-of-the-art video recognition approaches are in favor of an end-to-end pipeline. One exception is the framework of DevNet. The merit of DevNet is that they first use the video data to learn a network (i.e. fine-tuning or training from scratch). Instead of directly using the end-to-end classification scores (e.g. softmax scores), they extract the features from the learned network and then fed them into the off-the-shelf machine learning models to conduct video classification. However, the effectiveness of this line work has long-term been ignored and underestimated. In this submission, we extensively use this strategy. Particularly, we investigate four temporal modeling approaches using the learned features: Multi-group Shifting Attention Network, Temporal Xception Network, Multi-stream sequence Model and Fast-Forward Sequence Model. Experiment results on the challenging Kinetics dataset demonstrate that our proposed temporal modeling approaches can significantly improve existing approaches in the large-scale video recognition tasks. Most remarkably, our best single Multi-group Shifting Attention Network can achieve 77.7% in term of top-1 accuracy and 93.2% in term of top-5 accuracy on the validation set.
Structure in scientific networks: towards predictions of research dynamism
Venue: arXiv cs.SI 2017
Year: 2017

Certain areas of scientific research flourish while others lose advocates and attention. We are interested in whether structural patterns within citation networks correspond to the growth or decline of the research areas to which those networks belong. We focus on three topic areas within optical physics as a set of cases; those areas have developed along different trajectories: one continues to expand rapidly; another is on the wane after an earlier peak; the final area has re-emerged after a short waning period. These three areas have substantial overlaps in the types of equipment they use and general methodology; at the same time, their citation networks are largely independent of each other. For each of our three areas, we map the citation networks of the top-100 most-cited papers, published pre-1999. In order to quantify the structures of the selected articles' citation networks, we use a modified version of weak tie theory in tandem with entropy measures. Although the fortunes of a given research area are most obviously the result of accumulated innovations and impasses, our preliminary study provides evidence that these citation networks' emergent structures reflect those developments and may shape evolving conversations in the scholarly literature.
The Parameterized Complexity of Happy Colorings
Venue: arXiv cs.DS 2017
Year: 2017

Consider a graph $G = (V,E)$ and a coloring $c$ of vertices with colors from $[\ell]$. A vertex $v$ is said to be happy with respect to $c$ if $c(v) = c(u)$ for all neighbors $u$ of $v$. Further, an edge $(u,v)$ is happy if $c(u) = c(v)$. Given a partial coloring $c$ of $V$, the Maximum Happy Vertex (Edge) problem asks for a total coloring of $V$ extending $c$ to all vertices of $V$ that maximises the number of happy vertices (edges). Both problems are known to be NP-hard in general even when $\ell = 3$, and is polynomially solvable when $\ell = 2$. In [IWOCA 2016] it was shown that both problems are polynomially solvable on trees, and for arbitrary $k$, it was shown that MHE is \NPH{} on planar graphs and is \FPT{} parameterized by the number of precolored vertices and branchwidth. We continue the study of this problem from a parameterized prespective. Our focus is on both structural and standard parameterizations. To begin with, we establish that the problems are \FPT{} when parameterized by the treewidth and the number of colors used in the precoloring, which is a potential improvement over the total number of precolored vertices. Further, we show that both the vertex and edge variants of the problem is \FPT{} when parameterized by vertex cover and distance-to-clique parameters. We also show that the problem of maximizing the number of happy edges is \FPT{} when parameterized by the standard parameter, the number of happy edges. We show that the maximum happy vertex (edge) problem is \NPH{} on split graphs and bipartite graphs and polynomially solvable on cographs.
Monadic Remote Invocation
Venue: arXiv cs.PL 2017
Year: 2017

In order to achieve Separation of Concerns in the domain of remote method invocation, a small functional adapter is added atop Java RMI, eliminating the need for every remote object to implement java.rmi.Remote and making it possible to remotely access existing code, unchanged. The Remote monad is introduced, and its implementation and usage are detailed. Reusing the existing, proven technology of RMI allows not to re-invent the underlying network protocol. As a result, orthogonal remote invocation is achieved with little or no implementation effort.
Sensitivity Analysis of Core Specialization Techniques
Venue: arXiv cs.AR 2017
Year: 2017

