Reviewerly/exHarmony huggingface dataset:
State and parameter estimation using extended Kitanidis Kalman filter

State estimation in presence of unknown parameter variations has received considerable attention in literature. The conventional approach is to model the unknown drifting parameters as random walk model and estimate them simultaneously with states by augmenting them with the states of the system. However, the estimates critically depend on the tuning of the random walk model. Kitanidis Kalman Filter (KKF) [1] is an unbiased minimum variance estimator for only the states in presence of unknown inputs for linear systems. KKF allows optimal estimates of states to be obtained in presence of unknown inputs by appropriately choosing the gain matrix during the state update step. In this work, we investigate the problem of state and parameter estimation for nonlinear systems using extended Kitanidis Kalman Filter (EKKF). EKKF enables state and parameter estimation separately. In particular, we extend the work of Ganesh et al. [2] by providing a maximum likelihood based moving window framework for parameter estimation based on the innovations generated by EKKF. Towards this end, we characterise the innovations generated by KKF and show that unlike Kalman filter where the innovations are white, the innovations generated in KKF are a coloured Gaussian stochastic process. We also present a recursive approach for computing the covariance kernel of the innovations. The proposed method is applied to a simulation and an experimental case study to demonstrate its utility. The results indicate that the proposed method can be a useful tool for state and parameter estimation of nonlinear systems.
Understanding the Security of ARM Debugging Features

Processors nowadays are consistently equipped with debugging features to facilitate the program analysis. Specifically, the ARM debugging architecture involves a series of CoreSight components and debug registers to aid the system debugging, and a group of debug authentication signals are designed to restrict the usage of these components and registers. Meantime, the security of the debugging features is under-examined since it normally requires physical access to use these features in the traditional debugging model. However, ARM introduces a new debugging model that requires no physical access since ARMv7, which exacerbates our concern on the security of the debugging features. In this paper, we perform a comprehensive security analysis of the ARM debugging features, and summarize the security and vulnerability implications. To understand the impact of the implications, we also investigate a series of ARM-based platforms in different product domains (i.e., development boards, IoT devices, cloud servers, and mobile devices). We consider the analysis and investigation expose a new attacking surface that universally exists in ARM-based platforms. To verify our concern, we further craft Nailgun attack, which obtains sensitive information (e.g., AES encryption key and fingerprint image) and achieves arbitrary payload execution in a high-privilege mode from a low-privilege mode via misusing the debugging features. This attack does not rely on software bugs, and our experiments show that almost all the platforms we investigated are vulnerable to the attack. The potential mitigations are discussed from different perspectives in the ARM ecosystem.
Risk and the Five Hard Problems of Cybersecurity

Abstract This perspectives article addresses risk in cyber defense and identifies opportunities to incorporate risk analysis principles into the cybersecurity field. The Science of Security (SoS) initiative at the National Security Agency seeks to further and promote interdisciplinary research in cybersecurity. SoS organizes its research into the Five Hard Problems (5HP): (1) scalability and composability; (2) policy‐governed secure collaboration; (3) security‐metrics–driven evaluation, design, development, and deployment; (4) resilient architectures; and (5) understanding and accounting for human behavior. However, a vast majority of the research sponsored by SoS does not consider risk and when it does so, only implicitly. Therefore, we identify opportunities for risk analysis in each hard problem and propose approaches to address these objectives. Such collaborations between risk and cybersecurity researchers will enable growth and insight in both fields, as risk analysts may apply existing methodology in a new realm, while the cybersecurity community benefits from accepted practices for describing, quantifying, working with, and mitigating risk.
Session Resumption Protocols and Efficient Forward Security for TLS 1.3 0-RTT

