Reviewerly/exHarmony huggingface dataset:
Efficient ciphertext-policy attribute-based encryption with blackbox traceability

Traitor tracing scheme is a paradigm to classify the users who illegal use of their decryption keys in cryptosystems. In the ciphertext-policy attribute-based cryptosystem, the decryption key usually contains the users' attributes, while the real identities are hidden. The decryption key with hidden identities enables malicious users to intentionally leak decryption keys or embed the decryption keys in the decryption device to gain illegal profits with a little risk of being discovered. To mitigate this problem, the concept of blackbox traceability in the ciphertext-policy attribute-based scheme was proposed to identify the malicious user via observing the I/O streams of the decryption device. However, current solutions with blackbox traceability are impractical since either the composite-order group or the linear complexity of system users is required. In this article, we proposed a secure ciphertext-policy attribute-based set encryption scheme with the short decryption key. The proposed scheme bases on the prime-order group to improve computational performances and aggregates multiple attributes into a constant-size attribute set to reduce the costs of communication overheads. By applying our proposed scheme with fingerprint codes, we then give an instantiation of the ciphertext-policy attribute-based scheme with blackbox traceability. Our scheme is provably secure under various q-type assumptions.
I know what you're probably going to say: Listener adaptation to variable use of uncertainty expressions

Pragmatic theories of utterance interpretation share the assumption that listeners reason about alternative utterances that a speaker could have produced, but didn't. For such reasoning to be successful, listeners must have precise expectations about a speaker's production choices. This is at odds with the considerable variability across speakers that exists at all levels of linguistic representation. This tension can be reconciled by listeners adapting to the statistics of individual speakers. While linguistic adaptation is increasingly widely attested, semantic/pragmatic adaptation is underexplored. Moreover, what kind of representations listeners update during semantic/pragmatic adaptation – estimates of the speaker's lexicon, or estimates of the speaker's utterance preferences – remains poorly understood. In this work, we investigate semantic/pragmatic adaptation in the domain of uncertainty expressions like might and probably. In a series of web-based experiments, we find 1) that listeners vary in their expectations about a generic speaker's use of uncertainty expressions; 2) that listeners rapidly update their expectations about the use of uncertainty expressions after brief exposure to a speaker with a specific usage of uncertainty expressions; and 3) that listeners' interpretations of uncertainty expressions change after being exposed to a specific speaker. We present a novel computational model of semantic/pragmatic adaptation based on Bayesian belief updating and show, through a series of model comparisons, that semantic/pragmatic adaptation is best captured by listeners updating their beliefs both about the speaker's lexicon and their utterance preferences. This work has implications for both semantic theories of uncertainty expressions and psycholinguistic theories of adaptation: it highlights the need for dynamic semantic representations and suggests that listeners integrate their general linguistic knowledge with speaker-specific experiences to arrive at more precise interpretations.
UNAS: Differentiable Architecture Search Meets Reinforcement Learning

Neural architecture search (NAS) aims to discover network architectures with desired properties such as high accuracy or low latency. Recently, differentiable NAS (DNAS) has demonstrated promising results while maintaining a search cost orders of magnitude lower than reinforcement learning (RL) based NAS. However, DNAS models can only optimize differentiable loss functions in search, and they require an accurate differentiable approximation of non-differentiable criteria. In this work, we present UNAS, a unified framework for NAS, that encapsulates recent DNAS and RL-based approaches under one framework. Our framework brings the best of both worlds, and it enables us to search for architectures with both differentiable and non-differentiable criteria in one unified framework while maintaining a low search cost. Further, we introduce a new objective function for search based on the generalization gap that prevents the selection of architectures prone to overfitting. We present extensive experiments on the CIFAR-10, CIFAR-100 and ImageNet datasets and we perform search in two fundamentally different search spaces. We show that UNAS obtains the state-of-the-art average accuracy on all three datasets when compared to the architectures searched in the DARTS space. Moreover, we show that UNAS can find an efficient and accurate architecture in the ProxylessNAS search space, that outperforms existing MobileNetV2 based architectures. The source code is available at \url{https://github.com/NVlabs/unas}.
Hyperspectral Image Restoration Using Adaptive Anisotropy Total Variation and Nuclear Norms

