OpenRaiser/Intern-Atlas huggingface dataset:
How to Train Your Deep Neural Network with Dictionary Learning
Venue: arXiv cs.LG 2016
Year: 2016

Currently there are two predominant ways to train deep neural networks. The first one uses restricted Boltzmann machine (RBM) and the second one autoencoders. RBMs are stacked in layers to form deep belief network (DBN); the final representation layer is attached to the target to complete the deep neural network. Autoencoders are nested one inside the other to form stacked autoencoders; once the stcaked autoencoder is learnt the decoder portion is detached and the target attached to the deepest layer of the encoder to form the deep neural network. This work proposes a new approach to train deep neural networks using dictionary learning as the basic building block; the idea is to use the features from the shallower layer as inputs for training the next deeper layer. One can use any type of dictionary learning (unsupervised, supervised, discriminative etc.) as basic units till the pre-final layer. In the final layer one needs to use the label consistent dictionary learning formulation for classification. We compare our proposed framework with existing state-of-the-art deep learning techniques on benchmark problems; we are always within the top 10 results. In actual problems of age and gender classification, we are better than the best known techniques.
Using UDP for Internet Transport Evolution
Venue: arXiv cs.NI 2016
Year: 2016

The increasing use of middleboxes (e.g., NATs, firewalls) in the Internet has made it harder and harder to deploy new transport or higher layer protocols, or even extensions to existing ones. Current work to address this Internet transport ossification has led to renewed interest in UDP as an encapsulation for making novel transport protocols deployable in the Internet. Examples include Google's QUIC and the WebRTC data channel. The common assumption made by these approaches is that encapsulation over UDP works in the present Internet. This paper presents a measurement study to examine this assumption, and provides guidance for protocol design based on our measurements. The key question is "can we run new transport protocols for the Internet over UDP?" We find that the answer is largely "yes": UDP works on most networks, and impairments are generally confined to access networks. This allows relatively simple fallback strategies to work around it. Our answer is based on a twofold methodology. First, we use the RIPE Atlas platform to basically check UDP connectivity and first-packet latency. Second, we deploy copycat, a new tool for comparing TCP loss, latency, and throughput with UDP by generating TCP-shaped traffic with UDP headers.
Spectral algorithms for tensor completion
Venue: arXiv cs.DS 2016
Year: 2016

In the tensor completion problem, one seeks to estimate a low-rank tensor based on a random sample of revealed entries. In terms of the required sample size, earlier work revealed a large gap between estimation with unbounded computational resources (using, for instance, tensor nuclear norm minimization) and polynomial-time algorithms. Among the latter, the best statistical guarantees have been proved, for third-order tensors, using the sixth level of the sum-of-squares (SOS) semidefinite programming hierarchy (Barak and Moitra, 2014). However, the SOS approach does not scale well to large problem instances. By contrast, spectral methods --- based on unfolding or matricizing the tensor --- are attractive for their low complexity, but have been believed to require a much larger sample size. This paper presents two main contributions. First, we propose a new unfolding-based method, which outperforms naive ones for symmetric $k$-th order tensors of rank $r$. For this result we make a study of singular space estimation for partially revealed matrices of large aspect ratio, which may be of independent interest. For third-order tensors, our algorithm matches the SOS method in terms of sample size (requiring about $rd^{3/2}$ revealed entries), subject to a worse rank condition ($r\ll d^{3/4}$ rather than $r\ll d^{3/2}$). We complement this result with a different spectral algorithm for third-order tensors in the overcomplete ($r\ge d$) regime. Under a random model, this second approach succeeds in estimating tensors of rank $d\le r \ll d^{3/2}$ from about $rd^{3/2}$ revealed entries.
Negotiation as Concurrency Primitive
Venue: arXiv cs.LO 2016
Year: 2016

This paper introduces negotiations, a model of concurrency close to Petri nets, with multi-party negotiations as concurrency primitive. We study two fundamental analysis problems. The soundness problem consists in deciding if it is always possible for a negotiation to terminate successfully, whatever the current state is. Given a sound negotiation, the summarization problem aims at computing an equivalent one-step negotiation with the same input/output behavior. The soundness and summarization problems can be solved by means of simple algorithms acting on the state space of the negotiation, which however face the well-known state explosion problem. We study alternative algorithms that avoid the construction of the state space. In particular, we define reduction rules that simplify a negotiation while preserving the sound/non-sound character of the negotiation and its summary. In a first result we show that our rules are complete for the class of weakly deterministic acyclic negotiations, meaning that they reduce all sound negotiations in this class, and only them, to equivalent one-step negotiations. This provides algorithms for both the soundness and summarization problems that avoid the construction of the state space. We then study the class of deterministic negotiations. Our second main result shows that the rules are also complete for this class, even if the negotiation contains cycles. Moreover, we present an algorithm that completely reduces all sound deterministic negotiations, and only them, in polynomial time.
Understanding Non-optical Remote-sensed Images: Needs, Challenges and Ways Forward
Venue: arXiv cs.CV 2016
Year: 2016

