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Corpora design and score calibration for text dependent pronunciation proficiency recognition

Published in:
8th ISCA Workshop on Speech and Language Technology in Education, SLaTe 2019, 20-21 September 2019.

Summary

This work investigates methods for improving a pronunciation proficiency recognition system, both in terms of phonetic level posterior probability calibration, and in ordinal utterance level classification, for Modern Standard Arabic (MSA), Spanish and Russian. To support this work, utterance level labels were obtained by crowd-sourcing the annotation of language learners' recordings. Phonetic posterior probability estimates extracted using automatic speech recognition systems trained in each language were estimated using a beta calibration approach [1] and language proficiency level was estimated using an ordinal regression [2]. Fusion with language recognition (LR) scores from an i-vector system [3] trained on 23 languages is also explored. Initial results were promising for all three languages and it was demonstrated that the calibrated posteriors were effective for predicting pronunciation proficiency. Significant relative gains of 16% mean absolute error for the ordinal regression and 17% normalized cross entropy for the binary beta regression were achieved on MSA through fusion with LR scores.
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Summary

This work investigates methods for improving a pronunciation proficiency recognition system, both in terms of phonetic level posterior probability calibration, and in ordinal utterance level classification, for Modern Standard Arabic (MSA), Spanish and Russian. To support this work, utterance level labels were obtained by crowd-sourcing the annotation of language learners'...

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Using K-means in SVR-based text difficulty estimation

Published in:
8th ISCA Workshop on Speech and Language Technology in Education, SLaTE, 20-21 September 2019.

Summary

A challenge for second language learners, educators, and test creators is the identification of authentic materials at the right level of difficulty. In this work, we present an approach to automatically measure text difficulty, integrated into Auto-ILR, a web-based system that helps find text material at the right level for learners in 18 languages. The Auto-ILR subscription service scans web feeds, extracts article content, evaluates the difficulty, and notifies users of documents that match their skill level. Difficulty is measured on the standard ILR scale with language-specific support vector machine regression (SVR) models built from vectors incorporating length features, term frequencies, relative entropy, and K-means clustering.
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Summary

A challenge for second language learners, educators, and test creators is the identification of authentic materials at the right level of difficulty. In this work, we present an approach to automatically measure text difficulty, integrated into Auto-ILR, a web-based system that helps find text material at the right level for...

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The leakage-resilience dilemma

Published in:
Proc. European Symp. on Research in Computer Security, ESORICS 2019, pp. 87-106.

Summary

Many control-flow-hijacking attacks rely on information leakage to disclose the location of gadgets. To address this, several leakage-resilient defenses, have been proposed that fundamentally limit the power of information leakage. Examples of such defenses include address-space re-randomization, destructive code reads, and execute-only code memory. Underlying all of these defenses is some form of code randomization. In this paper, we illustrate that randomization at the granularity of a page or coarser is not secure, and can be exploited by generalizing the idea of partial pointer overwrites, which we call the Relative ROP (RelROP) attack. We then analyzed more that 1,300 common binaries and found that 94% of them contained sufficient gadgets for an attacker to spawn a shell. To demonstrate this concretely, we built a proof-of-concept exploit against PHP 7.0.0. Furthermore, randomization at a granularity finer than a memory page faces practicality challenges when applied to shared libraries. Our findings highlight the dilemma that faces randomization techniques: course-grained techniques are efficient but insecure and fine-grained techniques are secure but impractical.
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Summary

Many control-flow-hijacking attacks rely on information leakage to disclose the location of gadgets. To address this, several leakage-resilient defenses, have been proposed that fundamentally limit the power of information leakage. Examples of such defenses include address-space re-randomization, destructive code reads, and execute-only code memory. Underlying all of these defenses is...

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State-of-the-art speaker recognition for telephone and video speech: the JHU-MIT submission for NIST SRE18

Summary

We present a condensed description of the joint effort of JHUCLSP, JHU-HLTCOE, MIT-LL., MIT CSAIL and LSE-EPITA for NIST SRE18. All the developed systems consisted of xvector/i-vector embeddings with some flavor of PLDA backend. Very deep x-vector architectures–Extended and Factorized TDNN, and ResNets– clearly outperformed shallower xvectors and i-vectors. The systems were tailored to the video (VAST) or to the telephone (CMN2) condition. The VAST data was challenging, yielding 4 times worse performance than other video based datasets like Speakers in the Wild. We were able to calibrate the VAST data with very few development trials by using careful adaptation and score normalization methods. The VAST primary fusion yielded EER=10.18% and Cprimary= 0.431. By improving calibration in post-eval, we reached Cprimary=0.369. In CMN2, we used unsupervised SPLDA adaptation based on agglomerative clustering and score normalization to correct the domain shift between English and Tunisian Arabic models. The CMN2 primary fusion yielded EER=4.5% and Cprimary=0.313. Extended TDNN x-vector was the best single system obtaining EER=11.1% and Cprimary=0.452 in VAST; and 4.95% and 0.354 in CMN2.
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Summary

