Reviewerly/exHarmony huggingface dataset:
Track Segment Association With Dual Contrast Neural Network

Track interruption will cause serious interference to target tracking, situation awareness, and information fusion. And it is often triggered by a variety of reasons, such as target maneuver, the blind spot of Doppler radar, and excessive long sampling interval, among other causes. All the existing algorithms are based on the hypothetical target motion models and adopt the theory of statistical estimation to measure the similarity between predicted tracks to associate, which have some disadvantages such as unreasonable hypotheses, inapplicable models, and uncertain thresholds. In order to address these problems, in this article, we propose a track segment association dual contrast neural network (TSADCNN) to complete track segment association tasks. First, based on deep metric learning, we propose a temporal and spatial information extraction module to extract track information, especially symmetric correlation information and raise dimension. Second, by constructing an appropriate optimization function, TSADCNN makes track segments belonging to the same target closer, track segments belonging to different targets farther in high-dimensional space. Finally, TSADCNN will pick the nearest neighbor vectors in high-dimensional space as associated tracks. Extensive experiments on simulation data are conducted to evaluate the effectiveness of TSADCNN, and the results demonstrate that this method has good adaptability and anti-noise ability. Furthermore, the contrast experiment shows that it can outperform other TSA methods in both quality and efficiency. Compared with the best performance, the average true association rate of TSADCNN can be increased by 1.3% in long interrupt intervals.
Learning to Match Anchor-Target Video Pairs With Dual Attentional Holographic Networks

Video hyperlinking is the task of linking two video fragments/clips based on their multi-modal contents. Specifically, given an anchor video as a query, machine techniques automatically generate links between the anchor and target videos by modeling and comparing their content aboutness. The term “aboutness” specifically refers to contextually relevant multimedia content, i.e., a fragment is on or of something. Since video contents are multi-modal (e.g., audio and vision), the content aboutness may be reflected across different modalities. Existing approaches regard hyperlinking as a retrieval task, by embedding multi-modal video contents into one or multiple common video representation space(s) for cross-modal comparison. As a result, the aboutness between videos is scored by computing the vector-distance based similarity in the learnt common feature space. However, these methods suffer from two main limitations: (1) the video modality descriptors/features are treated equally in representation learning, which hinders the effective modeling of their respective capabilities in linking; and (2) directly using the vector-distance based similarity to measure aboutness bears the risk of returning more duplicates. This paper focuses on addressing these two problems. Specifically, we firstly build attentional neural networks to learn a compact fragment-level representation, assigning different importance weights to different descriptor/feature contents by an attention mechanism. We believe that the potentially interesting content(s) should be highlighted in the representation. Furthermore, instead of directly computing the similarity of two representation embeddings, we secondly build a holographic composition network to model the aboutness for link establishment, with the core use of circular correlation. The two networks string together to form the final hyperlinking matching system. The entire model is trained in an end-to-end fashion. We examine its effectiveness by creating four train/validate/test partitioning schemes on the Blip10000 dataset and employing two video fragmentation methods.
FPGA Implementations of 256-Bit SNOW Stream Ciphers for Postquantum Mobile Security

Quantum computing is a serious threat for contemporary cryptography and, in order to address this threat, key sizes used for symmetric cryptography should be doubled, most typically from 128 to 256 bits. The third-generation partnership project (3GPP), the standards organization for mobile communications, is currently in the process of adding support for 256-bit keys into future mobile standards for the purpose of confidentiality and integrity protection. The current standard defines three ciphers with 128-bit keys: AES, SNOW 3G, and ZUC. There are two competing stream ciphers for the replacement of the 128-bit SNOW 3G: a 256-bit variant of SNOW 3G and a new design SNOW-V. SNOW-V has superior performance in software platforms. In this article, we investigate the implementations of these ciphers on field-programmable gate arrays (FPGAs) for both confidentiality and integrity protection. We develop optimized architectures and introduce a new parallelization technique that applies to both ciphers and offers significant throughput improvements in particular when the ciphers are used in the integrity protection modes. Our results show that SNOW-V has a significant performance advantage over the 256-bit SNOW 3G also on FPGAs.
Robustifying Deep Networks for Medical Image Segmentation