The TLS 1.3 0-RTT mode enables a client reconnecting to a server to send encrypted application-layer data in “0-RTT” (“zero round-trip time”), without the need for a prior interactive handshake. This fundamentally requires the server to reconstruct the previous session’s encryption secrets upon receipt of the client’s first message. The standard techniques to achieve this are Session Caches or, alternatively, Session Tickets. The former provides forward security and resistance against replay attacks, but requires a large amount of server-side storage. The latter requires negligible storage, but provides no forward security and is known to be vulnerable to replay attacks. In this paper, we first formally define session resumption protocols as an abstract perspective on mechanisms like Session Caches and Session Tickets. We give a new generic construction that provably provides forward security and replay resilience, based on puncturable pseudorandom functions (PPRFs). This construction can immediately be used in TLS 1.3 0-RTT and deployed unilaterally by servers, without requiring any changes to clients or the protocol. We then describe two new constructions of PPRFs, which are particularly suitable for use for forward-secure and replay-resilient session resumption in TLS 1.3. The first construction is based on the strong RSA assumption. Compared to standard Session Caches, for “128-bit security” it reduces the required server storage by a factor of almost 20, when instantiated in a way such that key derivation and puncturing together are cheaper on average than one full exponentiation in an RSA group. Hence, a 1 GB Session Cache can be replaced with only about 51 MBs of storage, which significantly reduces the amount of secure memory required. For larger security parameters or in exchange for more expensive computations, even larger storage reductions are achieved. The second construction combines a standard binary tree PPRF with a new “domain extension” technique. For a reasonable choice of parameters, this reduces the required storage by a factor of up to 5 compared to a standard Session Cache. It employs only symmetric cryptography, is suitable for high-traffic scenarios, and can serve thousands of tickets per second.
Benchmark Revision for HOG-SVM Pedestrian Detector Through Reinvigorated Training and Evaluation Methodologies

Histogram of oriented gradient (HOG) features in combination with support vector machine (SVM) classification are still widely reported as a de facto standard benchmark to evaluate contemporary pedestrian detectors employing a vast spectrum of sophisticated features and classification schemes. In this paper, however, it has been shown that these benchmark figures for the original HOG-linear SVM detector grossly underestimate its true working potential. An improved SVM training methodology has been proposed that can make this important detector yield up to 14% lower miss rates than being currently reported on standard datasets. The proposed method accomplishes this by considering only a small set of the most relevant training examples and mitigating the class imbalance problem through manipulation of misclassification cost ratios. The second contribution of this work is the investigation of quantization errors which are induced when fast look-up table (LUT)-based kernel SVM classification is used. Based on the findings, a non-linear quantization scheme for the HOG features has been proposed to facilitate fast evaluation of kernel SVM resulting in up to 4% further reduction in miss rates without incurring additional computational cost. Hence, the linear and kernel SVM-based detectors proposed in this paper not only serve as much more stringent benchmarks for pedestrian detection but also provide the practitioners with an improved training framework for similar detection tasks involving other features and objects. The retrained models have been publicly released and provide a much better replacement for those currently being shipped with OpenCV and MATLAB computer vision libraries.
Cyber Security Threats and Challenges in Collaborative Mixed-Reality

Collaborative Mixed-Reality (CMR) applications are gaining interest in a wide range of areas including games, social interaction, design and health-care. The vast majority of published work to date has focused on display technology advancements, software, collaboration architectures and applications. However, the potential security concerns that affect collaborative platforms have received limited research attention. In this position paper, we investigate the challenges posed by cyber-security threats to CMR systems. We focus on how typical network architectures facilitating CMR and how their vulnerabilities can be exploited by attackers, and discuss the degree of potential social, monetary impacts, psychological and other harms that may result from such exploits. The main purpose of this paper is to provoke a discussion on CMR security concerns. We highlight insights from a cyber-security threat modelling perspective and also propose potential directions for research and development towards better mitigation strategies. We present a simple, systematic approach to understanding a CMR attack surface through an abstraction-based reasoning framework to identify potential attack vectors. Using this framework, security analysts, engineers, designers and users alike (stakeholders) can identify potential Indicators of Exposures (IoE) and Indicators of Compromise (IoC). Our framework should allow stakeholders to reduce their CMR attack surface as well as understand how Intrusion Detection System (IDS) approaches can be adopted for CMR systems. To demonstrate the validity to our framework, we illustrate several CMR attack surfaces with a set of use-cases. Finally, we also present a discussion on the future directions this line of research should take.
Multi-teacher Knowledge Distillation for Compressed Video Action Recognition on Deep Neural Networks

Recently, convolutional neural networks (CNNs) have seen great progress in classifying images. Action recognition is different from still image classification; video data contains temporal information that plays an important role in video understanding. Currently, most CNN-based approaches for action recognition have excessive computational costs, with an explosion of parameters and computation time. The currently most efficient method trains a deep network directly on compressed video containing the motion information. However, this method has a large number of parameters. We propose a multi-teacher knowledge distillation framework for compressed video action recognition to compress this model. With this framework, the model is compressed by transferring the knowledge from multiple teachers to a single small student model. With multi-teacher knowledge distillation, students learn better than with single-teacher knowledge distillation. Experiments show that we can reach a 2.4× compression rate in a number of parameters and a 1.2× computation reduction with 1.79% loss of accuracy on the UCF-101 dataset and 0.35% loss of accuracy on the HMDB51 dataset.
A novel differential evolution mapping technique for generic combinatorial optimization problems

