Reviewerly/exHarmony huggingface dataset:
When Do Neural Networks Outperform Kernel Methods

For a certain scaling of the initialization of stochastic gradient descent (SGD), wide neural networks (NN) have been shown to be well approximated by reproducing kernel Hilbert space (RKHS) methods. Recent empirical work showed that, for some classification tasks, RKHS methods can replace NNs without a large loss in performance. On the other hand, two-layers NNs are known to encode richer smoothness classes than RKHS and we know of special examples for which SGD-trained NN provably outperform RKHS. This is true even in the wide network limit, for a different scaling of the initialization. How can we reconcile the above claims? For which tasks do NNs outperform RKHS? If feature vectors are nearly isotropic, RKHS methods suffer from the curse of dimensionality, while NNs can overcome it by learning the best low-dimensional representation. Here we show that this curse of dimensionality becomes milder if the feature vectors display the same low-dimensional structure as the target function, and we precisely characterize this tradeoff. Building on these results, we present a model that can capture in a unified framework both behaviors observed in earlier work. We hypothesize that such a latent low-dimensional structure is present in image classification. We test numerically this hypothesis by showing that specific perturbations of the training distribution degrade the performances of RKHS methods much more significantly than NNs.
SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows

Normalizing flows and variational autoencoders are powerful generative models that can represent complicated density functions. However, they both impose constraints on the models: Normalizing flows use bijective transformations to model densities whereas VAEs learn stochastic transformations that are non-invertible and thus typically do not provide tractable estimates of the marginal likelihood. In this paper, we introduce SurVAE Flows: A modular framework of composable transformations that encompasses VAEs and normalizing flows. SurVAE Flows bridge the gap between normalizing flows and VAEs with surjective transformations, wherein the transformations are deterministic in one direction -- thereby allowing exact likelihood computation, and stochastic in the reverse direction -- hence providing a lower bound on the corresponding likelihood. We show that several recently proposed methods, including dequantization and augmented normalizing flows, can be expressed as SurVAE Flows. Finally, we introduce common operations such as the max value, the absolute value, sorting and stochastic permutation as composable layers in SurVAE Flows.
Distributionally Robust Federated Averaging

In this paper, we study communication efficient distributed algorithms for distributionally robust federated learning via periodic averaging with adaptive sampling. In contrast to standard empirical risk minimization, due to the minimax structure of the underlying optimization problem, a key difficulty arises from the fact that the global parameter that controls the mixture of local losses can only be updated infrequently on the global stage. To compensate for this, we propose a Distributionally Robust Federated Averaging (DRFA) algorithm that employs a novel snapshotting scheme to approximate the accumulation of history gradients of the mixing parameter. We analyze the convergence rate of DRFA in both convex-linear and nonconvex-linear settings. We also generalize the proposed idea to objectives with regularization on the mixture parameter and propose a proximal variant, dubbed as DRFA-Prox, with provable convergence rates. We also analyze an alternative optimization method for regularized cases in strongly-convex-strongly-concave and non-convex (under PL condition)-strongly-concave settings. To the best of our knowledge, this paper is the first to solve distributionally robust federated learning with reduced communication, and to analyze the efficiency of local descent methods on distributed minimax problems. We give corroborating experimental evidence for our theoretical results in federated learning settings.
COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning

Many real-world video-text tasks involve different levels of granularity, such as frames and words, clip and sentences or videos and paragraphs, each with distinct semantics. In this paper, we propose a Cooperative hierarchical Transformer (COOT) to leverage this hierarchy information and model the interactions between different levels of granularity and different modalities. The method consists of three major components: an attention-aware feature aggregation layer, which leverages the local temporal context (intra-level, e.g., within a clip), a contextual transformer to learn the interactions between low-level and high-level semantics (inter-level, e.g. clip-video, sentence-paragraph), and a cross-modal cycle-consistency loss to connect video and text. The resulting method compares favorably to the state of the art on several benchmarks while having few parameters. All code is available open-source at this https URL
Owicki-Gries Reasoning for C11 RAR

