Reviewerly/exHarmony huggingface dataset:
Knowledge Distillation for Object Detection via Rank Mimicking and Prediction-Guided Feature Imitation

Knowledge Distillation (KD) is a widely-used technology to inherit information from cumbersome teacher models to compact student models, consequently realizing model compression and acceleration. Compared with image classification, object detection is a more complex task, and designing specific KD methods for object detection is non-trivial. In this work, we elaborately study the behaviour difference between the teacher and student detection models, and obtain two intriguing observations: First, the teacher and student rank their detected candidate boxes quite differently, which results in their precision discrepancy. Second, there is a considerable gap between the feature response differences and prediction differences between teacher and student, indicating that equally imitating all the feature maps of the teacher is the sub-optimal choice for improving the student's accuracy. Based on the two observations, we propose Rank Mimicking (RM) and Prediction-guided Feature Imitation (PFI) for distilling one-stage detectors, respectively. RM takes the rank of candidate boxes from teachers as a new form of knowledge to distill, which consistently outperforms the traditional soft label distillation. PFI attempts to correlate feature differences with prediction differences, making feature imitation directly help to improve the student's accuracy. On MS COCO and PASCAL VOC benchmarks, extensive experiments are conducted on various detectors with different backbones to validate the effectiveness of our method. Specifically, RetinaNet with ResNet50 achieves 40.4% mAP on MS COCO, which is 3.5% higher than its baseline, and also outperforms previous KD methods.
Motion Capture Data Denoising Based on LSTNet Autoencoder

&lt;p&gt;This paper proposes a novel deep learning-based optical motion capture denoising model encoder-LSTNet- decoder (ELD). ELD uses an autoencoder for manifold learning and decoder to remove jitter noise and missing noise effectively. It uses recurrent units in LSTNet to effectively obtain the spatial-temporal information of motion sequences, especially the periodic long-term and short-term features. In the denoising procedure, the kinetical characteristics of the motion are also considered so that the reconstructed deviation is smaller and can more accurately reflect the real action. We simulated ELD with the CMU database and compared it with the art-of-state methods. The experiment shows that ELD is a very effective denoising technique with lower reconstruction error, stronger robustness, and shorter running time.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Graph Neural Networks: Taxonomy, Advances, and Trends

Graph neural networks provide a powerful toolkit for embedding real-world graphs into low-dimensional spaces according to specific tasks. Up to now, there have been several surveys on this topic. However, they usually lay emphasis on different angles so that the readers cannot see a panorama of the graph neural networks. This survey aims to overcome this limitation and provide a systematic and comprehensive review on the graph neural networks. First of all, we provide a novel taxonomy for the graph neural networks, and then refer to up to 327 relevant literatures to show the panorama of the graph neural networks. All of them are classified into the corresponding categories. In order to drive the graph neural networks into a new stage, we summarize four future research directions so as to overcome the challenges faced. It is expected that more and more scholars can understand and exploit the graph neural networks and use them in their research community.
MedGCN: Medication recommendation and lab test imputation via graph convolutional networks

Laboratory testing and medication prescription are two of the most important routines in daily clinical practice. Developing an artificial intelligence system that can automatically make lab test imputations and medication recommendations can save costs on potentially redundant lab tests and inform physicians of a more effective prescription. We present an intelligent medical system (named MedGCN) that can automatically recommend the patients' medications based on their incomplete lab tests, and can even accurately estimate the lab values that have not been taken. In our system, we integrate the complex relations between multiple types of medical entities with their inherent features in a heterogeneous graph. Then we model the graph to learn a distributed representation for each entity in the graph based on graph convolutional networks (GCN). By the propagation of graph convolutional networks, the entity representations can incorporate multiple types of medical information that can benefit multiple medical tasks. Moreover, we introduce a cross regularization strategy to reduce overfitting for multi-task training by the interaction between the multiple tasks. In this study, we construct a graph to associate 4 types of medical entities, i.e., patients, encounters, lab tests, and medications, and applied a graph neural network to learn node embeddings for medication recommendation and lab test imputation. we validate our MedGCN model on two real-world datasets: NMEDW and MIMIC-III. The experimental results on both datasets demonstrate that our model can outperform the state-of-the-art in both tasks. We believe that our innovative system can provide a promising and reliable way to assist physicians to make medication prescriptions and to save costs on potentially redundant lab tests.
Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation

