OpenRaiser/Intern-Atlas huggingface dataset:
Integrating DRAM Power-Down Modes in gem5 and Quantifying their Impact
Venue: arXiv cs.AR 2018
Year: 2018

Across applications, DRAM is a significant contributor to the overall system power, with the DRAM access energy per bit up to three orders of magnitude higher compared to on-chip memory accesses. To improve the power efficiency, DRAM technology incorporates multiple power-down modes, each with different trade-offs between achievable power savings and performance impact due to entry and exit delay requirements. Accurate modeling of these low power modes and entry and exit control is crucial to analyze the trade-offs across controller configurations and workloads with varied memory access characteristics. To address this, we integrate the power-down modes into the DRAM controller model in the open-source simulator gem5. This is the first publicly available full-system simulator with DRAM power-down modes, providing the research community a tool for DRAM power analysis for a breadth of use cases. We validate the power-down functionality with sweep tests, which trigger defined memory access characteristics. We further evaluate the model with real HPC workloads, illustrating the value of integrating low power functionality into a full system simulator.
An Online Algorithm for Power-proportional Data Centers with Switching Cost
Venue: arXiv cs.NI 2018
Year: 2018

Recent studies have shown that power-proportional data centers can save energy cost by dynamically "right-sizing" the data centers based on real-time workload. More servers are activated when the workload increases while some servers can be put into the sleep mode during periods of low load. In this paper, we revisit the dynamic right-sizing problem for heterogeneous data centers with various operational cost and switching cost. We propose a new online algorithm based on a regularization technique, which achieves a better competitive ratio compared to the state-of-the-art greedy algorithm. We further introduce a switching cost offset into the model and extend our algorithm to this new setting. Simulations based on real workload and renewable energy traces show that our algorithms outperform the greedy algorithm in both settings.
Balanced Black and White Coloring Problem on knights chessboards
Venue: arXiv cs.DM 2018
Year: 2018

Graph anticoloring problem is partial coloring problem where the main feature is the opposite rule of the graph coloring problem, i.e., if two vertices are adjacent, their assigned colors must be the same or at least one of them is uncolored. In the same way, Berge in 1972 proposed the problem of placing b black queens and w white queens on a $n \times n$ chessboard such that no two queens of different color can attack to each other, the complexity of this problem remains open. In this work we deal with the knight piece under the balance property, since this special case is the most difficult for brute force algorithms.
Exploiting deep residual networks for human action recognition from skeletal data
Venue: arXiv cs.CV 2018
Year: 2018

The computer vision community is currently focusing on solving action recognition problems in real videos, which contain thousands of samples with many challenges. In this process, Deep Convolutional Neural Networks (D-CNNs) have played a significant role in advancing the state-of-the-art in various vision-based action recognition systems. Recently, the introduction of residual connections in conjunction with a more traditional CNN model in a single architecture called Residual Network (ResNet) has shown impressive performance and great potential for image recognition tasks. In this paper, we investigate and apply deep ResNets for human action recognition using skeletal data provided by depth sensors. Firstly, the 3D coordinates of the human body joints carried in skeleton sequences are transformed into image-based representations and stored as RGB images. These color images are able to capture the spatial-temporal evolutions of 3D motions from skeleton sequences and can be efficiently learned by D-CNNs. We then propose a novel deep learning architecture based on ResNets to learn features from obtained color-based representations and classify them into action classes. The proposed method is evaluated on three challenging benchmark datasets including MSR Action 3D, KARD, and NTU-RGB+D datasets. Experimental results demonstrate that our method achieves state-of-the-art performance for all these benchmarks whilst requiring less computation resource. In particular, the proposed method surpasses previous approaches by a significant margin of 3.4% on MSR Action 3D dataset, 0.67% on KARD dataset, and 2.5% on NTU-RGB+D dataset.
Domain Adaptation for Ear Recognition Using Deep Convolutional Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we have extensively investigated the unconstrained ear recognition problem. We have first shown the importance of domain adaptation, when deep convolutional neural network models are used for ear recognition. To enable domain adaptation, we have collected a new ear dataset using the Multi-PIE face dataset, which we named as Multi-PIE ear dataset. To improve the performance further, we have combined different deep convolutional neural network models. We have analyzed in depth the effect of ear image quality, for example illumination and aspect ratio, on the classification performance. Finally, we have addressed the problem of dataset bias in the ear recognition field. Experiments on the UERC dataset have shown that domain adaptation leads to a significant performance improvement. For example, when VGG-16 model is used and the domain adaptation is applied, an absolute increase of around 10\% has been achieved. Combining different deep convolutional neural network models has further improved the accuracy by 4\%. It has also been observed that image quality has an influence on the results. In the experiments that we have conducted to examine the dataset bias, given an ear image, we were able to classify the dataset that it has come from with 99.71\% accuracy, which indicates a strong bias among the ear recognition datasets.
Introducing higher order correlations to marginals' subset of multivariate data by means of Archimedean copulas
Venue: arXiv cs.DS 2018
Year: 2018

