OpenRaiser/Intern-Atlas huggingface dataset:
Computational Theories of Curiosity-Driven Learning
Venue: arXiv cs.AI 2018
Year: 2018

What are the functions of curiosity? What are the mechanisms of curiosity-driven learning? We approach these questions about the living using concepts and tools from machine learning and developmental robotics. We argue that curiosity-driven learning enables organisms to make discoveries to solve complex problems with rare or deceptive rewards. By fostering exploration and discovery of a diversity of behavioural skills, and ignoring these rewards, curiosity can be efficient to bootstrap learning when there is no information, or deceptive information, about local improvement towards these problems. We also explain the key role of curiosity for efficient learning of world models. We review both normative and heuristic computational frameworks used to understand the mechanisms of curiosity in humans, conceptualizing the child as a sense-making organism. These frameworks enable us to discuss the bi-directional causal links between curiosity and learning, and to provide new hypotheses about the fundamental role of curiosity in self-organizing developmental structures through curriculum learning. We present various developmental robotics experiments that study these mechanisms in action, both supporting these hypotheses to understand better curiosity in humans and opening new research avenues in machine learning and artificial intelligence. Finally, we discuss challenges for the design of experimental paradigms for studying curiosity in psychology and cognitive neuroscience. Keywords: Curiosity, intrinsic motivation, lifelong learning, predictions, world model, rewards, free-energy principle, learning progress, machine learning, AI, developmental robotics, development, curriculum learning, self-organization.
Equi-coverage Contours in Cellular Networks
Venue: arXiv cs.IT 2018
Year: 2018

In this letter, we introduce a general cellular network model where i) users and BSs are distributed as two general point processes that may be coupled, ii) pathloss is assumed to follow a multi-slope power-law pathloss model, and iii) fading (power) is assumed to be independent across all wireless links. For this setup, we first obtain a set of contours representing the same meta distribution of SIR, which is the distribution of the conditional coverage probability given the point process, for different values of the parameters of the pathloss function and BS and user point processes. This general result is then specialized to 3GPP-inspired user and BS configurations obtained by combining Poisson point process (PPP) and Poisson cluster process (PCP).
Invariant properties of a locally salient dither pattern with a spatial-chromatic histogram
Venue: arXiv cs.CV 2018
Year: 2018

Compacted Dither Pattern Code (CDPC) is a recently found feature which is successful in irregular shapes based visual depiction. Locally salient dither pattern feature is an attempt to expand the capability of CDPC for both regular and irregular shape based visual depiction. This paper presents an analysis of rotational and scale invariance property of locally salient dither pattern feature with a two dimensional spatialchromatic histogram, which expands the applicability of the visual feature. Experiments were conducted to exhibit rotational and scale invariance of the feature. These experiments were conducted by combining linear Support Vector Machine (SVM) classifier to the new feature. The experimental results revealed that the locally salient dither pattern feature with the spatialchromatic histogram is rotationally and scale invariant.
Super-Efficient Spatially Adaptive Contrast Enhancement Algorithm for Superficial Vein Imaging
Venue: arXiv cs.CV 2018
Year: 2018

This paper presents a super-efficient spatially adaptive contrast enhancement algorithm for enhancing infrared (IR) radiation based superficial vein images in real-time. The super-efficiency permits the algorithm to run in consumer-grade handheld devices, which ultimately reduces the cost of vein imaging equipment. The proposed method utilizes the response from the low-frequency range of the IR image signal to adjust the boundaries of the reference dynamic range in a linear contrast stretching process with a tunable contrast enhancement parameter, as opposed to traditional approaches which use costly adaptive histogram equalization based methods. The algorithm has been implemented and deployed in a consumer grade Android-based mobile device to evaluate the performance. The results revealed that the proposed algorithm can process IR images of veins in real-time on low-performance computers. It was compared with several well-performed traditional methods and the results revealed that the new algorithm stands out with several beneficial features, namely, the fastest processing, the ability to enhance the desired details, the excellent illumination normalization capability and the ability to enhance details where the traditional methods failed.
A Global Information Based Adaptive Threshold for Grouping Large Scale Global Optimization Problems
Venue: arXiv cs.NE 2018
Year: 2018

By taking the idea of divide-and-conquer, cooperative coevolution (CC) provides a powerful architecture for large scale global optimization (LSGO) problems, but its efficiency relies highly on the decomposition strategy. It has been shown that differential grouping (DG) performs well on decomposing LSGO problems by effectively detecting the interaction among decision variables. However, its decomposition accuracy depends highly on the threshold. To improve the decomposition accuracy of DG, a global information based adaptive threshold setting algorithm (GIAT) is proposed in this paper. On the one hand, by reducing the sensitivity of the indicator in DG to the roundoff error and the magnitude of contribution weight of subcomponent, we proposed a new indicator for two variables which is much more sensitive to their interaction. On the other hand, instead of setting the threshold only based on one pair of variables, the threshold is generated from the interaction information for all pair of variables. By conducting the experiments on two sets of LSGO benchmark functions, the correctness and robustness of this new indicator and GIAT were verified.
A Class-Incremental Learning Method Based on One Class Support Vector Machine
Venue: arXiv cs.CV 2018
Year: 2018