Owicki-Gries reasoning for concurrent programs uses Hoare logic together with an interference freedom rule for concurrency. In this paper, we develop a new proof calculus for the C11 RAR memory model (a fragment of C11 with both relaxed and release-acquire accesses) that allows all Owicki-Gries proof rules for compound statements, including non-interference, to remain unchanged. Our proof method features novel assertions specifying thread-specific views on the state of programs. This is combined with a set of Hoare logic rules that describe how these assertions are affected by atomic program steps. We demonstrate the utility of our proof calculus by verifying a number of standard C11 litmus tests and Peterson’s algorithm adapted for C11. Our proof calculus and its application to program verification have been fully mechanised in the theorem prover Isabelle.
Robust Variational-Based Kalman Filter for Outlier Rejection With Correlated Measurements

State estimation is a fundamental task in many engineering fields, and therefore robust nonlinear filtering techniques able to cope with misspecified, uncertain and/or corrupted models must be designed for real-life applicability. In this contribution we explore nonlinear Gaussian filtering problems where measurements may be corrupted by outliers, and propose a new robust variational-based filtering methodology able to detect and mitigate their impact. This method generalizes previous contributions to the case of multiple outlier indicators for both independent and dependent observation models. An illustrative example is provided to support the discussion and show the performance improvement.
Seam-Carved Image Tampering Detection Based on the Cooccurrence of Adjacent LBPs

Seam carving has been widely used in image resizing due to its superior performance in avoiding image distortion and deformation, which can maliciously be used on purpose, such as tampering contents of an image. As a result, seam-carving detection is becoming crucially important to recognize the image authenticity. However, existing methods do not perform well in the accuracy of seam-carving detection especially when the scaling ratio is low. In this paper, we propose an image forensic approach based on the cooccurrence of adjacent local binary patterns (LBPs), which employs LBP to better display texture information. Specifically, a total of 24 energy-based, seam-based, half-seam-based, and noise-based features in the LBP domain are applied to the seam-carving detection. Moreover, the cooccurrence features of adjacent LBPs are combined to highlight the local relationship between LBPs. Besides, SVM after training is adopted for feature classification to determine whether an image is seam-carved or not. Experimental results demonstrate the effectiveness in improving the detection accuracy with respect to different scaling ratios, especially under low scaling ratios.
Design preferences on Industrial Augmented Reality: a survey with potential technical writers

The research presented in this contribution aims to investigate user preferences about how to convey information in Industrial Augmented Reality (IAR) interfaces to the user. Our interest is focused on the opinion of potential technical writers of IAR documentation for assembly or maintenance operations. Authoring of IAR interfaces imply a choice among various visual assets, that is influenced by the information type and the AR display used. There are no specific standards in the literature to follow and it is challenging to extract guidelines from the literature. This study gathers preferences of 105 selected users that have knowledge about IAR issues, graphical user interfaces (GUI) designing, and assembly/maintenance procedures. The results of this survey show a great preference for 3D CAD models of components (product model) for almost all the information types. However, some alternative visual assets have also been proposed, such as video and auxiliary models. Contrary to common practices in industry, text was the least preferred visual asset. The insights from this research can help other IAR technical writers in the authoring of their interfaces.
Results of the WMT20 Metrics Shared Task.

This paper presents the results of the WMT20 Metrics Shared Task. Participants were asked to score the outputs of the translation systems competing in the WMT20 News Translation Task with automatic metrics. Ten research groups submitted 27 metrics, four of which are reference-less “metrics”. In addition, we computed five baseline metrics, including sentBLEU, BLEU, TER and using the SacreBLEU scorer. All metrics were evaluated on how well they correlate at the system-, document- and segment-level with the WMT20 official human scores. We present an extensive analysis on influence of different reference translations on metric reliability, how well automatic metrics score human translations, and we also flag major discrepancies between metric and human scores when evaluating MT systems. Finally, we investigate whether we can use automatic metrics to flag incorrect human ratings.
Lipschitz Recurrent Neural Networks

Viewing recurrent neural networks (RNNs) as continuous-time dynamical systems, we propose a recurrent unit that describes the hidden state's evolution with two parts: a well-understood linear component plus a Lipschitz nonlinearity. This particular functional form facilitates stability analysis of the long-term behavior of the recurrent unit using tools from nonlinear systems theory. In turn, this enables architectural design decisions before experimentation. Sufficient conditions for global stability of the recurrent unit are obtained, motivating a novel scheme for constructing hidden-to-hidden matrices. Our experiments demonstrate that the Lipschitz RNN can outperform existing recurrent units on a range of benchmark tasks, including computer vision, language modeling and speech prediction tasks. Finally, through Hessian-based analysis we demonstrate that our Lipschitz recurrent unit is more robust with respect to input and parameter perturbations as compared to other continuous-time RNNs.
FRCNN-GNB: Cascade Faster R-CNN With Gabor Filters and Naïve Bayes for Enhanced Eye Detection