In this paper, we present the algorithm that alters the subset of marginals of multivariate standard distributed data into such modelled by an Archimedean copula. Proposed algorithm leaves a correlation matrix almost unchanged, but introduces a higher order correlation into a subset of marginals. Our data transformation algorithm can be used to analyse whether particular machine learning algorithm, especially a dimensionality reduction one, utilises higher order correlations or not. We present an exemplary application on two features selection algorithms, mention that features selection is one of the approaches to dimensionality reduction. To measure higher order correlation, we use multivariate higher order cumulants, hence to utilises higher order correlations be to use the Joint Skewness Band Selection (JSBS) algorithm that uses third-order multivariate cumulant. We show the robust performance of the JSBS in contrary to the poor performance of the Maximum Ellipsoid Volume (MEV) algorithm that does not utilise such higher order correlations. With this result, we confirm the potential application of our data generation algorithm to analyse a performance of various dimensionality reduction algorithms.
Patch-based Fake Fingerprint Detection Using a Fully Convolutional Neural Network with a Small Number of Parameters and an Optimal Threshold
Venue: arXiv cs.CV 2018
Year: 2018

Fingerprint authentication is widely used in biometrics due to its simple process, but it is vulnerable to fake fingerprints. This study proposes a patch-based fake fingerprint detection method using a fully convolutional neural network with a small number of parameters and an optimal threshold to solve the above-mentioned problem. Unlike the existing methods that classify a fingerprint as live or fake, the proposed method classifies fingerprints as fake, live, or background, so preprocessing methods such as segmentation are not needed. The proposed convolutional neural network (CNN) structure applies the Fire module of SqueezeNet, and the fewer parameters used require only 2.0 MB of memory. The network that has completed training is applied to the training data in a fully convolutional way, and the optimal threshold to distinguish fake fingerprints is determined, which is used in the final test. As a result of this study experiment, the proposed method showed an average classification error of 1.35%, demonstrating a fake fingerprint detection method using a high-performance CNN with a small number of parameters.
End-to-End Fingerprints Liveness Detection using Convolutional Networks with Gram module
Venue: arXiv cs.CV 2018
Year: 2018

This paper proposes an end-to-end CNN(Convolutional Neural Networks) model that uses gram modules with parameters that are approximately 1.2MB in size to detect fake fingerprints. The proposed method assumes that texture is the most appropriate characteristic in fake fingerprint detection, and implements the gram module to extract textures from the CNN. The proposed CNN structure uses the fire module as the base model and uses the gram module for texture extraction. Tensors that passed the fire module will be joined with gram modules to create a gram matrix with the same spatial size. After 3 gram matrices extracted from different layers are combined with the channel axis, it becomes the basis for categorizing fake fingerprints. The experiment results had an average detection error of 2.61% from the LivDet 2011, 2013, 2015 data, proving that an end-to-end CNN structure with few parameters that is able to be used in fake fingerprint detection can be designed.
Modelling the Influence of Cultural Information on Vision-Based Human Home Activity Recognition
Venue: arXiv cs.CV 2018
Year: 2018

Daily life activities, such as eating and sleeping, are deeply influenced by a person's culture, hence generating differences in the way a same activity is performed by individuals belonging to different cultures. We argue that taking cultural information into account can improve the performance of systems for the automated recognition of human activities. We propose four different solutions to the problem and present a system which uses a Naive Bayes model to associate cultural information with semantic information extracted from still images. Preliminary experiments with a dataset of images of individuals lying on the floor, sleeping on a futon and sleeping on a bed suggest that: i) solutions explicitly taking cultural information into account are more accurate than culture-unaware solutions; and ii) the proposed system is a promising starting point for the development of culture-aware Human Activity Recognition methods.
Tracking Control of Marine Craft in the port-Hamiltonian Framework: A Virtual Differential Passivity Approach
Venue: arXiv cs.SY 2018
Year: 2018

In this work we propose a family of trajectory tracking controllers for marine craft in the port-Hamiltonian (pH) framework using virtual differential passivity based control (v-dPBC). Two pH models of marine craft are considered, one in a body frame and another in an inertial frame. The structure and workless forces of pH models are exploited to design two virtual control systems which are related to the original marine craft's pH models. These virtual systems are rendered differentially passive with an imposed steady-state trajectory, both by means of a control scheme. Finally, the original marine craft pH models in closed-loop with above controllers solve the trajectory tracking problem. The performance of the closedloop system is evaluated on numerical simulations.
Age of Information in a Network of Preemptive Servers
Venue: arXiv cs.IT 2018
Year: 2018

