OpenRaiser/Intern-Atlas huggingface dataset:
Multi-level Wavelet-CNN for Image Restoration
Venue: arXiv cs.CV 2018
Year: 2018

The tradeoff between receptive field size and efficiency is a crucial issue in low level vision. Plain convolutional networks (CNNs) generally enlarge the receptive field at the expense of computational cost. Recently, dilated filtering has been adopted to address this issue. But it suffers from gridding effect, and the resulting receptive field is only a sparse sampling of input image with checkerboard patterns. In this paper, we present a novel multi-level wavelet CNN (MWCNN) model for better tradeoff between receptive field size and computational efficiency. With the modified U-Net architecture, wavelet transform is introduced to reduce the size of feature maps in the contracting subnetwork. Furthermore, another convolutional layer is further used to decrease the channels of feature maps. In the expanding subnetwork, inverse wavelet transform is then deployed to reconstruct the high resolution feature maps. Our MWCNN can also be explained as the generalization of dilated filtering and subsampling, and can be applied to many image restoration tasks. The experimental results clearly show the effectiveness of MWCNN for image denoising, single image super-resolution, and JPEG image artifacts removal.
EthIR: A Framework for High-Level Analysis of Ethereum Bytecode
Venue: arXiv cs.PL 2018
Year: 2018

Analyzing Ethereum bytecode, rather than the source code from which it was generated, is a necessity when: (1) the source code is not available (e.g., the blockchain only stores the bytecode), (2) the information to be gathered in the analysis is only visible at the level of bytecode (e.g., gas consumption is specified at the level of EVM instructions), (3) the analysis results may be affected by optimizations performed by the compiler (thus the analysis should be done ideally after compilation). This paper presents EthIR, a framework for analyzing Ethereum bytecode, which relies on (an extension of) OYENTE, a tool that generates CFGs; EthIR produces from the CFGs, a rule-based representation (RBR) of the bytecode that enables the application of (existing) high-level analyses to infer properties of EVM code.
Learning and Inferring Movement with Deep Generative Model
Venue: arXiv cs.LG 2018
Year: 2018

Learning and inference movement is a very challenging problem due to its high dimensionality and dependency to varied environments or tasks. In this paper, we propose an effective probabilistic method for learning and inference of basic movements. The motion planning problem is formulated as learning on a directed graphic model and deep generative model is used to perform learning and inference from demonstrations. An important characteristic of this method is that it flexibly incorporates the task descriptors and context information for long-term planning and it can be combined with dynamic systems for robot control. The experimental validations on robotic approaching path planning tasks show the advantages over the base methods with limited training data.
Neural Network Compression using Transform Coding and Clustering
Venue: arXiv cs.CV 2018
Year: 2018

With the deployment of neural networks on mobile devices and the necessity of transmitting neural networks over limited or expensive channels, the file size of the trained model was identified as bottleneck. In this paper, we propose a codec for the compression of neural networks which is based on transform coding for convolutional and dense layers and on clustering for biases and normalizations. By using this codec, we achieve average compression factors between 7.9-9.3 while the accuracy of the compressed networks for image classification decreases only by 1%-2%, respectively.
Mixup-Based Acoustic Scene Classification Using Multi-Channel Convolutional Neural Network
Venue: arXiv cs.CV 2018
Year: 2018

Audio scene classification, the problem of predicting class labels of audio scenes, has drawn lots of attention during the last several years. However, it remains challenging and falls short of accuracy and efficiency. Recently, Convolutional Neural Network (CNN)-based methods have achieved better performance with comparison to the traditional methods. Nevertheless, conventional single channel CNN may fail to consider the fact that additional cues may be embedded in the multi-channel recordings. In this paper, we explore the use of Multi-channel CNN for the classification task, which aims to extract features from different channels in an end-to-end manner. We conduct the evaluation compared with the conventional CNN and traditional Gaussian Mixture Model-based methods. Moreover, to improve the classification accuracy further, this paper explores the using of mixup method. In brief, mixup trains the neural network on linear combinations of pairs of the representation of audio scene examples and their labels. By employing the mixup approach for data argumentation, the novel model can provide higher prediction accuracy and robustness in contrast with previous models, while the generalization error can also be reduced on the evaluation data.
Model-Driven Engineering of Self-Adaptive Software with EUREMA
Venue: arXiv cs.SE 2018
Year: 2018