Random Gaussian noise and striping artifacts are common phenomena in hyperspectral images (HSI). In this article, an effective restoration method is proposed to simultaneously remove Gaussian noise and stripes by merging a denoising and a destriping submodel. A denoising submodel performs a multiband denoising, i.e., Gaussian noise removal, considering Gaussian noise variations between different bands, to restore the striped HSI from the corrupted image, in which the striped HSI is constrained by a weighted nuclear norm. For the destriping submodel, we propose an adaptive anisotropy total variation method to adaptively smoothen the striped HSI, and we apply, for the first time, the truncated nuclear norm to constrain the rank of the stripes to 1. After merging the above two submodels, an ultimate image restoration model is obtained for both denoising and destriping. To solve the obtained optimization problem, the alternating direction method of multipliers (ADMM) is carefully schemed to perform an alternative and mutually constrained execution of denoising and destriping. Experiments on both synthetic and real data demonstrate the effectiveness and superiority of the proposed approach.
Moving in the Right Direction: A Regularization for Deep Metric Learning

Deep metric learning leverages carefully designed sampling strategies and loss functions that aid in optimizing the generation of a discriminable embedding space. While effective sampling of pairs is critical for shaping the metric space during training, the relative interactions between pairs, and consequently the forces exerted on these pairs that direct their displacement in the embedding space can significantly impact the formation of well separated clusters. In this work, we identify a shortcoming of existing loss formulations which fail to consider more optimal directions of pair displacements as another criterion for optimization. We propose a novel direction regularization to explicitly account for the layout of sampled pairs and attempt to introduce orthogonality in the representations. The proposed regularization is easily integrated into existing loss functions providing considerable performance improvements. We experimentally validate our hypothesis on the Cars-196, CUB-200 and InShop datasets and outperform existing methods to yield state-of-the-art results on these datasets.
Towards Inheritable Models for Open-Set Domain Adaptation

There has been a tremendous progress in Domain Adaptation (DA) for visual recognition tasks. Particularly, open-set DA has gained considerable attention wherein the target domain contains additional unseen categories. Existing open-set DA approaches demand access to a labeled source dataset along with unlabeled target instances. However, this reliance on co-existing source and target data is highly impractical in scenarios where data-sharing is restricted due to its proprietary nature or privacy concerns. Addressing this, we introduce a practical DA paradigm where a source-trained model is used to facilitate adaptation in the absence of the source dataset in future. To this end, we formalize knowledge inheritability as a novel concept and propose a simple yet effective solution to realize inheritable models suitable for the above practical paradigm. Further, we present an objective way to quantify inheritability to enable the selection of the most suitable source model for a given target domain, even in the absence of the source data. We provide theoretical insights followed by a thorough empirical evaluation demonstrating state-of-the-art open-set domain adaptation performance.
Eye contact in virtual reality – A psychophysiological study

This experiment investigated whether eye contact would evoke similar attention and emotion related psychophysiological responses in virtual reality (VR) as in a face-to-face interaction. Participants viewed a confederate in a live interaction (Live condition) and a confederate's avatar in VR (VR condition). In both conditions, the confederate/avatar was portraying direct and laterally averted gaze. Heart rate deceleration responses reflecting attention orienting were greater to direct gaze compared to averted gaze, and the effect was not significantly different between Live and VR conditions. However, skin conductance responses reflecting physiological arousal were larger in response to direct than averted gaze only in the Live condition. These results suggest that while eye contact with a live person evokes substantial attention and emotion related psychophysiological responses, the physiological effects of eye contact are diminished in VR.
Light-weight Calibrator: A Separable Component for Unsupervised Domain Adaptation

Existing domain adaptation methods aim at learning features that can be generalized among domains. These methods commonly require to update source classifier to adapt to the target domain and do not properly handle the trade-off between the source domain and the target domain. In this work, instead of training a classifier to adapt to the target domain, we use a separable component called data calibrator to help the fixed source classifier recover discrimination power in the target domain, while preserving the source domain's performance. When the difference between two domains is small, the source classifier's representation is sufficient to perform well in the target domain and outperforms GAN-based methods in digits. Otherwise, the proposed method can leverage synthetic images generated by GANs to boost performance and achieve state-of-the-art performance in digits datasets and driving scene semantic segmentation. Our method also empirically suggests the potential connection between domain adaptation and adversarial attacks.
Quantification of photoelastic fringe orders using polarized light camera and continuous loading

Quantification of fringe orders is important in visualizing full-field stress in digital photoelasticity. Various methods have been developed to improve the calculated accuracy of fringe orders, such as the phase shifting, carrier, and RGB methods. However, in these methods, fringe orders are calculated using the photoelastic patterns under a certain load but not the patterns in the whole time series; this impedes quantification of the fringe orders in dynamic photoelasticity. In this study, a polarized light camera was employed to simultaneously capture the photoelastic patterns of the dark and bright fields under circular polariscope. Based on the light intensity difference of these patterns in the entire loading process, a method of quantifying fringe orders is proposed. Full-field fringe orders in a disk fabricated by 3D printer and subjected to three compressive loads were calculated using this method. Its good accuracy, improved by removing the background light and choosing a suitable capture frame rate, was verified by comparing the fringe orders calculated by the traditional and proposed methods. Furthermore, the determination of the full-field fringe orders in complex structures indicated that the complexity of tested models had no influence on the calculated accuracy.
Better secret sharing via robust conditional disclosure of secrets

