OpenRaiser/Intern-Atlas huggingface dataset:
Region Based Extensive Response Index Pattern for Facial Expression Recognition
Venue: arXiv cs.CV 2018
Year: 2018

This paper presents a novel descriptor named Region based Extensive Response Index Pattern (RETRaIN) for facial expression recognition. The RETRaIN encodes the relation among the reference and neighboring pixels of facial active regions. These relations are computed by using directional compass mask on an input image and extract the high edge responses in foremost directions. Further extreme edge index positions are selected and encoded into six-bit compact code to reduce feature dimensionality and distinguish between the uniform and non-uniform patterns in the facial features. The performance of the proposed descriptor is tested and evaluated on three benchmark datasets Extended Cohn Kanade, JAFFE, and MUG. The RETRaIN achieves superior recognition accuracy in comparison to state-of-the-art techniques.
Distributed and Secure ML with Self-tallying Multi-party Aggregation
Venue: arXiv cs.CR 2018
Year: 2018

Privacy preserving multi-party computation has many applications in areas such as medicine and online advertisements. In this work, we propose a framework for distributed, secure machine learning among untrusted individuals. The framework consists of two parts: a two-step training protocol based on homomorphic addition and a zero knowledge proof for data validity. By combining these two techniques, our framework provides privacy of per-user data, prevents against a malicious user contributing corrupted data to the shared pool, enables each user to self-compute the results of the algorithm without relying on external trusted third parties, and requires no private channels between groups of users. We show how different ML algorithms such as Latent Dirichlet Allocation, Naive Bayes, Decision Trees etc. fit our framework for distributed, secure computing.
Multi-hierarchical Independent Correlation Filters for Visual Tracking
Venue: arXiv cs.CV 2018
Year: 2018

For visual tracking, most of the traditional correlation filters (CF) based methods suffer from the bottleneck of feature redundancy and lack of motion information. In this paper, we design a novel tracking framework, called multi-hierarchical independent correlation filters (MHIT). The framework consists of motion estimation module, hierarchical features selection, independent CF online learning, and adaptive multi-branch CF fusion. Specifically, the motion estimation module is introduced to capture motion information, which effectively alleviates the object partial occlusion in the temporal video. The multi-hierarchical deep features of CNN representing different semantic information can be fully excavated to track multi-scale objects. To better overcome the deep feature redundancy, each hierarchical features are independently fed into a single branch to implement the online learning of parameters. Finally, an adaptive weight scheme is integrated into the framework to fuse these independent multi-branch CFs for the better and more robust visual object tracking. Extensive experiments on OTB and VOT datasets show that the proposed MHIT tracker can significantly improve the tracking performance. Especially, it obtains a 20.1% relative performance gain compared to the top trackers on the VOT2017 challenge, and also achieves new state-of-the-art performance on the VOT2018 challenge.
Investigation of Nonlinear Communication Channel with Small Dispersion via Stochastic Correlator Approach
Venue: arXiv cs.IT 2018
Year: 2018

We consider the optical fiber channel modelled by the nonlinear Schrödinger equation with additive white Gaussian noise and with large signal-to-noise ratio. For the small dispersion case we present the approach to analyze the stochastic nonlinear Schrödinger equation. Taking into account the averaging procedure (frequency filtering) of the output signal detector we find the first corrections in small dispersion parameter to the correlators of the input signal recovered by the backward propagation. These correlators are the important ingredients for the calculation of the channel capacity and the optimal input signal distribution. We assert that the information channel characteristics essentially depend on the procedures of the output signal filtering and the recovery of the transmitted signal.
Unsupervised learning with sparse space-and-time autoencoders
Venue: arXiv cs.CV 2018
Year: 2018

We use spatially-sparse two, three and four dimensional convolutional autoencoder networks to model sparse structures in 2D space, 3D space, and 3+1=4 dimensional space-time. We evaluate the resulting latent spaces by testing their usefulness for downstream tasks. Applications are to handwriting recognition in 2D, segmentation for parts in 3D objects, segmentation for objects in 3D scenes, and body-part segmentation for 4D wire-frame models generated from motion capture data.
Quantifying Attacker Capability Via Model Checking Multiple Properties (Extended Version)
Venue: arXiv cs.LO 2018
Year: 2018

