OpenRaiser/Intern-Atlas huggingface dataset:
Contextual object categorization with energy-based model
Venue: arXiv cs.CV 2016
Year: 2016

Object categorization is a hot issue of an image mining. Contextual information between objects is one of the important semantic knowledge of an image. However, the previous researches for an object categorization have not made full use of the contextual information, especially the spatial relations between objects. In addition, the object categorization methods, which generally use the probabilistic graphical models to implement the incorporation of contextual information with appearance of objects, are almost inevitable to evaluate the intractable partition function for normalization. In this work, we introduced fully-connected fuzzy spatial relations including directional, distance and topological relations between object regions, so the spatial relational information could be fully utilized. Then, the spatial relations were considered as well as co-occurrence and appearance of objects by using energy-based model, where the energy function was defined as the region-object association potential and the configuration potential of objects. Minimizing the energy function of whole image arrangement, we obtained the optimal label set about the image regions and addressed the evaluation of intractable partition function in conditional random fields. Experimental results show the validity and reliability of this proposed method.
Text Flow: A Unified Text Detection System in Natural Scene Images
Venue: arXiv cs.CV 2016
Year: 2016

The prevalent scene text detection approach follows four sequential steps comprising character candidate detection, false character candidate removal, text line extraction, and text line verification. However, errors occur and accumulate throughout each of these sequential steps which often lead to low detection performance. To address these issues, we propose a unified scene text detection system, namely Text Flow, by utilizing the minimum cost (min-cost) flow network model. With character candidates detected by cascade boosting, the min-cost flow network model integrates the last three sequential steps into a single process which solves the error accumulation problem at both character level and text line level effectively. The proposed technique has been tested on three public datasets, i.e, ICDAR2011 dataset, ICDAR2013 dataset and a multilingual dataset and it outperforms the state-of-the-art methods on all three datasets with much higher recall and F-score. The good performance on the multilingual dataset shows that the proposed technique can be used for the detection of texts in different languages.
Deep Learning with Eigenvalue Decay Regularizer
Venue: arXiv cs.LG 2016
Year: 2016

This paper extends our previous work on regularization of neural networks using Eigenvalue Decay by employing a soft approximation of the dominant eigenvalue in order to enable the calculation of its derivatives in relation to the synaptic weights, and therefore the application of back-propagation, which is a primary demand for deep learning. Moreover, we extend our previous theoretical analysis to deep neural networks and multiclass classification problems. Our method is implemented as an additional regularizer in Keras, a modular neural networks library written in Python, and evaluated in the benchmark data sets Reuters Newswire Topics Classification, IMDB database for binary sentiment classification, MNIST database of handwritten digits and CIFAR-10 data set for image classification.
Parity check systems of nonlinear codes over finite commutative Frobenius rings
Venue: arXiv cs.IT 2016
Year: 2016

The concept of parity check matrices of linear binary codes has been extended by Heden [9] to parity check systems of nonlinear binary codes. In the present paper we extend this concept to parity check systems of nonlinear codes over finite commutative Frobenius rings. Using parity check systems, results on how to get some fundamental properties of the codes are given. Moreover, parity check systems and its connection to characters is investigated and a MacWilliams type theorem on the distance distribution is given.
Lossless Intra Coding in HEVC with Adaptive 3-tap Filters
Venue: arXiv cs.MM 2016
Year: 2016

In pixel-by-pixel spatial prediction methods for lossless intra coding, the prediction is obtained by a weighted sum of neighbouring pixels. The proposed prediction approach in this paper uses a weighted sum of three neighbor pixels according to a two-dimensional correlation model. The weights are obtained after a three step optimization procedure. The first two stages are offline procedures where the computed prediction weights are obtained offline from training sequences. The third stage is an online optimization procedure where the offline obtained prediction weights are further fine-tuned and adapted to each encoded block during encoding using a rate-distortion optimized method and the modification in this third stage is transmitted to the decoder as side information. The results of the simulations show average bit rate reductions of 12.02% and 3.28% over the default lossless intra coding in HEVC and the well-known Sample-based Angular Prediction (SAP) method, respectively.
Neurohex: A Deep Q-learning Hex Agent
Venue: arXiv cs.AI 2016
Year: 2016

DeepMind's recent spectacular success in using deep convolutional neural nets and machine learning to build superhuman level agents --- e.g. for Atari games via deep Q-learning and for the game of Go via Reinforcement Learning --- raises many questions, including to what extent these methods will succeed in other domains. In this paper we consider DQL for the game of Hex: after supervised initialization, we use selfplay to train NeuroHex, an 11-layer CNN that plays Hex on the 13x13 board. Hex is the classic two-player alternate-turn stone placement game played on a rhombus of hexagonal cells in which the winner is whomever connects their two opposing sides. Despite the large action and state space, our system trains a Q-network capable of strong play with no search. After two weeks of Q-learning, NeuroHex achieves win-rates of 20.4% as first player and 2.1% as second player against a 1-second/move version of MoHex, the current ICGA Olympiad Hex champion. Our data suggests further improvement might be possible with more training time.
On Guarding Orthogonal Polygons with Sliding Cameras
Venue: arXiv cs.CG 2016
Year: 2016