Non-optical remote-sensed images are going to be used more often in man- aging disaster, crime and precision agriculture. With more small satellites and unmanned air vehicles planning to carry radar and hyperspectral image sensors there is going to be an abundance of such data in the recent future. Understanding these data in real-time will be crucial in attaining some of the important sustain- able development goals. Processing non-optical images is, in many ways, different from that of optical images. Most of the recent advances in the domain of image understanding has been using optical images. In this article we shall explain the needs for image understanding in non-optical domain and the typical challenges. Then we shall describe the existing approaches and how we can move from there to the desired goal of a reliable real-time image understanding system.
DeMIAN: Deep Modality Invariant Adversarial Network
Venue: arXiv cs.LG 2016
Year: 2016

Obtaining common representations from different modalities is important in that they are interchangeable with each other in a classification problem. For example, we can train a classifier on image features in the common representations and apply it to the testing of the text features in the representations. Existing multi-modal representation learning methods mainly aim to extract rich information from paired samples and train a classifier by the corresponding labels; however, collecting paired samples and their labels simultaneously involves high labor costs. Addressing paired modal samples without their labels and single modal data with their labels independently is much easier than addressing labeled multi-modal data. To obtain the common representations under such a situation, we propose to make the distributions over different modalities similar in the learned representations, namely modality-invariant representations. In particular, we propose a novel algorithm for modality-invariant representation learning, named Deep Modality Invariant Adversarial Network (DeMIAN), which utilizes the idea of Domain Adaptation (DA). Using the modality-invariant representations learned by DeMIAN, we achieved better classification accuracy than with the state-of-the-art methods, especially for some benchmark datasets of zero-shot learning.
EgoReID: Cross-view Self-Identification and Human Re-identification in Egocentric and Surveillance Videos
Venue: arXiv cs.CV 2016
Year: 2016

Human identification remains to be one of the challenging tasks in computer vision community due to drastic changes in visual features across different viewpoints, lighting conditions, occlusion, etc. Most of the literature has been focused on exploring human re-identification across viewpoints that are not too drastically different in nature. Cameras usually capture oblique or side views of humans, leaving room for a lot of geometric and visual reasoning. Given the recent popularity of egocentric and top-view vision, re-identification across these two drastically different views can now be explored. Having an egocentric and a top view video, our goal is to identify the cameraman in the content of the top-view video, and also re-identify the people visible in the egocentric video, by matching them to the identities present in the top-view video. We propose a CRF-based method to address the two problems. Our experimental results demonstrates the efficiency of the proposed approach over a variety of video recorded from two views.
Unsupervised Video Segmentation via Spatio-Temporally Nonlocal Appearance Learning
Venue: arXiv cs.CV 2016
Year: 2016

Video object segmentation is challenging due to the factors like rapidly fast motion, cluttered backgrounds, arbitrary object appearance variation and shape deformation. Most existing methods only explore appearance information between two consecutive frames, which do not make full use of the usefully long-term nonlocal information that is helpful to make the learned appearance stable, and hence they tend to fail when the targets suffer from large viewpoint changes and significant non-rigid deformations. In this paper, we propose a simple yet effective approach to mine the long-term sptatio-temporally nonlocal appearance information for unsupervised video segmentation. The motivation of our algorithm comes from the spatio-temporal nonlocality of the region appearance reoccurrence in a video. Specifically, we first generate a set of superpixels to represent the foreground and background, and then update the appearance of each superpixel with its long-term sptatio-temporally nonlocal counterparts generated by the approximate nearest neighbor search method with the efficient KD-tree algorithm. Then, with the updated appearances, we formulate a spatio-temporal graphical model comprised of the superpixel label consistency potentials. Finally, we generate the segmentation by optimizing the graphical model via iteratively updating the appearance model and estimating the labels. Extensive evaluations on the SegTrack and Youtube-Objects datasets demonstrate the effectiveness of the proposed method, which performs favorably against some state-of-art methods.
KS_JU@DPIL-FIRE2016:Detecting Paraphrases in Indian Languages Using Multinomial Logistic Regression Model
Venue: arXiv cs.CL 2016
Year: 2016

