OpenRaiser/Intern-Atlas huggingface dataset:
Tracking Single-Cells in Overcrowded Bacterial Colonies
Venue: arXiv cs.CV 2017
Year: 2017

Cell tracking enables data extraction from time-lapse "cell movies" and promotes modeling biological processes at the single-cell level. We introduce a new fully automated computational strategy to track accurately cells across frames in time-lapse movies. Our method is based on a dynamic neighborhoods formation and matching approach, inspired by motion estimation algorithms for video compression. Moreover, it exploits "divide and conquer" opportunities to solve effectively the challenging cells tracking problem in overcrowded bacterial colonies. Using cell movies generated by different labs we demonstrate that the accuracy of the proposed method remains very high (exceeds 97%) even when analyzing large overcrowded microbial colonies.
Universal Sampling Rate Distortion
Venue: arXiv cs.IT 2017
Year: 2017

We examine the coordinated and universal rate-efficient sampling of a subset of correlated discrete memoryless sources followed by lossy compression of the sampled sources. The goal is to reconstruct a predesignated subset of sources within a specified level of distortion. The combined sampling mechanism and rate distortion code are universal in that they are devised to perform robustly without exact knowledge of the underlying joint probability distribution of the sources. In Bayesian as well as nonBayesian settings, single-letter characterizations are provided for the universal sampling rate distortion function for fixed-set sampling, independent random sampling and memoryless random sampling. It is illustrated how these sampling mechanisms are successively better. Our achievability proofs bring forth new schemes for joint source distribution-learning and lossy compression.
Mining Features Associated with Effective Tweets
Venue: arXiv cs.SI 2017
Year: 2017

What tweet features are associated with higher effectiveness in tweets? Through the mining of 122 million engagements of 2.5 million original tweets, we present a systematic review of tweet time, entities, composition, and user account features. We show that the relationship between various features and tweeting effectiveness is non-linear; for example, tweets that use a few hashtags have higher effectiveness than using no or too many hashtags. This research closely relates to various industrial applications that are based on tweet features, including the analysis of advertising campaigns, the prediction of user engagement, the extraction of signals for automated trading, etc.
Neural Machine Translation with Gumbel-Greedy Decoding
Venue: arXiv cs.CL 2017
Year: 2017

Previous neural machine translation models used some heuristic search algorithms (e.g., beam search) in order to avoid solving the maximum a posteriori problem over translation sentences at test time. In this paper, we propose the Gumbel-Greedy Decoding which trains a generative network to predict translation under a trained model. We solve such a problem using the Gumbel-Softmax reparameterization, which makes our generative network differentiable and trainable through standard stochastic gradient methods. We empirically demonstrate that our proposed model is effective for generating sequences of discrete words.
Coupled Support Vector Machines for Supervised Domain Adaptation
Venue: arXiv cs.CV 2017
Year: 2017

Popular domain adaptation (DA) techniques learn a classifier for the target domain by sampling relevant data points from the source and combining it with the target data. We present a Support Vector Machine (SVM) based supervised DA technique, where the similarity between source and target domains is modeled as the similarity between their SVM decision boundaries. We couple the source and target SVMs and reduce the model to a standard single SVM. We test the Coupled-SVM on multiple datasets and compare our results with other popular SVM based DA approaches.
Listen to Your Face: Inferring Facial Action Units from Audio Channel
Venue: arXiv cs.CV 2017
Year: 2017

Extensive efforts have been devoted to recognizing facial action units (AUs). However, it is still challenging to recognize AUs from spontaneous facial displays especially when they are accompanied with speech. Different from all prior work that utilized visual observations for facial AU recognition, this paper presents a novel approach that recognizes speech-related AUs exclusively from audio signals based on the fact that facial activities are highly correlated with voice during speech. Specifically, dynamic and physiological relationships between AUs and phonemes are modeled through a continuous time Bayesian network (CTBN); then AU recognition is performed by probabilistic inference via the CTBN model. A pilot audiovisual AU-coded database has been constructed to evaluate the proposed audio-based AU recognition framework. The database consists of a "clean" subset with frontal and neutral faces and a challenging subset collected with large head movements and occlusions. Experimental results on this database show that the proposed CTBN model achieves promising recognition performance for 7 speech-related AUs and outperforms the state-of-the-art visual-based methods especially for those AUs that are activated at low intensities or "hardly visible" in the visual channel. Furthermore, the CTBN model yields more impressive recognition performance on the challenging subset, where the visual-based approaches suffer significantly.
How Much Data is Enough? A Statistical Approach with Case Study on Longitudinal Driving Behavior
Venue: arXiv cs.LG 2017
Year: 2017

