OpenRaiser/Intern-Atlas huggingface dataset:
DeepIoT: Compressing Deep Neural Network Structures for Sensing Systems with a Compressor-Critic Framework
Venue: arXiv cs.LG 2017
Year: 2017

Recent advances in deep learning motivate the use of deep neutral networks in sensing applications, but their excessive resource needs on constrained embedded devices remain an important impediment. A recently explored solution space lies in compressing (approximating or simplifying) deep neural networks in some manner before use on the device. We propose a new compression solution, called DeepIoT, that makes two key contributions in that space. First, unlike current solutions geared for compressing specific types of neural networks, DeepIoT presents a unified approach that compresses all commonly used deep learning structures for sensing applications, including fully-connected, convolutional, and recurrent neural networks, as well as their combinations. Second, unlike solutions that either sparsify weight matrices or assume linear structure within weight matrices, DeepIoT compresses neural network structures into smaller dense matrices by finding the minimum number of non-redundant hidden elements, such as filters and dimensions required by each layer, while keeping the performance of sensing applications the same. Importantly, it does so using an approach that obtains a global view of parameter redundancies, which is shown to produce superior compression. We conduct experiments with five different sensing-related tasks on Intel Edison devices. DeepIoT outperforms all compared baseline algorithms with respect to execution time and energy consumption by a significant margin. It reduces the size of deep neural networks by 90% to 98.9%. It is thus able to shorten execution time by 71.4% to 94.5%, and decrease energy consumption by 72.2% to 95.7%. These improvements are achieved without loss of accuracy. The results underscore the potential of DeepIoT for advancing the exploitation of deep neural networks on resource-constrained embedded devices.
Aiding autobiographic memory by using wearable devices
Venue: arXiv cs.HC 2017
Year: 2017

In this paper, we investigate the effectiveness of two distinct techniques (Special Moment Approach & Spatial Frequency Approach) for reviewing the lifelogs, which were collected by lifeloggers who were willing to use a wearable camera and a bracelet simultaneously for two days. Generally, Special moment approach is a technique for extracting episodic events and Spatial frequency approach is a technique for associating visual with temporal and location information, especially heat map is applied as the spatial data for expressing frequency awareness. Based on that, the participants were asked to fill in two post-study questionnaires for evaluating the effectiveness of those two techniques and their combination. The preliminary result showed the positive potential of exploring individual lifelogs using our approaches.
Language Generation with Recurrent Generative Adversarial Networks without Pre-training
Venue: arXiv cs.CL 2017
Year: 2017

Generative Adversarial Networks (GANs) have shown great promise recently in image generation. Training GANs for language generation has proven to be more difficult, because of the non-differentiable nature of generating text with recurrent neural networks. Consequently, past work has either resorted to pre-training with maximum-likelihood or used convolutional networks for generation. In this work, we show that recurrent neural networks can be trained to generate text with GANs from scratch using curriculum learning, by slowly teaching the model to generate sequences of increasing and variable length. We empirically show that our approach vastly improves the quality of generated sequences compared to a convolutional baseline.
A method for the online construction of the set of states of a Markov Decision Process using Answer Set Programming
Venue: arXiv cs.AI 2017
Year: 2017

Non-stationary domains, that change in unpredicted ways, are a challenge for agents searching for optimal policies in sequential decision-making problems. This paper presents a combination of Markov Decision Processes (MDP) with Answer Set Programming (ASP), named {\em Online ASP for MDP} (oASP(MDP)), which is a method capable of constructing the set of domain states while the agent interacts with a changing environment. oASP(MDP) updates previously obtained policies, learnt by means of Reinforcement Learning (RL), using rules that represent the domain changes observed by the agent. These rules represent a set of domain constraints that are processed as ASP programs reducing the search space. Results show that oASP(MDP) is capable of finding solutions for problems in non-stationary domains without interfering with the action-value function approximation process.
UCB Exploration via Q-Ensembles
Venue: arXiv cs.LG 2017
Year: 2017

We show how an ensemble of $Q^*$-functions can be leveraged for more effective exploration in deep reinforcement learning. We build on well established algorithms from the bandit setting, and adapt them to the $Q$-learning setting. We propose an exploration strategy based on upper-confidence bounds (UCB). Our experiments show significant gains on the Atari benchmark.
Robust and efficient validation of the linear hexahedral element
Venue: arXiv cs.CG 2017
Year: 2017