The purpose of this study is to investigate the robustness of a commonly used convolutional neural network for image segmentation with respect to nearly unnoticeable adversarial perturbations, and suggest new methods to make these networks more robust to such perturbations. In this retrospective study, the accuracy of brain tumor segmentation was studied in subjects with low- and high-grade gliomas. Two representative UNets were implemented to segment four different MR series (T1-weighted, post-contrast T1-weighted, T2-weighted, and T2-weighted FLAIR) into four pixelwise labels (Gd-enhancing tumor, peritumoral edema, necrotic and non-enhancing tumor, and background). We developed attack strategies based on the fast gradient sign method (FGSM), iterative FGSM (i-FGSM), and targeted iterative FGSM (ti-FGSM) to produce effective but imperceptible attacks. Additionally, we explored the effectiveness of distillation and adversarial training via data augmentation to counteract these adversarial attacks. Robustness was measured by comparing the Dice coefficients for the attacks using Wilcoxon signed-rank tests. The experimental results show that attacks based on FGSM, i-FGSM, and ti-FGSM were effective in reducing the quality of image segmentation by up to 65% in the Dice coefficient. For attack defenses, distillation performed significantly better than adversarial training approaches. However, all defense approaches performed worse compared to unperturbed test images. Therefore, segmentation networks can be adversely affected by targeted attacks that introduce visually minor (and potentially undetectable) modifications to existing images. With an increasing interest in applying deep learning techniques to medical imaging data, it is important to quantify the ramifications of adversarial inputs (either intentional or unintentional).
Applicability of Accurate Ground Motion Estimation Using Initial P Wave for Earthquake Early Warning

The earthquake early warning (EEW) system is capable of mitigating seismic hazards and reducing deaths, injuries, and economic losses. Although EEW approaches have already been developed worldwide, improving the accuracy and applicability is still controversial. Aiming at the ground motion estimation using the initial P wave, we investigated eight representative characteristic parameters, i.e., the peak measurements and integral quantities, using the database of the 2008 Wenchuan earthquake, where the aftershocks with the criteria that 4.0 ≤ Ms ≤ 6.5 and epicentral distance less than 150 km are analyzed. We established the relationships between the eight characteristic parameters and four ground motion parameters, respectively, based on which the estimation accuracy and reliability and the extent to which the increasingly expanding time windows could affect the estimates are analyzed accordingly. We found that the integral quantities could also be a robust estimator for peak ground acceleration (PGA), peak ground velocity (PGV), and spectral intensity (SI), while the peak measurement is more useful in estimating peak ground displacement (PGD). In addition, for estimating the ground motion of events with magnitudes less than 6.5, a 2-s window could effectively improve the estimation accuracy by approximately 11.5–18.5% compared with using a 1-s window, as the window increases to 3 s, the accuracy would further improve while the growth rate will be reduced to around 3.0–8.0%.
Wage growth and inflation in Europe: a puzzle?

Abstract In many European countries, prior to the pandemic, wages rose faster than productivity, yet consumer price pressures remained subdued. This is puzzling as theory suggests that inflation would pick up with a rise in wage growth that exceeds productivity growth. To shed light on this puzzle, we analyze the transmission of wage shocks to core inflation for a sample of 27 European countries over the period 1995Q1–2019Q1. More importantly, we assess how the transmission depends on a set of factors. We find that historically wage growth has led to higher inflation but the impact has weakened since the global financial crisis. Our analysis shows that the passthrough from wage growth to inflation is significantly lower in periods of subdued inflation and inflation expectations, greater competitive pressures, and robust corporate profitability.
GAP-WF: Graph Attention Pooling Network for Fine-grained SSL/TLS Website Fingerprinting

As an important part of network management, website fingerprinting has become one of the hottest topics in the field of encrypted traffic classification. Website fingerprinting aims to identify the specific webpages in encrypted traffic by observing patterns of traffic traces. Prior studies proposed several machine-learning-based methods using statistical features and deep-learning-based methods using packet length sequences. However, these works mainly focus on the website homepage fingerprinting. In fact, people are usually not limited to visiting the homepage. Compared with the homepage classification of websites, it is more difficult to identify different webpages within the same website due to the traffic traces are very similar. In this paper, we propose the Graph Attention Pooling Network for fine-grained website fingerprinting (GAP-WF). We introduce the trace graph to describe the contextual relationship between flows in webpage loading. Then we utilize the Graph Neural Networks to learn the intra-flow and inter-flow features. Considering different flows may have different importance, we utilize the graph attention mechanism to pay attention to key nodes. We collect four datasets covering three different granularity scenarios to evaluate our proposed method. Experimental results demonstrate that GAP-WF not only achieves the best performance of 99.86% in website homepage fingerprinting, but also outperforms other state-of-art methods in all fine-grained webpage fingerprinting scenarios. Moreover, GAP-WF can achieve better performance with fewer training samples.
Activity Worksheets for Teaching and Learning Data Visualization