We present a condensed description of the joint effort of JHUCLSP, JHU-HLTCOE, MIT-LL., MIT CSAIL and LSE-EPITA for NIST SRE18. All the developed systems consisted of xvector/i-vector embeddings with some flavor of PLDA backend. Very deep x-vector architectures–Extended and Factorized TDNN, and ResNets– clearly outperformed shallower xvectors and i-vectors. The...

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Guest editorial: special issue on hardware solutions for cyber security

Published in:
J. Hardw. Syst. Secur., Vol. 3, No. 199, 2019.

Summary

A cyber system could be viewed as an architecture consisting of application software, system software, and system hardware. The hardware layer, being at the foundation of the overall architecture, must be secure itself and also provide effective security features to the software layers. In order to seamlessly integrate security hardware into a system with minimal performance compromises, designers must develop and understand tangible security specifications and metrics to trade between security, performance, and cost for an optimal solution. Hardware security components, libraries, and reference architecture are increasingly important in system design and security. This special issue includes four exciting manuscripts on several aspects of developing hardware-oriented security for systems.
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Summary

A cyber system could be viewed as an architecture consisting of application software, system software, and system hardware. The hardware layer, being at the foundation of the overall architecture, must be secure itself and also provide effective security features to the software layers. In order to seamlessly integrate security hardware...

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Improving robustness to attacks against vertex classification

Published in:
15th Intl. Workshop on Mining and Learning with Graphs, 5 August 2019.

Summary

Vertex classification—the problem of identifying the class labels of nodes in a graph—has applicability in a wide variety of domains. Examples include classifying subject areas of papers in citation networks or roles of machines in a computer network. Recent work has demonstrated that vertex classification using graph convolutional networks is susceptible to targeted poisoning attacks, in which both graph structure and node attributes can be changed in an attempt to misclassify a target node. This vulnerability decreases users' confidence in the learning method and can prevent adoption in high-stakes contexts. This paper presents work in progress aiming to make vertex classification robust to these types of attacks. We investigate two aspects of this problem: (1) the classification model and (2) the method for selecting training data. Our alternative classifier is a support vector machine (with a radial basis function kernel), which is applied to an augmented node feature-vector obtained by appending the node’s attributes to a Euclidean vector representing the node based on the graph structure. Our alternative methods of selecting training data are (1) to select the highest-degree nodes in each class and (2) to iteratively select the node with the most neighbors minimally connected to the training set. In the datasets on which the original attack was demonstrated, we show that changing the training set can make the network much harder to attack. To maintain a given probability of attack success, the adversary must use far more perturbations; often a factor of 2–4 over the random training baseline. Even in cases where success is relatively easy for the attacker, we show that the classification and training alternatives allow classification performance to degrade much more gradually, with weaker incorrect predictions for the attacked nodes.
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Summary

Vertex classification—the problem of identifying the class labels of nodes in a graph—has applicability in a wide variety of domains. Examples include classifying subject areas of papers in citation networks or roles of machines in a computer network. Recent work has demonstrated that vertex classification using graph convolutional networks is...

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A compact end cryptographic unit for tactical unmanned systems

Summary

Under the Navy's Flexible Cyber-Secure Radio (FlexCSR) program, the Naval Information Warfare Center Pacific and the Massachusetts Institute of Technology's Lincoln Laboratory are jointly developing a unique cybersecurity solution for tactical unmanned systems (UxS): the FlexCSR Security/Cyber Module (SCM) End Cryptographic Unit (ECU). To deal with possible loss of unmanned systems that contain the device, the SCM ECU uses only publicly available Commercial National Security Algorithms and a Tactical Key Management system to generate and distribute onboard mission keys that are destroyed at mission completion or upon compromise. This also significantly reduces the logistic complexity traditionally involved with protection and loading of classified cryptographic keys. The SCM ECU is on track to be certified by the National Security Agency for protecting tactical data-in-transit up to Secret level. The FlexCSR SCM ECU is the first stand-alone cryptographic module that conforms to the United States Department of Defense (DoD) Joint Communications Architecture for Unmanned Systems, an initiative by the Office of the Secretary of Defense supporting the interoperability pillar of the DoD Unmanned Systems Integrated Roadmap. It is a credit card-sized enclosed unit that provides USB interfaces for plaintext and ciphertext, support for radio controls and management, and a software Application Programming Interface that together allow easy integration into tactical UxS communication systems. This paper gives an overview of the architecture, interfaces, usage, and development and approval schedule of the device.
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Summary

