OpenRaiser/Intern-Atlas huggingface dataset:
A Survey of Electromagnetic Side-Channel Attacks and Discussion on their Case-Progressing Potential for Digital Forensics
Venue: arXiv cs.CR 2019
Year: 2019

The increasing prevalence of Internet of Things (IoT) devices has made it inevitable that their pertinence to digital forensic investigations will increase into the foreseeable future. These devices produced by various vendors often posses limited standard interfaces for communication, such as USB ports or WiFi/Bluetooth wireless interfaces. Meanwhile, with an increasing mainstream focus on the security and privacy of user data, built-in encryption is becoming commonplace in consumer-level computing devices, and IoT devices are no exception. Under these circumstances, a significant challenge is presented to digital forensic investigations where data from IoT devices needs to be analysed. This work explores the electromagnetic (EM) side-channel analysis literature for the purpose of assisting digital forensic investigations on IoT devices. EM side-channel analysis is a technique where unintentional electromagnetic emissions are used for eavesdropping on the operations and data handling of computing devices. The non-intrusive nature of EM side-channel approaches makes it a viable option to assist digital forensic investigations as these attacks require, and must result in, no modification to the target device. The literature on various EM side-channel analysis attack techniques are discussed - selected on the basis of their applicability in IoT device investigation scenarios. The insight gained from the background study is used to identify promising future applications of the technique for digital forensic analysis on IoT devices - potentially progressing a wide variety of currently hindered digital investigations.
Between Promise and Performance: Science and Technology Policy Implementation through Network Governance
Venue: arXiv cs.CY 2019
Year: 2019

This research analyzes the effects of U.S. science and technology policy on the technological performance of organizations in a global strategic alliance network. During the mid-1980s the U.S. semiconductor industry appeared to be collapsing. Industry leaders and policymakers moved to support and protect U.S. firms by creating a program called Sematech. While many scholars regard Sematech as a success, how the program succeeded remains unclear. This study re-contextualizes Sematech as a network administrative organization which lowered cooperation costs and enhanced resource combination for innovation at the cutting edge. This study combines network analysis and longitudinal regression techniques to test the effects of public policy on organizational network position and technological performance in an unbalanced panel of semiconductor firms between 1986 and 2001. This research suggests governments might achieve policy through inter-organizational innovations aimed at the development and administration of robust governance networks.
Predicting Stochastic Human Forward Reachable Sets Based on Learned Human Behavior
Venue: arXiv cs.SY 2019
Year: 2019

With the recent surge of interest in introducing autonomous vehicles to the everyday lives of people, developing accurate and generalizable algorithms for predicting human behavior becomes highly crucial. Moreover, many of these emerging applications occur in a safety-critical context, making it even more urgent to develop good prediction models for human-operated vehicles. This is fundamentally a challenging task as humans are often noisy in their decision processes. Hamilton-Jacobi (HJ) reachability is a useful tool in control theory that provides safety guarantees for collision avoidance. In this paper, we first demonstrate how to incorporate information derived from HJ reachability into a machine learning problem which predicts human behavior in a simulated collision avoidance context, and show that this yields a higher prediction accuracy than learning without this information. Then we propose a framework to generate stochastic forward reachable sets that flexibly provides different safety probabilities and generalizes to novel scenarios. We demonstrate that we can construct stochastic reachable sets that can capture the trajectories with probability from 0.75 to 1.
Distributed Subtrajectory Join on Massive Datasets
Venue: arXiv cs.DB 2019
Year: 2019

Joining trajectory datasets is a significant operation in mobility data analytics and the cornerstone of various methods that aim to extract knowledge out of them. In the era of Big Data, the production of mobility data has become massive and, consequently, performing such an operation in a centralized way is not feasible. In this paper, we address the problem of Distributed Subtrajectory Join processing by utilizing the MapReduce programming model. Compared to traditional trajectory join queries, this problem is even more challenging since the goal is to retrieve all the "maximal" portions of trajectories that are "similar". We propose three solutions: (i) a well-designed basic solution, coined DTJb, (ii) a solution that uses a preprocessing step that repartitions the data, labeled DTJr, and (iii) a solution that, additionally, employs an indexing scheme, named DTJi. In our experimental study, we utilize a 56GB dataset of real trajectories from the maritime domain, which, to the best of our knowledge, is the largest real dataset used for experimentation in the literature of trajectory data management. The results show that DTJi performs up to 16x faster compared with DTJb, 10x faster than DTJr and 3x faster than the closest related state of the art algorithm.
Distance Distribution Between Two Random Nodes in Arbitrary Polygons
Venue: arXiv cs.IT 2019
Year: 2019