Graph-level anomaly detection (GAD) describes the problem of detecting graphs that are abnormal in their structure and/or the features of their nodes, as compared to other graphs. One of the challenges in GAD is to devise graph representations that enable the detection of both locally- and globally-anomalous graphs, i.e., graphs that are abnormal in their fine-grained (node-level) or holistic (graph-level) properties, respectively. To tackle this challenge we introduce a novel deep anomaly detection approach for GAD that learns rich global and local normal pattern information by joint random distillation of graph and node representations. The random distillation is achieved by training one GNN to predict another GNN with randomly initialized network weights. Extensive experiments on 16 real-world graph datasets from diverse domains show that our model significantly outperforms seven state-of-the-art models. Code and datasets are available at https://git.io/GLocalKD.
Recommendations on compiling test datasets for evaluating artificial intelligence solutions in pathology

<h2>Abstract</h2> Artificial intelligence (AI) solutions that automatically extract information from digital histology images have shown great promise for improving pathological diagnosis. Prior to routine use, it is important to evaluate their predictive performance and obtain regulatory approval. This assessment requires appropriate test datasets. However, compiling such datasets is challenging and specific recommendations are missing. A committee of various stakeholders, including commercial AI developers, pathologists, and researchers, discussed key aspects and conducted extensive literature reviews on test datasets in pathology. Here, we summarize the results and derive general recommendations on compiling test datasets. We address several questions: Which and how many images are needed? How to deal with low-prevalence subsets? How can potential bias be detected? How should datasets be reported? What are the regulatory requirements in different countries? The recommendations are intended to help AI developers demonstrate the utility of their products and to help pathologists and regulatory agencies verify reported performance measures. Further research is needed to formulate criteria for sufficiently representative test datasets so that AI solutions can operate with less user intervention and better support diagnostic workflows in the future.
Context-Based Contrastive Learning for Scene Text Recognition

Pursuing accurate and robust recognizers has been a long-lasting goal for scene text recognition (STR) researchers. Recently, attention-based methods have demonstrated their effectiveness and achieved impressive results on public benchmarks. The attention mechanism enables models to recognize scene text with severe visual distortions by leveraging contextual information. However, recent studies revealed that the implicit over-reliance of context leads to catastrophic out-of-vocabulary performance. On the contrary to the superior accuracy of the seen text, models are prone to misrecognize unseen text even with good image quality. We propose a novel framework, Context-based contrastive learning (ConCLR), to alleviate this issue. Our proposed method first generates characters with different contexts via simple image concatenation operations and then optimizes contrastive loss on their embeddings. By pulling together clusters of identical characters within various contexts and pushing apart clusters of different characters in embedding space, ConCLR suppresses the side-effect of overfitting to specific contexts and learns a more robust representation. Experiments show that ConCLR significantly improves out-of-vocabulary generalization and achieves state-of-the-art performance on public benchmarks together with attention-based recognizers.
Cybersickness in Virtual Reality Questionnaire (CSQ-VR): A Validation and Comparison against SSQ and VRSQ

Cybersickness is a drawback of virtual reality (VR), which also affects the cognitive and motor skills of users. The Simulator Sickness Questionnaire (SSQ) and its variant, the Virtual Reality Sickness Questionnaire (VRSQ), are two tools that measure cybersickness. However, both tools suffer from important limitations which raise concerns about their suitability. Two versions of the Cybersickness in VR Questionnaire (CSQ-VR), a paper-and-pencil and a 3D–VR version, were developed. The validation of the CSQ-VR and a comparison against the SSQ and the VRSQ were performed. Thirty-nine participants were exposed to three rides with linear and angular accelerations in VR. Assessments of cognitive and psychomotor skills were performed at baseline and after each ride. The validity of both versions of the CSQ-VR was confirmed. Notably, CSQ-VR demonstrated substantially better internal consistency than both SSQ and VRSQ. Additionally, CSQ-VR scores had significantly better psychometric properties in detecting a temporary decline in performance due to cybersickness. Pupil size was a significant predictor of cybersickness intensity. In conclusion, the CSQ-VR is a valid assessment of cybersickness with superior psychometric properties to SSQ and VRSQ. The CSQ-VR enables the assessment of cybersickness during VR exposure, and it benefits from examining pupil size, a biomarker of cybersickness.
Champion-level drone racing using deep reinforcement learning

Abstract First-person view (FPV) drone racing is a televised sport in which professional competitors pilot high-speed aircraft through a 3D circuit. Each pilot sees the environment from the perspective of their drone by means of video streamed from an onboard camera. Reaching the level of professional pilots with an autonomous drone is challenging because the robot needs to fly at its physical limits while estimating its speed and location in the circuit exclusively from onboard sensors 1 . Here we introduce Swift, an autonomous system that can race physical vehicles at the level of the human world champions. The system combines deep reinforcement learning (RL) in simulation with data collected in the physical world. Swift competed against three human champions, including the world champions of two international leagues, in real-world head-to-head races. Swift won several races against each of the human champions and demonstrated the fastest recorded race time. This work represents a milestone for mobile robotics and machine intelligence 2 , which may inspire the deployment of hybrid learning-based solutions in other physical systems.
What About Bob? The Inadequacy of CPA Security for Proxy Reencryption

