Reviewerly/exHarmony huggingface dataset:
A digital image encryption algorithm based on chaotic mapping

In this article, we introduced a digital image encryption algorithm based on the chaotic mapping designed by Xiong et al. In their paper, the authors theoretically analyzed the algorithm and pointed out that the algorithm did not need to have the prior knowledge of the orbital distribution and one can select any chaotic model. In this way, the algorithm greatly expanded the cryptographic space and greatly reduced the number of iterations of the mapping. Since the algorithm has many characteristics, for instance, it is sensitive to the secret key, its key space is big, the pixel is well distributed after being encrypted, etc., the security of the encrypted images can be assured effectively. However, since the algorithm applied the image scrambling for encryption, and did not take the chosen-plaintext attacks into consideration, the algorithm is relatively weak in resisting the chosen-plaintext attacks. Therefore, we put forward a kind of image replacement method based on chaos, which can resist the chosen-plaintext attacks. And the experimental simulation proves that this algorithm not only has many characteristics, for instance, it is sensitive to the secret key, its key space is big, the pixel is well distributed after being encrypted, etc., but also can resist the chosen-plaintext attacks effectively. In the meanwhile, the algorithm is very sensitive to the small changes of the plaintexts, and its encrypted images will completely lose the features of the original ones.
Energy-Efficient Radio Resource Allocation for Federated Edge Learning

Edge machine learning involves the development of learning algorithms at the network edge to leverage massive distributed data and computation resources. Among others, the framework of federated edge learning (FEEL) is particularly promising for its data-privacy preservation. FEEL coordinates global model training at a server and local model training at edge devices over wireless links. In this work, we explore the new direction of energy-efficient radio resource management (RRM) for FEEL. To reduce devices' energy consumption, we propose energy-efficient strategies for bandwidth allocation and scheduling. They adapt to devices' channel states and computation capacities so as to reduce their sum energy consumption while warranting learning performance. In contrast with the traditional rate-maximization designs, the derived optimal policies allocate more bandwidth to those scheduled devices with weaker channels or poorer computation capacities, which are the bottlenecks of synchronized model updates in FEEL. On the other hand, the scheduling priority function derived in closed form gives preferences to devices with better channels and computation capacities. Substantial energy reduction contributed by the proposed strategies is demonstrated in learning experiments.
Evaluation of rolling shear modulus and strength of Japanese cedar cross-laminated timber (CLT) laminae

Cross-laminated timber (CLT) panels are fabricated with their layers stacked crosswise. Owing to the low shear modulus and strength in the rolling shear direction, the shear properties of cross-layers influence the overall deflection and shear capacities of CLT panels. The aim of the present study is to determine the rolling shear properties of Japanese cedar and investigate how annual ring patterns and lamina geometry influence shear properties. Using a test configuration similar to the standard shear test configuration prescribed in European Standards (EN 408), a single lamina shear test was conducted. To investigate the influence of thickness-to-width aspect ratio of the lamina on the rolling shear properties, samples with three different widths, including 62, 88, and 112 mm, with a constant thickness of 24 mm were tested. The geometrical features of the annual ring patterns of each test sample were measured. The mean rolling shear moduli were 72, 91, and 109 MPa, and the mean rolling shear strengths were 1.54, 1.83, and 2.02 MPa for the 62-, 88-, and 112-mm sample widths, respectively. Shear strength was highly correlated with shear modulus. The mean shear modulus and strength, in addition to the 5% quantile, increased with an increase in lamina width. Across all sample widths, rolling shear modulus and strength decreased with an increasing radial distance from the pith. Using the principle of continuum mechanics, the influence of the annual ring angle relative to the shear force direction was examined quantitatively using the finite element method. The results suggest that shear modulus and its variance are influenced greatly by the annual ring structure.
MAP-Elites for noisy domains by adaptive sampling