This work aims to solve a practical problem, i.e., how to quantify the risk brought upon a system by different attackers. The answer is useful for optimising resource allocation for system defence. Given a set of safety requirements, we quantify the attacker capability in terms of the set of safety requirements an attacker can compromise. Given a system (in the presence of an attacker), model checking it against each safety requirement one by one is expensive and wasteful since the same state space is explored many times. We thus propose model checking multiple properties efficiently by means of coalgebraic model checking using enhanced coinduction techniques. We apply the proposed technique to a real-world water treatment system and the results show that our approach can effectively reduce the effort required for model checking.
Structured Binary Neural Networks for Accurate Image Classification and Semantic Segmentation
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we propose to train convolutional neural networks (CNNs) with both binarized weights and activations, leading to quantized models specifically} for mobile devices with limited power capacity and computation resources. Previous works on quantizing CNNs seek to approximate the floating-point information using a set of discrete values, which we call value approximation, but typically assume the same architecture as the full-precision networks. In this paper, however, we take a novel 'structure approximation' view for quantization---it is very likely that a different architecture may be better for best performance. In particular, we propose a `network decomposition' strategy, named \textbf{Group-Net}, in which we divide the network into groups. In this way, each full-precision group can be effectively reconstructed by aggregating a set of homogeneous binary branches. In addition, we learn effective connections among groups to improve the representational capability. Moreover, the proposed Group-Net shows strong generalization to other tasks. For instance, we extend Group-Net for highly accurate semantic segmentation by embedding rich context into the binary structure. Experiments on both classification and semantic segmentation tasks demonstrate the superior performance of the proposed methods over various popular architectures. In particular, we outperform the previous best binary neural networks in terms of accuracy and major computation savings.
On Filter Size in Graph Convolutional Networks
Venue: arXiv cs.LG 2018
Year: 2018

Recently, many researchers have been focusing on the definition of neural networks for graphs. The basic component for many of these approaches remains the graph convolution idea proposed almost a decade ago. In this paper, we extend this basic component, following an intuition derived from the well-known convolutional filters over multi-dimensional tensors. In particular, we derive a simple, efficient and effective way to introduce a hyper-parameter on graph convolutions that influences the filter size, i.e. its receptive field over the considered graph. We show with experimental results on real-world graph datasets that the proposed graph convolutional filter improves the predictive performance of Deep Graph Convolutional Networks.
Sentence Encoding with Tree-constrained Relation Networks
Venue: arXiv cs.CL 2018
Year: 2018

The meaning of a sentence is a function of the relations that hold between its words. We instantiate this relational view of semantics in a series of neural models based on variants of relation networks (RNs) which represent a set of objects (for us, words forming a sentence) in terms of representations of pairs of objects. We propose two extensions to the basic RN model for natural language. First, building on the intuition that not all word pairs are equally informative about the meaning of a sentence, we use constraints based on both supervised and unsupervised dependency syntax to control which relations influence the representation. Second, since higher-order relations are poorly captured by a sum of pairwise relations, we use a recurrent extension of RNs to propagate information so as to form representations of higher order relations. Experiments on sentence classification, sentence pair classification, and machine translation reveal that, while basic RNs are only modestly effective for sentence representation, recurrent RNs with latent syntax are a reliably powerful representational device.
On the Performance of Backhaul Constrained Cell-Free Massive MIMO with Linear Receivers
Venue: arXiv cs.IT 2018
Year: 2018

Limited-backhaul cell-free Massive multiple-input multiple-output (MIMO), in which the fog radio access network (F-RAN) is implemented to exchange the information between access points (APs) and the central processing unit (CPU), is investigated. We introduce a novel approach where the APs estimate the channel and send back the quantized version of the estimated channel and the quantized version of the received signal to the central processing unit. The Max algorithm and the Bussgang theorem are exploited to model the optimum uniform quantization. The ergodic achievable rates are derived. We show that exploiting microwave wireless backhaul links and using a small number of bits to quantize the estimated channel and the received signal, the performance of limited-backhaul cell-free Massive MIMO closely approaches the performance of cell-free Massive MIMO with perfect backhaul links.
EVM analysis of an Interference Limited SIMO-SC System With Independent and Correlated Channels
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we derive the error vector magnitude (EVM) in a selection combining (SC) system experiencing co-channel interference, for arbitrary number of antennas and interferers when all the channels experience Rayleigh fading. We use a novel approach that uses the CCDF of SIR to derive EVM as using the conventional approach to derive EVM for a SC system is difficult. Considering two selection rules based on (a) maximum signal power (b) maximum signal to interference ratio, we observe that EVM is worse when maximum signal power based rule is used. Further, EVM is also derived considering (a) all the channels to be independent and (b) channels to be correlated due to insufficient antenna spacing at receiver. For some special cases, EVM is also derived when the desired channels experience Nakagami-m fading.
A Survey of Collection Methods and Cross-Data Set Comparison of Android Unlock Patterns
Venue: arXiv cs.CR 2018
Year: 2018