In the simplest setting of proxy reencryption, there are three parties: Alice, Bob, and Polly (the proxy). Alice keeps some encrypted data that she can decrypt with a secret key known only to her. She wants to communicate the data to Bob, but not to Polly (nor anybody else). Using proxy reencryption, Alice can create a reencryption key that will enable Polly to reencrypt the data for Bob’s use, but which will not help Polly learn anything about the data. There are two well-studied notions of security for proxy reencryption schemes: security under chosen-plaintext attacks (CPA) and security under chosen-ciphertext attacks (CCA). Both definitions aim to formalize the security that Alice enjoys against both Polly and Bob. In this work, we demonstrate that CPA security guarantees much less security against Bob than was previously understood. In particular, CPA security does not prevent Bob from learning Alice’s secret key after receiving a single honestly reencrypted ciphertext. As a result, CPA security provides scant guarantees in common applications. We propose security under honest reencryption attacks (HRA), a strengthening of CPA security that better captures the goals of proxy reencryption. In applications, HRA security provides much more robust security. We identify a property of proxy reencryption schemes that suffices to amplify CPA security to HRA security and show that two existing proxy reencryption schemes are in fact HRA secure.
Exploration Strategies for Discovery of Interactivity in Visualizations

We investigate how people discover the functionality of an interactive visualization that was designed for the general public. While interactive visualizations are increasingly available for public use, we still know little about how the general public discovers what they can do with these visualizations and what interactions are available. Developing a better understanding of this discovery process can help inform the design of visualizations for the general public, which in turn can help make data more accessible. To unpack this problem, we conducted a lab study in which participants were free to use their own methods to discover the functionality of a connected set of interactive visualizations of public energy data. We collected eye movement data and interaction logs as well as video and audio recordings. By analyzing this combined data, we extract exploration strategies that the participants employed to discover the functionality in these interactive visualizations. These exploration strategies illuminate possible design directions for improving the discoverability of a visualization's functionality.
Analysis and automatic parameter selection of a variational model for mixed Gaussian and salt-and-pepper noise removal

We analyse a variational regularisation problem for mixed noise removal that was recently proposed in [14]. The data discrepancy term of the model combines $L^1$ and $L^2$ terms in an infimal convolution fashion and it is appropriate for the joint removal of Gaussian and Salt & Pepper noise. In this work we perform a finer analysis of the model which emphasises on the balancing effect of the two parameters appearing in the discrepancy term. Namely, we study the asymptotic behaviour of the model for large and small values of these parameters and we compare it to the corresponding variational models with $L^1$ and $L^2$ data fidelity. Furthermore, we compute exact solutions for simple data functions taking the total variation as regulariser. Using these theoretical results, we then analytically study a bilevel optimisation strategy for automatically selecting the parameters of the model by means of a training set. Finally, we report some numerical results on the selection of the optimal noise model via such strategy which confirm the validity of our analysis and the use of popular data models in the case of "blind" model selection.
Dual vigilance fuzzy adaptive resonance theory

Clusters retrieved by generic Adaptive Resonance Theory (ART) networks are limited to their internal categorical representation. This study extends the capabilities of ART by incorporating multiple vigilance thresholds in a single network: stricter (data compression) and looser (cluster similarity) vigilance values are used to obtain a many-to-one mapping of categories-to-clusters. It demonstrates this idea in the context of Fuzzy ART, presented as Dual Vigilance Fuzzy ART (DVFA), to improve the ability to capture clusters with arbitrary geometry. DVFA outperformed Fuzzy ART for the datasets in our experiments while yielding a statistically-comparable performance to another more complex, multi-prototype Fuzzy ART-based architecture.
Reinforcement learning based methodology for energy-efficient resource allocation in cloud data centers

Energy-efficient Cloud Infrastructure Resource Allocation Framework is getting popularity as it is paying effective attention to cloud data management with a view to achieve maximize revenue and minimize cost. This infrastructure can encourage for both cloud providers and users for allocating cloud infrastructure resources for fulfilling not only good energy efficiency measured in Power Usage Effectiveness (PUE) and data center Infrastructure Efficiency (DCiE) but also high CPU utilization. Therefore, in this paper we proposed a framework which can show effective performance for achieving the high data center energy efficiency and preventing Service Level Agreement (SLA) violation respectively with the aim of green cloud resources deployment. The framework accomplishes cloud infrastructure resource allocation on the basic of Reinforcement Learning mechanism and Fuzzy Logic for green solutions. The evaluation for Energy-efficient Resource Allocation is experimented on the traces of the PlanetLab virtualized environment for gaining good PUE and CPU utilization.
The Oxford Multimotion Dataset: Multiple SE(3) Motions With Ground Truth