In this work, we describe a system that detects paraphrases in Indian Languages as part of our participation in the shared Task on detecting paraphrases in Indian Languages (DPIL) organized by Forum for Information Retrieval Evaluation (FIRE) in 2016. Our paraphrase detection method uses a multinomial logistic regression model trained with a variety of features which are basically lexical and semantic level similarities between two sentences in a pair. The performance of the system has been evaluated against the test set released for the FIRE 2016 shared task on DPIL. Our system achieves the highest f-measure of 0.95 on task1 in Punjabi language.The performance of our system on task1 in Hindi language is f-measure of 0.90. Out of 11 teams participated in the shared task, only four teams participated in all four languages, Hindi, Punjabi, Malayalam and Tamil, but the remaining 7 teams participated in one of the four languages. We also participated in task1 and task2 both for all four Indian Languages. The overall average performance of our system including task1 and task2 overall four languages is F1-score of 0.81 which is the second highest score among the four systems that participated in all four languages.
Neighbor-Neighbor Correlations Explain Measurement Bias in Networks
Venue: arXiv cs.SI 2016
Year: 2016

In numerous physical models on networks, dynamics are based on interactions that exclusively involve properties of a node's nearest neighbors. However, a node's local view of its neighbors may systematically bias perceptions of network connectivity or the prevalence of certain traits. We investigate the strong friendship paradox, which occurs when the majority of a node's neighbors have more neighbors than does the node itself. We develop a model to predict the magnitude of the paradox, showing that it is enhanced by negative correlations between degrees of neighboring nodes. We then show that by including neighbor-neighbor correlations, which are degree correlations one step beyond those of neighboring nodes, we accurately predict the impact of the strong friendship paradox in real-world networks. Understanding how the paradox biases local observations can inform better measurements of network structure and our understanding of collective phenomena.
Predicting the Industry of Users on Social Media
Venue: arXiv cs.CL 2016
Year: 2016

Automatic profiling of social media users is an important task for supporting a multitude of downstream applications. While a number of studies have used social media content to extract and study collective social attributes, there is a lack of substantial research that addresses the detection of a user's industry. We frame this task as classification using both feature engineering and ensemble learning. Our industry-detection system uses both posted content and profile information to detect a user's industry with 64.3% accuracy, significantly outperforming the majority baseline in a taxonomy of fourteen industry classes. Our qualitative analysis suggests that a person's industry not only affects the words used and their perceived meanings, but also the number and type of emotions being expressed.
Ideology Detection for Twitter Users with Heterogeneous Types of Links
Venue: arXiv cs.SI 2016
Year: 2016

The problem of ideology detection is to study the latent (political) placement for people, which is traditionally studied on politicians according to their voting behaviors. Recently, more and more studies begin to address the ideology detection problem for ordinary users based on their online behaviors that can be captured by social media, e.g., Twitter. As far as we are concerned, however, the vast majority of the existing methods on ideology detection on social media have oversimplified the problem as a binary classification problem (i.e., liberal vs. conservative). Moreover, though social links can play a critical role in deciding one's ideology, most of the existing work ignores the heterogeneous types of links in social media. In this paper we propose to detect \emph{numerical} ideology positions for Twitter users, according to their \emph{follow}, \emph{mention}, and \emph{retweet} links to a selected set of politicians. A unified probabilistic model is proposed that can (1) explain the reasons why links are built among people in terms of their ideology, (2) integrate heterogeneous types of links together in determining people's ideology, and (3) automatically learn the quality of each type of links in deciding one's ideology. Experiments have demonstrated the advantages of our model in terms of both ranking and political leaning classification accuracy. It is shown that (1) using multiple types of links is better than using any single type of links alone to determine one's ideology, and (2) our model is even more superior than baselines when dealing with people that are sparsely linked in one type of links. We also show that the detected ideology for Twitter users aligns with our intuition quite well.
Non-Linear Programming: Maximize SNR for Designing Spreading Sequence - Part I: SNR versus Mean-Square Correlation
Venue: arXiv cs.IT 2016
Year: 2016

Signal to Noise Ratio (SNR) is an important index for wireless communications. In CDMA systems, spreading sequences are utilized. This series of papers show the method to derive spreading sequences as the solutions of the non-linear programming: maximize SNR. In this paper, we consider a frequency-selective wide-sense-stationary uncorrelated-scattering (WSSUS) channel and evaluate the worst case of SNR. Then, we derive the new expression of SNR whose main term consists of the periodic correlation terms and the aperiodic correlation terms. In general, there is a relation between SNR and mean-square correlations, which are indices for performance of spreading sequences. Then, we show the relation between our expression and them. With this expression, we can maximize SNR with the Lagrange multiplier method. In Part II, with this expression, we construct two types optimization problems and evaluate them.
Distributed Infrastructure Inspection Path Planning subject to Time Constraints
Venue: arXiv cs.RO 2016
Year: 2016

