Reviewerly/exHarmony huggingface dataset:
Benchmarking Deep Learning Interpretability in Time Series Predictions

Saliency methods are used extensively to highlight the importance of input features in model predictions. These methods are mostly used in vision and language tasks, and their applications to time series data is relatively unexplored. In this paper, we set out to extensively compare the performance of various saliency-based interpretability methods across diverse neural architectures, including Recurrent Neural Network, Temporal Convolutional Networks, and Transformers in a new benchmark of synthetic time series data. We propose and report multiple metrics to empirically evaluate the performance of saliency methods for detecting feature importance over time using both precision (i.e., whether identified features contain meaningful signals) and recall (i.e., the number of features with signal identified as important). Through several experiments, we show that (i) in general, network architectures and saliency methods fail to reliably and accurately identify feature importance over time in time series data, (ii) this failure is mainly due to the conflation of time and feature domains, and (iii) the quality of saliency maps can be improved substantially by using our proposed two-step temporal saliency rescaling (TSR) approach that first calculates the importance of each time step before calculating the importance of each feature at a time step.
Solar Power Forecasting Based on Domain Adaptive Learning

Solar power forecasting is critical to ensure the safety and stability of the power grid with high photovoltaic power penetration.Machine learning methods are compelling in solar forecasting.These methods can capture the complex coupling relationship between different meteorological factors without physical modeling.Most of the existing machine learning based forecasts follow the batch learning manner.Once the training is completed, the structure and parameters of the model are usually no longer adjusted.However, the climate is complex and dynamic.It is difficult for a fixed model to adapt to the climate characteristics of different regions or periods.Therefore, an online domain adaptive learning approach is proposed in this paper.Knowledge can be selectively accumulated or forgotten in its iterative process.As weather changes, the model can dynamically adjust its structure to adapt to the latest weather conditions.Unlike existing adaptive iterative methods, the proposed adaptive learning approach does not rely on the labels of the test data in the updating process.Experiments show that this method can effectively track changes in data distribution and obtain reliable prediction results.
GRAMEVAL 2020 SHARED TASK: RUSSIAN FULL MORPHOLOGY AND UNIVERSAL DEPENDENCIES PARSING

The paper presents the results of GramEval 2020, a shared task on Russian morphological and syntactic processing. The objective is to process Russian texts starting from provided tokens to parts of speech (pos), grammatical features, lemmas, and labeled dependency trees. To encourage the multi-domain processing, five genres of Modern Russian are selected as test data: news, social media and electronic communication, wiki-texts, fiction, poetry; Middle Russian texts are used as the sixth test set. The data annotation follows the Universal Dependencies scheme. Unlike in many similar tasks, the collection of existing resources, the annotation of which is not perfectly harmonized, is provided for training, so the variability in annotations is a further source of difficulties. The main metric is the average accuracy of pos, features, and lemma tagging, and LAS. In this report, the organizers of GramEval 2020 overview the task, training and test data, evaluation methodology, submission routine, and participating systems. The approaches proposed by the participating systems and their results are reported and analyzed.
Denoised Smoothing: A Provable Defense for Pretrained Classifiers

We present a method for provably defending any pretrained image classifier against lp adversarial attacks. This method, for instance, allows public vision API providers and users to seamlessly convert pretrained non-robust classification services into provably robust ones. By prepending a custom-trained denoiser to any off-the-shelf image classifier and using randomized smoothing, we effectively create a new classifier that is guaranteed to be lp-robust to adversarial examples, without modifying the pretrained classifier. Our approach applies to both the white-box and the black-box settings of the pretrained classifier. We refer to this defense as denoised smoothing, and we demonstrate its effectiveness through extensive experimentation on ImageNet and CIFAR-10. Finally, we use our approach to provably defend the Azure, Google, AWS, and ClarifAI image classification APIs. Our code replicating all the experiments in the paper can be found on GitHub.
Development of a Seamless Forecast for Solar Radiation Using ANAKLIM++

A novel approach for a blending between nowcasting and numerical weather prediction (NWP) for the surface incoming shortwave radiation (SIS) for a forecast horizon of 1–5 h is presented in this study. The blending is performed with a software tool called ANAKLIM++ (Adjustment of Assimilation Software for the Reanalysis of Climate Data) which was originally designed for the efficient assimilation of two-dimensional data sets using a variational approach. A nowcasting for SIS was already presented and validated in earlier publications as seamless solar radiation forecast (SESORA). For our blending, two NWP models, namely the ICON (Icosahedral Non-hydrostatic model) from the German weather Service (DWD) and the IFS (Integrated Forecasting System) from the European Centre for Medium-Range Weather Forecasts (ECMWF), were used. The weights for the input data for ANAKLIM++ vary for every single forecast time and pixel, depending on the error growth of the nowcasting. The results look promising, since the root mean square error (RMSE) and mean absolute error (MAE) of the blending are smaller than the error measures of the nowcasting or NWP models, respectively.
Variable-Depth Convolutional Neural Network for Text Classification