Differential evolution is primarily designed and used to solve continuous optimization problems. Therefore, it has not been widely considered as applicable for real-world problems that are characterized by permutation-based combinatorial domains. Many algorithms for solving discrete problems using differential evolution have been proposed, some of which have achieved promising results. However, to enhance their performance, they require improvements in many aspects, such as their convergence speeds, computational times and capabilities to solve large discrete problems. In this paper, we present a new mapping method that may be used with differential evolution to solve combinatorial optimization problems. This paper focuses specifically on the mapping component and its effect on the performance of differential evolution. Our method maps continuous variables to discrete ones, while at the same time, it directs the discrete solutions produced towards optimality, by using the best solution in each generation as a guide. To judge its performance, its solutions for instances of well-known discrete problems, namely: 0/1 knapsack, traveling salesman and traveling thief problems, are compared with those obtained by 8 other state-of-the-art mapping techniques. To do this, all mapping techniques are used with the same differential evolution settings. The results demonstrated that our technique significantly outperforms the other mapping methods in terms of the average error from the best-known solution for the traveling salesman problems, and achieves promising results for both the 0/1 knapsack and the traveling thief problems.
Fast spatial–temporal stereo matching for 3D face reconstruction under speckle pattern projection

Three-dimensional (3D) face reconstruction can be tackled in either measurement-based means or model-based means. The former requires special hardwares or devices, such as structured light setups. This paper addresses 3D face reconstruction by measurement-based means, more specifically a special kind of structured light called space–time speckle projection. Under such a setup, we propose a novel and efficient spatial–temporal stereo scheme towards fast and accurate 3D face recovery. To improve the overall computational efficiency, our scheme consists of a series of optimization strategies including face-cropping-based stereo matching, coarse-to-fine stereo matching strategy applied to face areas, and spatial–temporal integral image (STII) for accelerating the matching cost computation. Based on the results, the proposed scheme is able to reconstruct a 3D face in hundreds of milliseconds on a normal PC, and its performance is validated both qualitatively and quantitatively.
Automatic Radar-based Gesture Detection and Classification via a Region-based Deep Convolutional Neural Network

In this paper, a region-based deep convolutional neural network (R-DCNN) is proposed to detect and classify gestures measured by a frequency-modulated continuous wave radar system. Micro-Doppler (µD) signatures of gestures are exploited, and the resulting spectrograms are fed into a neural network. We are the first to use the R-DCNN for radar-based gesture recognition, such that multiple gestures could be automatically detected and classified without manually clipping the data streams according to each hand movement in advance. Further, along with the µD signatures, we incorporate phase-difference information of received signals from an L-shaped antenna array to enhance the classification accuracy. Finally, the classification results show that the proposed network trained with spectrogram and phase-difference information can guarantee a promising performance for nine gestures.
Attention-based Wavenet Autoencoder for Universal Voice Conversion

We present a method for converting any voice to a target voice. The method is based on a WaveNet autoencoder, with the addition of a novel attention component that supports the modification of timing between the input and the output samples. Training the attention is done in an unsupervised way, by teaching the neural network to recover the original timing from an artificially modified one. Adding a generic voice robot, which we convert to the target voice, we present a robust Text To Speech pipeline that is able to train without any transcript. Our experiments show that the proposed method is able to recover the timing of the speaker and that the proposed pipeline provides a competitive Text To Speech method.
Unsupervised Learning to Overcome Catastrophic Forgetting in Neural Networks

Continual learning is the ability to acquire a new task or knowledge without losing any previously collected information. Achieving continual learning in artificial intelligence (AI) is currently prevented by catastrophic forgetting, where training of a new task deletes all previously learned tasks. Here, we present a new concept of a neural network capable of combining supervised convolutional learning with bio-inspired unsupervised learning. Brain-inspired concepts such as spike-timing-dependent plasticity (STDP) and neural redundancy are shown to enable continual learning and prevent catastrophic forgetting without compromising standard accuracy achievable with state-of-the-art neural networks. Unsupervised learning by STDP is demonstrated by hardware experiments with a one-layer perceptron adopting phase-change memory (PCM) synapses. Finally, we demonstrate full testing classification of Modified National Institute of Standards and Technology (MNIST) database with an accuracy of 98% and continual learning of up to 30% non-trained classes with 83% average accuracy.
Truthful Crowdsensed Data Trading Based on Reverse Auction and Blockchain