Within this paper, the problem of 3D structural inspection path planning for distributed infrastructure using aerial robots that are subject to time constraints is addressed. The proposed algorithm handles varying spatial properties of the infrastructure facilities, accounts for their different importance and exploration function and computes an overall inspection path of high inspection reward while respecting the robot endurance or mission time constraints as well as the vehicle dynamics and sensor limitations. To achieve its goal, it employs an iterative, 3-step optimization strategy at each iteration of which it first randomly samples a set of possible structures to visit, subsequently solves the derived traveling salesman problem and computes the travel costs, while finally it samples and assigns inspection times to each structure and evaluates the total inspection reward. For the derivation of the inspection paths per each independent facility, it interfaces a path planner dedicated to the 3D coverage of single structures. The resulting algorithm properties, computational performance and path quality are evaluated using simulation studies as well as experimental test-cases employing a multirotor micro aerial vehicle.
Proactive QoE Provisioning in Heterogeneous Access Networks using Hidden Markov Models and Reinforcement Learning
Venue: arXiv cs.NI 2016
Year: 2016

Quality of Experience (QoE) provisioning in heterogeneous access networks (HANs) can be achieved via handoffs. The current approaches for QoE-aware handoffs either lack the availability of a network path probing method or lack the availability of efficient methods for QoE prediction. Further, the current approaches do not explore the benefits of proactive QoE-aware handoffs such that user's QoE is maximized by learning from past network conditions and by actions taken by the mobile device regarding handoffs. In this paper, our contributions are two-fold. First, we propose, develop and validate a novel method for QoE prediction based on passive probing. Our method is based on hidden Markov models and Multi-homed Mobility Management Protocol which eliminates the need for additional probe packets for QoE prediction. It achieves the average QoE prediction accuracy of 97%. Second, we propose, develop and validate a novel reinforcement learning based method for proactive QoE-aware handoffs. We show that our method outperforms existing approaches by reducing the number of vertical handoffs by 60.65% while maintaining high QoE levels and by extending crucial functionality such as passive probing mechanisms.
Audio-based Distributional Semantic Models for Music Auto-tagging and Similarity Measurement
Venue: arXiv cs.IR 2016
Year: 2016

The recent development of Audio-based Distributional Semantic Models (ADSMs) enables the computation of audio and lexical vector representations in a joint acoustic-semantic space. In this work, these joint representations are applied to the problem of automatic tag generation. The predicted tags together with their corresponding acoustic representation are exploited for the construction of acoustic-semantic clip embeddings. The proposed algorithms are evaluated on the task of similarity measurement between music clips. Acoustic-semantic models are shown to outperform the state-of-the-art for this task and produce high quality tags for audio/music clips.
Credibility and Dynamics of Collective Attention
Venue: arXiv cs.SI 2016
Year: 2016

Today, social media provide the means by which billions of people experience news and events happening around the world. However, the absence of traditional journalistic gatekeeping allows information to flow unencumbered through these platforms, often raising questions of veracity and credibility of the reported information. Here we ask: How do the dynamics of collective attention directed toward an event reported on social media vary with its perceived credibility? By examining the first large-scale, systematically tracked credibility database of public Twitter messages (47M messages corresponding to 1,138 real-world events over a span of three months), we established a relationship between the temporal dynamics of events reported on social media and their associated level of credibility judgments. Representing collective attention by the aggregate temporal signatures of an event reportage, we found that the amount of continued attention focused on an event provides information about its associated levels of perceived credibility. Events exhibiting sustained, intermittent bursts of attention were found to be associated with lower levels of perceived credibility. In other words, as more people showed interest during moments of transient collective attention, the associated uncertainty surrounding these events also increased.
Effect of white LED DC-bias on modulation speed for visible light communications
Venue: arXiv cs.IT 2016
Year: 2016

The light emitting diode (LED) nonlinearities distortion induced degradation in the performance of visible light communication (VLC) systems can be controlled by optimizing the DC bias point of the LED. In this paper, we theoretically analyze and experimentally demonstrate the effect of white LED DC bias on nonlinear modulation bandwidth and dynamic range of the VLC system. The linear dynamic range is enhanced by using series-connected LED chips, and the modulation bandwidth is extended to 40 MHz by post-equalization without using a blue filter. The experimental results well match the theoretical model of LED nonlinear modulation characteristics. The results show that the modulation bandwidth increases and saturates with an increase in LED DC bias current due to nonlinear effect of carrier lifetime and junction capacitance. The optimized DC-bias current that corresponds to the minimum BER increases with the increase of data rate. A 60-Mbps NRZ transmission can be achieved with BER threshold of 10-3 by properly adjusting LED DC bias point.