A source submits status updates to a network for delivery to a destination monitor. Updates follow a route through a series of network nodes. Each node is a last-come-first-served queue supporting preemption in service. We characterize the average age of information at the input and output of each node in the route induced by the updates passing through. For Poisson arrivals to a line network of preemptive memoryless servers, we show that average age accumulates through successive network nodes.
Adversarial Defense based on Structure-to-Signal Autoencoders
Venue: arXiv cs.LG 2018
Year: 2018

Adversarial attack methods have demonstrated the fragility of deep neural networks. Their imperceptible perturbations are frequently able fool classifiers into potentially dangerous misclassifications. We propose a novel way to interpret adversarial perturbations in terms of the effective input signal that classifiers actually use. Based on this, we apply specially trained autoencoders, referred to as S2SNets, as defense mechanism. They follow a two-stage training scheme: first unsupervised, followed by a fine-tuning of the decoder, using gradients from an existing classifier. S2SNets induce a shift in the distribution of gradients propagated through them, stripping them from class-dependent signal. We analyze their robustness against several white-box and gray-box scenarios on the large ImageNet dataset. Our approach reaches comparable resilience in white-box attack scenarios as other state-of-the-art defenses in gray-box scenarios. We further analyze the relationships of AlexNet, VGG 16, ResNet 50 and Inception v3 in adversarial space, and found that VGG 16 is the easiest to fool, while perturbations from ResNet 50 are the most transferable.
Social Media Would Not Lie: Prediction of the 2016 Taiwan Election via Online Heterogeneous Data
Venue: arXiv cs.SI 2018
Year: 2018

The prevalence of online media has attracted researchers from various domains to explore human behavior and make interesting predictions. In this research, we leverage heterogeneous social media data collected from various online platforms to predict Taiwan's 2016 presidential election. In contrast to most existing research, we take a "signal" view of heterogeneous information and adopt the Kalman filter to fuse multiple signals into daily vote predictions for the candidates. We also consider events that influenced the election in a quantitative manner based on the so-called event study model that originated in the field of financial research. We obtained the following interesting findings. First, public opinions in online media dominate traditional polls in Taiwan election prediction in terms of both predictive power and timeliness. But offline polls can still function on alleviating the sample bias of online opinions. Second, although online signals converge as election day approaches, the simple Facebook "Like" is consistently the strongest indicator of the election result. Third, most influential events have a strong connection to cross-strait relations, and the Chou Tzu-yu flag incident followed by the apology video one day before the election increased the vote share of Tsai Ing-Wen by 3.66%. This research justifies the predictive power of online media in politics and the advantages of information fusion. The combined use of the Kalman filter and the event study method contributes to the data-driven political analytics paradigm for both prediction and attribution purposes.
How Do Practitioners Perceive Assurance Cases in Safety-Critical Software Systems?
Venue: arXiv cs.SE 2018
Year: 2018

Safety-critical software systems are those whose failure or malfunction could result in casualty and/or serious financial loss. In such systems, safety assurance cases (SACs) are an emerging approach that adopts a proactive strategy to produce structuralized safety justifications and arguments. While SACs are recommended in many software-intensive safety-critical domains, the lack of knowledge regarding the practitioners' perspectives on using SACs hinders effective adoption of this approach. To gain such knowledge, we interviewed nine practitioners and safety experts who focused on safety-critical software systems. In general, our participants found the SAC approach beneficial for communication of safety arguments and management of safety issues in a multidisciplinary setting. The challenges they faced when using SACs were primarily associated with (1) a lack of tool support, (2) insufficient process integration, and (3) scarcity of experienced personnel. To overcome those challenges, our participants suggested tactics that focused on creating direct safety arguments. Process and organizational adjustments are also needed to streamline SAC analysis and creation. Finally, our participants emphasized the importance of knowledge sharing about SACs across software-intensive safety-critical domains.
Can Decentralized Status Update Achieve Universally Near-Optimal Age-of-Information in Wireless Multiaccess Channels?
Venue: arXiv cs.IT 2018
Year: 2018

In an Internet-of-Things system where status data are collected from sensors and actuators for time-critical applications, the freshness of data is vital and can be quantified by the recently proposed age-of-information (AoI) metric. In this paper, we first consider a general scenario where multiple terminals share a common channel to transmit or receive randomly generated status packets. The optimal scheduling problem to minimize AoI is formulated as a restless multi-armed bandit problem. To solve the problem efficiently, we derive the Whittle's index in closed-form and establish the indexability thereof. Compared with existing work, we extend the index policy for AoI optimization to incorporate stochastic packet arrivals and optimal packet management (buffering the latest packet). Inspired by the index policy which has near-optimal performance but is centralized by nature, a decentralized status update scheme, i.e., the index-prioritized random access policy (IPRA), is further proposed, achieving universally near-optimal AoI performance and outperforming state-of-the-arts in the literature.
SCISPACE: A Scientific Collaboration Workspace for File Systems in Geo-Distributed HPC Data Centers
Venue: arXiv cs.DC 2018
Year: 2018