The development of self-adaptive software requires the engineering of an adaptation engine that controls the underlying adaptable software by feedback loops. The engine often describes the adaptation by runtime models representing the adaptable software and by activities such as analysis and planning that use these models. To systematically address the interplay between runtime models and adaptation activities, runtime megamodels have been proposed. A runtime megamodel is a specific model capturing runtime models and adaptation activities. In this article, we go one step further and present an executable modeling language for ExecUtable RuntimE MegAmodels (EUREMA) that eases the development of adaptation engines by following a model-driven engineering approach. We provide a domain-specific modeling language and a runtime interpreter for adaptation engines, in particular feedback loops. Megamodels are kept alive at runtime and by interpreting them, they are directly executed to run feedback loops. Additionally, they can be dynamically adjusted to adapt feedback loops. Thus, EUREMA supports development by making feedback loops explicit at a higher level of abstraction and it enables solutions where multiple feedback loops interact or operate on top of each other and self-adaptation co-exists with offline adaptation for evolution.
A Testing Scheme for Self-Adaptive Software Systems with Architectural Runtime Models
Venue: arXiv cs.SE 2018
Year: 2018

Self-adaptive software systems (SASS) are equipped with feedback loops to adapt autonomously to changes of the software or environment. In established fields, such as embedded software, sophisticated approaches have been developed to systematically study feedback loops early during the development. In order to cover the particularities of feedback, techniques like one-way and in-the-loop simulation and testing have been included. However, a related approach to systematically test SASS is currently lacking. In this paper we therefore propose a systematic testing scheme for SASS that allows engineers to test the feedback loops early in the development by exploiting architectural runtime models. These models that are available early in the development are commonly used by the activities of a feedback loop at runtime and they provide a suitable high-level abstraction to describe test inputs as well as expected test results. We further outline our ideas with some initial evaluation results by means of a small case study.
Adaptation and Abstract Runtime Models
Venue: arXiv cs.SE 2018
Year: 2018

Runtime adaptability is often a crucial requirement for today's complex software systems. Several approaches use an architectural model as a runtime representation of a managed system for monitoring, reasoning and performing adaptation. To ease the causal connection between a system and a model, these models are often closely related to the implementation and at a rather low level of abstraction. This makes them as complex as the implementation and it impedes reusability and extensibility of autonomic managers. Moreover, the models often do not cover different concerns, like security or performance, and therefore they do not support several self-management capabilities at once. In this paper we propose a model-driven approach that provides multiple architectural runtime models at different levels of abstraction as a basis for adaptation. Each runtime model abstracts from the underlying system and platform leveraging reusability and extensibility of managers that work on these models. Moreover, each model focuses on a specific concern which simplifies the work of autonomic managers. The different models are maintained automatically at runtime using model-driven engineering techniques that also reduce development efforts. Our approach has been implemented for the broadly adopted Enterprise Java Beans component standard and its application is presented in a self-healing scenario requiring structural adaptation.
Incept-N: A Convolutional Neural Network based Classification Approach for Predicting Nationality from Facial Features
Venue: arXiv cs.CV 2018
Year: 2018

The nationality of a human being is a well-known identifying characteristic used for every major authentication purpose in every country. Albeit advances in the application of Artificial Intelligence and Computer Vision in different aspects, its contribution to this specific security procedure is yet to be cultivated. With a goal to successfully applying computer vision techniques to predict the nationality of a person based on his facial features, we have proposed this novel method and have achieved an average of 93.6% accuracy with very low misclassification rate.
Subspace Selection via DR-Submodular Maximization on Lattices
Venue: arXiv cs.DS 2018
Year: 2018

The subspace selection problem seeks a subspace that maximizes an objective function under some constraint. This problem includes several important machine learning problems such as the principal component analysis and sparse dictionary selection problem. Often, these problems can be solved by greedy algorithms. Here, we are interested in why these problems can be solved by greedy algorithms, and what classes of objective functions and constraints admit this property. To answer this question, we formulate the problems as optimization problems on lattices. Then, we introduce a new class of functions, directional DR-submodular functions, to characterize the approximability of problems. We see that the principal component analysis, sparse dictionary selection problem, and these generalizations have directional DR-submodularities. We show that, under several constraints, the directional DR-submodular function maximization problem can be solved efficiently with provable approximation factors.
Solving the Rubik's Cube Without Human Knowledge
Venue: arXiv cs.AI 2018
Year: 2018

A generally intelligent agent must be able to teach itself how to solve problems in complex domains with minimal human supervision. Recently, deep reinforcement learning algorithms combined with self-play have achieved superhuman proficiency in Go, Chess, and Shogi without human data or domain knowledge. In these environments, a reward is always received at the end of the game, however, for many combinatorial optimization environments, rewards are sparse and episodes are not guaranteed to terminate. We introduce Autodidactic Iteration: a novel reinforcement learning algorithm that is able to teach itself how to solve the Rubik's Cube with no human assistance. Our algorithm is able to solve 100% of randomly scrambled cubes while achieving a median solve length of 30 moves -- less than or equal to solvers that employ human domain knowledge.
Tell Me Something New: A New Framework for Asynchronous Parallel Learning
Venue: arXiv cs.LG 2018
Year: 2018