Research into biometric identification technologies has evolved in recent years, as most secure facilities and applications are now based on digital technology. Among the available biometric identification technologies is eye detection. The relevance and impact of the use of eye detection in a variety of biometric authentication systems are very high. The main problems associated with the accuracy of eye detection methods are occlusion or reflections from glass. In view of this, we propose a hybridized and enhanced eye detection method that uses a faster region-based convolutional neural network with Gabor filters and naive Bayes (FRCNN-GNB) model to address the problems associated with eye detection schemes. The proposed method consists of four components: convolution layers, a region proposal network, a detection network, and a decision model. The enhancement method is based on a cascade Faster R-CNN with Gabor filters and the naïve Bayes model, in which the initial bounding boxes of the eye region are detected using Faster R-CNN and the decision step is carried out using Gabor filters and the naïve Bayes model to determine which of the bounding boxes belong to the eye region. Experiments on the proposed FRCNN-GNB eye detection scheme are performed on the CASIA-IrisV4 database, and show that the accuracy in terms of eye detection is 100%. The results of the study demonstrate the efficiency of the proposed solution.
Internet of Things (IoT) Authentication and Access Control by Hybrid Deep Learning Method - A Study

In the history of device computing, Internet of Things (IoT) is one of the fastest growing field that facing many security challenges. The effective efforts should have been made to address the security and privacy issues in IoT networks. The IoT devices are basically resource control device which provide routine attract impression for cyber attackers. The IoT participation nodes are increasing rapidly with more resource constrained that creating more challenging conditions in the real time. The existing methods provide an ineffective response to the tasks for effective IoT device. Also, it is an insufficient to involve the complete security and safety spectrum of the IoT networks. Because of the existing algorithms are not enriched to secure IoT bionetwork in the real time environment. The existing system is not enough to detect the proxy to the authorized person in the embedding devices. Also, those methods are believed in single model domain. Therefore, the effectiveness is dropping for further multimodal domain such as combination of behavioral and physiological features. The embedding intelligent technique will be securitizing for the IoT devices and networks by deep learning (DL) techniques. The DL method is addressing different security and safety problems arise in real time environment. This paper is highlighting hybrid DL techniques with Reinforcement Learning (RL) for the better performance during attack and compared with existing one. Also, here we discussed about DL combined with RL of several techniques and identify the higher accuracy algorithm for security solutions. Finally, we discuss the future direction of decision making of DL based IoT security system.
EDAN: An EMG-controlled Daily Assistant to Help People With Physical Disabilities

Injuries, accidents, strokes, and other diseases can significantly degrade the capabilities to perform even the most simple activities in daily life. A large share of these cases involves neuromuscular diseases, which lead to severely reduced muscle function. However, even though affected people are no longer able to move their limbs, residual muscle function can still be existent. Previous work has shown that this residual muscular activity can suffice to apply an EMG-based user interface. In this paper, we introduce DLR's robotic wheelchair EDAN (EMG-controlled Daily Assistant), which is equipped with a torque-controlled, eight degree-of-freedom light-weight arm and a dexterous, five-fingered robotic hand. Using electromyography, muscular activity of the user is measured, processed and utilized to control both the wheelchair and the robotic manipulator. This EMG-based interface is enhanced with shared control functionality to allow for efficient and safe physical interaction with the environment.
Learning joint latent representations based on information maximization

Learning disentangled and interpretable representations is an important aspect of information understanding. In this paper, we propose a novel deep learning model representing both discrete and continuous latent variable spaces which can be used in either supervised or unsupervised learning. The proposed model is trained using an optimization function employing the mutual information maximization criterion. For the unsupervised learning setting we define a lower bound to the mutual information between the joint distribution of the latent variables corresponding to the real data and those generated by the model. The maximization of this lower bound during the training induces the learning of disentangled and interpretable data representations. Such representations can be used for attribute manipulation and image editing tasks.
Cyber security during the COVID-19 pandemic

