OpenRaiser/Intern-Atlas huggingface dataset:
Reward-Conditioned Policies
Venue: arXiv cs.LG 2019
Year: 2019

Reinforcement learning offers the promise of automating the acquisition of complex behavioral skills. However, compared to commonly used and well-understood supervised learning methods, reinforcement learning algorithms can be brittle, difficult to use and tune, and sensitive to seemingly innocuous implementation decisions. In contrast, imitation learning utilizes standard and well-understood supervised learning methods, but requires near-optimal expert data. Can we learn effective policies via supervised learning without demonstrations? The main idea that we explore in this work is that non-expert trajectories collected from sub-optimal policies can be viewed as optimal supervision, not for maximizing the reward, but for matching the reward of the given trajectory. By then conditioning the policy on the numerical value of the reward, we can obtain a policy that generalizes to larger returns. We show how such an approach can be derived as a principled method for policy search, discuss several variants, and compare the method experimentally to a variety of current reinforcement learning methods on standard benchmarks.
Water Supply Prediction Based on Initialized Attention Residual Network
Venue: arXiv cs.LG 2019
Year: 2019

Real-time and accurate water supply forecast is crucial for water plant. However, most existing methods are likely affected by factors such as weather and holidays, which lead to a decline in the reliability of water supply prediction. In this paper, we address a generic artificial neural network, called Initialized Attention Residual Network (IARN), which is combined with an attention module and residual modules. Specifically, instead of continuing to use the recurrent neural network (RNN) in time-series tasks, we try to build a convolution neural network (CNN)to recede the disturb from other factors, relieve the limitation of memory size and get a more credible results. Our method achieves state-of-the-art performance on several data sets, in terms of accuracy, robustness and generalization ability.
Deep Reinforced Self-Attention Masks for Abstractive Summarization (DR.SAS)
Venue: arXiv cs.CL 2019
Year: 2019

We present a novel architectural scheme to tackle the abstractive summarization problem based on the CNN/DMdataset which fuses Reinforcement Learning (RL) withUniLM, which is a pre-trained Deep Learning Model, to solve various natural language tasks. We have tested the limits of learning fine-grained attention in Transformers to improve the summarization quality. UniLM applies attention to the entire token space in a global fashion. We propose DR.SAS which applies the Actor-Critic (AC) algorithm to learn a dynamic self-attention distribution over the tokens to reduce redundancy and generate factual and coherent summaries to improve the quality of summarization. After performing hyperparameter tuning, we achievedbetter ROUGE results compared to the baseline. Our model tends to be more extractive/factual yet coherent in detail because of optimization over ROUGE rewards. We present detailed error analysis with examples of the strengths and limitations of our model. Our codebase will be publicly available on our GitHub.
Monte Carlo Techniques for Approximating the Myerson Value -- Theoretical and Empirical Analysis
Venue: arXiv cs.SI 2019
Year: 2019

Myerson first introduced graph-restricted games in order to model the interaction of cooperative players with an underlying communication network. A dedicated solution concept -- the Myerson value -- is perhaps the most important normative solution concept for cooperative games on graphs. Unfortunately, its computation is computationally challenging. In particular, although exact algorithms have been proposed, they must traverse all connected coalitions of the graph of which there may be exponentially many. In this paper, we consider the issue of approximating the Myerson value for arbitrary graphs and characteristic functions. While Monte Carlo approximations have been proposed for the related concept of the Shapley value, their suitability for the Myerson value has not been studied. Given this, we evaluate and compare (both theoretically and empiraclly) three Monte Carlo sampling methods for the Myerson value: conventional method of sampling permutations; a new, hybrid algorithm that combines exact computations and sampling; and sampling of connected coalitions. We find that our hybrid algorithm performs very well and also significantly improves on the conventional methods.
ATRIUM -- Architecting Under Uncertainty for ISO 26262 compliance
Venue: arXiv cs.CY 2019
Year: 2019

The ISO 26262 is currently the dominant standard for assuring functional safety of electrical and electronic systems in the automotive industry. The Functional Safety Concept (FSC) subphase in the standard requires the Preliminary Architectural Assumptions (PAA) for allocation of functional safety requirements (FSRs). This paper justifies the need for, and defines a process ATRIUM, for consistent design of the PAA. ATRIUM is subsequently applied in an industrial case study for a function enabling highly automated driving at one of the largest heavy vehicle manufacturers in Europe, Scania CV AB. The findings from this study, which contributed to ATRIUM's institutionalization at Scania, are presented. The benefits of the proposed process include (i) a fast and flexible way to refine the PAA, and a framework to (ii) incorporate information from legacy systems into safety design and (iii) rigorously track and document the assumptions and rationale behind architectural decisions under uncertain information. The contributions of this paper are the (i) analysis of the problem (ii) the process ATRIUM and (iii) findings and the discussion from the case study at Scania. Keywords: ISO 26262, functional safety, automation, HCV, HGV, architectures, highly automated driving, ATRIUM, decision making, architecting, uncertainty management
Schemes for Privacy Data Destruction in a NAND Flash Memory
Venue: arXiv cs.CR 2019
Year: 2019