We present a novel approach for parallel computation in the context of machine learning that we call "Tell Me Something New" (TMSN). This approach involves a set of independent workers that use broadcast to update each other when they observe "something new". TMSN does not require synchronization or a head node and is highly resilient against failing machines or laggards. We demonstrate the utility of TMSN by applying it to learning boosted trees. We show that our implementation is 10 times faster than XGBoost and LightGBM on the splice-site prediction problem.
ACR: a cluster-based routing protocol for VANET
Venue: arXiv cs.NI 2018
Year: 2018

Clustering is a technique used in network routing to enhance the performance and conserve the network resources. This paper presents a cluster-based routing protocol for VANET utilizing a new addressing scheme in which each node gets an address according to its mobility pattern. Hamming distance technique is used then to partition the network in an address-centric manner. The simulation results show that this protocol enhances routing reachability, whereas reduces routing end-to-end delay and traffic received comparing with two benchmarks namely AODV and DSDV.
Fast Object Classification in Single-pixel Imaging
Venue: arXiv cs.CV 2018
Year: 2018

In single-pixel imaging (SPI), the target object is illuminated with varying patterns sequentially and an intensity sequence is recorded by a single-pixel detector without spatial resolution. A high quality object image can only be computationally reconstructed after a large number of illuminations, with disadvantages of long imaging time and high cost. Conventionally, object classification is performed after a reconstructed object image with good fidelity is available. In this paper, we propose to classify the target object with a small number of illuminations in a fast manner for Fourier SPI. A naive Bayes classifier is employed to classify the target objects based on the single-pixel intensity sequence without any image reconstruction and each sequence element is regarded as an object feature in the classifier. Simulation results demonstrate our proposed scheme can classify the number digit object images with high accuracy (e.g. 80% accuracy using only 13 illuminations, at a sampling ratio of 0.3%).
A hybrid index model for efficient spatio-temporal search in HBase
Venue: arXiv cs.DB 2018
Year: 2018

With advances in geo-positioning technologies and geo-location services, there are a rapidly growing massive amount of spatio-temporal data collected in many applications such as location-aware devices and wireless communication, in which an object is described by its spatial location and its timestamp. Consequently, the study of spatio-temporal search which explores both geo-location information and temporal information of the data has attracted significant concern from research organizations and commercial communities. This work study the problem of spatio-temporal \emph{k}-nearest neighbors search (ST$k$NNS), which is fundamental in the spatial temporal queries. Based on HBase, a novel index structure is proposed, called \textbf{H}ybrid \textbf{S}patio-\textbf{T}emporal HBase \textbf{I}ndex (\textbf{HSTI} for short), which is carefully designed and takes both spatial and temporal information into consideration to effectively reduce the search space. Based on HSTI, an efficient algorithm is developed to deal with spatio-temporal \emph{k}-nearest neighbors search. Comprehensive experiments on real and synthetic data clearly show that HSTI is three to five times faster than the state-of-the-art technique.
Learning Hierarchical Visual Representations in Deep Neural Networks Using Hierarchical Linguistic Labels
Venue: arXiv cs.CV 2018
Year: 2018

Modern convolutional neural networks (CNNs) are able to achieve human-level object classification accuracy on specific tasks, and currently outperform competing models in explaining complex human visual representations. However, the categorization problem is posed differently for these networks than for humans: the accuracy of these networks is evaluated by their ability to identify single labels assigned to each image. These labels often cut arbitrarily across natural psychological taxonomies (e.g., dogs are separated into breeds, but never jointly categorized as "dogs"), and bias the resulting representations. By contrast, it is common for children to hear both "dog" and "Dalmatian" to describe the same stimulus, helping to group perceptually disparate objects (e.g., breeds) into a common mental class. In this work, we train CNN classifiers with multiple labels for each image that correspond to different levels of abstraction, and use this framework to reproduce classic patterns that appear in human generalization behavior.
On Attention Models for Human Activity Recognition
Venue: arXiv cs.CV 2018
Year: 2018

Most approaches that model time-series data in human activity recognition based on body-worn sensing (HAR) use a fixed size temporal context to represent different activities. This might, however, not be apt for sets of activities with individ- ually varying durations. We introduce attention models into HAR research as a data driven approach for exploring relevant temporal context. Attention models learn a set of weights over input data, which we leverage to weight the temporal context being considered to model each sensor reading. We construct attention models for HAR by adding attention layers to a state- of-the-art deep learning HAR model (DeepConvLSTM) and evaluate our approach on benchmark datasets achieving sig- nificant increase in performance. Finally, we visualize the learned weights to better understand what constitutes relevant temporal context.
DFCV: A Novel Approach for Message Dissemination in Connected Vehicles using Dynamic Fog
Venue: arXiv cs.NI 2018
Year: 2018