Quality Diversity algorithms (QD) evolve a set of high-performing phenotypes that each behaves as differently as possible. However, current algorithms are all elitist, which make them unable to cope with stochastic fitness functions and behavior evaluations. In fact, many of the promising applications of QD algorithms, for instance, games and robotics, are stochastic. Here we propose two new extensions to the QD-algorithm MAP-Elites --- adaptive sampling and drifting-elites --- and demonstrate empirically that these extensions increase the quality of solutions in a noisy artificial test function and the behavioral diversity in a 2D bipedal walker environment.
Operational Data Analytics

In 2017/2018, the Energy Efficient HPC Working Group (EE HPC WG) Dashboard Team conducted an analysis that assessed the current use of information dashboards for operational facility management in major supercomputing centers around the globe, resulting in the formalization of a process now referred to as Operational Data Analytics (ODA). Subsequent to surveys of multiple HPC facilities, the EE HPC WG determined that case studies were needed to help HPC facilities evaluate the value of implementing ODA practices. This paper provides a summary of the successful use of an ODA approach being used by the National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory (Berkeley Lab) in meeting organizational energy efficiency performance goals for the NERSC HPC cooling systems.
Investigating Statistical Privacy Frameworks from the Perspective of Hypothesis Testing

Abstract Over the last decade, differential privacy (DP) has emerged as the gold standard of a rigorous and provable privacy framework. However, there are very few practical guidelines on how to apply differential privacy in practice, and a key challenge is how to set an appropriate value for the privacy parameter ɛ. In this work, we employ a statistical tool called hypothesis testing for discovering useful and interpretable guidelines for the state-of-the-art privacy-preserving frameworks. We formalize and implement hypothesis testing in terms of an adversary’s capability to infer mutually exclusive sensitive information about the input data (such as whether an individual has participated or not) from the output of the privacy-preserving mechanism. We quantify the success of the hypothesis testing using the precision- recall-relation , which provides an interpretable and natural guideline for practitioners and researchers on selecting ɛ. Our key results include a quantitative analysis of how hypothesis testing can guide the choice of the privacy parameter ɛ in an interpretable manner for a differentially private mechanism and its variants. Importantly, our findings show that an adversary’s auxiliary information - in the form of prior distribution of the database and correlation across records and time - indeed influences the proper choice of ɛ. Finally, we also show how the perspective of hypothesis testing can provide useful insights on the relationships among a broad range of privacy frameworks including differential privacy, Pufferfish privacy, Blowfish privacy, dependent differential privacy, inferential privacy, membership privacy and mutual-information based differential privacy.
Photorealistic Facial Synthesis in the Dimensional Affect Space

This paper presents a novel approach for synthesizing facial affect, which is based on our annotating 600,000 frames of the 4DFAB database in terms of valence and arousal. The input of this approach is a pair of these emotional state descriptors and a neutral 2D image of a person to whom the corresponding affect will be synthesized. Given this target pair, a set of 3D facial meshes is selected, which is used to build a blendshape model and generate the new facial affect. To synthesize the affect on the 2D neutral image, 3DMM fitting is performed and the reconstructed face is deformed to generate the target facial expressions. Last, the new face is rendered into the original image. Both qualitative and quantitative experimental studies illustrate the generation of realistic images, when the neutral image is sampled from a variety of well known databases, such as the Aff-Wild, AFEW, Multi-PIE, AFEW-VA, BU-3DFE, Bosphorus.
Bayesian Analysis of Dynamic Linear Topic Models