Distance distributions are a key building block in stochastic geometry modelling of wireless networks and in many other fields in mathematics and science. In this paper, we propose a novel framework for analytically computing the closed form probability density function (PDF) of the distance between two random nodes each uniformly randomly distributed in respective arbitrary (convex or concave) polygon regions (which may be disjoint or overlap or coincide). The proposed framework is based on measure theory and uses polar decomposition for simplifying and calculating the integrals to obtain closed form results. We validate our proposed framework by comparison with simulations and published closed form results in the literature for simple cases. We illustrate the versatility and advantage of the proposed framework by deriving closed form results for a case not yet reported in the literature. Finally, we also develop a Mathematica implementation of the proposed framework which allows a user to define any two arbitrary polygons and conveniently determine the distance distribution numerically.
Deep Reinforcement Learning with Decorrelation
Venue: arXiv cs.LG 2019
Year: 2019

Learning an effective representation for high-dimensional data is a challenging problem in reinforcement learning (RL). Deep reinforcement learning (DRL) such as Deep Q networks (DQN) achieves remarkable success in computer games by learning deeply encoded representation from convolution networks. In this paper, we propose a simple yet very effective method for representation learning with DRL algorithms. Our key insight is that features learned by DRL algorithms are highly correlated, which interferes with learning. By adding a regularized loss that penalizes correlation in latent features (with only slight computation), we decorrelate features represented by deep neural networks incrementally. On 49 Atari games, with the same regularization factor, our decorrelation algorithms perform $70\%$ in terms of human-normalized scores, which is $40\%$ better than DQN. In particular, ours performs better than DQN on 39 games with 4 close ties and lost only slightly on $6$ games. Empirical results also show that the decorrelation method applies to Quantile Regression DQN (QR-DQN) and significantly boosts performance. Further experiments on the losing games show that our decorelation algorithms can win over DQN and QR-DQN with a fined tuned regularization factor.
Energy-Aware Digital Signatures for Embedded Medical Devices
Venue: arXiv cs.CR 2019
Year: 2019

Authentication is vital for the Internet of Things (IoT) applications involving sensitive data (e.g., medical and financial systems). Digital signatures offer scalable authentication with non-repudiation and public verifiability, which are necessary for auditing and dispute resolution in such IoT applications. However, digital signatures have been shown to be highly costly for low-end IoT devices, especially when embedded devices (e.g., medical implants) must operate without a battery replacement for a long time. We propose an Energy-aware Signature for Embedded Medical devices (ESEM) that achieves near-optimal signer efficiency. ESEM signature generation does not require any costly operations (e.g., elliptic curve (EC) scalar multiplication/addition), but only a small constant-number of pseudo-random function calls, additions, and a single modular multiplication. ESEM has the smallest signature size among its EC-based counterparts with an identical private key size. We achieve this by eliminating the use of the ephemeral public key (i.e, commitment) in Schnorr-type signatures from the signing via a distributed construction at the verifier without interaction with the signer while permitting a constant-size public key. We proved that ESEM is secure (in random oracle model), and fully implemented it on an 8-bit AVR microcontroller that is commonly used in medical devices. Our experiments showed that ESEM achieves 8.4x higher energy efficiency over its closest counterpart while offering a smaller signature and code size. Hence, ESEM can be suitable for deployment on resource limited embedded devices in IoT. We open-sourced our software for public testing and wide-adoption.
Performance Analysis of Urban Mmwave Multi-hop V2V Communications with Shifted-Exponential Distribution Headway
Venue: arXiv cs.NI 2019
Year: 2019

With the emergence of autonomous driving and 5G mobile communication systems, how to design vehicular networks to meet the requirements of ultra-reliable low-latency communication (URLLC) between autonomous vehicles is widely concerned by the academia and industry. In this paper, message transmission delay and reliability between autonomous vehicles based on millimeter wave (mmwave) multi-hop V2V communications under urban road environment are investigated. Considering the fact that vehicles cannot come arbitrarily close to each other, shifted-exponential distribution is proposed to model the headway distance (the distance between the head of a vehicle and the head of its follower). Simulation results show that message transmission delay and reliability decrease with the increase of the path loss exponent and are also affected by the minimum safe distance between adjacent vehicles.
Robust Visual Tracking Using Dynamic Classifier Selection with Sparse Representation of Label Noise
Venue: arXiv cs.CV 2019
Year: 2019

Recently a category of tracking methods based on "tracking-by-detection" is widely used in visual tracking problem. Most of these methods update the classifier online using the samples generated by the tracker to handle the appearance changes. However, the self-updating scheme makes these methods suffer from drifting problem because of the incorrect labels of weak classifiers in training samples. In this paper, we split the class labels into true labels and noise labels and model them by sparse representation. A novel dynamic classifier selection method, robust to noisy training data, is proposed. Moreover, we apply the proposed classifier selection algorithm to visual tracking by integrating a part based online boosting framework. We have evaluated our proposed method on 12 challenging sequences involving severe occlusions, significant illumination changes and large pose variations. Both the qualitative and quantitative evaluations demonstrate that our approach tracks objects accurately and robustly and outperforms state-of-the-art trackers.
Self-Weighted Multiview Metric Learning by Maximizing the Cross Correlations
Venue: arXiv cs.CV 2019
Year: 2019

