Reviewerly/exHarmony huggingface dataset:
Demon: Improved Neural Network Training With Momentum Decay

Momentum is a widely used technique for gradient-based optimizers in deep learning. Here, we propose a decaying momentum (DEMON) hyperparameter rule. We conduct large-scale empirical analysis of momentum decay methods for modern neural network optimization and compare to the most popular learning rate decay schedules. Across 28 relevant combinations of models, epochs, datasets, and optimizers, DEMON achieves Top-1 and Top-3 performance in 39% and 85% of cases, respectively, almost doubling the second-placed cosine learning rate schedule at 17% and 60%, respectively. DEMON consistently outperforms other widely-used schedulers including, but not limited to, the learning rate step schedule, linear schedule, OneCycle schedule, and exponential schedule. Compared with the widely-used learning rate step schedule, DEMON is less sensitive to parameter tuning, which is critical to training neural networks in practice. Results are demonstrated across a variety of settings and architectures, including image classification models, generative models, and language models. DEMON is easy to implement, requires no additional tuning, and incurs almost no extra computational overhead compared to the vanilla counterparts. Code is readily available.
What does a digital forensics opinion look like? A comparative study of digital forensics and forensic science reporting practices

This study explores digital forensics (DF) reporting practices and compares the results with other forensic science disciplines. Forty reports were obtained from a quasi-experiment involving DF examiners, and a quantitative content analysis was performed to determine which conclusion types they applied and which content they included with relevance to the credibility of the reported results. A qualitative analysis was performed to examine the certainty expressions used in the conclusions. The results were compared to a study of eight forensic science disciplines performed by Bali et al. [24,26]. The results show that the DF examiners tend to present their conclusions either as Categorical conclusion or Strength of support (SoS) conclusion types and that they address source, activity, and offence level issues in their conclusions. The content analysis indicates deficiencies in DF reporting practices, and several of the challenges seem to be shared with other FS disciplines. The analysis of certainty expressions showed that a plethora of expressions was used, and that they lacked reference to an established framework. The results indicate that more research on DF evaluation and reporting practices is necessary and justifies a need for enhanced focus on quality control and peer review within the DF discipline.
SRDRL: A Blind Super-Resolution Framework With Degradation Reconstruction Loss

Recent years have witnessed the remarkable success of deep learning-based single image super-resolution (SISR) methods. However, most of the existing SISR methods assume that low-resolution (LR) images are purely bicubic downsampled from high-resolution (HR) images. Once the actual degradation is not bicubic, their outstanding performance is hard to maintain. Since the real-world image degradation process can be modeled by a combination of downsampling, blurring, and noise, several SR methods have been proposed to super-resolve LR images with multiple blur kernels and noise levels. However, these SR methods require prior knowledge of the degradation process, which is difficult to obtain in practical applications. To address these issues, we propose a degradation reconstruction loss (DRL), which captures the degradation-wise differences between SR images and HR images via a degradation simulator. Empowered by the degradation simulator, the proposed loss, and an efficient SR network, a blind SR framework (SRDRL) without prior knowledge that can handle multiple degradations is formed. Extensive experimental results demonstrate that the proposed SRDRL outperforms the state-of-the-art blind SR methods and denosing+SR methods on multi-degraded datasets. The degradation reconstruction loss can be a plug-and-play loss for existing SR methods to handle multiple degradations. The source code can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/FVL2020/SRDRL</uri> .
An Automatic Biopsy Needle Detection and Segmentation on Ultrasound Images Using a Convolutional Neural Network

Needle visualization in the ultrasound image is essential to successfully perform the ultrasound-guided core needle biopsy. Automatic needle detection can significantly reduce the procedure time, false-negative rate, and highly improve the diagnosis. In this paper, we present a CNN-based, fully automatic method for detection of core needle in 2D ultrasound images. Adaptive moment estimation optimizer is proposed as CNN architecture. Radon transform is applied to locate the needle. The network's model was trained and tested on the total of 619 2D images from 91 cases of breast cancer. The model has achieved an average weighted intersection over union (the weighted Jaccard Index) of 0.986, F1 Score of 0.768, and angle RMSE of 3.73°. The obtained results exceed the other solutions by at least 0.27 and 7° in case of F1 score and angle RMSE, respectively. Finally, the needle is detected in a single frame averagely in 21.6 ms on a modern PC.
Attack graph reachability: concept, analysis, challenges and issues