Discovering temporal evolution of themes from a time-stamped collection of text poses a challenging statistical learning problem.Dynamic topic models offer a probabilistic modeling framework to decompose a corpus of text documents into "topics", i.e., probability distributions over vocabulary terms, while simultaneously learning the temporal dynamics of the relative prevalence of these topics.We extend the dynamic topic model of Blei and Lafferty (2006) by fusing its multinomial factor model on topics with dynamic linear models that account for time trends and seasonality in topic prevalence.A Markov chain Monte Carlo (MCMC) algorithm that utilizes Pólya-Gamma data augmentation is developed for posterior sampling.Conditional independencies in the model and sampling are made explicit, and our MCMC algorithm is parallelized where possible to allow for inference in large corpora.Our model and inference algorithm are validated with multiple synthetic examples, and we consider the applied problem of modeling trends in real estate listings from the housing website Zillow.We demonstrate in synthetic examples that sharing information across documents is critical for accurately estimating document-specific topic proportions.Analysis of the Zillow corpus demonstrates that the method is able to learn seasonal patterns and locally linear trends in topic prevalence.
Scene Coordinate Regression with Angle-Based Reprojection Loss for Camera Relocalization

Image-based camera relocalization is an important problem in computer vision and robotics. Recent works utilize convolutional neural networks (CNNs) to regress for pixels in a query image their corresponding 3D world coordinates in the scene. The final pose is then solved via a RANSAC-based optimization scheme using the predicted coordinates. Usually, the CNN is trained with ground truth scene coordinates, but it has also been shown that the network can discover 3D scene geometry automatically by minimizing single-view reprojection loss. However, due to the deficiencies of the reprojection loss, the network needs to be carefully initialized. In this paper, we present a new angle-based reprojection loss, which resolves the issues of the original reprojection loss. With this new loss function, the network can be trained without careful initialization, and the system achieves more accurate results. The new loss also enables us to utilize available multi-view constraints, which further improve performance.
Face Photo-Sketch Synthesis via Knowledge Transfer

Despite deep neural networks have demonstrated strong power in face photo-sketch synthesis task, their performance, however, are still limited by the lack of training data (photo-sketch pairs). Knowledge Transfer (KT), which aims at training a smaller and fast student network with the information learned from a larger and accurate teacher network, has attracted much attention recently due to its superior performance in the acceleration and compression of deep neural networks. This work has brought us great inspiration that we can train a relatively small student network on very few training data by transferring knowledge from a larger teacher model trained on enough training data for other tasks. Therefore, we propose a novel knowledge transfer framework to synthesize face photos from face sketches or synthesize face sketches from face photos. Particularly, we utilize two teacher networks trained on large amount of data in related task to learn the knowledge of face photos and face sketches separately and transfer them to two student networks simultaneously. In addition, the two student networks, one for photo ? sketch task and the other for sketch ? photo task, can transfer their knowledge mutually. With the proposed method, we can train our model which has superior performance using a small set of photo-sketch pairs. We validate the effectiveness of our method across several datasets. Quantitative and qualitative evaluations illustrate that our model outperforms other state-of-the-art methods in generating face sketches (or photos) with high visual quality and recognition ability.
A tight runtime analysis for the (1 + (λ, λ)) GA on leadingones

We conduct a rigorous runtime analysis of the (1 + (λ, λ)) evolutionary algorithm with standard parameter settings, that is, a mutation rate of p = λ/n and a crossover bias of c = 1/λ when optimizing the classic LeadingOnes benchmark function. We show that, for all λ ∈ [1..n/2], the runtime is Θ(n2/λ) iterations and Θ(n2) fitness evaluations. This is, asymptotically, the same number of iterations as for the (1 + λ) EA and the same number of fitness evaluations as for the (1 + λ) EA for any value of λ = O(n). We also extend our results to parameter control techniques and prove that for any dynamic choice of λ the bound of Θ(n2) fitness evaluations still holds.
GACOforRec: Session-Based Graph Convolutional Neural Networks Recommendation Model

The biggest challenge to recommendation systems based on user preferences is how to improve the ability of the recommendation system to mine and analyse user preferences and behaviours. In this process, we must not only consider the continuation of the user's long-term preference but also improve the system's ability to accommodate short-term preferences and discrete preferences. To this end, we focus on the performance of time factors of user preferences. However, the issue we are concerned about has not received much attention in the existing research. We propose a new recommendation model based on the perspective of user sessions, namely GACOforRec. This model can handle long-term and stable preferences at the same time and preserve the hierarchy of potential preferences. We conducted a large number of comparative experiments on two real datasets, and the results show that GACOforRec is significantly better than other state-of-the-art methods in the study of user sessions.
CaRe: Open Knowledge Graph Embeddings