With the development of multimedia time, one sample can always be described from multiple views which contain compatible and complementary information. Most algorithms cannot take information from multiple views into considerations and fail to achieve desirable performance in most situations. For many applications, such as image retrieval, face recognition, etc., an appropriate distance metric can better reflect the similarities between various samples. Therefore, how to construct a good distance metric learning methods which can deal with multiview data has been an important topic during the last decade. In this paper, we proposed a novel algorithm named Self-weighted Multiview Metric Learning (SM2L) which can finish this task by maximizing the cross correlations between different views. Furthermore, because multiple views have different contributions to the learning procedure of SM2L, we adopt a self-weighted learning framework to assign multiple views with different weights. Various experiments on benchmark datasets can verify the performance of our proposed method.
A semi-supervised deep learning algorithm for abnormal EEG identification
Venue: arXiv cs.LG 2019
Year: 2019

Systems that can automatically analyze EEG signals can aid neurologists by reducing heavy workload and delays. However, such systems need to be first trained using a labeled dataset. While large corpuses of EEG data exist, a fraction of them are labeled. Hand-labeling data increases workload for the very neurologists we try to aid. This paper proposes a semi-supervised learning workflow that can not only extract meaningful information from large unlabeled EEG datasets but also make predictions with minimal supervision, using labeled datasets as small as 5 examples.
Non-negative representation based discriminative dictionary learning for face recognition
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we propose a non-negative representation based discriminative dictionary learning algorithm (NRDL) for multicategory face classification. In contrast to traditional dictionary learning methods, NRDL investigates the use of non-negative representation (NR), which contributes to learning discriminative dictionary atoms. In order to make the learned dictionary more suitable for classification, NRDL seamlessly incorporates nonnegative representation constraint, discriminative dictionary learning and linear classifier training into a unified model. Specifically, NRDL introduces a positive constraint on representation matrix to find distinct atoms from heterogeneous training samples, which results in sparse and discriminative representation. Moreover, a discriminative dictionary encouraging function is proposed to enhance the uniqueness of class-specific sub-dictionaries. Meanwhile, an inter-class incoherence constraint and a compact graph based regularization term are constructed to respectively improve the discriminability of learned classifier. Experimental results on several benchmark face data sets verify the advantages of our NRDL algorithm over the state-of-the-art dictionary learning methods.
Turing-Completeness of Dynamics in Abstract Persuasion Argumentation
Venue: arXiv cs.LO 2019
Year: 2019

Abstract Persuasion Argumentation (APA) is a dynamic argumentation formalism that extends Dung argumentation with persuasion relations. In this work, we show through two-counter Minsky machine encoding that APA dynamics is Turing-complete.
The Probabilistic Object Detection Challenge
Venue: arXiv cs.RO 2019
Year: 2019

We introduce a new challenge for computer and robotic vision, the first ACRV Robotic Vision Challenge, Probabilistic Object Detection. Probabilistic object detection is a new variation on traditional object detection tasks, requiring estimates of spatial and semantic uncertainty. We extend the traditional bounding box format of object detection to express spatial uncertainty using gaussian distributions for the box corners. The challenge introduces a new test dataset of video sequences, which are designed to more closely resemble the kind of data available to a robotic system. We evaluate probabilistic detections using a new probability-based detection quality (PDQ) measure. The goal in creating this challenge is to draw the computer and robotic vision communities together, toward applying object detection solutions for practical robotics applications.
Hindsight Generative Adversarial Imitation Learning
Venue: arXiv cs.LG 2019
Year: 2019

Compared to reinforcement learning, imitation learning (IL) is a powerful paradigm for training agents to learn control policies efficiently from expert demonstrations. However, in most cases, obtaining demonstration data is costly and laborious, which poses a significant challenge in some scenarios. A promising alternative is to train agent learning skills via imitation learning without expert demonstrations, which, to some extent, would extremely expand imitation learning areas. To achieve such expectation, in this paper, we propose Hindsight Generative Adversarial Imitation Learning (HGAIL) algorithm, with the aim of achieving imitation learning satisfying no need of demonstrations. Combining hindsight idea with the generative adversarial imitation learning (GAIL) framework, we realize implementing imitation learning successfully in cases of expert demonstration data are not available. Experiments show that the proposed method can train policies showing comparable performance to current imitation learning methods. Further more, HGAIL essentially endows curriculum learning mechanism which is critical for learning policies.
Geometry-constrained Car Recognition Using a 3D Perspective Network
Venue: arXiv cs.CV 2019
Year: 2019