A secret-sharing scheme allows to distribute a secret s among n parties such that only some predefined “authorized” sets of parties can reconstruct the secret, and all other “unauthorized” sets learn nothing about s. For over 30 years, it was known that any (monotone) collection of authorized sets can be realized by a secret-sharing scheme whose shares are of size 2 n−o(n) and until recently no better scheme was known. In a recent breakthrough, Liu and Vaikuntanathan (STOC 2018) have reduced the share size to 20.994n+o(n), which was later improved to 20.892n+o(n) by Applebaum et al. (EUROCRYPT 2019).
Machine learning for point counting and segmentation of arenite in thin section

Thin sections provide geoscientists with a wealth of information about composition and diagenetic history of sedimentary rocks. From a practical perspective, the quantity of detrital clay minerals or percentage of porosity can play a large role in the quality of a reservoir. However, the quantitative analysis of thin sections often requires many hours of manual labor, which limits the number of samples a single person can analyze in a reasonable time frame. Here we apply a supervised machine-learning method that requires only traced grains as inputs, which eliminates the need for an expert to hand design input features. We also present a data-augmentation method to reduce the amount of tracing required. The traced grains form a multi-channel input that takes into account plane- and cross-polarized images, and a segmented image is output. Using a simplified grain categorization (quartz-feldspar-rock fragments-dense minerals) the statistical error for results on grain composition is comparable to a point count with 350 points. Once the model is trained, it can be applied quickly to additional images. In addition to providing component percentages, a segmented thin section can be used further to describe the morphology of grains (e.g., angularity, ellipticity) or serve as the basis for digital rock-physics experiments. This test of supervised machine learning does not reproduce the level of detailed component identification that is typical of manual point-counting, but it provides a clear indication that a diverse and fully representative data set will be required to achieve automated component identification that is both accurate and precise.
High-Quality Shared-Memory Graph Partitioning

Partitioning graphs into blocks of roughly equal size such that few edges run between blocks is a frequently needed operation in processing graphs. Recently, size, variety, and structural complexity of these networks has grown dramatically. Unfortunately, previous approaches to parallel graph partitioning have problems in this context since they often show a negative trade-off between speed and quality. We present an approach to multi-level shared-memory parallel graph partitioning that produces balanced solutions, shows high speedups for a variety of large graphs and yields very good quality independently of the number of cores used. For example, in an extensive experimental study, at 79 cores, one of our closest competitors is faster but fails to meet the balance criterion in the majority of cases and another is mostly slower and incurs about 13 percent larger cut size. Important ingredients include parallel label propagation for both coarsening and refinement, parallel initial partitioning, a simple yet effective approach to parallel localized local search, and fast locality preserving hash tables.
Regularization on Spatio-Temporally Smoothed Feature for Action Recognition

Deep neural networks for video action recognition frequently require 3D convolutional filters and often encounter overfitting due to a larger number of parameters. In this paper, we propose Random Mean Scaling (RMS), a simple and effective regularization method, to relieve the overfitting problem in 3D residual networks. The key idea of RMS is to randomly vary the magnitude of low-frequency components of the feature to regularize the model. The low-frequency component can be derived by a spatio-temporal mean on the local patch of a feature. We present that selective regularization on this locally smoothed feature makes a model handle the low-frequency and high-frequency component distinctively, resulting in performance improvement. RMS can enhance a model with little additional computation only during training, similar to other regularization methods. RMS also can be incorporated into typical training process without any bells and whistles. Experimental results show the improvement in generalization performance on a popular action recognition datasets demonstrating the effectiveness of RMS as a regularization technique, compared to other state-of-the-art regularization methods.
Nonlinear control of aerial manipulation systems