Open Information Extraction (OpenIE) methods are effective at extracting (noun phrase, relation phrase, noun phrase) triples from text, e.g., (Barack Obama, took birth in, Honolulu).Organization of such triples in the form of a graph with noun phrases (NPs) as nodes and relation phrases (RPs) as edges results in the construction of Open Knowledge Graphs (OpenKGs).In order to use such OpenKGs in downstream tasks, it is often desirable to learn embeddings of the NPs and RPs present in the graph.Even though several Knowledge Graph (KG) embedding methods have been recently proposed, all of those methods have targeted Ontological KGs, as opposed to OpenKGs.Straightforward application of existing Ontological KG embedding methods to OpenKGs is challenging, as unlike Ontological KGs, OpenKGs are not canonicalized, i.e., a real-world entity may be represented using multiple nodes in the OpenKG, with each node corresponding to a different NP referring to the entity.For example, nodes with labels Barack Obama, Obama, and President Obama may refer to the same real-world entity Barack Obama.Even though canonicalization of OpenKGs has received some attention lately, output of such methods has not been used to improve OpenKG embeddings.We fill this gap in the paper and propose Canonicalization-infused Representations (CaRe) for OpenKGs.Through extensive experiments, we observe that CaRe enables existing models to adapt to the challenges in OpenKGs and achieve substantial improvements for the link prediction task. USA
PANLP at MEDIQA 2019: Pre-trained Language Models, Transfer Learning and Knowledge Distillation

This paper describes the models designated for the MEDIQA 2019 shared tasks by the team PANLP.We take advantages of the recent advances in pre-trained bidirectional transformer language models such as BERT (Devlin et al., 2018) and MT-DNN (Liu et al., 2019b).We find that pre-trained language models can significantly outperform traditional deep learning models.Transfer learning from the NLI task to the RQE task is also experimented, which proves to be useful in improving the results of fine-tuning MT-DNN large.A knowledge distillation process is implemented, to distill the knowledge contained in a set of models and transfer it into an single model, whose performance turns out to be comparable with that obtained by the ensemble of that set of models.Finally, for test submissions, model ensemble and a re-ranking process are implemented to boost the performances.Our models participated in all three tasks and ranked the 1st place for the RQE task, and the 2nd place for the NLI task, and also the 2nd place for the QA task.
ShrinkML: End-to-End ASR Model Compression Using Reinforcement Learning

End-to-end automatic speech recognition (ASR) models are increasingly large and complex to achieve the best possible accuracy. In this paper, we build an AutoML system that uses reinforcement learning (RL) to optimize the per-layer compression ratios when applied to a state-of-the-art attention based end-to-end ASR model composed of several LSTM layers. We use singular value decomposition (SVD) low-rank matrix factorization as the compression method. For our RL-based AutoML system, we focus on practical considerations such as the choice of the reward/punishment functions, the formation of an effective search space, and the creation of a representative but small data set for quick evaluation between search steps. Finally, we present accuracy results on LibriSpeech of the model compressed by our AutoML system, and we compare it to manually-compressed models. Our results show that in the absence of retraining our RL-based search is an effective and practical method to compress a production-grade ASR system. When retraining is possible, we show that our AutoML system can select better highly-compressed seed models compared to manually hand-crafted rank selection, thus allowing for more compression than previously possible.
Identification and authentication for wireless transmission security based on RF-DNA fingerprint

