OpenRaiser/Intern-Atlas huggingface dataset:
Motion Prediction Under Multimodality with Conditional Stochastic Networks
Venue: arXiv cs.CV 2017
Year: 2017

Given a visual history, multiple future outcomes for a video scene are equally probable, in other words, the distribution of future outcomes has multiple modes. Multimodality is notoriously hard to handle by standard regressors or classifiers: the former regress to the mean and the latter discretize a continuous high dimensional output space. In this work, we present stochastic neural network architectures that handle such multimodality through stochasticity: future trajectories of objects, body joints or frames are represented as deep, non-linear transformations of random (as opposed to deterministic) variables. Such random variables are sampled from simple Gaussian distributions whose means and variances are parametrized by the output of convolutional encoders over the visual history. We introduce novel convolutional architectures for predicting future body joint trajectories that outperform fully connected alternatives \cite{DBLP:journals/corr/WalkerDGH16}. We introduce stochastic spatial transformers through optical flow warping for predicting future frames, which outperform their deterministic equivalents \cite{DBLP:journals/corr/PatrauceanHC15}. Training stochastic networks involves an intractable marginalization over stochastic variables. We compare various training schemes that handle such marginalization through a) straightforward sampling from the prior, b) conditional variational autoencoders \cite{NIPS2015_5775,DBLP:journals/corr/WalkerDGH16}, and, c) a proposed K-best-sample loss that penalizes the best prediction under a fixed "prediction budget". We show experimental results on object trajectory prediction, human body joint trajectory prediction and video prediction under varying future uncertainty, validating quantitatively and qualitatively our architectural choices and training schemes.
Blind Detection of Polar Codes
Venue: arXiv cs.IT 2017
Year: 2017

Polar codes were recently chosen to protect the control channel information in the next-generation mobile communication standard (5G) defined by the 3GPP. As a result, receivers will have to implement blind detection of polar coded frames in order to keep complexity, latency, and power consumption tractable. As a newly proposed class of block codes, the problem of polar-code blind detection has received very little attention. In this work, we propose a low-complexity blind-detection algorithm for polar-encoded frames. We base this algorithm on a novel detection metric with update rules that leverage the a priori knowledge of the frozen-bit locations, exploiting the inherent structures that these locations impose on a polar-encoded block of data. We show that the proposed detection metric allows to clearly distinguish polar-encoded frames from other types of data by considering the cumulative distribution functions of the detection metric, and the receiver operating characteristic. The presented results are tailored to the 5G standardization effort discussions, i.e., we consider a short low-rate polar code concatenated with a CRC.
Social Media Advertisement Outreach: Learning the Role of Aesthetics
Venue: arXiv cs.IR 2017
Year: 2017

Corporations spend millions of dollars on developing creative image-based promotional content to advertise to their user-base on platforms like Twitter. Our paper is an initial study, where we propose a novel method to evaluate and improve outreach of promotional images from corporations on Twitter, based purely on their describable aesthetic attributes. Existing works in aesthetic based image analysis exclusively focus on the attributes of digital photographs, and are not applicable to advertisements due to the influences of inherent content and context based biases on outreach. Our paper identifies broad categories of biases affecting such images, describes a method for normalization to eliminate effects of those biases and score images based on their outreach, and examines the effects of certain handcrafted describable aesthetic features on image outreach. Optimizing on the describable aesthetic features resulting from this research is a simple method for corporations to complement their existing marketing strategy to gain significant improvement in user engagement on social media for promotional images.
Analysis of Computational Science Papers from ICCS 2001-2016 using Topic Modeling and Graph Theory
Venue: arXiv cs.DL 2017
Year: 2017

This paper presents results of topic modeling and network models of topics using the International Conference on Computational Science corpus, which contains domain-specific (computational science) papers over sixteen years (a total of 5695 papers). We discuss topical structures of International Conference on Computational Science, how these topics evolve over time in response to the topicality of various problems, technologies and methods, and how all these topics relate to one another. This analysis illustrates multidisciplinary research and collaborations among scientific communities, by constructing static and dynamic networks from the topic modeling results and the keywords of authors. The results of this study give insights about the past and future trends of core discussion topics in computational science. We used the Non-negative Matrix Factorization topic modeling algorithm to discover topics and labeled and grouped results hierarchically.
A Generic Approach to Flow-Sensitive Polymorphic Effects (Extended Version)
Venue: arXiv cs.PL 2017
Year: 2017