We propose schemes for efficiently destroying privacy data in a NAND flash memory. Generally, even if privcy data is discarded from NAND flash memories, there is a high probability that the data will remain in an invalid block. This is a management problem that arises from the specificity of a program operation and an erase operation of NAND flash memories. When updating pages or performing a garbage collection, there is a problem that valid data remains in at least one unmapped memory block. Is it possible to apply the obligation to delete privacy data from existing NAND flash memory? This paper is the answer to this question. We propose a partial overwriting scheme, a SLC programming scheme, and a deletion duty pulse application scheme for invalid pages to effectively solve privacy data destruction issues due to the remaining data. Such privacy data destruction schemes basically utilize at least one state in which data can be written to the programmed cells based on a multi-level cell program operation. Our privacy data destruction schemes have advantages in terms of block management as compared with conventional erase schemes, and are very economical in terms of time and cost. The proposed privacy data destruction schemes can be easily applied to many storage devices and data centers using NAND flash memories.
BehavDT: A Behavioral Decision Tree Learning to Build User-Centric Context-Aware Predictive Model
Venue: arXiv cs.LG 2019
Year: 2019

This paper formulates the problem of building a context-aware predictive model based on user diverse behavioral activities with smartphones. In the area of machine learning and data science, a tree-like model as that of decision tree is considered as one of the most popular classification techniques, which can be used to build a data-driven predictive model. The traditional decision tree model typically creates a number of leaf nodes as decision nodes that represent context-specific rigid decisions, and consequently may cause overfitting problem in behavior modeling. However, in many practical scenarios within the context-aware environment, the generalized outcomes could play an important role to effectively capture user behavior. In this paper, we propose a behavioral decision tree, "BehavDT" context-aware model that takes into account user behavior-oriented generalization according to individual preference level. The BehavDT model outputs not only the generalized decisions but also the context-specific decisions in relevant exceptional cases. The effectiveness of our BehavDT model is studied by conducting experiments on individual user real smartphone datasets. Our experimental results show that the proposed BehavDT context-aware model is more effective when compared with the traditional machine learning approaches, in predicting user diverse behaviors considering multi-dimensional contexts.
Analytic Continued Fractions for Regression: A Memetic Algorithm Approach
Venue: arXiv cs.NE 2019
Year: 2019

We present an approach for regression problems that employs analytic continued fractions as a novel representation. Comparative computational results using a memetic algorithm are reported in this work. Our experiments included fifteen other different machine learning approaches including five genetic programming methods for symbolic regression and ten machine learning methods. The comparison on training and test generalization was performed using 94 datasets of the Penn State Machine Learning Benchmark. The statistical tests showed that the generalization results using analytic continued fractions provides a powerful and interesting new alternative in the quest for compact and interpretable mathematical models for artificial intelligence.
PI-GAN: Learning Pose Independent representations for multiple profile face synthesis
Venue: arXiv cs.CV 2019
Year: 2019

Generating a pose-invariant representation capable of synthesizing multiple face pose views from a single pose is still a difficult problem. The solution is demanded in various areas like multimedia security, computer vision, robotics, etc. Generative adversarial networks (GANs) have encoder-decoder structures possessing the capability to learn pose-independent representation incorporated with discriminator network for realistic face synthesis. We present PIGAN, a cyclic shared encoder-decoder framework, in an attempt to solve the problem. As compared to traditional GAN, it consists of secondary encoder-decoder framework sharing weights from the primary structure and reconstructs the face with the original pose. The primary framework focuses on creating disentangle representation, and secondary framework aims to restore the original face. We use CFP high-resolution, realistic dataset to check the performance.
Deep Learning-Based Intrusion Detection System for Advanced Metering Infrastructure
Venue: arXiv cs.CR 2019
Year: 2019