Abstract For wireless transmission, radio-frequency device anti-cloning has become a major security issue. Radio-frequency distinct native attribute (RF-DNA) fingerprint is a developing technology to find the difference among RF devices and identify them. Comparing with previous research, (1) this paper proposed that mean ( μ ) feature should be added into RF-DNA fingerprint. Thus, totally four statistics (mean, standard deviation, skewness, and kurtosis) were calculated on instantaneous amplitude, phase, and frequency generated by Hilbert transform. (2) We first proposed using the logistic regression (LR) and support vector machine (SVM) to recognize such extracted fingerprint at different signal-to-noise ratio (SNR) environment. We compared their performance with traditional multiple discriminant analysis (MDA). (3) In addition, this paper also proposed to extract three sub-features (amplitude, phase, and frequency) separately to recognize extracted fingerprint under MDA. In order to make our results more universal, additive white Gaussian noise was adopted to simulate the real environment. The results show that (1) mean feature conducts an improvement in the classification accuracy, especially in low SNR environment. (2) MDA and SVM could successfully identify these RF devices, and the classification accuracy could reach 94%. Although the classification accuracy of LR is 89.2%, it could get the probability of each class. After adding a different noise, the recognition accuracy is more than 80% when SNR ≥5 dB using MDA or SVM. (3) Frequency feature has more discriminant information. Phase and amplitude play an auxiliary but also pivotal role in classification recognition.
A condition for scattered linearized polynomials involving Dickson matrices

A linearized polynomial over $${{\mathbb {F}}}_{q^n}$$ is called scattered when for any $$t,x\in {{\mathbb {F}}}_{q^n}$$, the condition $$xf(t)-tf(x)=0$$ holds if and only if x and t are $${\mathbb {F}}_q$$-linearly dependent. General conditions for linearized polynomials over $${{\mathbb {F}}}_{q^n}$$ to be scattered can be deduced from the recent results in Csajbók (Scalar q-subresultants and Dickson matrices, 2018), Csajbók et al. (Finite Fields Appl 56:109–130, 2019), McGuire and Sheekey (Finite Fields Appl 57:68–91, 2019), Polverino and Zullo (On the number of roots of some linearized polynomials, 2019). Some of them are based on the Dickson matrix associated with a linearized polynomial. Here a new condition involving Dickson matrices is stated. This condition is then applied to the Lunardon–Polverino binomial $$x^{q^s}+\delta x^{q^{n-s}}$$, allowing to prove that for any n and s, if $${{\,\mathrm{N}\,}}_{q^n/q}(\delta )=1$$, then the binomial is not scattered. Also, a necessary and sufficient condition for $$x^{q^s}+bx^{q^{2s}}$$ to be scattered is shown which is stated in terms of a special plane algebraic curve.
EKF-Based Parameter Identification of Multi-Rotor Unmanned Aerial VehiclesModels

This work presents a method for estimating the model parameters of multi-rotor unmanned aerial vehicles by means of an extended Kalman filter. Different from test-bed based identification methods, the proposed approach estimates all the model parameters of a multi-rotor aerial vehicle, using a single online estimation process that integrates measurements that can be obtained directly from onboard sensors commonly available in this kind of UAV. In order to develop the proposed method, the observability property of the system is investigated by means of a nonlinear observability analysis. First, the dynamic models of three classes of multi-rotor aerial vehicles are presented. Then, in order to carry out the observability analysis, the state vector is augmented by considering the parameters to be identified as state variables with zero dynamics. From the analysis, the sets of measurements from which the model parameters can be estimated are derived. Furthermore, the necessary conditions that must be satisfied in order to obtain the observability results are given. An extensive set of computer simulations is carried out in order to validate the proposed method. According to the simulation results, it is feasible to estimate all the model parameters of a multi-rotor aerial vehicle in a single estimation process by means of an extended Kalman filter that is updated with measurements obtained directly from the onboard sensors. Furthermore, in order to better validate the proposed method, the model parameters of a custom-built quadrotor were estimated from actual flight log data. The experimental results show that the proposed method is suitable to be practically applied.