Vehicular Ad-hoc Network (VANET) has emerged as a promising solution for enhancing road safety. Routing of messages in VANET is challenging due to packet delays arising from high mobility of vehicles, frequently changing topology, and high density of vehicles, leading to frequent route breakages and packet losses. Previous researchers have used either mobility in vehicular fog computing or cloud computing to solve the routing issue, but they suffer from large packet delays and frequent packet losses. We propose Dynamic Fog for Connected Vehicles (DFCV), a fog computing based scheme which dynamically creates, increments and destroys fog nodes depending on the communication needs. The novelty of DFCV lies in providing lower delays and guaranteed message delivery at high vehicular densities. Simulations were conducted using hybrid simulation consisting of ns-2, SUMO, and Cloudsim. Results show that DFCV ensures efficient resource utilization, lower packet delays and losses at high vehicle densities.
An Evaluation of Trajectory Prediction Approaches and Notes on the TrajNet Benchmark
Venue: arXiv cs.CV 2018
Year: 2018

In recent years, there is a shift from modeling the tracking problem based on Bayesian formulation towards using deep neural networks. Towards this end, in this paper the effectiveness of various deep neural networks for predicting future pedestrian paths are evaluated. The analyzed deep networks solely rely, like in the traditional approaches, on observed tracklets without human-human interaction information. The evaluation is done on the publicly available TrajNet benchmark dataset, which builds up a repository of considerable and popular datasets for trajectory-based activity forecasting. We show that a Recurrent-Encoder with a Dense layer stacked on top, referred to as RED-predictor, is able to achieve sophisticated results compared to elaborated models in such scenarios. Further, we investigate failure cases and give explanations for observed phenomena and give some recommendations for overcoming demonstrated shortcomings.
Generating High-Quality Surface Realizations Using Data Augmentation and Factored Sequence Models
Venue: arXiv cs.CL 2018
Year: 2018

This work presents a new state of the art in reconstruction of surface realizations from obfuscated text. We identify the lack of sufficient training data as the major obstacle to training high-performing models, and solve this issue by generating large amounts of synthetic training data. We also propose preprocessing techniques which make the structure contained in the input features more accessible to sequence models. Our models were ranked first on all evaluation metrics in the English portion of the 2018 Surface Realization shared task.
A PTAS for a Class of Stochastic Dynamic Programs
Venue: arXiv cs.DS 2018
Year: 2018

We develop a framework for obtaining polynomial time approximation schemes (PTAS) for a class of stochastic dynamic programs. Using our framework, we obtain the first PTAS for the following stochastic combinatorial optimization problems: \probemax: We are given a set of $n$ items, each item $i\in [n]$ has a value $X_i$ which is an independent random variable with a known (discrete) distribution $π_i$. We can {\em probe} a subset $P\subseteq [n]$ of items sequentially. Each time after {probing} an item $i$, we observe its value realization, which follows the distribution $π_i$. We can {\em adaptively} probe at most $m$ items and each item can be probed at most once. The reward is the maximum among the $m$ realized values. Our goal is to design an adaptive probing policy such that the expected value of the reward is maximized. To the best of our knowledge, the best known approximation ratio is $1-1/e$, due to Asadpour \etal~\cite{asadpour2015maximizing}. We also obtain PTAS for some generalizations and variants of the problem and some other problems.
Network Reconstruction and Controlling Based on Structural Regularity Analysis
Venue: arXiv cs.SI 2018
Year: 2018

From the perspective of network analysis, the ubiquitous networks are comprised of regular and irregular components, which makes uncovering the complexity of network structures to be a fundamental challenge. Exploring the regular information and identifying the roles of microscopic elements in network data can help us recognize the principle of network organization and contribute to network data utilization. However, the intrinsic structural properties of networks remain so far inadequately explored and theorised. With the realistic assumption that there are consistent features across the local structures of networks, we propose a low-rank pursuit based self-representation network model, in which the principle of network organization can be uncovered by a representation matrix. According to this model, original true networks can be reconstructed based on the observed unreliable network topology. In particular, the proposed model enables us to estimate the extent to which the networks are regulable, i.e., measuring the reconstructability of networks. In addition, the model is capable of measuring the importance of microscopic network elements, i.e., nodes and links, in terms of network regularity thereby allowing us to regulate the reconstructability of networks based on them. Extensive experiments on disparate real-world networks demonstrate the effectiveness of the proposed network reconstruction and regulation algorithm. Specifically, the network regularity metric can reflect the reconstructability of networks, and the reconstruction accuracy can be improved by removing irregular network links. Lastly, our approach provides an unique and novel insight into the organization exploring of complex networks.