Crowdsensed Data Trading (CDT) is a novel data trading paradigm, in which each data consumer can publicize its demand as some crowdsensing tasks, and some mobile users can compete for these tasks, collect the corresponding data, and sell the results to the consumers. Existing CDT systems either depend on a trusted data trading broker or cannot ensure sellers to report costs honestly. To address this problem, we propose a Reverse-Auction-and-blockchain-based crowdsensed Data Trading (RADT) system, mainly containing a smart contract, called RADToken. We adopt a greedy strategy to determine winners, and prove the truthfulness and individual rationality of the whole reverse auction process. Moreover, we exploit the smart contract with a series of devises to enforce mutually untrusted parties to participate in the data trading honestly. Additionally, we also deploy RADToken on an Ethereum test network to demonstrate its significant performances. To the best of our knowledge, this is the first CDT work that exploits both auction and blockchain to ensure the truthfulness of the whole data trading process.
Automatic Classification of Algorithm Citation Functions in Scientific Literature

Computer sciences and related disciplines evolve around developing, evaluating, and applying algorithms. Typically, an algorithm is not developed from scratch, but uses and builds upon existing ones, which often are proposed and published in scholarly articles. The ability to capture this evolution relationship among these algorithms in scientific literature would not only allow us to understand how a particular algorithm is composed, but also shed light on large-scale analysis of algorithmic evolution through different temporal spans and thematic scales. We propose to capture such evolution relationship between two algorithms by investigating the knowledge represented in citation contexts, where authors explain how cited algorithms are used in their works. A set of heterogeneous ensemble machine-learning methods is proposed, where the combination of two base classifiers trained with heterogeneous feature types is used to automatically identify the algorithm usage relationship. The proposed heterogeneous ensemble methods achieve the best average F1 of 0.749 and 0.905 for fine-grained and binary algorithm citation function classification, respectively. The success of this study will allow us to generate a large-scale algorithm citation network from a collection of scholarly documents representing multiple time spans, venues, and fields of study. Such a network will be used as an instrument not only to answer critical questions in algorithm search, such as identifying the most influential and generalizable algorithms, but also to study the evolution of algorithmic development and trends over time.
Bipartite Network Embedding via Effective Integration of Explicit and Implicit Relations

Network representation learning, or network embedding, aims at mapping the nodes of the network to low-dimensional vector space, in which the learned node representations can be used for a variety of tasks, such as node classification, link prediction, and visualization. As a special class of complex networks, the bipartite network is composed of two different types of nodes in which the links only exist among different types of nodes, has important applications in the recommendation system, link prediction, and disease diagnosis. However, most existing methods for network representation learning are aimed at homogeneous networks in general, while the special properties of bipartite networks are not taken into account, such as the implicit relations (i.e., unobserved links) between nodes of the same type. In this paper, we propose a novel deep learning framework for bipartite networks, which integrates the explicit and implicit relations, while preserving the local and global structure, to learn the highly non-linear representations of nodes. Extensive experiments conducted on several real-world datasets, based on the link prediction, recommendation, and visualization, demonstrate the effectiveness of our proposed method compared with state-of-the-art network representation learning based methods.
MDCGen: Multidimensional Dataset Generator for Clustering

We present a tool for generating multidimensional synthetic datasets for testing, evaluating, and benchmarking unsupervised classification algorithms. Our proposal fills a gap observed in previous approaches with regard to underlying distributions for the creation of multidimensional clusters. As a novelty, normal and non-normal distributions can be combined for either independently defining values feature by feature (i.e., multivariate distributions) or establishing overall intra-cluster distances. Being highly flexible, parameterizable, and randomizable, MDCGen also implements classic pursued features: (a) customization of cluster-separation, (b) overlap control, (c) addition of outliers and noise, (d) definition of correlated variables and rotations, (e) flexibility for allowing or avoiding isolation constraints per dimension, (f) creation of subspace clusters and subspace outliers, (g) importing arbitrary distributions for the value generation, and (h) dataset quality evaluations, among others. As a result, the proposed tool offers an improved range of potential datasets to perform a more comprehensive testing of clustering algorithms.