Effect systems are lightweight extensions to type systems that can verify a wide range of important properties with modest developer burden. But our general understanding of effect systems is limited primarily to systems where the order of effects is irrelevant. Understanding such systems in terms of a lattice of effects grounds understanding of the essential issues, and provides guidance when designing new effect systems. By contrast, sequential effect systems --- where the order of effects is important --- lack a clear algebraic characterization. We derive an algebraic characterization from the shape of prior concrete sequential effect systems. We present an abstract polymorphic effect system with singleton effects parameterized by an effect quantale --- an algebraic structure with well-defined properties that can model a range of existing order-sensitive effect systems. We define effect quantales, derive useful properties, and show how they cleanly model a variety of known sequential effect systems. We show that effect quantales provide a free, general notion of iterating a sequential effect, and that for systems we consider the derived iteration agrees with the manually designed iteration operators in prior work. Identifying and applying the right algebraic structure led us to subtle insights into the design of order-sensitive effect systems, which provides guidance on non-obvious points of designing order-sensitive effect systems. Effect quantales have clear relationships to the recent category theoretic work on order-sensitive effect systems, but are explained without recourse to category theory. In addition, our derived iteration construct should generalize to these semantic structures, addressing limitations of that work.
Deep Speaker: an End-to-End Neural Speaker Embedding System
Venue: arXiv cs.CL 2017
Year: 2017

We present Deep Speaker, a neural speaker embedding system that maps utterances to a hypersphere where speaker similarity is measured by cosine similarity. The embeddings generated by Deep Speaker can be used for many tasks, including speaker identification, verification, and clustering. We experiment with ResCNN and GRU architectures to extract the acoustic features, then mean pool to produce utterance-level speaker embeddings, and train using triplet loss based on cosine similarity. Experiments on three distinct datasets suggest that Deep Speaker outperforms a DNN-based i-vector baseline. For example, Deep Speaker reduces the verification equal error rate by 50% (relatively) and improves the identification accuracy by 60% (relatively) on a text-independent dataset. We also present results that suggest adapting from a model trained with Mandarin can improve accuracy for English speaker recognition.
Building Morphological Chains for Agglutinative Languages
Venue: arXiv cs.CL 2017
Year: 2017

In this paper, we build morphological chains for agglutinative languages by using a log-linear model for the morphological segmentation task. The model is based on the unsupervised morphological segmentation system called MorphoChains. We extend MorphoChains log linear model by expanding the candidate space recursively to cover more split points for agglutinative languages such as Turkish, whereas in the original model candidates are generated by considering only binary segmentation of each word. The results show that we improve the state-of-art Turkish scores by 12% having a F-measure of 72% and we improve the English scores by 3% having a F-measure of 74%. Eventually, the system outperforms both MorphoChains and other well-known unsupervised morphological segmentation systems. The results indicate that candidate generation plays an important role in such an unsupervised log-linear model that is learned using contrastive estimation with negative samples.
On Algorithms for $L$-bounded Cut Problem
Venue: arXiv cs.DS 2017
Year: 2017

Given a graph $G=(V,E)$ with two distinguished vertices $s,t\in V$ and an integer parameter $L>0$, an {\em $L$-bounded cut} is a subset $F$ of edges (vertices) such that the every path between $s$ and $t$ in $G\setminus F$ has length more than $L$. The task is to find an $L$-bounded cut of minimum cardinality. Though the problem is very simple to state and has been studied since the beginning of the 70's, it is not much understood yet. The problem is known to be $\cal{NP}$-hard to approximate within a small constant factor even for $L\geq 4$ (for $L\geq 5$ for the vertex cuts). On the other hand, the best known approximation algorithm for general graphs has approximation ratio only $\mathcal{O}({n^{2/3}})$ in the edge case, and $\mathcal{O}({\sqrt{n}})$ in the vertex case, where $n$ denotes the number of vertices. We show that for planar graphs, it is possible to solve both the edge- and the vertex-version of the problem optimally in time $\mathcal{O}(L^{3L}n)$. That is, the problem is fixed parameter tractable (FPT) with respect to $L$ on planar graphs. Furthermore, we show that the problem remains FPT even for bounded genus graphs, a super class of planar graphs. Our second contribution deals with approximations of the vertex version of the problem. We describe an algorithm that for a given a graph $G$, its tree decomposition of treewidth $τ$ and vertices $s$ and $t$ computes a $τ$-approximation of the minimum $L$-bounded $s-t$ vertex cut; if the decomposition is not given, then the approximation ratio is $\mathcal{O}(τ\sqrt{\log τ})$. For graphs with treewidth bounded by $\mathcal{O}(n^{1/2-ε})$ for any $ε>0$, but not by a constant, this is the best approximation in terms of~$n$ that we are aware of.
On Using Active Learning and Self-Training when Mining Performance Discussions on Stack Overflow
Venue: arXiv cs.CL 2017
Year: 2017