We present a novel learning framework for vehicle recognition from a single RGB image. Unlike existing methods which only use attention mechanisms to locate 2D discriminative information, our work learns a novel 3D perspective feature representation of a vehicle, which is then fused with 2D appearance feature to predict the category. The framework is composed of a global network (GN), a 3D perspective network (3DPN), and a fusion network. The GN is used to locate the region of interest (RoI) and generate the 2D global feature. With the assistance of the RoI, the 3DPN estimates the 3D bounding box under the guidance of the proposed vanishing point loss, which provides a perspective geometry constraint. Then the proposed 3D representation is generated by eliminating the viewpoint variance of the 3D bounding box using perspective transformation. Finally, the 3D and 2D feature are fused to predict the category of the vehicle. We present qualitative and quantitative results on the vehicle classification and verification tasks in the BoxCars dataset. The results demonstrate that, by learning such a concise 3D representation, we can achieve superior performance to methods that only use 2D information while retain 3D meaningful information without the challenge of requiring a 3D CAD model.
An Evaluation of Bitcoin Address Classification based on Transaction History Summarization
Venue: arXiv cs.CR 2019
Year: 2019

Bitcoin is a cryptocurrency that features a distributed, decentralized and trustworthy mechanism, which has made Bitcoin a popular global transaction platform. The transaction efficiency among nations and the privacy benefiting from address anonymity of the Bitcoin network have attracted many activities such as payments, investments, gambling, and even money laundering in the past decade. Unfortunately, some criminal behaviors which took advantage of this platform were not identified. This has discouraged many governments to support cryptocurrency. Thus, the capability to identify criminal addresses becomes an important issue in the cryptocurrency network. In this paper, we propose new features in addition to those commonly used in the literature to build a classification model for detecting abnormality of Bitcoin network addresses. These features include various high orders of moments of transaction time (represented by block height) which summarizes the transaction history in an efficient way. The extracted features are trained by supervised machine learning methods on a labeling category data set. The experimental evaluation shows that these features have improved the performance of Bitcoin address classification significantly. We evaluate the results under eight classifiers and achieve the highest Micro-F1/Macro-F1 of 87%/86% with LightGBM.
Multi timescale bandwidth profile and its application for burst-aware fairness
Venue: arXiv cs.NI 2019
Year: 2019

We propose a resource sharing scheme that takes into account the traffic history over several predefined time scales and provides fair resource sharing considering the traffic history. Our concept builds on a simplified version of core-stateless resource sharing, where we only use a few Drop Precedences (DPs). For packet marking we introduce Multi timescale bandwidth profile. Additionally, we provide basic dimensioning concepts for the proposed schema and present its simulation based performance analysis.
Device-to-Device Coded Caching with Distinct Cache Sizes
Venue: arXiv cs.IT 2019
Year: 2019

This paper considers a cache-aided device-to-device (D2D) system where the users are equipped with cache memories of different size. During low traffic hours, a server places content in the users' cache memories, knowing that the files requested by the users during peak traffic hours will have to be delivered by D2D transmissions only. The worst-case D2D delivery load is minimized by jointly designing the uncoded cache placement and linear coded D2D delivery. Next, a novel lower bound on the D2D delivery load with uncoded placement is proposed and used in explicitly characterizing the minimum D2D delivery load (MD2DDL) with uncoded placement for several cases of interest. In particular, having characterized the MD2DDL for equal cache sizes, it is shown that the same delivery load can be achieved in the network with users of unequal cache sizes, provided that the smallest cache size is greater than a certain threshold. The MD2DDL is also characterized in the small cache size regime, the large cache size regime, and the three-user case. Comparisons of the server-based delivery load with the D2D delivery load are provided. Finally, connections and mathematical parallels between cache-aided D2D systems and coded distributed computing (CDC) systems are discussed.
Sum Spectral Efficiency Maximization in Massive MIMO Systems: Benefits from Deep Learning
Venue: arXiv cs.IT 2019
Year: 2019

This paper investigates the joint data and pilot power optimization for maximum sum spectral efficiency (SE) in multi-cell Massive MIMO systems, which is a non-convex problem. We first propose a new optimization algorithm, inspired by the weighted minimum mean square error (MMSE) approach, to obtain a stationary point in polynomial time. We then use this algorithm together with deep learning to train a convolutional neural network to perform the joint data and pilot power control in sub-millisecond runtime, making it suitable for online optimization in real multi-cell Massive MIMO systems. The numerical result demonstrates that the solution obtained by the neural network is $1\%$ less than the stationary point for four-cell systems, while the sum SE loss is $2\%$ in a nine-cell system.