Smart grid is an alternative solution of the conventional power grid which harnesses the power of the information technology to save the energy and meet today's environment requirements. Due to the inherent vulnerabilities in the information technology, the smart grid is exposed to a wide variety of threats that could be translated into cyber-attacks. In this paper, we develop a deep learning-based intrusion detection system to defend against cyber-attacks in the advanced metering infrastructure network. The proposed machine learning approach is trained and tested extensively on an empirical industrial dataset which is composed of several attack categories including the scanning, buffer overflow, and denial of service attacks. Then, an experimental comparison in terms of detection accuracy is conducted to evaluate the performance of the proposed approach with Naive Bayes, Support Vector Machine, and Random Forest. The obtained results suggest that the proposed approaches produce optimal results comparing to the other algorithms. Finally, we propose a network architecture to deploy the proposed anomaly-based intrusion detection system across the Advanced Metering Infrastructure network. In addition, we propose a network security architecture composed of two types of Intrusion detection system types, Host and Network-based, deployed across the Advanced Metering Infrastructure network to inspect the traffic and detect the malicious one at all the levels.
A Promise Theoretic Account of the Boeing 737 Max MCAS Algorithm Affair
Venue: arXiv cs.OH 2019
Year: 2019

Many public controversies involve the assessment of statements about which we have imperfect information. Without a structured approach, it is quite difficult to develop an approach to reasoning which is not based on ad hoc choices. Forms of logic have been used in the past to try to bring such clarity, but these fail for a variety of reasons. We demonstrate a simple approach to bringing a standardized approach to semantics, in certain discourse, using Promise Theory. As a case, we use Promise Theory (PT) to collect and structure publicly available information about the case of the MCAS software component for the Boeing 737 Max flight control system.
Universal Hysteresis Identification Using Extended Preisach Neural Network
Venue: arXiv cs.NE 2019
Year: 2019

Hysteresis phenomena have been observed in different branches of physics and engineering sciences. Therefore, several models have been proposed for hysteresis simulation in different fields; however, almost neither of them can be utilized universally. In this paper by inspiring of Preisach Neural Network which was inspired by the Preisach model that basically stemmed from Madelungs rules and using the learning capability of the neural networks, an adaptive universal model for hysteresis is introduced and called Extended Preisach Neural Network Model. It is comprised of input, output and, two hidden layers. The input and output layers contain linear neurons while the first hidden layer incorporates neurons called Deteriorating Stop neurons, which their activation function follows Deteriorating Stop operator. Deteriorating Stop operators can generate non-congruent hysteresis loops. The second hidden layer includes Sigmoidal neurons. Adding the second hidden layer, helps the neural network learn non-Masing and asymmetric hysteresis loops very smoothly. At the input layer, besides input data the rate at which input data changes, is included as well in order to give the model the capability of learning rate-dependent hysteresis loops. Hence, the proposed approach has the capability of the simulation of both rate-independent and rate-dependent hysteresis with either congruent or non-congruent loops as well as symmetric and asymmetric loops. A new hybridized algorithm has been adopted for training the model which is based on a combination of the Genetic Algorithm and the optimization method of sub-gradient with space dilatation. The generality of the proposed model has been evaluated by applying it to various hysteresis from different areas of engineering with different characteristics. The results show that the model is successful in the identification of the considered hystereses.
Are skip connections necessary for biologically plausible learning rules?
Venue: arXiv cs.NE 2019
Year: 2019

Backpropagation is the workhorse of deep learning, however, several other biologically-motivated learning rules have been introduced, such as random feedback alignment and difference target propagation. None of these methods have produced a competitive performance against backpropagation. In this paper, we show that biologically-motivated learning rules with skip connections between intermediate layers can perform as well as backpropagation on the MNIST dataset and are robust to various sets of hyper-parameters.
Design of optical neural networks with component imprecisions
Venue: arXiv cs.NE 2019
Year: 2019

For the benefit of designing scalable, fault resistant optical neural networks (ONNs), we investigate the effects architectural designs have on the ONNs' robustness to imprecise components. We train two ONNs -- one with a more tunable design (GridNet) and one with better fault tolerance (FFTNet) -- to classify handwritten digits. When simulated without any imperfections, GridNet yields a better accuracy (~98%) than FFTNet (~95%). However, under a small amount of error in their photonic components, the more fault tolerant FFTNet overtakes GridNet. We further provide thorough quantitative and qualitative analyses of ONNs' sensitivity to varying levels and types of imprecisions. Our results offer guidelines for the principled design of fault-tolerant ONNs as well as a foundation for further research.
Evolution Strategies Converges to Finite Differences
Venue: arXiv cs.NE 2019
Year: 2019

Since the debut of Evolution Strategies (ES) as a tool for Reinforcement Learning by Salimans et al. 2017, there has been interest in determining the exact relationship between the Evolution Strategies gradient and the gradient of a similar class of algorithms, Finite Differences (FD).(Zhang et al. 2017, Lehman et al. 2018) Several investigations into the subject have been performed, investigating the formal motivational differences(Lehman et al. 2018) between ES and FD, as well as the differences in a standard benchmark problem in Machine Learning, the MNIST classification problem(Zhang et al. 2017). This paper proves that while the gradients are different, they converge as the dimension of the vector under optimization increases.
A Supervised Modified Hebbian Learning Method On Feed-forward Neural Networks
Venue: arXiv cs.NE 2019
Year: 2019