The instruction footprint of OS-intensive workloads such as web servers, database servers, and file servers typically exceeds the size of the instruction cache (32 KB). Consequently, such workloads incur a lot of i-cache misses, which reduces their performance drastically. Several papers have proposed to improve the performance of such workloads using core specialization. In this scheme, tasks with different instruction footprints are executed on different cores. In this report, we study the performance of five state of the art core specialization techniques: SelectiveOffload [6], FlexSC [8], DisAggregateOS [5], SLICC [2], and SchedTask [3] for different system parameters. Our studies show that for a suite of 8 popular OS-intensive workloads, SchedTask performs best for all evaluated configurations.
Towards Speech Emotion Recognition "in the wild" using Aggregated Corpora and Deep Multi-Task Learning
Venue: arXiv cs.CL 2017
Year: 2017

One of the challenges in Speech Emotion Recognition (SER) "in the wild" is the large mismatch between training and test data (e.g. speakers and tasks). In order to improve the generalisation capabilities of the emotion models, we propose to use Multi-Task Learning (MTL) and use gender and naturalness as auxiliary tasks in deep neural networks. This method was evaluated in within-corpus and various cross-corpus classification experiments that simulate conditions "in the wild". In comparison to Single-Task Learning (STL) based state of the art methods, we found that our MTL method proposed improved performance significantly. Particularly, models using both gender and naturalness achieved more gains than those using either gender or naturalness separately. This benefit was also found in the high-level representations of the feature space, obtained from our method proposed, where discriminative emotional clusters could be observed.
System-Level Performance of mmWave Cellular Networks for Urban Micro Environments
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we investigate the propagation coupling loss (captures all sources of attenuation between serving cell and mobile station (MS)) and geometry metric (GM) (downlink average signal-to-interference plus noise ratio) performance of mmWave cellular networks for outdoor and indoor MSs, considering urban micro (UMi) environments. Based on these studies, we identify effective mmWave frequency bands for cellular communication. We consider 3GPP compliant system-level simulations with two power allocation schemes: 1) transmit power scaled with communication bandwidth, and 2) constant total transmit power. Simulation results show that with scaled transmit power allocation, GM performance degradation is small: 20% of MSs experience GM less than 0 dB at all mmWave frequencies considered, for outdoor MSs. With constant Tx power allocation, 20% of MSs experience GM less than 0 dB for frequencies up to 30 GHz. Furthermore, 35% (48%) of outdoor MSs experience GM performance less than 0 dB at 60 GHz (100 GHz). On the other hand, for indoor MSs, even with scaled Tx power allocation, favorable GM performance is observed only at low frequencies, i.e., 2 GHz.
Assentication: User Deauthentication and Lunchtime Attack Mitigation with Seated Posture Biometric
Venue: arXiv cs.CR 2017
Year: 2017

Biometric techniques are often used as an extra security factor in authenticating human users. Numerous biometrics have been proposed and evaluated, each with its own set of benefits and pitfalls. Static biometrics (such as fingerprints) are geared for discrete operation, to identify users, which typically involves some user burden. Meanwhile, behavioral biometrics (such as keystroke dynamics) are well suited for continuous, and sometimes more unobtrusive, operation. One important application domain for biometrics is deauthentication, a means of quickly detecting absence of a previously authenticated user and immediately terminating that user's active secure sessions. Deauthentication is crucial for mitigating so called Lunchtime Attacks, whereby an insider adversary takes over (before any inactivity timeout kicks in) authenticated state of a careless user who walks away from her computer. Motivated primarily by the need for an unobtrusive and continuous biometric to support effective deauthentication, we introduce PoPa, a new hybrid biometric based on a human user's seated posture pattern. PoPa captures a unique combination of physiological and behavioral traits. We describe a low cost fully functioning prototype that involves an office chair instrumented with 16 tiny pressure sensors. We also explore (via user experiments) how PoPa can be used in a typical workplace to provide continuous authentication (and deauthentication) of users. We experimentally assess viability of PoPa in terms of uniqueness by collecting and evaluating posture patterns of a cohort of users. Results show that PoPa exhibits very low false positive, and even lower false negative, rates. In particular, users can be identified with, on average, 91.0% accuracy. Finally, we compare pros and cons of PoPa with those of several prominent biometric based deauthentication techniques.
AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild
Venue: arXiv cs.CV 2017
Year: 2017