A sliding camera inside an orthogonal polygon $P$ is a point guard that travels back and forth along an orthogonal line segment $γ$ in $P$. The sliding camera $g$ can see a point $p$ in $P$ if the perpendicular from $p$ onto $γ$ is inside $P$. In this paper, we give the first constant-factor approximation algorithm for the problem of guarding $P$ with the minimum number of sliding cameras. Next, we show that the sliding guards problem is linear-time solvable if the (suitably defined) dual graph of the polygon has bounded treewidth. Finally, we study art gallery theorems for sliding cameras, thus, give upper and lower bounds in terms of the number of guards needed relative to the number of vertices $n$.
Closed-form Approximations for Coverage and Rate in a Multi-tier Heterogeneous Network in Nakagami-m Fading
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we consider the downlink in a K-tier heterogeneous network in the presence of Nakagami-m fading and noise. For such a system, we derive closed-form approximations of coverage probability and average rate achievable. A piece-wise linear approximation is employed in obtaining the simplified expressions. The proposed results are verified numerically through simulations. A comparison with existing work shows that the proposed work is a good approximation.
Information Cascades on Arbitrary Topologies
Venue: arXiv cs.SI 2016
Year: 2016

In this paper, we study information cascades on graphs. In this setting, each node in the graph represents a person. One after another, each person has to take a decision based on a private signal as well as the decisions made by earlier neighboring nodes. Such information cascades commonly occur in practice and have been studied in complete graphs where everyone can overhear the decisions of every other player. It is known that information cascades can be fragile and based on very little information, and that they have a high likelihood of being wrong. Generalizing the problem to arbitrary graphs reveals interesting insights. In particular, we show that in a random graph $G(n,q)$, for the right value of $q$, the number of nodes making a wrong decision is logarithmic in $n$. That is, in the limit for large $n$, the fraction of players that make a wrong decision tends to zero. This is intriguing because it contrasts to the two natural corner cases: empty graph (everyone decides independently based on his private signal) and complete graph (all decisions are heard by all nodes). In both of these cases a constant fraction of nodes make a wrong decision in expectation. Thus, our result shows that while both too little and too much information sharing causes nodes to take wrong decisions, for exactly the right amount of information sharing, asymptotically everyone can be right. We further show that this result in random graphs is asymptotically optimal for any topology, even if nodes follow a globally optimal algorithmic strategy. Based on the analysis of random graphs, we explore how topology impacts global performance and construct an optimal deterministic topology among layer graphs.
Towards Reduced Reference Parametric Models for Estimating Audiovisual Quality in Multimedia Services
Venue: arXiv cs.MM 2016
Year: 2016

We have developed reduced reference parametric models for estimating perceived quality in audiovisual multimedia services. We have created 144 unique configurations for audiovisual content including various application and network parameters such as bitrates and distortions in terms of bandwidth, packet loss rate and jitter. To generate the data needed for model training and validation we have tasked 24 subjects, in a controlled environment, to rate the overall audiovisual quality on the absolute category rating (ACR) 5-level quality scale. We have developed models using Random Forest and Neural Network based machine learning methods in order to estimate Mean Opinion Scores (MOS) values. We have used information retrieved from the packet headers and side information provided as network parameters for model training. Random Forest based models have performed better in terms of Root Mean Square Error (RMSE) and Pearson correlation coefficient. The side information proved to be very effective in developing the model. We have found that, while the model performance might be improved by replacing the side information with more accurate bit stream level measurements, they are performing well in estimating perceived quality in audiovisual multimedia services.
Blind Identification of Graph Filters
Venue: arXiv cs.IT 2016
Year: 2016

Network processes are often represented as signals defined on the vertices of a graph. To untangle the latent structure of such signals, one can view them as outputs of linear graph filters modeling underlying network dynamics. This paper deals with the problem of joint identification of a graph filter and its input signal, thus broadening the scope of classical blind deconvolution of temporal and spatial signals to the less-structured graph domain. Given a graph signal $\mathbf{y}$ modeled as the output of a graph filter, the goal is to recover the vector of filter coefficients $\mathbf{h}$, and the input signal $\mathbf{x}$ which is assumed to be sparse. While $\mathbf{y}$ is a bilinear function of $\mathbf{x}$ and $\mathbf{h}$, the filtered graph signal is also a linear combination of the entries of the lifted rank-one, row-sparse matrix $\mathbf{x} \mathbf{h}^T$. The blind graph-filter identification problem can thus be tackled via rank and sparsity minimization subject to linear constraints, an inverse problem amenable to convex relaxations offering provable recovery guarantees under simplifying assumptions. Numerical tests using both synthetic and real-world networks illustrate the merits of the proposed algorithms, as well as the benefits of leveraging multiple signals to aid the blind identification task.
Persistence Lenses: Segmentation, Simplification, Vectorization, Scale Space and Fractal Analysis of Images
Venue: arXiv cs.CV 2016
Year: 2016