This paper presents four effective control algorithms to overcome challenges in controlling Aerial Manipulation Systems (AMSs). Interaction with the environment, nonlinear couplings, and structural and non-structural uncertainties are some issues that make the AMS controller difficult to develop and implement. Because of the complexity in the control design process, many research studies employed simplified AMS dynamics in the design of the controller. In this study, all the proposed control algorithms, with the exception of the hierarchical LQR method, are designed considering complete dynamics of the system without any need for simplification. Proposed control methods include (i) inverse dynamic, (ii) hierarchical LQR, (iii) sliding mode, and (iv) semi-optimal nonlinear control, which show appropriate behavior in the presence of some of the above-mentioned challenges. In order to assess the effectiveness of the control methods, coupled dynamics of a quadcopter endowed with a robotic manipulator is modeled. These controllers are compared to identify their effectiveness in simultaneous control of the quadcopter and its manipulator in the context of control accuracy and control effort. Additionally, sensitivity of the proposed approaches to an increase in the uncertainty levels are determined.
Extensions and adaptations of existing medical information system in order to reduce social contacts during COVID-19 pandemic

The main objective of this paper is the reduction of the COVID-19 pandemic spread by increasing the degree of social distancing by using and upgrading the existing Medical Information System (MIS). The existing MIS MEDIS.NET, currently used in the largest health center in the Balkans, has been adapted and further developed. During the adaptation of existing MIS MEDIS.NET 4 new and 9 existing modules were developed. A quick questionnaire for the smart triage of patients was also implemented. The adapted MIS successfully influenced the reduction of social contacts within the Health Center Nis. The need for the arrival of children and their parents to receive appropriate health certificates for the school enrolment is reduced. The therapy of chronic patients has been prolonged for 6 months via an electronic prescription. An online service for the communication between patients and the chosen physicians is provided. Possible social contacts and exposure to the viral environment of patients are reduced by making appointments in extended slots and at determined physical locations. Patients are notified per SMS or email about the availability of chosen and physician on duty. The social distancing of patients and physicians is also established by sending laboratory analyses per email or SMS. Keeping the central registry for COVID-19 is enabled throughout the country. The smart adaptation of MIS, and its collaboration with other state systems can significantly influence the reduction of social contacts and thus mitigate the consequences of COVID-19 pandemic.
Merkle Tree and Blockchain-Based Cloud Data Auditing

Cloud computing is the fastest growing and most promising field in the service provisioning segment. It has become a challenging task to provide security in the cloud. The purpose of this article is to suggest a better and efficient integrity verification technique for data referred to as cloud audit. The deployment of cloud storage services has significant benefits in the management of data for users. However, this raises many security concerns, and one of them is data integrity. Though public verification techniques serve the purpose they are vulnerable to procrastinating auditors who may not perform verifications on time. In this article, a cloud data auditing system is proposed. The proposed cloud data auditing system integrates Merkle Tree-based Cloud audit and the blockchain-based audit recording system, thus the core idea is to record each verification result into a blockchain as a transaction. Utilizing the time-sensitive nature of blockchain, the verifications are time-stamped after the corresponding transaction is recorded into the blockchain, which enables users to check whether auditors have performed the verifications at the prescribed time. The proposed cloud data auditing system is experimentally validated. The investigations with varied dataset size revealed less time taken, on an average of 0.25 milliseconds with the use of Merkle Tree. Further results reveal consistency of the data integrity checking.
From Paris to Berlin: Discovering Fashion Style Influences Around the World

The evolution of clothing styles and their migration across the world is intriguing, yet difficult to describe quantitatively. We propose to discover and quantify fashion influences from everyday images of people wearing clothes. We introduce an approach that detects which cities influence which other cities in terms of propagating their styles. We then leverage the discovered influence patterns to inform a forecasting model that predicts the popularity of any given style at any given city into the future. Demonstrating our idea with GeoStyle-a large-scale dataset of 7.7M images covering 44 major world cities, we present the discovered influence relationships, revealing how cities exert and receive fashion influence for an array of 50 observed visual styles. Furthermore, the proposed forecasting model achieves state-of-the-art results for a challenging style forecasting task, showing the advantage of grounding visual style evolution both spatially and temporally.
Poisoning the (Data) Well in ML-Based CAD: A Case Study of Hiding Lithographic Hotspots

Machine learning (ML) provides state-of-the-art performance in many parts of computer-aided design (CAD) flows. However, deep neural networks (DNNs) are susceptible to various adversarial attacks, including data poisoning to compromise training to insert backdoors. Sensitivity to training data integrity presents a security vulnerability, especially in light of malicious insiders who want to cause targeted neural network misbehavior. In this study, we explore this threat in lithographic hotspot detection via training data poisoning, where hotspots in a layout clip can be "hidden" at inference time by including a trigger shape in the input. We show that training data poisoning attacks are feasible and stealthy, demonstrating a backdoored neural network that performs normally on clean inputs but misbehaves on inputs when a backdoor trigger is present. Furthermore, our results raise some fundamental questions about the robustness of ML-based systems in CAD.