Reviewerly/exHarmony huggingface dataset:
Specific Emitter Identification based on Multi-Domain Features Learning

Specific emitter identification (SEI) is a technology to extract the subtle fingerprint features of the received electromagnetic signal, and identify the emitters to which the signal belongs. It has important applications in military and civil occasions. Traditionally, expert-experience is used for feature extraction, which is time-consuming and unstable. In order to overcome this shortcoming, this paper proposes an Intelligent Radiometric Identification algorithm base on Time and Frequency domain feature Fusion (IRI-TFF) which uses deep learning technology. The algorithm designs a new multi-domain fused one-dimensional complex-valued densely connected convolutional network (DenseNet) model after the accurate “calibration” preprocessing of the received signal and the combination of time and frequency domain data as training examples. Meanwhile, three fusion strategies are proposed. The experimental results show that the proposed algorithm is superior to the traditional expert-experience based SEI algorithm or other similar deep learning based SEI algorithm, and is robust to noises.
MetaAugment: Sample-Aware Data Augmentation Policy Learning

Automated data augmentation has shown superior performance in image recognition. Existing works search for dataset-level augmentation policies without considering individual sample variations, which are likely to be sub-optimal. On the other hand, learning different policies for different samples naively could greatly increase the computing cost. In this paper, we learn a sample-aware data augmentation policy efficiently by formulating it as a sample reweighting problem. Specifically, an augmentation policy network takes a transformation and the corresponding augmented image as inputs, and outputs a weight to adjust the augmented image loss computed by a task network. At training stage, the task network minimizes the weighted losses of augmented training images, while the policy network minimizes the loss of the task network on a validation set via meta-learning. We theoretically prove the convergence of the training procedure and further derive the exact convergence rate. Superior performance is achieved on widely-used benchmarks including CIFAR-10/100, Omniglot, and ImageNet.
Model-based segmentation using neural network-based boundary detectors: Application to prostate and heart segmentation in MR images

Model-based segmentation (MBS) is a variant of active surfaces and active shape models that has successfully been used to segment anatomical structures such as the heart or the brain. We propose to integrate neural networks (NNs) into MBS for boundary detection. We formulate boundary detection as a regression task and use a NN to predict the distances between a surface mesh and the corresponding boundary points. The proposed approach has been applied to two tasks — prostate segmentation in MR images and the segmentation of the left and right ventricle in MR images. For the first task, data from the Prostate MR Image Segmentation 2012 (PROMISE12) challenge has been used. For the second task, a diverse database with cardiac MR images from six clinical sites has been used. We compare the results to the popular U-net approaches using the nnU-net implementation that is among the top performing segmentation algorithms in various challenges. In cross-validation experiments, the mean Dice scores are very similar and no statistically significant difference is observed. On the PROMISE12 test set, nnU-net Dice scores are significantly better. This is achieved by using an ensemble of 2D and 3D U-nets to generate the final segmentation, a concept that may also be adapted to NN-based boundary detection in the future. While the U-net provides a voxel labeling, our approach provides a 3D surface mesh with pre-defined mesh topology, establishes correspondences with respect to the reference mesh, avoids isolated falsely segmented regions and ensures proper connectivity of different regions.
PENGEMBANGAN MEDIA PEMBELAJARAN PERANGKAT KERAS JARINGAN BERBASIS AUGMENTED REALITY PADA PLATFORM ANDROID

The limitedness number of teaching media such as projector and networking hardware in SMK N 2 of padang causes the learning activities especially WAN (Wide Area Network) Technology to be not optimal. Therefore, the material learning activities become one-way and unattractive. The school prohibit students to borrow networking hardware, so that the process of recognizing the physical form of network devices occurs only during laboratory activities. These learning causes inefficient use of study time. Students often abuse the use of smartphones in school as entertainment media. ARSchool networking Hardware Learning Media was developed to overcome this problem. The Marker Based Tracking Method of Augmented Reality technology was utilized to display three-dimensional objects (3D) on an Android Smartphone. The 3D objects of network devices was designed by using the Blender application. The making of media was based on WAN Technology syllabus used by school. The design of media used Unity and Vuforia application as database of image target or marker. With this learning, students could easily learn the networking hardware of WAN Technology anywhere and anytime. The feasibility test shows that this learning media is very practical (95,45%) and very valid (94,47%). This media is equipped with the explanation of parts of the devices that is displayed in the form of text, image and audioKeywords: Learning Media, Augmented Reality, Android, Network Hardware.
Cloud based private data analytic using secure computation over encrypted data