This article introduces a recurrent CNN based framework for the classification of arbitrary length text in natural sentence. In our model, we present a complete CNN design with recurrent structure to capture the contextual information as far as possible when learning sentences, which allows arbitrary-length sentences and more flexibility to analyze complete sentences compared with traditional CNN based neural networks. In addition, our model greatly reduces the number of layers in the architecture and requires fewer training parameters, which leads to less memory consumption, and it can reach $$O\left( \log n\right) $$ time complexity. As a result, this model can achieve enhancement in training accuracy. Moreover, the design and implementation can be easily deployed in the current text classification systems.
Investigating representations of verb bias in neural language models

Languages typically provide more than one grammatical construction to express certain types of messages. A speaker’s choice of construction is known to depend on multiple factors, including the choice of main verb – a phenomenon known as verb bias. Here we introduce DAIS, a large benchmark dataset containing 50K human judgments for 5K distinct sentence pairs in the English dative alternation. This dataset includes 200 unique verbs and systematically varies the definiteness and length of arguments. We use this dataset, as well as an existing corpus of naturally occurring data, to evaluate how well recent neural language models capture human preferences. Results show that larger models perform better than smaller models, and transformer architectures (e.g. GPT-2) tend to out-perform recurrent architectures (e.g. LSTMs) even under comparable parameter and training settings. Additional analyses of internal feature representations suggest that transformers may better integrate specific lexical information with grammatical constructions.
Spatial-light-modulator-based dual shearing direction shearography

We propose a dual shearing shearography system based on a spatial light modulator (SLM). Compared to spatial phase shift shearography, the advantages of this system include its simple structure, relatively high light efficiency, and good phase map quality. Digital shearography is a fast, practical, non-contact, whole-field, and anti-turbulent optical approach to non-destructive testing (NDT) and strain measurement. Because the shearing direction determines the strain direction being measured, tests using multiple shearing directions are sometimes required to obtain strain in different directions and detect all defects. Various setups, based on the spatial phase shift method, have been proposed to solve the issue. While some of these setups perform well, they may also introduce new problems, such as poor phase map quality and low light efficiency. We present a sequential dual shearing shearographic system with good phase map quality and high light efficiency. Due to the SLM’s high-speed response, capable of reaching hundreds of hertz, SLM-based dual shearing direction shearography allows for fast temporal phase shifting and shearing direction switching while providing very good phase map quality. Unlike the spatial phase shift method, which has low light efficiency due to its need for a small aperture to enable a relatively large speckle size to cover multiple pixels, the proposed method is based on a fast temporal phase shift and does not have this limitation. In addition, SLM can provide a programmable and adjustable shearing method in any direction and distance, which is beneficial for strain measurements and NDT requiring strain measurements in different directions using a small and precise shearing distance. We describe in detail the theory derivation and non-destructive testing application results for the SLM-based dual shearing direction shearography system.
Heuristic Domain Adaptation

In visual domain adaptation (DA), separating the domain-specific characteristics from the domain-invariant representations is an ill-posed problem. Existing methods apply different kinds of priors or directly minimize the domain discrepancy to address this problem, which lack flexibility in handling real-world situations. Another research pipeline expresses the domain-specific information as a gradual transferring process, which tends to be suboptimal in accurately removing the domain-specific properties. In this paper, we address the modeling of domain-invariant and domain-specific information from the heuristic search perspective. We identify the characteristics in the existing representations that lead to larger domain discrepancy as the heuristic representations. With the guidance of heuristic representations, we formulate a principled framework of Heuristic Domain Adaptation (HDA) with well-founded theoretical guarantees. To perform HDA, the cosine similarity scores and independence measurements between domain-invariant and domain-specific representations are cast into the constraints at the initial and final states during the learning procedure. Similar to the final condition of heuristic search, we further derive a constraint enforcing the final range of heuristic network output to be small. Accordingly, we propose Heuristic Domain Adaptation Network (HDAN), which explicitly learns the domain-invariant and domain-specific representations with the above mentioned constraints. Extensive experiments show that HDAN has exceeded state-of-the-art on unsupervised DA, multi-source DA and semi-supervised DA. The code is available at this https URL.
Learning from the Guidance: Knowledge Embedded Meta-learning for Medical Visual Question Answering