Checking mesh validity is a mandatory step before doing any finite element analysis. If checking the validity of tetrahedra is trivial, checking the validity of hexahedral elements is far from being obvious. In this paper, a method that robustly and efficiently compute the validity of standard linear hexahedral elements is presented. This method is a significant improvement of a previous work on the validity of curvilinear elements. The new implementation is simple and computationally efficient. The key of the algorithm is still to compute Bézier coefficients of the Jacobian determinant. We show that only 20 Jacobian determinants are necessary to compute the 27 Bézier coefficients. Those 20 Jacobians can be efficiently computed by calculating the volume of 20 tetrahedra. The new implementation is able to check the validity of about 6 million hexahedra per second on one core of a personal computer. Through the paper, all the necessary information is provided that allow to easily reproduce the results, \ie write a simple code that takes the coordinates of 8 points as input and outputs the validity of the hexahedron.
A Flexible Modeling Approach for Robust Multi-Lane Road Estimation
Venue: arXiv cs.RO 2017
Year: 2017

A robust estimation of road course and traffic lanes is an essential part of environment perception for next generations of Advanced Driver Assistance Systems and development of self-driving vehicles. In this paper, a flexible method for modeling multiple lanes in a vehicle in real time is presented. Information about traffic lanes, derived by cameras and other environmental sensors, that is represented as features, serves as input for an iterative expectation-maximization method to estimate a lane model. The generic and modular concept of the approach allows to freely choose the mathematical functions for the geometrical description of lanes. In addition to the current measurement data, the previously estimated result as well as additional constraints to reflect parallelism and continuity of traffic lanes, are considered in the optimization process. As evaluation of the lane estimation method, its performance is showcased using cubic splines for the geometric representation of lanes in simulated scenarios and measurements recorded using a development vehicle. In a comparison to ground truth data, robustness and precision of the lanes estimated up to a distance of 120 m are demonstrated. As a part of the environmental modeling, the presented method can be utilized for longitudinal and lateral control of autonomous vehicles.
TDNetGen: An open-source, parametrizable, large-scale, transmission and distribution test system
Venue: arXiv cs.SY 2017
Year: 2017

In this paper, an open-source MATLAB toolbox is presented that is able to generate synthetic, combined transmission and distribution network models. These can be used to analyse the interactions between transmission and multiple distribution systems, such as the provision of ancillary services by active distribution grids, the co-optimization of planning and operation, the development of emergency control and protection schemes spanning over different voltage levels, the analysis of combined market aspects, etc. The generated test-system models are highly customizable, providing the user with the flexibility to easily choose the desired characteristics, such as the level of renewable energy penetration, the size of the final system, etc.
Learning Pairwise Disjoint Simple Languages from Positive Examples
Venue: arXiv cs.LG 2017
Year: 2017

A classical problem in grammatical inference is to identify a deterministic finite automaton (DFA) from a set of positive and negative examples. In this paper, we address the related - yet seemingly novel - problem of identifying a set of DFAs from examples that belong to different unknown simple regular languages. We propose two methods based on compression for clustering the observed positive examples. We apply our methods to a set of print jobs submitted to large industrial printers.
Blockchain based trust & authentication for decentralized sensor networks
Venue: arXiv cs.CR 2017
Year: 2017

Sensor networks and Wireless Sensor Networks (WSN) are key components for the development of the Internet of Things. These networks are subject of two kinds of constraints. Adaptability by the mean of mutability and evolutivity, and constrained node resources such as energy consumption, computational complexity or memory usage. In this context, none of the existing protocols and models allows reliable peer authentication and trust level management. In the field of virtual economic transactions, Bitcoin has proposed a new decentralized and evolutive way to model and acknowledge trust and data validity in a peer network by the mean of the blockchain. We propose a new security model and its protocol based on the blockchain technology to ensure validity and integrity of cryptographic authentication data and associate peer trust level, from the beginning to the end of the sensor network lifetime.
Specifying Transaction Control to Serialize Concurrent Program Executions
Venue: arXiv cs.DB 2017
Year: 2017

We define a programming language independent transaction controller and an operator which when applied to concurrent programs with shared locations turns their behavior with respect to some abstract termination criterion into a transactional behavior. We prove the correctness property that concurrent runs under the transaction controller are serialisable. We specify the transaction controller TaCtl and the operator TA in terms of Abstract State Machines. This makes TaCtl applicable to a wide range of programs and in particular provides the possibility to use it as a plug-in when specifying concurrent system components in terms of Abstract State Machines.
Adversarial-Playground: A Visualization Suite for Adversarial Sample Generation
Venue: arXiv cs.CR 2017
Year: 2017