Big data has shown its uniquely powerful ability to reveal, model, and understand driver behaviors. The amount of data affects the experiment cost and conclusions in the analysis. Insufficient data may lead to inaccurate models while excessive data waste resources. For projects that cost millions of dollars, it is critical to determine the right amount of data needed. However, how to decide the appropriate amount has not been fully studied in the realm of driver behaviors. This paper systematically investigates this issue to estimate how much naturalistic driving data (NDD) is needed for understanding driver behaviors from a statistical point of view. A general assessment method is proposed using a Gaussian kernel density estimation to catch the underlying characteristics of driver behaviors. We then apply the Kullback-Liebler divergence method to measure the similarity between density functions with differing amounts of NDD. A max-minimum approach is used to compute the appropriate amount of NDD. To validate our proposed method, we investigated the car-following case using NDD collected from the University of Michigan Safety Pilot Model Deployment (SPMD) program. We demonstrate that from a statistical perspective, the proposed approach can provide an appropriate amount of NDD capable of capturing most features of the normal car-following behavior, which is consistent with the experiment settings in many literatures.
Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks
Venue: arXiv cs.IR 2017
Year: 2017

Recommendations can greatly benefit from good representations of the user state at recommendation time. Recent approaches that leverage Recurrent Neural Networks (RNNs) for session-based recommendations have shown that Deep Learning models can provide useful user representations for recommendation. However, current RNN modeling approaches summarize the user state by only taking into account the sequence of items that the user has interacted with in the past, without taking into account other essential types of context information such as the associated types of user-item interactions, the time gaps between events and the time of day for each interaction. To address this, we propose a new class of Contextual Recurrent Neural Networks for Recommendation (CRNNs) that can take into account the contextual information both in the input and output layers and modifying the behavior of the RNN by combining the context embedding with the item embedding and more explicitly, in the model dynamics, by parametrizing the hidden unit transitions as a function of context information. We compare our CRNNs approach with RNNs and non-sequential baselines and show good improvements on the next event prediction task.
Study Morphology of Minimum Spanning Tree Problem and Generalized Algorithms
Venue: arXiv cs.DM 2017
Year: 2017

In this paper, we study the form over the minimum spanning tree problem (MST) from which we will derive an intuitively generalized model and new methods with the upper bound of runtimes of logarithm. The new pattern we made has taken successful to better equilibrium the benefits of local and global when we employ the strategy of divide and conquer to optimize solutions on problem. Under new model, we let the course of clustering become more transparent with many details, so that the whole solution may be featured of much reasonable, flexibility, efficiency and approach to reveal or reflect the reality. There are some important methods and avenues as fruits derived from discussions or trial which can be broad usefulness in the fields of graphic analysis, data mining, k-means clustering problem and so forth.
Integrating self-efficacy into a gamified approach to thwart phishing attacks
Venue: arXiv cs.CR 2017
Year: 2017

Security exploits can include cyber threats such as computer programs that can disturb the normal behavior of computer systems (viruses), unsolicited e-mail (spam), malicious software (malware), monitoring software (spyware), attempting to make computer resources unavailable to their intended users (Distributed Denial-of-Service or DDoS attack), the social engineering, and online identity theft (phishing). One such cyber threat, which is particularly dangerous to computer users is phishing. Phishing is well known as online identity theft, which targets to steal victims' sensitive information such as username, password and online banking details. This paper focuses on designing an innovative and gamified approach to educate individuals about phishing attacks. The study asks how one can integrate self-efficacy, which has a co-relation with the user's knowledge, into an anti-phishing educational game to thwart phishing attacks? One of the main reasons would appear to be a lack of user knowledge to prevent from phishing attacks. Therefore, this research investigates the elements that influence (in this case, either conceptual or procedural knowledge or their interaction effect) and then integrate them into an anti-phishing educational game to enhance people's phishing prevention behaviour through their motivation.
Effective optimization using sample persistence: A case study on quantum annealers and various Monte Carlo optimization methods
Venue: arXiv cs.DM 2017
Year: 2017

We present and apply a general-purpose, multi-start algorithm for improving the performance of low-energy samplers used for solving optimization problems. The algorithm iteratively fixes the value of a large portion of the variables to values that have a high probability of being optimal. The resulting problems are smaller and less connected, and samplers tend to give better low-energy samples for these problems. The algorithm is trivially parallelizable, since each start in the multi-start algorithm is independent, and could be applied to any heuristic solver that can be run multiple times to give a sample. We present results for several classes of hard problems solved using simulated annealing, path-integral quantum Monte Carlo, parallel tempering with isoenergetic cluster moves, and a quantum annealer, and show that the success metrics as well as the scaling are improved substantially. When combined with this algorithm, the quantum annealer's scaling was substantially improved for native Chimera graph problems. In addition, with this algorithm the scaling of the time to solution of the quantum annealer is comparable to the Hamze--de Freitas--Selby algorithm on the weak-strong cluster problems introduced by Boixo et al. Parallel tempering with isoenergetic cluster moves was able to consistently solve 3D spin glass problems with 8000 variables when combined with our method, whereas without our method it could not solve any.
A Semantic Cross-Species Derived Data Management Application
Venue: arXiv cs.DB 2017
Year: 2017