A method based on one class support vector machine (OCSVM) is proposed for class incremental learning. Several OCSVM models divide the input space into several parts. Then, the 1VS1 classifiers are constructed for the confuse part by using the support vectors. During the class incremental learning process, the OCSVM of the new class is trained at first. Then the support vectors of the old classes and the support vectors of the new class are reused to train 1VS1 classifiers for the confuse part. In order to bring more information to the certain support vectors, the support vectors are at the boundary of the distribution of samples as much as possible when the OCSVM is built. Compared with the traditional methods, the proposed method retains the original model and thus reduces memory consumption and training time cost. Various experiments on different datasets also verify the efficiency of the proposed method.
The Effect of Instruction Padding on SFI Overhead
Venue: arXiv cs.SE 2018
Year: 2018

Software-based fault isolation (SFI) is a technique to isolate a potentially faulty or malicious software module from the rest of a system using instruction-level rewriting. SFI implementations on CISC architectures, including Google Native Client, use instruction padding to enforce an address layout invariant and restrict control flow. However this padding decreases code density and imposes runtime overhead. We analyze this overhead, and show that it can be reduced by allowing some execution of overlapping instructions, as long as those overlapping instructions are still safe according to the original per-instruction policy. We implemented this change for both 32-bit and 64-bit x86 versions of Native Client, and analyzed why the performance benefit is higher on 32-bit. The optimization leads to a consistent decrease in the number of instructions executed and savings averaging 8.6% in execution time (over compatible benchmarks from SPECint2006) for x86-32. We describe how to modify the validation algorithm to check the more permissive policy, and extend a machine-checked Coq proof to confirm that the system's security is preserved.
XNMT: The eXtensible Neural Machine Translation Toolkit
Venue: arXiv cs.CL 2018
Year: 2018

This paper describes XNMT, the eXtensible Neural Machine Translation toolkit. XNMT distin- guishes itself from other open-source NMT toolkits by its focus on modular code design, with the purpose of enabling fast iteration in research and replicable, reliable results. In this paper we describe the design of XNMT and its experiment configuration system, and demonstrate its utility on the tasks of machine translation, speech recognition, and multi-tasked machine translation/parsing. XNMT is available open-source at https://github.com/neulab/xnmt
Securing OFDM-Based Wireless Links Using Temporal Artificial-Noise Injection
Venue: arXiv cs.IT 2018
Year: 2018

We investigate the physical layer security of wireless single-input single-output orthogonal-division multiplexing (OFDM) when a transmitter, which we refer to as Alice, sends her information to a receiver, which we refer to as Bob, in the presence of an eavesdropping node, Eve. To prevent information leakage, Alice sends an artificial-noise (AN) signal superimposed over her information signal. We investigate the impact of the channel delay spread, OFDM cyclic prefix, information/AN power allocation, and information and AN precoders design on the achievable average secrecy rate. We consider the two cases of known and unknown channel state information (CSI) at Alice. Furthermore, we compare both cases of per-sub-channel processing and joint sub-channels processing at Eve's receiver. Our numerical results show the gain of AN injection in terms of average secrecy rate for different OFDM operating conditions. Moreover, based on our new insights, we demonstrate that the AN-aided scheme is effective and achieves almost the same average secrecy rate as the full-CSI case without the need for Eve's instantaneous CSI at Alice.
DRUNET: A Dilated-Residual U-Net Deep Learning Network to Digitally Stain Optic Nerve Head Tissues in Optical Coherence Tomography Images
Venue: arXiv cs.CV 2018
Year: 2018

Given that the neural and connective tissues of the optic nerve head (ONH) exhibit complex morphological changes with the development and progression of glaucoma, their simultaneous isolation from optical coherence tomography (OCT) images may be of great interest for the clinical diagnosis and management of this pathology. A deep learning algorithm was designed and trained to digitally stain (i.e. highlight) 6 ONH tissue layers by capturing both the local (tissue texture) and contextual information (spatial arrangement of tissues). The overall dice coefficient (mean of all tissues) was $0.91 \pm 0.05$ when assessed against manual segmentations performed by an expert observer. We offer here a robust segmentation framework that could be extended for the automated parametric study of the ONH tissues.
Entanglement-assisted quantum MDS codes constructed from constacyclic codes
Venue: arXiv cs.IT 2018
Year: 2018