A persistence lens is a hierarchy of disjoint upper and lower level sets of a continuous luminance image's Reeb graph. The boundary components of a persistence lens's interior components are Jordan curves that serve as a hierarchical segmentation of the image, and may be rendered as vector graphics. A persistence lens determines a varilet basis for the luminance image, in which image simplification is a realized by subspace projection. Image scale space, and image fractal analysis, result from applying a scale measure to each basis function.
Do the Hard Stuff First: Scheduling Dependent Computations in Data-Analytics Clusters
Venue: arXiv cs.DC 2016
Year: 2016

We present a scheduler that improves cluster utilization and job completion times by packing tasks having multi-resource requirements and inter-dependencies. While the problem is algorithmically very hard, we achieve near-optimality on the job DAGs that appear in production clusters at a large enterprise and in benchmarks such as TPC-DS. A key insight is that carefully handling the long-running tasks and those with tough-to-pack resource needs will produce good-enough schedules. However, which subset of tasks to treat carefully is not clear (and intractable to discover). Hence, we offer a search procedure that evaluates various possibilities and outputs a preferred schedule order over tasks. An online component enforces the schedule orders desired by the various jobs running on the cluster. In addition, it packs tasks, overbooks the fungible resources and guarantees bounded unfairness for a variety of desirable fairness schemes. Relative to the state-of-the art schedulers, we speed up 50% of the jobs by over 30% each.
Physical Layer Security in Millimeter Wave Cellular Networks
Venue: arXiv cs.IT 2016
Year: 2016

Recent researches show that millimeter wave (mmWave) communications can offer orders of magnitude increases in the cellular capacity. However, the secrecy performance of a mmWave cellular network has not been investigated so far. Leveraging the new path-loss and blockage models for mmWave channels, which are significantly different from the conventional microwave channel, this paper comprehensively studies the network-wide physical layer security performance of the downlink transmission in a mmWave cellular network under a stochastic geometry framework. We first study the secure connectivity probability and the average number of perfect communication links per unit area in a noise-limited mmWave network for both non-colluding and colluding eavesdroppers scenarios, respectively. Then, we evaluate the effect of the artificial noise (AN) on the secrecy performance, and derive the analysis result of average number of perfect communication links per unit area in an interference-limited mmWave network. Numerical results are demonstrated to show the network-wide secrecy performance, and provide interesting insights into how the secrecy performance is influenced by various network parameters: antenna array pattern, base station (BS) intensity, and AN power allocation, etc.
Once for All: a Two-flow Convolutional Neural Network for Visual Tracking
Venue: arXiv cs.CV 2016
Year: 2016

One of the main challenges of visual object tracking comes from the arbitrary appearance of objects. Most existing algorithms try to resolve this problem as an object-specific task, i.e., the model is trained to regenerate or classify a specific object. As a result, the model need to be initialized and retrained for different objects. In this paper, we propose a more generic approach utilizing a novel two-flow convolutional neural network (named YCNN). The YCNN takes two inputs (one is object image patch, the other is search image patch), then outputs a response map which predicts how likely the object appears in a specific location. Unlike those object-specific approach, the YCNN is trained to measure the similarity between two image patches. Thus it will not be confined to any specific object. Furthermore the network can be end-to-end trained to extract both shallow and deep convolutional features which are dedicated for visual tracking. And once properly trained, the YCNN can be applied to track all kinds of objects without further training and updating. Benefiting from the once-for-all model, our algorithm is able to run at a very high speed of 45 frames-per-second. The experiments on 51 sequences also show that our algorithm achieves an outstanding performance.
A Novel Similarity Measure for Intrusion Detection using Gaussian Function
Venue: arXiv cs.CR 2016
Year: 2016

In this paper the major objective is to design and analyze the suitability of Gaussian similarity measure for intrusion detection. The objective is to use this as a distance measure to find the distance between any two data samples of training set such as DARPA Data Set, KDD Data Set. This major objective is to use this measure as a distance metric when applying k- means algorithm. The novelty of this approach is making use of the proposed distance function as part of k-means algorithm so as to obtain disjoint clusters. This is followed by a case study, which demonstrates the process of Intrusion Detection. The proposed similarity has fixed upper and lower bounds. The proposed similarity measure satisfies all properties of a typical similarity measure.
Feedback-based Approach to Introduce Freshness in Recommendations
Venue: arXiv cs.IR 2016
Year: 2016