An attack graph (AG) is an abstraction that represents the paths by which an attacker could break a security policy, leveraging interdependencies among discovered vulnerabilities. However, current AG implementations are inefficient on large-scale networks. The increase of the number of hosts in networks causes an increase in the time it takes to generate the AG, especially the calculation time and the complexity of determining reachability. An attack graph (AG) is an abstraction that represents the paths by which an attacker could break a security policy, leveraging interdependencies among discovered vulnerabilities. However, current AG implementations are inefficient on large-scale networks. The increase of the number of hosts in networks causes an increase in the time it takes to generate the AG, especially the calculation time and the complexity of determining reachability. In this article, the authors examine techniques that calculate the reachability using a matrix or a hyper-graph.
Robust Image Representation via Low Rank Locality Preserving Projection

Locality preserving projection (LPP) is a dimensionality reduction algorithm preserving the neighhorhood graph structure of data. However, the conventional LPP is sensitive to outliers existing in data. This article proposes a novel low-rank LPP model called LR-LPP. In this new model, original data are decomposed into the clean intrinsic component and noise component. Then the projective matrix is learned based on the clean intrinsic component which is encoded in low-rank features. The noise component is constrained by the ℓ 1 -norm which is more robust to outliers. Finally, LR-LPP model is extended to LR-FLPP in which low-dimensional feature is measured by F-norm. LR-FLPP will reduce aggregated error and weaken the effect of outliers, which will make the proposed LR-FLPP even more robust for outliers. The experimental results on public image databases demonstrate the effectiveness of the proposed LR-LPP and LR-FLPP.
A Survey of Collaborative Reinforcement Learning: Interactive Methods and Design Patterns

Recently, methods enabling humans and Artificial Intelligent (AI) agents to collaborate towards improving the efficiency of Reinforcement Learning - also called Collaborative Reinforcement Learning (CRL) - have been receiving increasing attention. In this paper, we provide a long-term, in-depth survey, investigating human-AI collaborative methods based on both interactive reinforcement learning algorithms and human-AI collaborative frameworks, between 2011 and 2020. We elucidate and discuss synergistic analysis methods of both the growth of the field and the state-of-the-art; we suggest novel technical directions and new collaboration design ideas. Specifically, we provide a new CRL classification taxonomy, as a systematic modelling tool for selecting and improving new CRL designs. Furthermore, we propose generic CRL challenges providing the research community with a guide towards effective implementation of human-AI collaboration. The aim is to empower researchers to develop more efficient and natural human-AI collaborative methods that could utilise the different strengths of humans and AI.
The Office Jungle: Exploring Unusual Ways of Working through Bodily Experimentations

Modern office environments foster sitting, a major public health risk, with physical inactivity being the fourth cause of death worldwide. This provocative pictorial presents the design explorations and bodily experimentations culminating in The Office Jungle, a critical and speculative redesign of the office environment that encourages physical activity by embracing wildness. The Office Jungle is a design exemplar of a “wild” office space presented as a suspended geodesic structure. It is built to experienc e how our office environment and our behaviour at work affect each other. We advocate that bringing wildness into office spaces will create more durable office environments that foster movement. With this pictorial, we aim to spark discussion amongst designers to think in new ways and to consider new opportunities to design for workplaces that integrate physical activity with work.
Instagram Filter Removal on Fashionable Images

Social media images are generally transformed by filtering to obtain aesthetically more pleasing appearances. However, CNNs generally fail to interpret both the image and its filtered version as the same in the visual analysis of social media images. We introduce Instagram Filter Removal Network (IFRNet) to mitigate the effects of image filters for social media analysis applications. To achieve this, we assume any filter applied to an image substantially injects a piece of additional style information to it, and we consider this problem as a reverse style transfer problem. The visual effects of filtering can be directly removed by adaptively normalizing external style information in each level of the encoder. Experiments demonstrate that IFRNet outperforms all compared methods in quantitative and qualitative comparisons, and has the ability to remove the visual effects to a great extent. Additionally, we present the filter classification performance of our proposed model, and analyze the dominant color estimation on the images unfiltered by all compared methods.
A Protocol for Secure Verification of Watermarks Embedded into Machine Learning Models