Managing dynamic information in large multi-site, multi-species, and multi-discipline consortia is a challenging task for data management applications. Often in academic research studies the goals for informatics teams are to build applications that provide extract-transform-load (ETL) functionality to archive and catalog source data that has been collected by the research teams. In consortia that cross species and methodological or scientific domains, building interfaces that supply data in a usable fashion and make intuitive sense to scientists from dramatically different backgrounds increases the complexity for developers. Further, reusing source data from outside one's scientific domain is fraught with ambiguities in understanding the data types, analysis methodologies, and how to combine the data with those from other research teams. We report on the design, implementation, and performance of a semantic data management application to support the NIMH funded Conte Center at the University of California, Irvine. The Center is testing a theory of the consequences of "fragmented" (unpredictable, high entropy) early-life experiences on adolescent cognitive and emotional outcomes in both humans and rodents. It employs cross-species neuroimaging, epigenomic, molecular, and neuroanatomical approaches in humans and rodents to assess the potential consequences of fragmented unpredictable experience on brain structure and circuitry. To address this multi-technology, multi-species approach, the system uses semantic web techniques based on the Neuroimaging Data Model (NIDM) to facilitate data ETL functionality. We find this approach enables a low-cost, easy to maintain, and semantically meaningful information management system, enabling the diverse research teams to access and use the data.
Speaker Recognition with Cough, Laugh and "Wei"
Venue: arXiv cs.SD 2017
Year: 2017

This paper proposes a speaker recognition (SRE) task with trivial speech events, such as cough and laugh. These trivial events are ubiquitous in conversations and less subjected to intentional change, therefore offering valuable particularities to discover the genuine speaker from disguised speech. However, trivial events are often short and idiocratic in spectral patterns, making SRE extremely difficult. Fortunately, we found a very powerful deep feature learning structure that can extract highly speaker-sensitive features. By employing this tool, we studied the SRE performance on three types of trivial events: cough, laugh and "Wei" (a short Chinese "Hello"). The results show that there is rich speaker information within these trivial events, even for cough that is intuitively less speaker distinguishable. With the deep feature approach, the EER can reach 10%-14% with the three trivial events, despite their extremely short durations (0.2-1.0 seconds).
Preserving Intermediate Objectives: One Simple Trick to Improve Learning for Hierarchical Models
Venue: arXiv cs.LG 2017
Year: 2017

Hierarchical models are utilized in a wide variety of problems which are characterized by task hierarchies, where predictions on smaller subtasks are useful for trying to predict a final task. Typically, neural networks are first trained for the subtasks, and the predictions of these networks are subsequently used as additional features when training a model and doing inference for a final task. In this work, we focus on improving learning for such hierarchical models and demonstrate our method on the task of speaker trait prediction. Speaker trait prediction aims to computationally identify which personality traits a speaker might be perceived to have, and has been of great interest to both the Artificial Intelligence and Social Science communities. Persuasiveness prediction in particular has been of interest, as persuasive speakers have a large amount of influence on our thoughts, opinions and beliefs. In this work, we examine how leveraging the relationship between related speaker traits in a hierarchical structure can help improve our ability to predict how persuasive a speaker is. We present a novel algorithm that allows us to backpropagate through this hierarchy. This hierarchical model achieves a 25% relative error reduction in classification accuracy over current state-of-the art methods on the publicly available POM dataset.
Joint and Competitive Caching Designs in Large-Scale Multi-Tier Wireless Multicasting Networks
Venue: arXiv cs.IT 2017
Year: 2017

Caching and multicasting are two promising methods to support massive content delivery in multi-tier wireless networks. In this paper, we consider a random caching and multicasting scheme with caching distributions in the two tiers as design parameters, to achieve efficient content dissemination in a two-tier large-scale cache-enabled wireless multicasting network. First, we derive tractable expressions for the successful transmission probabilities in the general region as well as the high SNR and high user density region, respectively, utilizing tools from stochastic geometry. Then, for the case of a single operator for the two tiers, we formulate the optimal joint caching design problem to maximize the successful transmission probability in the asymptotic region, which is nonconvex in general. By using the block successive approximate optimization technique, we develop an iterative algorithm, which is shown to converge to a stationary point. Next, for the case of two different operators, one for each tier, we formulate the competitive caching design game where each tier maximizes its successful transmission probability in the asymptotic region. We show that the game has a unique Nash equilibrium (NE) and develop an iterative algorithm, which is shown to converge to the NE under a mild condition. Finally, by numerical simulations, we show that the proposed designs achieve significant gains over existing schemes.
Cluster Based Symbolic Representation for Skewed Text Categorization
Venue: arXiv cs.IR 2017
Year: 2017