Traditional medical visual question answering approaches require a large amount of labeled data for training, but still cannot jointly consider both image and text information. To address this issue, we propose a novel framework called Knowledge Embedded Meta-Learning. In particular, we present a deep relation network to capture and memorize the relation among different samples. First, we introduce the embedding approach to perform feature fusion representation learning. Then, we present the construction of our knowledge graph that relates image with text, as the guidance of our meta-learner. We design a knowledge embedding mechanism to incorporate the knowledge representation into our network. Final result is derived from our relation network by learning to compare the features of samples. Experimental results demonstrate that the proposed approach achieves significantly higher performance compared with other state-of-the-arts.
Vehicle Spatial Distribution and 3D Trajectory Extraction Algorithm in a Cross-Camera Traffic Scene

The three-dimensional trajectory data of vehicles have important practical meaning for traffic behavior analysis. To solve the problems of narrow visual angle in single-camera scenes and lack of continuous trajectories in 3D space by current cross-camera trajectory extraction methods, we propose an algorithm of vehicle spatial distribution and 3D trajectory extraction in this paper. First, a panoramic image of a road with spatial information is generated based on camera calibration, which is used to convert cross-camera perspectives into 3D physical space. Then, we choose YOLOv4 to obtain 2D bounding boxes of vehicles in cross-camera scenes. Based on the above information, 3D bounding boxes around vehicles are built with geometric constraints which are used to obtain projection centroids of vehicles. Finally, by calculating the spatial distribution of projection centroids in the panoramic image, 3D trajectories of vehicles are extracted. The experimental results indicate that our algorithm can effectively complete vehicle spatial distribution and 3D trajectory extraction in various traffic scenes, which outperforms other comparison algorithms.
Towards Quantum Satellite Internetworking: A Software-Defined Networking Perspective

Recently, quantum computing and communications rapidly developed to interconnect heterogeneous quantum devices. In particular, some researchers have been performed about terrestrial quantum communications over typical optical fiber links. However, this technology is affected by extremely high losses that can be faced only through the deployment of several repeaters, which in turn involve impractical costs for end-to-end (E2E) route management. Quantum Satellite Networks (QSNs) can overcome the limitations of terrestrial optical networks, such as a remarkable signal attenuation over long distances and difficulty of intercontinental communications. The recent studies on quantum satellite communications motivated our research towards a Low Earth Orbit (LEO) quantum satellite backbone for interconnecting quantum on Earth Servers in order to achieve an unprecedented computational capacity. Specifically, our paper proposes a near optimum E2E path evaluation procedure allowing an efficient switching in order to maximize the entanglement generation rate. Indeed, this is one of the main issues that involve the Data Link Layer and the Network Layer of the Quantum Internet (QI) protocol stack, which is in its early standardization phase. In particular, the design of our approach is based on the Software-Defined Networking (SDN) paradigm with the aim of minimizing the number of hops for E2E connection and maximizing network capacity. Therefore, we compare distributed and centralized approaches in order to achieve a trade-off between performance and cost.
Garbage object recognition and classification based on Mask Scoring RCNN

In order to protect the ecological environment on which human beings depend for existence and make the society sustainable development, it is more and more necessary to classify the garbage. However, people are not familiar with the classification method, so it is difficult for us to accurately understand the classification of each kind of garbage. In order to guide people to classify garbage correctly, this paper proposes a garbage classification system based on Mask Scoring RCNN. Make a new data set based on Beijing municipal domestic garbage classification criteria, and use Mask Scoring RCNN for training. It is hoped that this system can help people classify garbage accurately and reduce the waste of time caused by garbage classification. The test results show that the garbage classification system in this paper has a high accuracy, and the segmentation recognition effect is better in the case of complex picture, which can efficiently and conveniently complete the garbage classification guidance task.
Design of Graph Filter Using Least-Squares Method with Parameter Norm Penalty

In this paper, the designs of graph filters using leastsquares (LS) method with parameter norm penalty are studied. First, the design problem of the graph filter which can be used to process irregular sensor network data is described. Then, the conventional LS method is employed to design graph filter and its numerical stability problem in high-order filter case is presented. Next, to solve numerical stability problem, the LS method with parameter norm penalty is applied to design the graph filter. Finally, the design examples are demonstrated to show the effectiveness of the proposed design method.
Fill your Boots: Enhanced Embedded Bootloader Exploits via Fault Injection and Binary Analysis