With growing interest in adversarial machine learning, it is important for machine learning practitioners and users to understand how their models may be attacked. We propose a web-based visualization tool, Adversarial-Playground, to demonstrate the efficacy of common adversarial methods against a deep neural network (DNN) model, built on top of the TensorFlow library. Adversarial-Playground provides users an efficient and effective experience in exploring techniques generating adversarial examples, which are inputs crafted by an adversary to fool a machine learning system. To enable Adversarial-Playground to generate quick and accurate responses for users, we use two primary tactics: (1) We propose a faster variant of the state-of-the-art Jacobian saliency map approach that maintains a comparable evasion rate. (2) Our visualization does not transmit the generated adversarial images to the client, but rather only the matrix describing the sample and the vector representing classification likelihoods. The source code along with the data from all of our experiments are available at \url{https://github.com/QData/AdversarialDNN-Playground}.
Two Categories of Indoor Interactive Dynamics of a Large-scale Human Population in a WiFi covered university campus
Venue: arXiv cs.SI 2017
Year: 2017

To explore large-scale population indoor interactions, we analyze 18,715 users' WiFi access logs recorded in a Chinese university campus during 3 months, and define two categories of human interactions, the event interaction (EI) and the temporal interaction (TI). The EI helps construct a transmission graph, and the TI helps build an interval graph. The dynamics of EIs show that their active durations are truncated power-law distributed, which is independent on the number of involved individuals. The transmission duration presents a truncated power-law behavior at the daily timescale with weekly periodicity. Besides, those `leaf' individuals in the aggregated contact network may participate in the `super-connecting cliques' in the aggregated transmission graph. Analyzing the dynamics of the interval graph, we find that the probability distribution of TIs' inter-event duration also displays a truncated power-law pattern at the daily timescale with weekly periodicity, while the pairwise individuals with burst interactions are prone to randomly select their interactive locations, and those individuals with periodic interactions have preferred interactive locations.
Face Alignment Using K-Cluster Regression Forests With Weighted Splitting
Venue: arXiv cs.CV 2017
Year: 2017

In this work we present a face alignment pipeline based on two novel methods: weighted splitting for K-cluster Regression Forests and 3D Affine Pose Regression for face shape initialization. Our face alignment method is based on the Local Binary Feature framework, where instead of standard regression forests and pixel difference features used in the original method, we use our K-cluster Regression Forests with Weighted Splitting (KRFWS) and Pyramid HOG features. We also use KRFWS to perform Affine Pose Regression (APR) and 3D-Affine Pose Regression (3D-APR), which intend to improve the face shape initialization. APR applies a rigid 2D transform to the initial face shape that compensates for inaccuracy in the initial face location, size and in-plane rotation. 3D-APR estimates the parameters of a 3D transform that additionally compensates for out-of-plane rotation. The resulting pipeline, consisting of APR and 3D-APR followed by face alignment, shows an improvement of 20% over standard LBF on the challenging IBUG dataset, and state-of-theart accuracy on the entire 300-W dataset.
Assessing the Linguistic Productivity of Unsupervised Deep Neural Networks
Venue: arXiv cs.CL 2017
Year: 2017

Increasingly, cognitive scientists have demonstrated interest in applying tools from deep learning. One use for deep learning is in language acquisition where it is useful to know if a linguistic phenomenon can be learned through domain-general means. To assess whether unsupervised deep learning is appropriate, we first pose a smaller question: Can unsupervised neural networks apply linguistic rules productively, using them in novel situations? We draw from the literature on determiner/noun productivity by training an unsupervised, autoencoder network measuring its ability to combine nouns with determiners. Our simple autoencoder creates combinations it has not previously encountered and produces a degree of overlap matching adults. While this preliminary work does not provide conclusive evidence for productivity, it warrants further investigation with more complex models. Further, this work helps lay the foundations for future collaboration between the deep learning and cognitive science communities.
Added value of morphological features to breast lesion diagnosis in ultrasound
Venue: arXiv cs.CV 2017
Year: 2017

Ultrasound imaging plays an important role in breast lesion differentiation. However, diagnostic accuracy depends on ultrasonographer experience. Various computer aided diagnosis systems has been developed to improve breast cancer detection and reduce the number of unnecessary biopsies. In this study, our aim was to improve breast lesion classification based on the BI-RADS (Breast Imaging - Reporting and Data System). This was accomplished by combining the BI-RADS with morphological features which assess lesion boundary. A dataset of 214 lesion images was used for analysis. 30 morphological features were extracted and feature selection scheme was applied to find features which improve the BI-RADS classification performance. Additionally, the best performing morphological feature subset was indicated. We obtained a better classification by combining the BI-RADS with six morphological features. These features were the extent, overlap ratio, NRL entropy, circularity, elliptic-normalized circumference and the normalized residual value. The area under the receiver operating curve calculated with the use of the combined classifier was 0.986. The best performing morphological feature subset contained six features: the DWR, NRL entropy, normalized residual value, overlap ratio, extent and the morphological closing ratio. For this set, the area under the curve was 0.901. The combination of the radiologist's experience related to the BI-RADS and the morphological features leads to a more effective breast lesion classification.
A two-dimensional index to quantify both scientific research impact and scope
Venue: arXiv cs.DL 2017
Year: 2017