<abstract> COVID-19 has changed the way cyber security is viewed by corporations in the global community. Not only did COVID-19 make many individuals work at home, sometimes on their own computers, and using their own routers, virus protection, etc., the lack of cyber security protection that individuals can provide against hacker attacks, especially for highly sensitive information can be limited. This paper introduces major technologies (such as 5G, blockchain, telemedicine, and big data) in fighting COVID-19, cyber-attacks, and cyber risks (due to peopleos actions as well as systems and technology failures) during the COVID-19 pandemic, cyber security for telework, cyber security of Internet of Things (IoT) and telemedicine, and cyber security based on blockchain technology. Blockchain helps mitigate the risks of COVID-19 and improves the privacy and security of health systems. </abstract>
Ontology-Based Knowledge Representation in Robotic Systems: A Survey Oriented toward Applications

Knowledge representation in autonomous robots with social roles has steadily gained importance through their supportive task assistance in domestic, hospital, and industrial activities. For active assistance, these robots must process semantic knowledge to perform the task more efficiently. In this context, ontology-based knowledge representation and reasoning (KR &amp; R) techniques appear as a powerful tool and provide sophisticated domain knowledge for processing complex robotic tasks in a real-world environment. In this article, we surveyed ontology-based semantic representation unified into the current state of robotic knowledge base systems, with our aim being three-fold: (i) to present the recent developments in ontology-based knowledge representation systems that have led to the effective solutions of real-world robotic applications; (ii) to review the selected knowledge-based systems in seven dimensions: application, idea, development tools, architecture, ontology scope, reasoning scope, and limitations; (iii) to pin-down lessons learned from the review of existing knowledge-based systems for designing better solutions and delineating research limitations that might be addressed in future studies. This survey article concludes with a discussion of future research challenges that can serve as a guide to those who are interested in working on the ontology-based semantic knowledge representation systems for autonomous robots.
CoNet: Co-occurrence neural networks for recommendation

Assuming that both users and items are independent and identically distributed, most existing methods model user–item pairs, while ignoring the relationship between items, leading to limited performance. To solve this problem, we propose a novel neural network, CoNet, which can effectively model the co-occurrence pattern for Collaborative Filtering (CF). We argue that items always occur in pairs, i.e. an item co-occurrence pattern. For example, movies ”Harry Potter 1” and ”Harry Potter 2” are always viewed by users who like magic style films. To learn the latent features, CoNet is simultaneously modeled on user–item and item–item interactions. Compared with methods that train on a single user–item pair, CoNet can encode highly descriptive features from the co-occurrence pattern. To achieve a better performance, we design an attention network to learn the weight of a user’s preference for different items and subsequently aggregate the weighted embeddings to obtain the co-occurrence representations. Finally, we conducted extensive experiments using several data sets, which show that the proposed method is superior to other baseline approaches. Source code of CoNet is available from https://github.com/XiuzeZhou/conet.
A New Combinational Technique in Image Steganography

Internet is used for exchanging information. Sometimes it is needed to transmit confidential data via internet. Here the authors use image steganography to pass confidential data within a cover image. To construct the algorithm, they take the combinational help of particle swarm optimization (PSO), bi-orthogonal wavelet transform (BWT), and genetic algorithm (GA). They use PSO to take the enhanced version of cover image. They use BWT to choose the selective sub bands of cover image and we utilize GA to select a particular stego image among a set of stego images. Thus, an innovative technique of image steganography has been made to transmit confidential data via cover image generating stego image. This combinational approach of image steganography is quite safe for confidential data transmission and makes it hard for the attackers to retrieve the confidential data.
A Survey on Variational Autoencoders from a Green AI Perspective

Abstract Variational Autoencoders (VAEs) are powerful generative models that merge elements from statistics and information theory with the flexibility offered by deep neural networks to efficiently solve the generation problem for high-dimensional data. The key insight of VAEs is to learn the latent distribution of data in such a way that new meaningful samples can be generated from it. This approach led to tremendous research and variations in the architectural design of VAEs, nourishing the recent field of research known as unsupervised representation learning. In this article, we provide a comparative evaluation of some of the most successful, recent variations of VAEs. We particularly focus the analysis on the energetic efficiency of the different models, in the spirit of the so-called Green AI, aiming both to reduce the carbon footprint and the financial cost of generative techniques. For each architecture, we provide its mathematical formulation, the ideas underlying its design, a detailed model description, a running implementation and quantitative results.