In this work, a problem associated with imbalanced text corpora is addressed. A method of converting an imbalanced text corpus into a balanced one is presented. The presented method employs a clustering algorithm for conversion. Initially to avoid curse of dimensionality, an effective representation scheme based on term class relevancy measure is adapted, which drastically reduces the dimension to the number of classes in the corpus. Subsequently, the samples of larger sized classes are grouped into a number of subclasses of smaller sizes to make the entire corpus balanced. Each subclass is then given a single symbolic vector representation by the use of interval valued features. This symbolic representation in addition to being compact helps in reducing the space requirement and also the classification time. The proposed model has been empirically demonstrated for its superiority on bench marking datasets viz., Reuters 21578 and TDT2. Further, it has been compared against several other existing contemporary models including model based on support vector machine. The comparative analysis indicates that the proposed model outperforms the other existing models.
Online Participatory Sensing in Double Auction Environment with Location Information
Venue: arXiv cs.GT 2017
Year: 2017

As mobile devices have been ubiquitous, participatory sensing emerges as a powerful tool to solve many contemporary real life problems. Here, we contemplate the participatory sensing in online double auction environment by considering the location information of the participating agents. In this paper, we propose a truthful mechanism in this setting and the mechanism also satisfies the other economic properties such as budget balance and individual rationality.
Encoding Video and Label Priors for Multi-label Video Classification on YouTube-8M dataset
Venue: arXiv cs.CV 2017
Year: 2017

YouTube-8M is the largest video dataset for multi-label video classification. In order to tackle the multi-label classification on this challenging dataset, it is necessary to solve several issues such as temporal modeling of videos, label imbalances, and correlations between labels. We develop a deep neural network model, which consists of four components: the frame encoder, the classification layer, the label processing layer, and the loss function. We introduce our newly proposed methods and discusses how existing models operate in the YouTube-8M Classification Task, what insights they have, and why they succeed (or fail) to achieve good performance. Most of the models we proposed are very high compared to the baseline models, and the ensemble of the models we used is 8th in the Kaggle Competition.
Theory and Practice of Data Citation
Venue: arXiv cs.DL 2017
Year: 2017

Citations are the cornerstone of knowledge propagation and the primary means of assessing the quality of research, as well as directing investments in science. Science is increasingly becoming "data-intensive", where large volumes of data are collected and analyzed to discover complex patterns through simulations and experiments, and most scientific reference works have been replaced by online curated datasets. Yet, given a dataset, there is no quantitative, consistent and established way of knowing how it has been used over time, who contributed to its curation, what results have been yielded or what value it has. The development of a theory and practice of data citation is fundamental for considering data as first-class research objects with the same relevance and centrality of traditional scientific products. Many works in recent years have discussed data citation from different viewpoints: illustrating why data citation is needed, defining the principles and outlining recommendations for data citation systems, and providing computational methods for addressing specific issues of data citation. The current panorama is many-faceted and an overall view that brings together diverse aspects of this topic is still missing. Therefore, this paper aims to describe the lay of the land for data citation, both from the theoretical (the why and what) and the practical (the how) angle.
Network Traffic Forensics on Firefox Mobile OS: Facebook, Twitter and Telegram as Case Studies
Venue: arXiv cs.CR 2017
Year: 2017

Development of mobile web-centric OS such as Firefox OS has created new challenges, and opportunities for digital investigators. Network traffic forensic plays an important role in cybercrime investigation to detect subject(s) and object(s) of the crime. In this chapter, we detect and analyze residual network traffic artefacts of Firefox OS in relation to two popular social networking applications (Facebook and Twitter) and one instant messaging application (Telegram). We utilized a Firefox OS simulator to generate relevant traffic while all communication data were captured using network monitoring tools. Captured network packets were examined and remnants with forensic value were reported. This paper as the first focused study on mobile Firefox OS network traffic analysis should pave the way for the future research in this direction.
Centralized Radio Resource Management for 5G small cells as LSA enabler
Venue: arXiv cs.NI 2017
Year: 2017

The stringent requirements defined for 5G systems drive the need to promote new paradigms to the existing cellular networks. Dense and ultra-dense networks based on small cells, together with new spectrum sharing schemes seem to be key enabling technologies for emerging 5G mobile networks. This article explores the vision of the SPEED-5G project, analyzing the ability of a Centralized Radio Resource Management entity to support the Licensed Shared Access spectrum sharing framework in a deployment based on Network Slicing and Network Sharing paradigms.