Secure and efficient analytic over sensitive data becomes nowadays a big challenge for some enterprises (medical health care, insurance, etc). While these companies are collecting a large amount of sensitive data, cloud system is offering important opportunities for outsourcing of storage and computation. Thus, preserving the data privacy while maintaining the efficiency in computation at the cloud is considered an attractive research topic for computer scientists in academia as well as in industry. Homomorphic Encryption (HE) came as a new cryptographic topic that allows computation over encrypted data at the cloud side. Paillier is a famous HE scheme, that allows secure addition and average calculation over encrypted data. It presents the efficiency in implementation while preserving the security. In this paper, a new HE scheme competent for Paillier crypto-system is proposed. The presented scheme is called Secure Analytic using Vector based Homomorphic Operation (SAVHO). A deep security analysis has shown that the new scheme is resistant against several types of attacks. In addition, performance analysis has shown that SAVHO is a practical scheme by comparison with Pailler crypto-system. Different implementations are achieved under Python using SageMath Library.
Cooperative coevolutionary multiobjective genetic programming for microarray data classification

DNA microarray data contain valuable biological information, which makes it of great importance in disease analysis and cancer diagnosis. However, the classification of microarray data is still a challenging task because of the high dimension and small sample size, especially for multiclass data accompanied by class imbalance. In this paper, we propose a cooperative coevolutionary multiobjective genetic programming (CC-MOGP) for microarray data classification. It converts a multiclass problem into a set of tractable binary problems and coevolves the corresponding population. And a cooperative coevolutionary Pareto archived evolution strategy (CC-PAES) is employed to approximate the Pareto front. During this procedure, we propose a synergy test method to assist in guiding the coevolution between populations. Experimental results on 8 multiclass microarray data show that CC-MOGP can obtain competitive prediction accuracy compared with several state-of-art evolutionary computation and traditional methods.
Integration of “Offline + Online” Teaching Method of College English Based on Web Search Technology

The change of social demand for English application-oriented talents has affected the transformation of the way of cultivating talents in College English. The traditional training mode of indoctrination has exposed more and more disadvantages, and the demand of new teaching mode is imminent. This paper first analyzes the current situation of College English learning platform in China. Then, it discusses the design and implementation of “Online + offline” College English learning platform based on Web. The overall structure and function of the database are designed in detail. The teaching platform provides students with learning tools, learning resources, communication platform, testing and evaluation functions, and can evaluate students’ learning behavior, learning process and learning effect. This paper traces and collects a large amount of data left by learners in learning college English courses, and analyzes learners’ learning habits, learning progress and learning effect. Finally, according to the learning big data, this paper customized a reasonable personalized learning platform and improved the online teaching personalized service system.
Gaussian process regression for predicting water quality index: A case study on Ping River basin, Thailand

<abstract> The water quality index (WQI) is an aggregated indicator used to represent the overall quality of water for any intended use. It is typically calculated from several biological, chemical, and physical parameters. Assessment of factors that affect the WQI is then essential. Climate change is expected to impact a wide range of water quality issues; hence, climate variables are likely to be significant factors to evaluate the WQI. We propose three statistical models; multiple linear regression (MLR), artificial neuron network (ANN), and Gaussian process regression (GPR) to assess the WQI using the climate variables. The data is the WQI of Ping River, which flows through the provinces in the north of Thailand. The climate variables are temperature, humidity, total rainfall, and evaporation. A comparison between these models is determined by model prediction accuracy scores. The results show that the total rainfall is the most significant variable to predict the WQI for the Ping River. Although these three methods can predict the WQI relatively good, overall, the GPR model performs better than the MLR and the ANN. Besides, the GPR is more flexible as it can relax some restrictions and assumptions. Therefore, the GPR is appropriate to assess the WQI under the climate variables for the Ping River. </abstract>
The Neural Correlates of Computational Thinking: Collaboration of Distinct Cognitive Components Revealed by fMRI

Recent technical advance attracts great attention to the promotion of programming skills, in particular, and computational thinking (CT), in general, as a new intellectual competency. However, the understanding of its cognitive substrates is limited. The present study used functional magnetic resonance imaging to examine the neural correlates of programming to understand the cognitive substrates of CT. Specifically, magnetic resonance imaging signals were collected while the participants were mentally solving programming problems, and we found that CT recruited distributed cortical regions, including the posterior parietal cortex, the medial frontal cortex, and the left lateral frontal cortex. These regions showed extensive univariate and multivariate resemblance with arithmetic, reasoning, and spatial cognition tasks. Based on the resemblance, clustering analyses revealed that cortical regions involved in CT can be divided into Reasoning, Calculation, Visuospatial, and Shared components. Further, connectivity increased during programming within the CT network constructed by these four components and decreased between the CT network and other cortical regions. In sum, our study revealed the cognitive components underlying CT and their neural correlates and further suggests that CT is not a simple sum of parallel cognitive processes, but a composite cognitive process integrating a set of intellectual abilities, particularly those in the science, technology, engineering, and math domains.
Predictive and discriminative localization of pathology using high resolution class activation maps with CNNs