Recently, entanglement-assisted quantum error correcting codes (EAQECCs) have been constructed by cyclic codes and negacyclic codes. In this paper, by analyzing the cyclotomic cosets in the defining set of constacyclic codes, we constructed three classes of new EAQECCs which satisfy the entanglement-assisted quantum Singleton bound. Besides, three classes of EAQECCs with maximal entanglement from constacyclic codes are constructed in the meanwhile.
EvoCut : A new Generalization of Albert-Barabási Model for Evolution of Complex Networks
Venue: arXiv cs.SI 2018
Year: 2018

With the evolution of social networks, the network structure shows dynamic nature in which nodes and edges appear as well as disappear for various reasons. The role of a node in the network is presented as the number of interactions it has with the other nodes. For this purpose a network is modeled as a graph where nodes represent network members and edges represent a relationship among them. Several models for evolution of social networks has been proposed till date, most widely accepted being the Barabási-Albert \cite{Network science} model that is based on \emph{preferential attachment} of nodes according to the degree distribution. This model leads to generation of graphs that are called \emph{Scale Free} and the degree distribution of such graphs follow the \emph{power law}. Several generalizations of this model has also been proposed. In this paper we present a new generalization of the model and attempt to bring out its implications in real life.
Hybrid Precoding Based on Non-Uniform Quantization Codebook to Reduce Feedback Overhead in Millimeter Wave MIMO Systems
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we focus on the design of the hybrid analog/digital precoding in millimeter wave multiple-input multiple-output (MIMO) systems. To reduce the feedback overhead, we propose two non-uniform quantization (NUQ) codebook based hybrid precoding schemes for two main hybrid precoding implementations, i.e., the full-connected structure and the sub-connected structure. Specifically, we firstly group the angles of the arrive/departure (AOAs/AODs) of the scattering paths into several spatial lobes by exploiting the sparseness property of the millimeter wave in the angular domain, which divides the total angular domain into effective spatial lobes' coverage angles and ineffective coverage angles. Then, we map the quantization bits non-uniformly to different coverage angles and construct NUQ codebooks, where high numbers of quantization bits are employed for the effective coverage angles to quantize AoAs/AoDs and zero quantization bit is employed for ineffective coverage angles. Finally, two low-complexity hybrid analog/digital precoding schemes are proposed based on NUQ codebooks. Simulation results demonstrate that, the proposed two NUQ codebook based hybrid precoding schemes achieve near-optimal spectral efficiencies and show the superiority in reducing the feedback overhead compared with the uniform quantization (UQ) codebook based works, e.g., at least 12.5% feedback overhead could be reduced for a system with 144/36 transmitting/receiving antennas.
A Deep Learning Approach for Multimodal Deception Detection
Venue: arXiv cs.CL 2018
Year: 2018

Automatic deception detection is an important task that has gained momentum in computational linguistics due to its potential applications. In this paper, we propose a simple yet tough to beat multi-modal neural model for deception detection. By combining features from different modalities such as video, audio, and text along with Micro-Expression features, we show that detecting deception in real life videos can be more accurate. Experimental results on a dataset of real-life deception videos show that our model outperforms existing techniques for deception detection with an accuracy of 96.14% and ROC-AUC of 0.9799.
Fibres of Failure: Classifying errors in predictive processes
Venue: arXiv cs.CV 2018
Year: 2018

We describe Fibres of Failure (FiFa), a method to classify failure modes of predictive processes using the Mapper algorithm from Topological Data Analysis. Our method uses Mapper to build a graph model of input data stratified by prediction error. Groupings found in high-error regions of the Mapper model then provide distinct failure modes of the predictive process. We demonstrate FiFa on misclassifications of MNIST images with added noise, and demonstrate two ways to use the failure mode classification: either to produce a correction layer that adjusts predictions by similarity to the failure modes; or to inspect members of the failure modes to illustrate and investigate what characterizes each failure mode.
Poisson Image Denoising Using Best Linear Prediction: A Post-processing Framework
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we address the problem of denoising images degraded by Poisson noise. We propose a new patch-based approach based on best linear prediction to estimate the underlying clean image. A simplified prediction formula is derived for Poisson observations, which requires the covariance matrix of the underlying clean patch. We use the assumption that similar patches in a neighborhood share the same covariance matrix, and we use off-the-shelf Poisson denoising methods in order to obtain an initial estimate of the covariance matrices. Our method can be seen as a post-processing step for Poisson denoising methods and the results show that it improves upon several Poisson denoising methods by relevant margins.
Interactive Sound Rendering on Mobile Devices using Ray-Parameterized Reverberation Filters
Venue: arXiv cs.GR 2018
Year: 2018