Android's graphical password unlock remains one of the most widely used schemes for phone unlock authentication, and it is has been studied extensively in the last decade since its launch. We have learned that users' choice of patterns mimics the poor password choices in other systems, such as PIN or text-based passwords. A wide variety of analysis and data collections methods was used to reach these conclusions, but what is missing from the literature is a systemized comparison of the related work in this space that compares both the methodology and the results. In this paper, we take a detailed accounting of the different methods applied to data collection and analysis for Android unlock patterns. We do so in two dimensions. First we systemize prior work into a detailed taxonomy of collection methods, and in the second dimension, we perform a detailed analysis of 9 different data sets collected using different methods. While this study focuses singularly on the collection methods and comparisons of the Android pattern unlock scheme, we believe that many of the findings generalize to other graphical password schemes, unlock authentication technology, and other knowledge-based authentication schemes.
GRSUS: Gerenciamento De Recursos De Saúde, Um Estudo Sob A Ótica Da Portaria GM/MS 1631/2015 No Estado do Pará
Venue: arXiv cs.CY 2018
Year: 2018

Investments in public health had an increase of about R$ 20 bi in recent years. Even with the dynamism of the Unique Health System (SUS), only after 13 years the criteria and parameters for the planning and programming of health services have been updated. The calculations for health resources division are complex due to the nature of the SUS administrative organization, which has three administrative levels. Despite providing the criteria and parameters for the calculations, it was not provided any information system that would automate this process and provide reliable information for decision making. In order to fill such gap, this paper presents a system for health resource management from the perspective of GM/MS 1631/2015 ordinance. The tool has been validated using as case studies two municipalities in the interior of the state of Pará. The results were promising, with latent market potential, being possible to simulate various scenarios for a medium and long-term predictions.
GANsfer Learning: Combining labelled and unlabelled data for GAN based data augmentation
Venue: arXiv cs.CV 2018
Year: 2018

Medical imaging is a domain which suffers from a paucity of manually annotated data for the training of learning algorithms. Manually delineating pathological regions at a pixel level is a time consuming process, especially in 3D images, and often requires the time of a trained expert. As a result, supervised machine learning solutions must make do with small amounts of labelled data, despite there often being additional unlabelled data available. Whilst of less value than labelled images, these unlabelled images can contain potentially useful information. In this paper we propose combining both labelled and unlabelled data within a GAN framework, before using the resulting network to produce images for use when training a segmentation network. We explore the task of deep grey matter multi-class segmentation in an AD dataset and show that the proposed method leads to a significant improvement in segmentation results, particularly in cases where the amount of labelled data is restricted. We show that this improvement is largely driven by a greater ability to segment the structures known to be the most affected by AD, thereby demonstrating the benefits of exposing the system to more examples of pathological anatomical variation. We also show how a shift in domain of the training data from young and healthy towards older and more pathological examples leads to better segmentations of the latter cases, and that this leads to a significant improvement in the ability for the computed segmentations to stratify cases of AD.
DynamicGEM: A Library for Dynamic Graph Embedding Methods
Venue: arXiv cs.LG 2018
Year: 2018

DynamicGEM is an open-source Python library for learning node representations of dynamic graphs. It consists of state-of-the-art algorithms for defining embeddings of nodes whose connections evolve over time. The library also contains the evaluation framework for four downstream tasks on the network: graph reconstruction, static and temporal link prediction, node classification, and temporal visualization. We have implemented various metrics to evaluate the state-of-the-art methods, and examples of evolving networks from various domains. We have easy-to-use functions to call and evaluate the methods and have extensive usage documentation. Furthermore, DynamicGEM provides a template to add new algorithms with ease to facilitate further research on the topic.
MATCH-Net: Dynamic Prediction in Survival Analysis using Convolutional Neural Networks
Venue: arXiv cs.LG 2018
Year: 2018

Accurate prediction of disease trajectories is critical for early identification and timely treatment of patients at risk. Conventional methods in survival analysis are often constrained by strong parametric assumptions and limited in their ability to learn from high-dimensional data, while existing neural network models are not readily-adapted to the longitudinal setting. This paper develops a novel convolutional approach that addresses these drawbacks. We present MATCH-Net: a Missingness-Aware Temporal Convolutional Hitting-time Network, designed to capture temporal dependencies and heterogeneous interactions in covariate trajectories and patterns of missingness. To the best of our knowledge, this is the first investigation of temporal convolutions in the context of dynamic prediction for personalized risk prognosis. Using real-world data from the Alzheimer's Disease Neuroimaging Initiative, we demonstrate state-of-the-art performance without making any assumptions regarding underlying longitudinal or time-to-event processes attesting to the model's potential utility in clinical decision support.
A composition theorem for randomized query complexity via max conflict complexity
Venue: arXiv cs.CC 2018
Year: 2018