Existing class activation mapping (CAM) techniques extract the feature maps only from a single layer of the convolutional neural net (CNN), generally from the final layer and then interpolate to upsample to the original image resolution to locate the discriminative regions. Consequently these provide a coarse localization that may not be able to capture subtle abnormalities in medical images. To alleviate this, our work proposes a technique called high resolution class activation mapping (HR-CAMs) that can provide enhanced visual explainability to the CNN models.HR-CAMs fuse feature maps by training a network using the input from multiple layers of a trained CNN, thus gaining information from every layer that can localize abnormalities with greater details in original image resolution. The technique is validated qualitatively and quantitatively on a simulated dataset of 8,000 images followed by applications on multiple image analysis tasks that include (1) skin lesion classification (ISIC open dataset-25,331 cases) and (2) predicting bone fractures (MURA open dataset-40,561 images) (3) predicting Parkinson's disease (PD) from neuromelanin sensitive MRI (small cohort-80 subjects).We demonstrate that our model creates clinically interpretable subject specific high resolution discriminative localizations when compared to widely used CAMs and Gradient-CAMs.HR-CAMs provide finer delineation of abnormalities thus facilitating superior explainability to CNNs as has been demonstrated from its rigorous validation.
Near optimal compression of orthonormal wavelet expansions

We consider the problem of best approximation of a function by finite linear combinations of orthonormal wavelets with the error measured in a standard distribution space. We show that for a large class of spaces an algorithm based only on the size of the coefficients is essentially optimal. This allows us to sharpen some earlier results on nonlinear approximation by wavelets and to establish some new characterizations of functions with a given degree of approximation. We relate these results to the classical problem of comparing the Lebesgue space Lp to the sequence space lp , and obtain best possible imbeddings in terms of the discrete Lorentz spaces. We also consider compression of operators acting on certain Besov spaces, and characterize the operators that can be compressed well in terms of the size of the (rectangular) wavelet coefficients of the operator kernels.
On End-to-End White-Box Adversarial Attacks in Music Information Retrieval

Small adversarial perturbations of input data can drastically change the performance of machine learning systems, thereby challenging their validity. We compare several adversarial attacks targeting an instrument classifier, where for the first time in Music Information Retrieval (MIR) the perturbations are computed directly on the waveform. The attacks can reduce the accuracy of the classifier significantly, while at the same time keeping perturbations almost imperceptible. Furthermore, we show the potential of adversarial attacks being a security issue in MIR by artificially boosting playcounts through an attack on a real-world music recommender system.
End-to-End Speech Translation with Pre-trained Models and Adapters: UPC at IWSLT 2021

This paper describes the submission to the IWSLT 2021 offline speech translation task by the UPC Machine Translation group.The task consists of building a system capable of translating English audio recordings extracted from TED talks into German text.Submitted systems can be either cascade or end-to-end and use a custom or given segmentation.Our submission is an end-to-end speech translation system, which combines pre-trained models (Wav2Vec 2.0 and mBART) with coupling modules between the encoder and decoder, and uses an efficient fine-tuning technique, which trains only 20% of its total parameters.We show that adding an Adapter to the system and pre-training it, can increase the convergence speed and the final result, with which we achieve a BLEU score of 27.3 on the MuST-C test set.Our final model is an ensemble that obtains 28.22 BLEU score on the same set.Our submission also uses a custom segmentation algorithm that employs pre-trained Wav2Vec 2.0 for identifying periods of untranscribable text and can bring improvements of 2.5 to 3 BLEU score on the IWSLT 2019 test set, as compared to the result with the given segmentation.
Team “DaDeFrNi” at CASE 2021 Task 1: Document and Sentence Classification for Protest Event Detection

This paper accompanies our top-performing submission to the CASE 2021 shared task, which is hosted at the workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text.Subtasks 1 and 2 of Task 1 concern the classification of newspaper articles and sentences into "conflict" versus "not conflict"-related in four different languages.Our model performs competitively in both subtasks (up to 0.8662 macro F1), obtaining the highest score of all contributions for subtask 1 on Hindi articles (0.7877 macro F1).We describe all experiments conducted with the XLM-RoBERTa (XLM-R) model and report results obtained in each binary classification task.We propose supplementing the original training data with additional data on political conflict events.In addition, we provide an analysis of unigram probability estimates and geospatial references contained within the original training corpus.
On Coresets for Fair Clustering in Metric and Euclidean Spaces and Their Applications.