In this paper, we present a new supervised learning algorithm that is based on the Hebbian learning algorithm in an attempt to offer a substitute for back propagation along with the gradient descent for a more biologically plausible method. The best performance for the algorithm was achieved when it was run on a feed-forward neural network with the MNIST handwritten digits data set reaching an accuracy of 70.4% on the test data set and 71.48% on the validation data set.
QoS Provision for Controlling Energy Consumption in Ad-hoc Wireless Sensor Networks
Venue: arXiv cs.NI 2019
Year: 2019

Adhoc wireless sensor network is an architecture of connected nodes, each node has its main elements such as sensors, computation and communications capabilities. Adhoc WSNs restrained energy sources result in a shorter lifetime of the sensor network and inefficient topology. In this paper, a new approach for saving and energy controlling is introduced using quality of service, the main reason is to reduce the nodes energy through discovering the best optimum route that meets quality of service requirements; quality of service technique is used to find the optimum methodology for nodes packets transmission and energy consumption.
Short-Term Temporal Convolutional Networks for Dynamic Hand Gesture Recognition
Venue: arXiv cs.CV 2019
Year: 2019

The purpose of gesture recognition is to recognize meaningful movements of human bodies, and gesture recognition is an important issue in computer vision. In this paper, we present a multimodal gesture recognition method based on 3D densely convolutional networks (3D-DenseNets) and improved temporal convolutional networks (TCNs). The key idea of our approach is to find a compact and effective representation of spatial and temporal features, which orderly and separately divide task of gesture video analysis into two parts: spatial analysis and temporal analysis. In spatial analysis, we adopt 3D-DenseNets to learn short-term spatio-temporal features effectively. Subsequently, in temporal analysis, we use TCNs to extract temporal features and employ improved Squeeze-and-Excitation Networks (SENets) to strengthen the representational power of temporal features from each TCNs' layers. The method has been evaluated on the VIVA and the NVIDIA Gesture Dynamic Hand Gesture Datasets. Our approach obtains very competitive performance on VIVA benchmarks with the classification accuracies of 91.54%, and achieve state-of-the art performance with 86.37% accuracy on NVIDIA benchmark.
Wi2Vi: Generating Video Frames from WiFi CSI Samples
Venue: arXiv cs.CV 2019
Year: 2019

Objects in an environment affect electromagnetic waves. While this effect varies across frequencies, there exists a correlation between them, and a model with enough capacity can capture this correlation between the measurements in different frequencies. In this paper, we propose the Wi2Vi model for associating variations in the WiFi channel state information with video frames. The proposed Wi2Vi system can generate video frames entirely using CSI measurements. The produced video frames by the Wi2Vi provide auxiliary information to the conventional surveillance system in critical circumstances. Our implementation of the Wi2Vi system confirms the feasibility of constructing a system capable of deriving the correlations between measurements in different frequency spectrums.
Adversarial Example Generation using Evolutionary Multi-objective Optimization
Venue: arXiv cs.CV 2019
Year: 2019

This paper proposes Evolutionary Multi-objective Optimization (EMO)-based Adversarial Example (AE) design method that performs under black-box setting. Previous gradient-based methods produce AEs by changing all pixels of a target image, while previous EC-based method changes small number of pixels to produce AEs. Thanks to EMO's property of population based-search, the proposed method produces various types of AEs involving ones locating between AEs generated by the previous two approaches, which helps to know the characteristics of a target model or to know unknown attack patterns. Experimental results showed the potential of the proposed method, e.g., it can generate robust AEs and, with the aid of DCT-based perturbation pattern generation, AEs for high resolution images.
Translating multispectral imagery to nighttime imagery via conditional generative adversarial networks
Venue: arXiv cs.CV 2019
Year: 2019

Nighttime satellite imagery has been applied in a wide range of fields. However, our limited understanding of how observed light intensity is formed and whether it can be simulated greatly hinders its further application. This study explores the potential of conditional Generative Adversarial Networks (cGAN) in translating multispectral imagery to nighttime imagery. A popular cGAN framework, pix2pix, was adopted and modified to facilitate this translation using gridded training image pairs derived from Landsat 8 and Visible Infrared Imaging Radiometer Suite (VIIRS). The results of this study prove the possibility of multispectral-to-nighttime translation and further indicate that, with the additional social media data, the generated nighttime imagery can be very similar to the ground-truth imagery. This study fills the gap in understanding the composition of satellite observed nighttime light and provides new paradigms to solve the emerging problems in nighttime remote sensing fields, including nighttime series construction, light desaturation, and multi-sensor calibration.