Let $R_ε(\cdot)$ stand for the bounded-error randomized query complexity with error $ε> 0$. For any relation $f \subseteq \{0,1\}^n \times S$ and partial Boolean function $g \subseteq \{0,1\}^m \times \{0,1\}$, we show that $R_{1/3}(f \circ g^n) \in Ω(R_{4/9}(f) \cdot \sqrt{R_{1/3}(g)})$, where $f \circ g^n \subseteq (\{0,1\}^m)^n \times S$ is the composition of $f$ and $g$. We give an example of a relation $f$ and partial Boolean function $g$ for which this lower bound is tight. We prove our composition theorem by introducing a new complexity measure, the max conflict complexity $\bar χ(g)$ of a partial Boolean function $g$. We show $\bar χ(g) \in Ω(\sqrt{R_{1/3}(g)})$ for any (partial) function $g$ and $R_{1/3}(f \circ g^n) \in Ω(R_{4/9}(f) \cdot \bar χ(g))$; these two bounds imply our composition result. We further show that $\bar χ(g)$ is always at least as large as the sabotage complexity of $g$, introduced by Ben-David and Kothari.
Informative Path Planning for Location Fingerprint Collection
Venue: arXiv cs.RO 2018
Year: 2018

Fingerprint-based indoor localization methods are promising due to the high availability of deployed access points and compatibility with commercial-off-the-shelf user devices. However, to train regression models for localization, an extensive site survey is required to collect fingerprint data from the target areas. In this paper, we consider the problem of informative path planning (IPP) to find the optimal walk for site survey subject to a budget constraint. IPP for location fingerprint collection is related to the well-known orienteering problem (OP) but is more challenging due to edge-based non-additive rewards and revisits. Given the NP-hardness of IPP, we propose two heuristic approaches: a Greedy algorithm and a genetic algorithm. We show through experimental data collected from two indoor environments with different characteristics that the two algorithms have low computation complexity, can generally achieve higher utility and lower localization errors compared to the extension of two state-of-the-art approaches to OP.
A Scalable Optimization Mechanism for Pairwise based Discrete Hashing
Venue: arXiv cs.IR 2018
Year: 2018

Maintaining the pair similarity relationship among originally high-dimensional data into a low-dimensional binary space is a popular strategy to learn binary codes. One simiple and intutive method is to utilize two identical code matrices produced by hash functions to approximate a pairwise real label matrix. However, the resulting quartic problem is difficult to directly solve due to the non-convex and non-smooth nature of the objective. In this paper, unlike previous optimization methods using various relaxation strategies, we aim to directly solve the original quartic problem using a novel alternative optimization mechanism to linearize the quartic problem by introducing a linear regression model. Additionally, we find that gradually learning each batch of binary codes in a sequential mode, i.e. batch by batch, is greatly beneficial to the convergence of binary code learning. Based on this significant discovery and the proposed strategy, we introduce a scalable symmetric discrete hashing algorithm that gradually and smoothly updates each batch of binary codes. To further improve the smoothness, we also propose a greedy symmetric discrete hashing algorithm to update each bit of batch binary codes. Moreover, we extend the proposed optimization mechanism to solve the non-convex optimization problems for binary code learning in many other pairwise based hashing algorithms. Extensive experiments on benchmark single-label and multi-label databases demonstrate the superior performance of the proposed mechanism over recent state-of-the-art methods.
Isabelle/jEdit as IDE for Domain-specific Formal Languages and Informal Text Documents
Venue: arXiv cs.HC 2018
Year: 2018

Isabelle/jEdit is the main application of the Prover IDE (PIDE) framework and the default user-interface of Isabelle, but it is not limited to theorem proving. This paper explores possibilities to use it as a general IDE for formal languages that are defined in user-space, and embedded into informal text documents. It covers overall document structure with auxiliary files and document antiquotations, formal text delimiters and markers for interpretation (via control symbols). The ultimate question behind this: How far can we stretch a plain text editor like jEdit in order to support semantic text processing, with support by the underlying PIDE framework?
Object Tracking by Reconstruction with View-Specific Discriminative Correlation Filters
Venue: arXiv cs.CV 2018
Year: 2018

Standard RGB-D trackers treat the target as an inherently 2D structure, which makes modelling appearance changes related even to simple out-of-plane rotation highly challenging. We address this limitation by proposing a novel long-term RGB-D tracker - Object Tracking by Reconstruction (OTR). The tracker performs online 3D target reconstruction to facilitate robust learning of a set of view-specific discriminative correlation filters (DCFs). The 3D reconstruction supports two performance-enhancing features: (i) generation of accurate spatial support for constrained DCF learning from its 2D projection and (ii) point cloud based estimation of 3D pose change for selection and storage of view-specific DCFs which are used to robustly localize the target after out-of-view rotation or heavy occlusion. Extensive evaluation of OTR on the challenging Princeton RGB-D tracking and STC Benchmarks shows it outperforms the state-of-the-art by a large margin.