Fair clustering is a variant of constrained clustering where the goal is to partition a set of colored points. The fraction of points of each color in every cluster should be more or less equal to the fraction of points of this color in the dataset. This variant was recently introduced by Chierichetti et al. [NeurIPS 2017] and became widely popular. This paper proposes a new construction of coresets for fair k-means and k-median clustering for Euclidean and general metrics based on random sampling. For the Euclidean space ℝ^d, we provide the first coresets whose size does not depend exponentially on the dimension d. The question of whether such constructions exist was asked by Schmidt, Schwiegelshohn, and Sohler [WAOA 2019] and Huang, Jiang, and Vishnoi [NeurIPS 2019]. For general metric, our construction provides the first coreset for fair k-means and k-median.New coresets appear to be a handy tool for designing better approximation and streaming algorithms for fair and other constrained clustering variants. In particular, we obtain - the first fixed-parameter tractable (FPT) PTAS for fair k-means and k-median clustering in ℝ^d. The near-linear time of our PTAS improves over the previous scheme of Bohm, Fazzone, Leonardi, and Schwiegelshohn [ArXiv 2020] with running time n^{poly(k/e)};- FPT true constant-approximation for metric fair clustering. All previous algorithms for fair k-means and k-median in general metric are bicriteria and violate the fairness constraints; - FPT 3-approximation for lower-bounded k-median improving the best-known 3.736 factor of Bera, Chakrabarty, and Negahbani [ArXiv 2019];- the first FPT constant-approximations for metric chromatic clustering and 𝓁-Diversity clustering; - near linear-time (in n) PTAS for capacitated and lower-bounded clustering improving over PTAS of Bhattacharya, Jaiswal, and Kumar [TOCS 2018] with super-quadratic running time;- a streaming (1+e)-approximation for fair k-means and k-median of space complexity polynomial in k, d, e and log{n} (the previous algorithms have exponential space complexity on either d or k).
MultiRPN-DIDNet: Multiple RPNs and Distance-IoU Discriminative Network for Real-Time UAV Target Tracking

Target tracking in low-altitude Unmanned Aerial Vehicle (UAV) videos faces many technical challenges due to the relatively small sizes, various orientation changes of the objects and diverse scenes. As a result, the tracking performance is still not satisfactory. In this paper, we propose a real-time single-target tracking method with multiple Region Proposal Networks (RPNs) and Distance-Intersection-over-Union (Distance-IoU) Discriminative Network (DIDNet), namely MultiRPN-DIDNet, in which ResNet50 is used as the backbone network for feature extraction. Firstly, an instance-based RPN suitable for the target tracking task is constructed under the framework of Simases Neural Network. RPN is to perform bounding box regression and classification, in which channel attention mechanism is integrated to improve the representative capability of the deep features. The RPNs built on the Block 2, Block 3 and Block 4 of ResNet50 output their own Regression (Reg) coefficients and Classification scores (Cls) respectively, which are weighted and then fused to determine the high-quality region proposals. Secondly, a DIDNet is designed to correct the candidate target’s bounding box finely through the fusion of multi-layer features, which is trained with the Distance-IoU loss. Experimental results on the public datasets of UAV20L and DTB70 show that, compared with the state-of-the-art UAV trackers, the proposed MultiRPN-DIDNet can obtain better tracking performance with fewer region proposals and correction iterations. As a result, the tracking speed has reached 33.9 frames per second (FPS), which can meet the requirements of real-time tracking tasks.
Extracting chemical-induced disease relation by integrating a hierarchical concentrative attention and a hybrid graph-based neural network

Extracting the chemical-induced disease relation from literatures is important for biomedical research. On one hand, it is challenging to capture the interactions among remote words and the long-distance information is not adequately exploited by existing systems for document-level relation extraction. On the other hand, there is some information particularly important to the target relations in documents, which should attract more attention than the less relevant information for the relation extraction. However, this issue is not well addressed in existing methods. In this paper, we present a method that integrates a hybrid graph and a hierarchical concentrative attention to overcome these problems. The hybrid graph is constructed by synthesizing the syntactic graph and Abstract Meaning Representation graph to acquire the long-distance information for document-level relation extraction. Meanwhile, the concentrative attention is used to focus on the most important information, and alleviate the disturbance brought by the less relevant items in the document. The experimental results demonstrate that our model yields competitive performance on the dataset of chemical-induced disease relations.