Abundant data is the key to successful machine learning. However, supervised learning requires annotated data that are often hard to obtain. In a classification task with limited resources, Active Learning (AL) promises to guide annotators to examples that bring the most value for a classifier. AL can be successfully combined with self-training, i.e., extending a training set with the unlabelled examples for which a classifier is the most certain. We report our experiences on using AL in a systematic manner to train an SVM classifier for Stack Overflow posts discussing performance of software components. We show that the training examples deemed as the most valuable to the classifier are also the most difficult for humans to annotate. Despite carefully evolved annotation criteria, we report low inter-rater agreement, but we also propose mitigation strategies. Finally, based on one annotator's work, we show that self-training can improve the classification accuracy. We conclude the paper by discussing implication for future text miners aspiring to use AL and self-training.
A Study and Comparison of Human and Deep Learning Recognition Performance Under Visual Distortions
Venue: arXiv cs.CV 2017
Year: 2017

Deep neural networks (DNNs) achieve excellent performance on standard classification tasks. However, under image quality distortions such as blur and noise, classification accuracy becomes poor. In this work, we compare the performance of DNNs with human subjects on distorted images. We show that, although DNNs perform better than or on par with humans on good quality images, DNN performance is still much lower than human performance on distorted images. We additionally find that there is little correlation in errors between DNNs and human subjects. This could be an indication that the internal representation of images are different between DNNs and the human visual system. These comparisons with human performance could be used to guide future development of more robust DNNs.
Credible Review Detection with Limited Information using Consistency Analysis
Venue: arXiv cs.AI 2017
Year: 2017

Online reviews provide viewpoints on the strengths and shortcomings of products/services, influencing potential customers' purchasing decisions. However, the proliferation of non-credible reviews -- either fake (promoting/ demoting an item), incompetent (involving irrelevant aspects), or biased -- entails the problem of identifying credible reviews. Prior works involve classifiers harnessing rich information about items/users -- which might not be readily available in several domains -- that provide only limited interpretability as to why a review is deemed non-credible. This paper presents a novel approach to address the above issues. We utilize latent topic models leveraging review texts, item ratings, and timestamps to derive consistency features without relying on item/user histories, unavailable for "long-tail" items/users. We develop models, for computing review credibility scores to provide interpretable evidence for non-credible reviews, that are also transferable to other domains -- addressing the scarcity of labeled data. Experiments on real-world datasets demonstrate improvements over state-of-the-art baselines.
Item Recommendation with Continuous Experience Evolution of Users using Brownian Motion
Venue: arXiv cs.AI 2017
Year: 2017

Online review communities are dynamic as users join and leave, adopt new vocabulary, and adapt to evolving trends. Recent work has shown that recommender systems benefit from explicit consideration of user experience. However, prior work assumes a fixed number of discrete experience levels, whereas in reality users gain experience and mature continuously over time. This paper presents a new model that captures the continuous evolution of user experience, and the resulting language model in reviews and other posts. Our model is unsupervised and combines principles of Geometric Brownian Motion, Brownian Motion, and Latent Dirichlet Allocation to trace a smooth temporal progression of user experience and language model respectively. We develop practical algorithms for estimating the model parameters from data and for inference with our model (e.g., to recommend items). Extensive experiments with five real-world datasets show that our model not only fits data better than discrete-model baselines, but also outperforms state-of-the-art methods for predicting item ratings.
Handwritten Bangla Digit Recognition Using Deep Learning
Venue: arXiv cs.CV 2017
Year: 2017

In spite of the advances in pattern recognition technology, Handwritten Bangla Character Recognition (HBCR) (such as alpha-numeric and special characters) remains largely unsolved due to the presence of many perplexing characters and excessive cursive in Bangla handwriting. Even the best existing recognizers do not lead to satisfactory performance for practical applications. To improve the performance of Handwritten Bangla Digit Recognition (HBDR), we herein present a new approach based on deep neural networks which have recently shown excellent performance in many pattern recognition and machine learning applications, but has not been throughly attempted for HBDR. We introduce Bangla digit recognition techniques based on Deep Belief Network (DBN), Convolutional Neural Networks (CNN), CNN with dropout, CNN with dropout and Gaussian filters, and CNN with dropout and Gabor filters. These networks have the advantage of extracting and using feature information, improving the recognition of two dimensional shapes with a high degree of invariance to translation, scaling and other pattern distortions. We systematically evaluated the performance of our method on publicly available Bangla numeral image database named CMATERdb 3.1.1. From experiments, we achieved 98.78% recognition rate using the proposed method: CNN with Gabor features and dropout, which outperforms the state-of-the-art algorithms for HDBR.
Generating Memorable Mnemonic Encodings of Numbers
Venue: arXiv cs.CL 2017
Year: 2017