Future terabit networks are committed to dramatically improving big data motion between geographically dispersed HPC data centers.The scientific community takes advantage of the terabit networks such as DOE's ESnet and accelerates the trend to build a small world of collaboration between geospatial HPC data centers. It improves information and resource sharing for joint simulation and analysis between the HPC data centers. In this paper, we propose to build SCISPACE (Scientific Collaboration Workspace) for collaborative data centers. It provides a global view of information shared from multiple geo-distributed HPC data centers under a single workspace. SCISPACE supports native data-access to gain high-performance when data read or write is required in native data center namespace. It is accomplished by integrating a metadata export protocol. To optimize scientific collaborations across HPC data centers, SCISPACE implements search and discovery service. To evaluate, we configured two geo-distributed small-scale HPC data centers connected via high-speed Infiniband network, equipped with LustreFS. We show the feasibility of SCISPACE using real scientific datasets and applications. The evaluation results show average 36\% performance boost when the proposed native-data access is employed in collaborations.
Head-up Displays (HUD) in driving
Venue: arXiv cs.HC 2018
Year: 2018

Head Up Displays (HUDs) were designed originally to present at the usual viewpoints of the pilot the main sensor data during aircraft missions, because of placing instrument information in the forward field of view enhances pilots ability to utilize both instrument and environmental information simultaneously. The first civilian motor vehicle had a monochrome HUD that was released in 1988 by General Motors as a technological improvement of HeadDown Display (HDD) interface, which is commonly used in automobile industry. The HUD reduces the number and duration of the drivers sight deviations from the road, by projecting the required information directly into the drivers line of vision. There are many studies about ways of presenting the information: standard oneearpiece presentation, threedimensional audio presentation, visual only or audiovisual presentation. Results have shown that using a 3D auditory display the time of acquiring targets is approximately 2.2 seconds faster than using a oneearpiece way. Nevertheless, a disadvantage is when the drivers attention unconsciously shifts away from the road and goes focused on processing the information presented by the HUD. By this reason, the time, the way and the channel are important to represent the information on a HUD. A solution is a context aware multimodal proactive recommended system that features personalized content combined with the use of car sensors to determine when the information has to be presented.
Buried object detection from B-scan ground penetrating radar data using Faster-RCNN
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we adapt the Faster-RCNN framework for the detection of underground buried objects (i.e. hyperbola reflections) in B-scan ground penetrating radar (GPR) images. Due to the lack of real data for training, we propose to incorporate more simulated radargrams generated from different configurations using the gprMax toolbox. Our designed CNN is first pre-trained on the grayscale Cifar-10 database. Then, the Faster-RCNN framework based on the pre-trained CNN is trained and fine-tuned on both real and simulated GPR data. Preliminary detection results show that the proposed technique can provide significant improvements compared to classical computer vision methods and hence becomes quite promising to deal with this kind of specific GPR data even with few training samples.
An asymptotically optimal indirect approach to continuous-time system identification
Venue: arXiv cs.SY 2018
Year: 2018

The indirect approach to continuous-time system identification consists in estimating continuous-time models by first determining an appropriate discrete-time model. For a zero-order hold sampling mechanism, this approach usually leads to a transfer function estimate with relative degree 1, independent of the relative degree of the strictly proper real system. In this paper, a refinement of these methods is developed. Inspired by indirect PEM, we propose a method that enforces a fixed relative degree in the continuous-time transfer function estimate, and show that the resulting estimator is consistent and asymptotically efficient. Extensive numerical simulations are put forward to show the performance of this estimator when contrasted with other indirect and direct methods for continuous-time system identification.
Deep Reinforcement Learning with Model Learning and Monte Carlo Tree Search in Minecraft
Venue: arXiv cs.AI 2018
Year: 2018

Deep reinforcement learning has been successfully applied to several visual-input tasks using model-free methods. In this paper, we propose a model-based approach that combines learning a DNN-based transition model with Monte Carlo tree search to solve a block-placing task in Minecraft. Our learned transition model predicts the next frame and the rewards one step ahead given the last four frames of the agent's first-person-view image and the current action. Then a Monte Carlo tree search algorithm uses this model to plan the best sequence of actions for the agent to perform. On the proposed task in Minecraft, our model-based approach reaches the performance comparable to the Deep Q-Network's, but learns faster and, thus, is more training sample efficient.