Modern management of research is increasingly based on quantitative bibliometric indices. Nowadays, the h-index is a major measure of research output that has supplanted all other citation-based indices. In this context, indicators that complement the h-index by evaluating different facets of research achievement are compelling. As an additional bibliometric source that can be easily extracted from available databases, we propose to use the number of distinct journals Nj in which an individual's papers were published. We show that Nj is independent of citation counts, and argue that it is a relevant indicator of research scope, since it quantifies readership extent and scientific multidisciplinarity. Combining the h-index and Nj, we define a two-dimensional index (H,M) that measures both the output (through H) and the outreach (through M) of individual's research. In order to probe the relevance of this two dimensional index, we have analysed the scientific production of a panel of physicists belonging to the same Department but with different research themes. The analysis of bibliometric data confirms that the two indices are uncorrelated and shows that while H reliably ranks the impact of researchers, M accurately sorts multidisciplinary or readership aspects. We conclude that the two indices together offer a more complete picture of research performance and can be applied to either individuals, research groups or institutions.
Deep Learning: Generalization Requires Deep Compositional Feature Space Design
Venue: arXiv cs.LG 2017
Year: 2017

Generalization error defines the discriminability and the representation power of a deep model. In this work, we claim that feature space design using deep compositional function plays a significant role in generalization along with explicit and implicit regularizations. Our claims are being established with several image classification experiments. We show that the information loss due to convolution and max pooling can be marginalized with the compositional design, improving generalization performance. Also, we will show that learning rate decay acts as an implicit regularizer in deep model training.
SCW Codes for Maximum Likelihood Detection in Diffusive Molecular Communications without Channel State Information
Venue: arXiv cs.IT 2017
Year: 2017

Instantaneous or statistical channel state information (CSI) is needed for most detection schemes developed for molecular communication (MC) systems. Since the MC channel changes over time, e.g., due to variations in the velocity of flow, the temperature, or the distance between transmitter and receiver, CSI acquisition has to be conducted repeatedly to keep track of CSI variations. Frequent CSI acquisition may entail a large overhead whereas infrequent CSI acquisition may result in a low CSI estimation accuracy. To overcome these challenges, we design codes which enable maximum likelihood sequence detection at the receiver without instantaneous or statistical CSI. In particular, assuming concentration shift keying modulation, we show that a class of codes, referred to as strongly constant-weight (SCW) codes, enables optimal CSI-free sequence detection at the expense of a decrease in data rate. For the proposed SCW codes, we analyze the code rate, the error rate, and the average number of released molecules. In addition, we study the properties of binary SCW codes and balanced SCW codes in further detail. Simulation results verify our analytical derivations and reveal that SCW codes with CSI-free detection outperform uncoded transmission with optimal coherent and non-coherent detection.
Synthesizing Filamentary Structured Images with GANs
Venue: arXiv cs.CV 2017
Year: 2017

This paper aims at synthesizing filamentary structured images such as retinal fundus images and neuronal images, as follows: Given a ground-truth, to generate multiple realistic looking phantoms. A ground-truth could be a binary segmentation map containing the filamentary structured morphology, while the synthesized output image is of the same size as the ground-truth and has similar visual appearance to what have been presented in the training set. Our approach is inspired by the recent progresses in generative adversarial nets (GANs) as well as image style transfer. In particular, it is dedicated to our problem context with the following properties: Rather than large-scale dataset, it works well in the presence of as few as 10 training examples, which is common in medical image analysis; It is capable of synthesizing diverse images from the same ground-truth; Last and importantly, the synthetic images produced by our approach are demonstrated to be useful in boosting image analysis performance. Empirical examination over various benchmarks of fundus and neuronal images demonstrate the advantages of the proposed approach.
Bounds and approximation results for scheduling coupled-tasks with compatibility constraints
Venue: arXiv cs.CC 2017
Year: 2017

This article is devoted to propose some lower and upper bounds for the coupled-tasks scheduling problem in presence of compatibility constraints according to classical complexity hypothesis ($\mathcal{P} \neq \mathcal{NP}$, $\mathcal{ETH}$). Moreover, we develop an efficient polynomial-time approximation algorithm for the specific case for which the topology describing the compatibility constraints is a quasi split-graph.