The major system is a mnemonic system that can be used to memorize sequences of numbers. In this work, we present a method to automatically generate sentences that encode a given number. We propose several encoding models and compare the most promising ones in a password memorability study. The results of the study show that a model combining part-of-speech sentence templates with an $n$-gram language model produces the most memorable password representations.
A Scalable, Low-Overhead Finite-State Machine Overlay for Rapid FPGA Application Development
Venue: arXiv cs.AR 2017
Year: 2017

Productivity issues such as lengthy compilation and limited code reuse have restricted usage of field-programmable gate arrays (FPGAs), despite significant technical advantages. Recent work into overlays -- virtual coarse-grained architectures implemented atop FPGAs -- has aimed to address these concerns through abstraction, but have mostly focused on pipelined applications with minimal control requirements. Although research has introduced overlays for finite-state machines, those architectures suffer from limited scalability and flexibility, which we address with a new overlay architecture using memory decomposition on transitional logic. Although our overlay provides modest average improvements of 15% to 29% fewer lookup tables for individual finite-state machines, for the more common usage of an overlay supporting different finite-state machines, our overlay achieves a 77% to 99% reduction in lookup tables. In addition, our overlay reduces compilation time to tenths of a second to enable rapid iterative-development methodologies.
ChineseFoodNet: A large-scale Image Dataset for Chinese Food Recognition
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we introduce a new and challenging large-scale food image dataset called "ChineseFoodNet", which aims to automatically recognizing pictured Chinese dishes. Most of the existing food image datasets collected food images either from recipe pictures or selfie. In our dataset, images of each food category of our dataset consists of not only web recipe and menu pictures but photos taken from real dishes, recipe and menu as well. ChineseFoodNet contains over 180,000 food photos of 208 categories, with each category covering a large variations in presentations of same Chinese food. We present our efforts to build this large-scale image dataset, including food category selection, data collection, and data clean and label, in particular how to use machine learning methods to reduce manual labeling work that is an expensive process. We share a detailed benchmark of several state-of-the-art deep convolutional neural networks (CNNs) on ChineseFoodNet. We further propose a novel two-step data fusion approach referred as "TastyNet", which combines prediction results from different CNNs with voting method. Our proposed approach achieves top-1 accuracies of 81.43% on the validation set and 81.55% on the test set, respectively. The latest dataset is public available for research and can be achieved at https://sites.google.com/view/chinesefoodnet.
Deterministic Genericity for Polynomial Ideals
Venue: arXiv cs.SC 2017
Year: 2017

We consider several notions of genericity appearing in algebraic geometry and commutative algebra. Special emphasis is put on various stability notions which are defined in a combinatorial manner and for which a number of equivalent algebraic characterisations are provided. It is shown that in characteristic zero the corresponding generic positions can be obtained with a simple deterministic algorithm. In positive characteristic, only adapted stable positions are reachable except for quasi-stability which is obtainable in any characteristic.
Aggregation and Disaggregation of Energetic Flexibility from Distributed Energy Resources
Venue: arXiv cs.SY 2017
Year: 2017

A variety of energy resources has been identified as being flexible in their electric energy consumption or generation. This energetic flexibility can be used for various purposes such as minimizing energy procurement costs or providing ancillary services to power grids. To fully leverage the flexibility available from distributed small-scale resources, their flexibility must be quantified and aggregated. This paper introduces a generic and scalable approach for flexible energy systems to quantitatively describe and price their flexibility based on zonotopic sets. The description proposed allows aggregators to efficiently pool the flexibility of large numbers of systems and to make control and market decisions on the aggregate level. In addition, an algorithm is presented that distributes aggregate-level control decisions among the individual systems of the pool in an economically fair and computationally efficient way. Finally, it is shown how the zonotopic description of flexibility enables an efficient computation of aggregate regulation power bid-curves.
Ultra Reliable UAV Communication Using Altitude and Cooperation Diversity
Venue: arXiv cs.IT 2017
Year: 2017

The use of unmanned aerial vehicles (UAVs) that serve as aerial base stations is expected to become predominant in the next decade. However, in order for this technology to unfold its full potential it is necessary to develop a fundamental understanding of the distinctive features of air-to-ground (A2G) links. As a contribution in this direction, this paper proposes a generic framework for the analysis and optimization of the A2G systems. In contrast to the existing literature, this framework incorporates both height-dependent path loss exponent and small-scale fading, and unifies a widely used ground-to-ground channel model with that of A2G for analysis of large-scale wireless networks. We derive analytical expressions for the optimal UAV height that minimizes the outage probability of a given A2G link. Moreover, our framework allows us to derive a height-dependent closed-form expression and a tight lower bound for the outage probability of an \textit{A2G cooperative communication} network. Our results suggest that the optimal location of the UAVs with respect to the ground nodes does not change by the inclusion of ground relays. This enables interesting insights in the deployment of future A2G networks, as the system reliability could be adjusted dynamically by adding relaying nodes without requiring changes in the position of the corresponding UAVs.