The bootloader of an embedded microcontroller is responsible for guarding the device’s internal (flash) memory, enforcing read/write protection mechanisms. Fault injection techniques such as voltage or clock glitching have been proven successful in bypassing such protection for specific microcontrollers, but this often requires expensive equipment and/or exhaustive search of the fault parameters. When multiple glitches are required (e.g., when countermeasures are in place) this search becomes of exponential complexity and thus infeasible. Another challenge which makes embedded bootloaders notoriously hard to analyse is their lack of debugging capabilities.This paper proposes a grey-box approach that leverages binary analysis and advanced software exploitation techniques combined with voltage glitching to develop a powerful attack methodology against embedded bootloaders. We showcase our techniques with three real-world microcontrollers as case studies: 1) we combine static and on-chip dynamic analysis to enable a Return-Oriented Programming exploit on the bootloader of the NXP LPC microcontrollers; 2) we leverage on-chip dynamic analysis on the bootloader of the popular STM8 microcontrollers to constrain the glitch parameter search, achieving the first fully-documented multi-glitch attack on a real-world target; 3) we apply symbolic execution to precisely aim voltage glitches at target instructions based on the execution path in the bootloader of the Renesas 78K0 automotive microcontroller. For each case study, we show that using inexpensive, open-design equipment, we are able to efficiently breach the security of these microcontrollers and get full control of the protected memory, even when multiple glitches are required. Finally, we identify and elaborate on several vulnerable design patterns that should be avoided when implementing embedded bootloaders.
Sample Complexity of Kalman Filtering for Unknown Systems

In this paper, we consider the task of designing a Kalman Filter (KF) for an unknown and partially observed autonomous linear time invariant system driven by process and sensor noise. To do so, we propose studying the following two step process: first, using system identification tools rooted in subspace methods, we obtain coarse finite-data estimates of the state-space parameters and Kalman gain describing the autonomous system; and second, we use these approximate parameters to design a filter which produces estimates of the system state. We show that when the system identification step produces sufficiently accurate estimates, or when the underlying true KF is sufficiently robust, that a Certainty Equivalent (CE) KF, i.e., one designed using the estimated parameters directly, enjoys provable sub-optimality guarantees. We further show that when these conditions fail, and in particular, when the CE KF is marginally stable (i.e., has eigenvalues very close to the unit circle), that imposing additional robustness constraints on the filter leads to similar sub-optimality guarantees. We further show that with high probability, both the CE and robust filters have mean prediction error bounded by $\tilde O(1/\sqrt{N})$, where $N$ is the number of data points collected in the system identification step. To the best of our knowledge, these are the first end-to-end sample complexity bounds for the Kalman Filtering of an unknown system.
ASSESSMENT OF MATHEMATICS TEACHERS’ PROFESSIONAL COMPETENCE

Development of students’ mathematical skills is associated with quality teaching, which means that mathematics teachers should be able to successfully solve mathematical, teaching, and professional problems. The article aims to describe the assessment system of mathematics school teachers’ professional competence, which helps identify gaps in their training and design tailor-made retraining courses. 2,359 mathematics teachers from 13 regions of Russia participated in the research on 05–29 September 2017. Foremost, we conducted a survey and collected data about their teacher category and teacher expertise. Next, we provided a preliminary diagnostic test to enable the participants to self-assess their subject matter and teaching competencies. After that, they completed a three-part diagnostic test to assess their abilities to solve mathematical, teaching, and professional problems. Finally, the participants conducted video lessons. The three-part diagnostic test and video lessons allowed determining the professional competence level for every mathematics teacher. 24% participants showed level I of professional competence, 44% – level II, 9% – level II; 23% participants did not pass the basic level of professional competence. The results show that the mathematics teachers have difficulties in solving mathematical, teaching, or professional problems so tailor-made retraining courses are required. The developed assessment system underlies designing the courses.
Image denoising method based on variable exponential fractional‐integer‐order total variation and tight frame sparse regularization

This study presents a variational image restoration algorithm based on variable exponential fractional-order total variation (TV), variable exponential integer-order TV and tight frame sparse regularization. The energy functional of this variational problem is composed of four parts: a fractional-order TV regularization term with a variable exponent, an integer-order TV regularization term with a variable exponent, a data fidelity term and a tight frame regularization term. The variable exponent is a function of the gradient information of the image. The first three parts of the energy functional is the smooth part, and the last part is the non-smooth part. To minimize the smooth part, the optimization problem can be simply transformed into a gradient descent flow using the variational method. For the non-smooth part, we use the soft-thresholding method over the sparse framelet coefficients. As the combination of the fractional-order derivative and integer-order derivative, the variable exponent and the sparse regularisation, the proposed method can effectively remove noise of the image, protect the image boundary, and keep image texture details well. Experiments with simulated data and real data are provided to validate the effectiveness of proposed method. This method is robust to noise and is of some practical application value.