Machine Learning is a well established tool used in a variety of applications. As training advanced models requires considerable amounts of meaningful data in addition to specific knowledge, a new business model separate models creators from model users. Pre-trained models are sold or made available as a service. This raises several security challenges, among others the one of intellectual property protection. Therefore, a new research track actively seeks to provide techniques for model watermarking that would enable model identification in case of suspicion of model theft or misuse. In this paper, we focus on the problem of secure watermarks verification, which affects all of the proposed techniques and until now was barely tackled. First, we revisit the existing threat model. In particular, we explain the possible threats related to a semi-honest or dishonest verification authority. Secondly, we show how to reduce trust requirements between participants by performing the watermarks verification on encrypted data. Finally, we describe a novel secure verification protocol as well as detail its possible implementation using Multi-Party Computation. The proposed solution does not only preserve the confidentiality of the watermarks but also helps detecting evasion attacks. It could be adopted to work with other authentication schemes based on watermarking, especially with image watermarking schemes.
On the Robustness of Monte Carlo Dropout Trained with Noisy Labels

The memorization effect of deep learning hinders its performance to effectively generalize on test set when learning with noisy labels. Prior study has discovered that epistemic uncertainty techniques are robust when trained with noisy labels compared with neural networks without uncertainty estimation. They obtain prolonged memorization effect and better generalization performance under the adversarial setting of noisy labels. Due to its superior performance amongst other selected epistemic uncertainty methods under noisy labels, we focus on Monte Carlo Dropout (MCDropout) and investigate why it is robust when trained with noisy labels. Through empirical studies on datasets MNIST, CIFAR-10, Animal-10n, we deep dive into three aspects of MCDropout under noisy label setting: 1. efficacy: understanding the learning behavior and test accuracy of MCDropout when training set contains artificially generated or naturally embedded label noise; 2. representation volatility: studying the responsiveness of neurons by examining the mean and standard deviation on each neuron’s activation; 3. network sparsity: investigating the network support of MCDropout in comparison with deterministic neural networks. Our findings suggest that MCDropout further sparsifies and regularizes the deterministic neural networks and thus provides higher robustness against noisy labels.
A Mirror-Based Active Vision System for Underwater Robots: From the Design to Active Object Tracking Application

A mirror-based active system capable of changing the view’s direction of a pre-existing fixed camera is presented. The aim of this research work is to extend the perceptual tracking capabilities of an underwater robot without altering its structure. The ability to control the view’s direction allows the robot to explore its entire surroundings without any actual displacement, which can be useful for more effective motion planning and for different navigation strategies, such as object tracking and/or obstacle evasion, which are of great importance for natural preservation in environments as complex and fragile as coral reefs. Active vision systems based on mirrors had been used mainly in terrestrial platforms to capture the motion of fast projectiles using high-speed cameras of considerable size and weight, but they had not been used on underwater platforms. In this sense, our approach incorporates a lightweight design adapted to an underwater robot using affordable and easy-access technology ( i.e. , 3D printing). Our active system consists of two arranged mirrors, one of which remains static in front of the robot’s camera, while the orientation of the second mirror is controlled by two servomotors. Object tracking is performed by using only the pixels contained on the homography of a defined area in the active mirror. HSV color space is used to reduce lighting change effects. Since color and geometry information of the tracking object are previously known, a window filter is applied over the H-channel for color blobs detection, then, noise is filtered and the object’s centroid is estimated. If the object is lost, a Kalman filter is applied to predict its position. Finally, with this information, an image PD controller computes the servomotor articular values. We have carried out experiments in real environments, testing our active vision system in an object-tracking application where an artificial object is manually displaced on the periphery of the robot and the mirror system is automatically reconfigured to keep such object focused by the camera, having satisfactory results in real time for detecting objects of low complexity and in poor lighting conditions.
Can Conversational Agents Change the Way Children Talk to People?