In this work, the data visualization activity (DVA) worksheet method for teaching and learning data visualization is presented. The DVA worksheet method consists of a series of activity worksheets developed to guide novice instructors and students through the data visualization process. The activity worksheets help new faculty and visualization instructors, lacking formal training in pedagogy and data visualization, learn the data visualization process, design course work, and develop curriculum. The worksheets can be used for individual activities, or as a collection of activities to support data visualization capacity building. Each worksheet focuses on an individual step in the process, allowing the worksheets to be tailored to discipline-specific data visualization needs. We share the motivation and evolution of the worksheets from paper-based to the semi-automated process utilized in fall 2020. We conclude this work with a discussion and areas for improvement.
A Weighting Method for Feature Dimension by Semisupervised Learning With Entropy

In this article, a semisupervised weighting method for feature dimension based on entropy is proposed for classification, dimension reduction, and correlation analysis. For real-world data, different feature dimensions usually show different importance. Generally, data in the same class are supposed to be similar, so their entropy should be small; and those in different classes are supposed to be dissimilar, so their entropy should be large. According to this, we propose a way to construct the weights of feature dimensions with the whole entropy and the innerclass entropies. The weights indicate the contribution of their corresponding feature dimensions in classification. They can be used to improve the performance of classification by giving a weighted distance metric and can be applied to dimension reduction and correlation analysis as well. Some numerical experiments are given to test the proposed method by comparing it with some other representative methods. They demonstrate that the proposed method is feasible and efficient in classification, dimension reduction, and correlation analysis.
Evaluation Methods Applied to Virtual Reality Educational Applications: A Systematic Review

Evaluation is an important activity in software development since it allows detecting issues like design flaws under different analysis perspectives. However, conducting a satisfactory assessment is challenging, especially considering new interactive technologies such as virtual reality (VR). This paper presents a systematic review revealing the most used evaluation methods in this context to deepen the understanding of the evaluation of educational applications employing VR. Considering a search in the ACM database with a filter for the last six years, we selected 1351 initial studies and 81 for analysis. The results show that the tests of usability, flow, and technology acceptance model are the most common, as well as the methods used in many articles present weaknesses due to the lack of an adequate theoretical foundation. Accordingly, we strengthen the relevance of using well-defined support theories in all stages of an evaluation.
More General and Effective Model Compression via an Additive Combination of Compressions

Model compression is generally performed by using quantization, low-rank approximation or pruning, for which various algorithms have been researched in recent years. One fundamental question is: what types of compression work better for a given model? Or even better: can we improve by combining compressions in a suitable way? We formulate this generally as a problem of optimizing the loss but where the weights are constrained to equal an additive combination of separately compressed parts; and we give an algorithm to learn the corresponding parts' parameters. Experimentally with deep neural nets, we observe that 1) we can find significantly better models in the error-compression space, indicating that different compression types have complementary benefits, and 2) the best type of combination depends exquisitely on the type of neural net. For example, we can compress ResNets and AlexNet using only 1 bit per weight without error degradation at the cost of adding a few floating point weights. However, VGG nets can be better compressed by combining low-rank with a few floating point weights.
Security Analysis of Capsule Network Inference using Horizontal Collaboration

The traditional convolution neural networks (CNN) have several drawbacks like the "Picasso effect" and the loss of information by the pooling layer. The Capsule network (CapsNet) was proposed to address these challenges because its architecture can encode and preserve the spatial orientation of input images. Similar to traditional CNNs, CapsNet is also vulnerable to several malicious attacks, as studied by several researchers in the literature. However, most of these studies focus on single-device-based inference, but horizontally collaborative inference in state-of-the-art systems, like intelligent edge services in self-driving cars, voice controllable systems, and drones, nullify most of these analyses. Horizontal collaboration implies partitioning the trained CNN models or CNN tasks to multiple end devices or edge nodes. Therefore, it is imperative to examine the robustness of the CapsNet against malicious attacks when deployed in horizontally collaborative environments. Towards this, we examine the robustness of the CapsNet when subjected to noise-based inference attacks in a horizontal collaborative environment. In this analysis, we perturbed the feature maps of the different layers of four DNN models, i.e., CapsNet, mini-VGGNet, LeNet, and an in-house designed CNN (ConvNet) with the same number of parameters as CapsNet, using two types of noised-based attacks, i.e., Gaussian Noise Attack and FGSM noise attack. The experimental results show that similar to the traditional CNNs, depending upon the attacker’s access to the DNN layer, the classification accuracy of the CapsNet drops significantly. For example, when Gaussian Noise Attack classification is performed at the Digit-cap layer of the CapsNet, the maximum classification accuracy drop is approximately 97%. Similarly, the maximum classification accuracy drop is 90.1% when an FGSM noise attack is performed at the Conv layer of the CapsNet.
Transfer Learning in Collaborative Recommendation for Bias Reduction