We present a new sound rendering pipeline that is able to generate plausible sound propagation effects for interactive dynamic scenes. Our approach combines ray-tracing-based sound propagation with reverberation filters using robust automatic reverb parameter estimation that is driven by impulse responses computed at a low sampling rate.We propose a unified spherical harmonic representation of directional sound in both the propagation and auralization modules and use this formulation to perform a constant number of convolution operations for any number of sound sources while rendering spatial audio. In comparison to previous geometric acoustic methods, we achieve a speedup of over an order of magnitude while delivering similar audio to high-quality convolution rendering algorithms. As a result, our approach is the first capable of rendering plausible dynamic sound propagation effects on commodity smartphones.
Developing a functional prototype master patient index (MPI) for interoperability of e-health systems in Sri Lanka
Venue: arXiv cs.CY 2018
Year: 2018

Introduction: A Master Patient Index(MPI) is a centralized index of all patients in a healthcare system. This index is composed of a unique identifier for each patient link to his/her demographic data and clinical encounters. A MPI is essential to ensure data interoperability in the different healthcare institution. The The health ministry of Sri Lanka planning to develop MPI for the country. This project focused on developing the prototype MPI for Sri Lanka with the view to implementing and scaling up at the national level. Methods: This project consisted of 3 phases. Phase 1: requirement analysis using focus group discussions (FGD) with information system users. Phase 2: identification of the suitable Application Programming interface (API) model. Phase 3: development of the prototype MPI. Results: FGD were conducted in 6 hospitals. There were 78 interviewers (Male -36, and female - 42). They highlighted the key requirements for the MPI. Which were the unique identification method and different searching criteria and merging records to avoid duplication. Using this information, the requirements specification for MPI was developed. A combination of monolithic and microservices architecture was selected to develop the MPI. The API using the Personal Health Number (PHN) as the unique patient identifier and HL7 standard was developed and implemented. Conclusions: Development and implementation of a MPI has facilitated the long due need for interoperability among health information systems in Sri Lankan. KEYWORDS MPI, Interoperability, Unique Identifier, PHN, API
Joint Inter-flow Network Coding and Opportunistic Routing in Multi-hop Wireless Mesh Networks: A Comprehensive Survey
Venue: arXiv cs.NI 2018
Year: 2018

Network coding and opportunistic routing are two recognized innovative ideas to improve the performance of wireless networks by utilizing the broadcast nature of the wireless medium. In the last decade, there has been considerable research on how to synergize inter-flow network coding and opportunistic routing in a single joint protocol outperforming each in any scenario. This paper explains the motivation behind the integration of these two techniques, and highlights certain scenarios in which the joint approach may even degrade the performance, emphasizing the fact that their synergistic effect cannot be accomplished with a naive and perfunctory combination. This survey paper also provides a comprehensive taxonomy of the joint protocols in terms of their fundamental components and associated challenges, and compares existing joint protocols. We also present concluding remarks along with an outline of future research directions.
Codes from surfaces with small Picard number
Venue: arXiv cs.IT 2018
Year: 2018

Extending work of M. Zarzar, we evaluate the potential of Goppa-type evaluation codes constructed from linear systems on projective algebraic surfaces with small Picard number. Putting this condition on the Picard number provides some control over the numbers of irreducible components of curves on the surface and hence over the minimum distance of the codes. We find that such surfaces do not automatically produce good codes; the sectional genus of the surface also has a major influence. Using that additional invariant, we derive bounds on the minimum distance under the assumption that the hyperplane section class generates the Néron-Severi group. We also give several examples of codes from such surfaces with minimum distance better than the best known bounds in Grassl's tables.
Graph Based Proactive Secure Decomposition Algorithm for Context Dependent Attribute Based Inference Control Problem
Venue: arXiv cs.DB 2018
Year: 2018

Relational DBMSs continue to dominate the database market, and inference problem on external schema of relational DBMS's is still an important issue in terms of data privacy.Especially for the last 10 years, external schema construction for application-specific database usage has increased its independency from the conceptual schema, as the definitions and implementations of views and procedures have been optimized. This paper offers an optimized decomposition strategy for the external schema, which concentrates on the privacy policy and required associations of attributes for the intended user roles. The method proposed in this article performs a proactive decomposition of the external schema, in order to satisfy both the forbidden and required associations of attributes.Functional dependency constraints of a database schema can be represented as a graph, in which vertices are attribute sets and edges are functional dependencies. In this representation, inference problem can be defined as a process of searching a subtree in the dependency graph containing the attributes that need to be related. The optimized decomposition process aims to generate an external schema, which guarantees the prevention of the inference of the forbidden attribute sets while guaranteeing the association of the required attribute sets with a minimal loss of possible association among other attributes, if the inhibited and required attribute sets are consistent with each other. Our technique is purely proactive, and can be viewed as a normalization process. Due to the usage independency of external schema construction tools, it can be easily applied to any existing systems without rewriting data access layer of applications. Our extensive experimental analysis shows the effectiveness of this optimized proactive strategy for a wide variety of logical schema volumes.