Automated affective computing in the wild setting is a challenging problem in computer vision. Existing annotated databases of facial expressions in the wild are small and mostly cover discrete emotions (aka the categorical model). There are very limited annotated facial databases for affective computing in the continuous dimensional model (e.g., valence and arousal). To meet this need, we collected, annotated, and prepared for public distribution a new database of facial emotions in the wild (called AffectNet). AffectNet contains more than 1,000,000 facial images from the Internet by querying three major search engines using 1250 emotion related keywords in six different languages. About half of the retrieved images were manually annotated for the presence of seven discrete facial expressions and the intensity of valence and arousal. AffectNet is by far the largest database of facial expression, valence, and arousal in the wild enabling research in automated facial expression recognition in two different emotion models. Two baseline deep neural networks are used to classify images in the categorical model and predict the intensity of valence and arousal. Various evaluation metrics show that our deep neural network baselines can perform better than conventional machine learning methods and off-the-shelf facial expression recognition systems.
Data Sets: Word Embeddings Learned from Tweets and General Data
Venue: arXiv cs.CL 2017
Year: 2017

A word embedding is a low-dimensional, dense and real- valued vector representation of a word. Word embeddings have been used in many NLP tasks. They are usually gener- ated from a large text corpus. The embedding of a word cap- tures both its syntactic and semantic aspects. Tweets are short, noisy and have unique lexical and semantic features that are different from other types of text. Therefore, it is necessary to have word embeddings learned specifically from tweets. In this paper, we present ten word embedding data sets. In addition to the data sets learned from just tweet data, we also built embedding sets from the general data and the combination of tweets with the general data. The general data consist of news articles, Wikipedia data and other web data. These ten embedding models were learned from about 400 million tweets and 7 billion words from the general text. In this paper, we also present two experiments demonstrating how to use the data sets in some NLP tasks, such as tweet sentiment analysis and tweet topic classification tasks.
Putting Self-Supervised Token Embedding on the Tables
Venue: arXiv cs.IR 2017
Year: 2017

Information distribution by electronic messages is a privileged means of transmission for many businesses and individuals, often under the form of plain-text tables. As their number grows, it becomes necessary to use an algorithm to extract text and numbers instead of a human. Usual methods are focused on regular expressions or on a strict structure in the data, but are not efficient when we have many variations, fuzzy structure or implicit labels. In this paper we introduce SC2T, a totally self-supervised model for constructing vector representations of tokens in semi-structured messages by using characters and context levels that address these issues. It can then be used for an unsupervised labeling of tokens, or be the basis for a semi-supervised information extraction system.
Understanding and Visualizing the District of Columbia Capital Bikeshare System Using Data Analysis for Balancing Purposes
Venue: arXiv cs.AI 2017
Year: 2017

Bike sharing systems' popularity has consistently been rising during the past years. Managing and maintaining these emerging systems are indispensable parts of these systems. Visualizing the current operations can assist in getting a better grasp on the performance of the system. In this paper, a data mining approach is used to identify and visualize some important factors related to bike-share operations and management. To consolidate the data, we cluster stations that have a similar pickup and drop-off profiles during weekdays and weekends. We provide the temporal profile of the center of each cluster which can be used as a simple and practical approach for approximating the number of pickups and drop-offs of the stations. We also define two indices based on stations' shortages and surpluses that reflect the degree of balancing aid a station needs. These indices can help stakeholders improve the quality of the bike-share user experience in at-least two ways. It can act as a complement to balancing optimization efforts, and it can identify stations that need expansion. We mine the District of Columbia's regional bike-share data and discuss the findings of this data set. We examine the bike-share system during different quarters of the year and during both peak and non-peak hours. Findings reflect that on weekdays most of the pickups and drop-offs happen during the morning and evening peaks whereas on weekends pickups and drop-offs are spread out throughout the day. We also show that throughout the day, more than 40% of the stations are relatively self-balanced. Not worrying about these stations during ordinary days can allow the balancing efforts to focus on a fewer stations and therefore potentially improve the efficiency of the balancing optimization models.
Emotion Detection on TV Show Transcripts with Sequence-based Convolutional Neural Networks
Venue: arXiv cs.CL 2017
Year: 2017