In a recommender system, a user’s interaction is often biased by the items’ displaying positions and popularity, as well as the user’s self-selection. Most existing recommendation models are built using such a biased user-system interaction data. In this paper, we first additionally introduce a specially collected unbiased data and then propose a novel transfer learning solution, i.e., transfer via joint reconstruction (TJR), to achieve knowledge transfer and sharing between the biased data and unbiased data. Specifically, in our TJR, we refine the prediction via the latent features containing bias information in order to obtain a more accurate and unbiased prediction. Moreover, we integrate the two data by reconstructing their interaction in a joint learning manner. We then adopt three representative methods as the backbone models of our TJR and conduct extensive empirical studies on two public datasets, showcasing the effectiveness of our transfer learning solution over some very competitive baselines.
Negative statements considered useful

Knowledge bases (KBs) about notable entities and their properties are an important asset in applications such as search, question answering and dialogue. All popular KBs capture virtually only positive statements, and abstain from taking any stance on statements not stored in the KB. This paper makes the case for explicitly stating salient statements that do not hold. Negative statements are useful to overcome limitations of question answering systems that are mainly geared for positive questions; they can also contribute to informative summaries of entities. Due to the abundance of such invalid statements, any effort to compile them needs to address ranking by saliency. We present a statisticalinference method for compiling and ranking negative statements, based on expectations from positive statements of related entities in peer groups. Experimental results, with a variety of datasets, show that the method can effectively discover notable negative statements, and extrinsic studies underline their usefulness for entity summarization. Datasets and code are released as resources for further research.
Where Should I Look? Optimized Gaze Control for Whole-Body Collision Avoidance in Dynamic Environments

As robots operate in increasingly complex and dynamic environments, fast motion re-planning has become a widely explored area of research.In a real-world deployment, we often lack the ability to fully observe the environment at all times, giving rise to the challenge of determining how to best perceive the environment given a continuously updated motion plan.We provide the first investigation into a 'smart' controller for gaze control with the objective of providing effective perception of the environment for obstacle avoidance and motion planning in dynamic and unknown environments.We detail the novel problem of determining the best head camera behaviour for mobile robots when constrained by a trajectory.Furthermore, we propose a greedy optimization-based solution that uses a combination of voxelised rewards and motion primitives.We demonstrate that our method outperforms the benchmark methods in 2D and 3D environments, in respect of both the ability to explore the local surroundings, as well as in a superior success rate of finding collision-free trajectories -our method is shown to provide 7.4x better map exploration while consistently achieving a higher success rate for generating collision-free trajectories.We verify our findings on a physical Toyota Human Support Robot (HSR) using a GPU-accelerated perception framework.
The Seismo-Performer: A Novel Machine Learning Approach for General and Efficient Seismic Phase Recognition from Local Earthquakes in Real Time

When recording seismic ground motion in multiple sites using independent recording stations one needs to recognize the presence of the same parts of seismic waves arriving at these stations. This problem is known in seismology as seismic phase picking. It is challenging to automate the accurate picking of seismic phases to the level of human capabilities. By solving this problem, it would be possible to automate routine processing in real time on any local network. A new machine learning approach was developed to classify seismic phases from local earthquakes. The resulting model is based on spectrograms and utilizes the transformer architecture with a self-attention mechanism and without any convolution blocks. The model is general for various local networks and has only 57 k learning parameters. To assess the generalization property, two new datasets were developed, containing local earthquake data collected from two different regions using a wide variety of seismic instruments. The data were not involved in the training process for any model to estimate the generalization property. The new model exhibits the best classification and computation performance results on its pre-trained weights compared with baseline models from related work. The model code is available online and is ready for day-to-day real-time processing on conventional seismic equipment without graphics processing units.