Recommender systems usually face the problem of serving the same recommendations across multiple sessions regardless of whether the user is interested in them or not, thereby reducing their effectiveness. To add freshness to the recommended products, we introduce a feedback loop where the set of recommended products in the current session depend on the user's interaction with the previously recommended sets. We also describe ways of addressing freshness when there is little or even no direct user interaction. We define a metric to quantify freshness by reducing the problem to measuring temporal diversity.
A Wised Routing Protocols for Leo Satellite Networks
Venue: arXiv cs.NI 2016
Year: 2016

This Study proposes a routing strategy of combining a packet scheduling with congestion control policy that applied for LEO satellite network with high speed and multiple traffic. It not only ensures the QoS of different traffic, but also can avoid low priority traffic to be "starve" due to their weak resource competitiveness, thus it guarantees the throughput and performance of the network. In the end, we set up a LEO satellite network simulation platform in OPNET to verify the effectiveness of the proposed algorithm.
Ozy: A General Orchestration Container
Venue: arXiv cs.SE 2016
Year: 2016

Service-Oriented Computing is a paradigm that uses services as building blocks for building distributed applications. The primary motivation for orchestrating services in the cloud used to be distributed business processes, which drove the standardization of the Business Process Execution Language (BPEL) and its central notion that a service is a business process. In recent years, there has been a transition towards other motivations for orchestrating services in the cloud, {\em e.g.}, XaaS, RMAD. Although it is theoretically possible to make all of those services into WSDL/SOAP services, it would be too complicated and costly for industry adoption. Therefore, the central notion that a service is a business process is too restrictive. Instead, we view a service as a technology neutral, loosely coupled, location transparent procedure. With these ideas in mind, we introduce a new approach to services orchestration: Ozy, a general orchestration container. We define this new approach in terms of existing technology, and we show that the Ozy container relaxes many traditional constraints and allows for simpler, more feature-rich applications.
The dynamics of publishing of posts and comments on facebook (the russian segment, the first five months of 2013)
Venue: arXiv cs.SI 2016
Year: 2016

The article presents a study of some characteristics of post and comment publishing in the Russian segment of Facebook. A number of non-trivial results has been obtained. For example, a significant anomaly has been detected in the number of user accounts with the rate of publishing posts of approximately two posts per three days. The analysis has been carried out at the level of basic characteristics that are shared by most social media platforms. It makes possible a direct comparison of obtained results with data from other platforms. The article presents an approach to formalization and ordering of structural and informational elements on social media platforms. The approach is based on the representation of these structural elements in the form of a coherent hierarchy of container objects and their relations. This method allows to structure and analyze raw data from different social media platforms in a unified algorithmic design. The described approach is more formal, universal and constructive than other known approaches.
Using Indirect Encoding of Multiple Brains to Produce Multimodal Behavior
Venue: arXiv cs.AI 2016
Year: 2016

An important challenge in neuroevolution is to evolve complex neural networks with multiple modes of behavior. Indirect encodings can potentially answer this challenge. Yet in practice, indirect encodings do not yield effective multimodal controllers. Thus, this paper introduces novel multimodal extensions to HyperNEAT, a popular indirect encoding. A previous multimodal HyperNEAT approach called situational policy geometry assumes that multiple brains benefit from being embedded within an explicit geometric space. However, experiments here illustrate that this assumption unnecessarily constrains evolution, resulting in lower performance. Specifically, this paper introduces HyperNEAT extensions for evolving many brains without assuming geometric relationships between them. The resulting Multi-Brain HyperNEAT can exploit human-specified task divisions to decide when each brain controls the agent, or can automatically discover when brains should be used, by means of preference neurons. A further extension called module mutation allows evolution to discover the number of brains, enabling multimodal behavior with even less expert knowledge. Experiments in several multimodal domains highlight that multi-brain approaches are more effective than HyperNEAT without multimodal extensions, and show that brains without a geometric relation to each other outperform situational policy geometry. The conclusion is that Multi-Brain HyperNEAT provides several promising techniques for evolving complex multimodal behavior.
Understanding Video-Ad Consumption on YouTube: A Measurement Study on User Behavior, Popularity, and Content Properties
Venue: arXiv cs.SI 2016
Year: 2016

Faced with the challenge of attracting user attention and revenue, social media websites have turned to video advertisements (video-ads). While in traditional media the video-ad market is mostly based on an interaction between content providers and marketers, the use of video-ads in social media has enabled a more complex interaction, that also includes content creator and viewer preferences. To better understand this novel setting, we present the first data-driven analysis of video-ad exhibitions on YouTube.