Datasets advance research by posing challenging new problems and providing standardized methods of algorithm comparison. High-quality datasets exist for many important problems in robotics and computer vision, including egomotion estimation and motion/scene segmentation, but not for techniques that estimate every motion in a scene. Metric evaluation of these multimotion estimation techniques requires datasets consisting of multiple, complex motions that also contain ground truth for every moving body. The Oxford Multimotion Dataset provides a number of multimotion estimation problems of varying complexity. It includes both complex problems that challenge existing algorithms as well as a number of simpler problems to support development. These include observations from both static and dynamic sensors, a varying number of moving bodies, and a variety of different three-dimensional motions. It also provides a number of experiments designed to isolate specific challenges of the multimotion problem, including rotation about the optical axis and occlusion. In total, the Oxford Multimotion Dataset contains over 110 min of multimotion data consisting of stereo and RGB-D camera images, IMU data, and Vicon ground-truth trajectories. The dataset culminates in a complex toy car segment representative of many challenging real-world scenarios. This letter describes each experiment with a focus on its relevance to the multimotion estimation problem.
A Probabilistic Generative Model of Linguistic Typology

Johannes Bjerva, Yova Kementchedjhieva, Ryan Cotterell, Isabelle Augenstein. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Evaluating Google Speech-to-Text API's Performance for Romanian e-Learning Resources

This paper presents a way of performing ASR on multimedia e-learning resources available in Romanian with the usage of the Google Cloud Speech-to-Text API. The material presents the history of ASR systems together with the main approaches used by the algorithms behind these systems. The cloud computing providers, that offer ASR solutions via SaaS, are analyzed as well. After performing a short literature review, the author focuses on applying the Google Cloud Speech-to-Text API on various video e-learning resources available online on YouTube. By doing this, the resources can be easily indexed and transformed into searchable materials. The WER score is used in order to measure the accuracy of the model and to compare it with similar works. The results are more than satisfying, thus the proposed model can be used as a method of automating the indexing of multimedia e-learning resources.
An Efficient Web Authentication Mechanism Preventing Man-In-The-Middle Attacks in Industry 4.0 Supply Chain

The fourth industrial revolution (Industry 4.0) is transforming the next generation of the supply chain by making it more agile and efficient compared with the traditional supply chain. However, data communication across the partners in the Industry 4.0 supply chain can be the target of a wide spectrum of attackers exploiting security breaches in the internal/external environment of the partners due to its heterogeneous and dynamic nature as well as the fact that the non-professional users in security issues usually operate their information systems. Attackers can compromise the data communication between legitimate parties in the Industry 4.0 Supply Chain, and thus, jeopardizing the delivery of services across the partners as well as the continuity of the service provision. Consequently, secure data communications across the partners in the Industry 4.0 Supply Chain are of utmost importance. Toward this direction, TLS protocol, which is the de facto standard for secure Internet communications, is employed to ensure secure communication between a user’s web browser and a remote web server located in the premises of the same or another partner. However, over the last few years, there have been several serious attacks on TLS, including man-in-the-middle attacks in web applications using TLS to secure HTTP communication. Therefore, in this paper, we propose an efficient TLS-based authentication mechanism, which is resistant against MITM in web applications.
A distributed, plug-n-play algorithm for multi-robot applications with a priori non-computable objective functions

This paper presents a distributed algorithm applicable to a wide range of practical multi-robot applications. In such multi-robot applications, the user-defined objectives of the mission can be cast as a general optimization problem, without explicit guidelines of the subtasks per different robot. Owing to the unknown environment, unknown robot dynamics, sensor nonlinearities, etc., the analytic form of the optimization cost function is not available a priori. Therefore, standard gradient-descent-like algorithms are not applicable to these problems. To tackle this, we introduce a new algorithm that carefully designs each robot’s subcost function, the optimization of which can accomplish the overall team objective. Upon this transformation, we propose a distributed methodology based on the cognitive-based adaptive optimization (CAO) algorithm, that is able to approximate the evolution of each robot’s cost function and to adequately optimize its decision variables (robot actions). The latter can be achieved by online learning only the problem-specific characteristics that affect the accomplishment of mission objectives. The overall, low-complexity algorithm can straightforwardly incorporate any kind of operational constraint, is fault tolerant, and can appropriately tackle time-varying cost functions. A cornerstone of this approach is that it shares the same convergence characteristics as those of block coordinate descent algorithms. The proposed algorithm is evaluated in three heterogeneous simulation set-ups under multiple scenarios, against both general-purpose and problem-specific algorithms.