Under the Navy's Flexible Cyber-Secure Radio (FlexCSR) program, the Naval Information Warfare Center Pacific and the Massachusetts Institute of Technology's Lincoln Laboratory are jointly developing a unique cybersecurity solution for tactical unmanned systems (UxS): the FlexCSR Security/Cyber Module (SCM) End Cryptographic Unit (ECU). To deal with possible loss of unmanned...

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Supporting security sensitive tenants in a bare-metal cloud

Summary

Bolted is a new architecture for bare-metal clouds that enables tenants to control tradeoffs between security, price, and performance. Security-sensitive tenants can minimize their trust in the public cloud provider and achieve similar levels of security and control that they can obtain in their own private data centers. At the same time, Bolted neither imposes overhead on tenants that are security insensitive nor compromises the flexibility or operational efficiency of the provider. Our prototype exploits a novel provisioning system and specialized firmware to enable elasticity similar to virtualized clouds. Experimentally we quantify the cost of different levels of security for a variety of workloads and demonstrate the value of giving control to the tenant.
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Summary

Bolted is a new architecture for bare-metal clouds that enables tenants to control tradeoffs between security, price, and performance. Security-sensitive tenants can minimize their trust in the public cloud provider and achieve similar levels of security and control that they can obtain in their own private data centers. At the...

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Control-flow integrity for real-time embedded systems

Published in:
31st Euromicro Conf. on Real-Time Systems, ECRTS, 9-12 July 2019.

Summary

Attacks on real-time embedded systems can endanger lives and critical infrastructure. Despite this, techniques for securing embedded systems software have not been widely studied. Many existing security techniques for general-purpose computers rely on assumptions that do not hold in the embedded case. This paper focuses on one such technique, control-flow integrity (CFI), that has been vetted as an effective countermeasure against control-flow hijacking attacks on general-purpose computing systems. Without the process isolation and fine-grained memory protections provided by a general-purpose computer with a rich operating system, CFI cannot provide any security guarantees. This work proposes RECFISH, a system for providing CFI guarantees on ARM Cortex-R devices running minimal real-time operating systems. We provide techniques for protecting runtime structures, isolating processes, and instrumenting compiled ARM binaries with CFI protection. We empirically evaluate RECFISH and its performance implications for real-time systems. Our results suggest RECFISH can be directly applied to binaries without compromising real-time performance; in a test of over six million realistic task systems running FreeRTOS, 85% were still schedulable after adding RECFISH.
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Summary

Attacks on real-time embedded systems can endanger lives and critical infrastructure. Despite this, techniques for securing embedded systems software have not been widely studied. Many existing security techniques for general-purpose computers rely on assumptions that do not hold in the embedded case. This paper focuses on one such technique, control-flow...

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Discriminative PLDA for speaker verification with X-vectors

Published in:
International Conference on Acoustics, Speech, and Signal Processing, May 2019 [submitted]

Summary

This paper proposes a novel approach to discriminative training ofprobabilistic linear discriminant analysis (PLDA) for speaker veri-fication with x-vectors. The Newton Method is used to discrimi-natively train the PLDA model by minimizing the log loss of ver-ification trials. By diagonalizing the across-class and within-classcovariance matrices as a pre-processing step, the PLDA model canbe trained without relying on approximations, and while maintain-ing important properties of the underlying covariance matrices. Thetraining procedure is extended to allow for efficient domain adapta-tion. When applied to the Speakers in the Wild and SRE16 tasks, theproposed approach provides significant performance improvementsrelative to conventional PLDA.
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Summary

This paper proposes a novel approach to discriminative training ofprobabilistic linear discriminant analysis (PLDA) for speaker veri-fication with x-vectors. The Newton Method is used to discrimi-natively train the PLDA model by minimizing the log loss of ver-ification trials. By diagonalizing the across-class and within-classcovariance matrices as a pre-processing step, the...

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