While there have been significant advances in detecting emotions from speech and image recognition, emotion detection on text is still under-explored and remained as an active research field. This paper introduces a corpus for text-based emotion detection on multiparty dialogue as well as deep neural models that outperform the existing approaches for document classification. We first present a new corpus that provides annotation of seven emotions on consecutive utterances in dialogues extracted from the show, Friends. We then suggest four types of sequence-based convolutional neural network models with attention that leverage the sequence information encapsulated in dialogue. Our best model shows the accuracies of 37.9% and 54% for fine- and coarse-grained emotions, respectively. Given the difficulty of this task, this is promising.
Deep Edge-Aware Saliency Detection
Venue: arXiv cs.CV 2017
Year: 2017

There has been profound progress in visual saliency thanks to the deep learning architectures, however, there still exist three major challenges that hinder the detection performance for scenes with complex compositions, multiple salient objects, and salient objects of diverse scales. In particular, output maps of the existing methods remain low in spatial resolution causing blurred edges due to the stride and pooling operations, networks often neglect descriptive statistical and handcrafted priors that have potential to complement saliency detection results, and deep features at different layers stay mainly desolate waiting to be effectively fused to handle multi-scale salient objects. In this paper, we tackle these issues by a new fully convolutional neural network that jointly learns salient edges and saliency labels in an end-to-end fashion. Our framework first employs convolutional layers that reformulate the detection task as a dense labeling problem, then integrates handcrafted saliency features in a hierarchical manner into lower and higher levels of the deep network to leverage available information for multi-scale response, and finally refines the saliency map through dilated convolutions by imposing context. In this way, the salient edge priors are efficiently incorporated and the output resolution is significantly improved while keeping the memory requirements low, leading to cleaner and sharper object boundaries. Extensive experimental analyses on ten benchmarks demonstrate that our framework achieves consistently superior performance and attains robustness for complex scenes in comparison to the very recent state-of-the-art approaches.
Continuous User Authentication via Unlabeled Phone Movement Patterns
Venue: arXiv cs.HC 2017
Year: 2017

In this paper, we propose a novel continuous authentication system for smartphone users. The proposed system entirely relies on unlabeled phone movement patterns collected through smartphone accelerometer. The data was collected in a completely unconstrained environment over five to twelve days. The contexts of phone usage were identified using k-means clustering. Multiple profiles, one for each context, were created for every user. Five machine learning algorithms were employed for classification of genuine and impostors. The performance of the system was evaluated over a diverse population of 57 users. The mean equal error rates achieved by Logistic Regression, Neural Network, kNN, SVM, and Random Forest were 13.7%, 13.5%, 12.1%, 10.7%, and 5.6% respectively. A series of statistical tests were conducted to compare the performance of the classifiers. The suitability of the proposed system for different types of users was also investigated using the failure to enroll policy.
Theoretical Foundation of Co-Training and Disagreement-Based Algorithms
Venue: arXiv cs.LG 2017
Year: 2017

Disagreement-based approaches generate multiple classifiers and exploit the disagreement among them with unlabeled data to improve learning performance. Co-training is a representative paradigm of them, which trains two classifiers separately on two sufficient and redundant views; while for the applications where there is only one view, several successful variants of co-training with two different classifiers on single-view data instead of two views have been proposed. For these disagreement-based approaches, there are several important issues which still are unsolved, in this article we present theoretical analyses to address these issues, which provides a theoretical foundation of co-training and disagreement-based approaches.
Ensemble Methods for Personalized E-Commerce Search Challenge at CIKM Cup 2016
Venue: arXiv cs.IR 2017
Year: 2017

Personalized search has been a hot research topic for many years and has been widely used in e-commerce. This paper describes our solution to tackle the challenge of personalized e-commerce search at CIKM Cup 2016. The goal of this competition is to predict search relevance and re-rank the result items in SERP according to the personalized search, browsing and purchasing preferences. Based on a detailed analysis of the provided data, we extract three different types of features, i.e., statistic features, query-item features and session features. Different models are used on these features, including logistic regression, gradient boosted decision trees, rank svm and a novel deep match model. With the blending of multiple models, a stacking ensemble model is built to integrate the output of individual models and produce a more accurate prediction result. Based on these efforts, our solution won the champion of the competition on all the evaluation metrics.