Millions of children now use conversational agents (CAs), leading researchers and the public alike to ask how interactions with these devices might shape children’s communication with people. We conducted a single-session observational lab study with 22 five-to-ten-year-old children as a step toward understanding whether and how children might transfer a linguistic routine they learned from a CA to a conversation with another person. We found that 68% of children spontaneously used this routine in a conversation with their parent in the lab, and 55% continued to use it at home. When addressing parents, children infused the routine with warmth and playfulness that they did not use when addressing the CA, adapting it to suit their relationship with their parent. However, only 18% of children used it in conversation with an unfamiliar researcher, where they instead were more likely to follow conventional conversational norms. These findings suggest children are quick to learn linguistic routines from CAs but use social differentiation when they apply them. Children’s willingness to expand on and share the routine with their parent is consistent with the principles of the Joint Media Engagement (JME) framework and suggests CAs may be a productive medium for creating JME experiences.
Optimal design of type-2 fuzzy systems for diabetes classification based on genetic algorithms

Diabetes has become a global health problem, where a proper diagnosis is vital for the life quality of patients. In this article, a genetic algorithm is put forward for designing type-2 fuzzy inference systems to perform Diabetes Classification. We aim at finding parameter values of Type-2 Trapezoidal membership functions and the type of model (Mamdani or Sugeno) with this optimization. To verify the effectiveness of the proposed approach, the PIMA Indian Diabetes dataset is used, and results are compared with type-1 fuzzy systems. Five attributes are used considered as the inputs of the fuzzy inference systems to obtain a Diabetes diagnosis. The instances are divided into design and testing sets, where the design set allows the genetic algorithm to minimize the error of classification, and finally, the real behavior of the fuzzy inference system is validated with the testing set.
Multi-Perspective LSTM for Joint Visual Representation Learning

We present a novel LSTM cell architecture capable of learning both intra- and inter-perspective relationships available in visual sequences captured from multiple perspectives. Our architecture adopts a novel recurrent joint learning strategy that uses additional gates and memories at the cell level. We demonstrate that by using the pro-posed cell to create a network, more effective and richer visual representations are learned for recognition tasks. We validate the performance of our proposed architecture in the context of two multi-perspective visual recognition tasks namely lip reading and face recognition. Three relevant datasets are considered and the results are compared against fusion strategies, other existing multi-input LSTM architectures, and alternative recognition solutions. The experiments show the superior performance of our solution over the considered benchmarks, both in terms of recognition accuracy and complexity. We make our code publicly available at https://github.com/arsm/MPLSTM.
Attention-based dynamic user preference modeling and nonlinear feature interaction learning for collaborative filtering recommendation

The traditional collaborative filtering (CF) method based on static user preference modeling and linear matching function learning severely limits the recommendation performance. To solve the above problem, in this article, we adopt dynamic user preference modeling and nonlinear matching function learning in the CF recommendation. For dynamic user preference modeling, a two-layer neural attention network is used, which fully considers the predicted item, the recent historical interacted items and their interaction time to estimate the contribution weight of each interacted item in user preferences modeling. For nonlinear matching function learning, we add a single hidden layer neural network on top of the traditional matrix factorization (MF) model, which can significantly improve the feature interaction learning capabilities of the model with only a few additional parameters. Extensive experiments show that our method significantly outperforms the state-of-the-art CF methods and the key technologies we proposed in this research have a positive effect on improving the recommendation performance.
Rapid search method for a spacecraft attitude maneuver path with multiple constraints

The rapid attitude maneuver capability of spacecraft is required in many space missions. Due to the complex attitude constraints and limited satellite resources, the rapid on-orbit attitude maneuver is very challenging for spacecraft attitude control systems. To address this problem, a rapid attitude path search method named Constraint Path Mapping (CPM) is proposed to quickly obtain a safe attitude path. In the CPM method, the attitude constraint safety-mapping strategy is designed to map the attitude constraint space to the attitude safety space, allowing for efficient planning and improved processing of complex attitude constraints. For resource use, this paper adopts the Euler forward search and node safety search to avoid algorithm redundancy caused by the repeated processing of the constraints. Finally, the numerical simulation is performed to validate the feasibility and effectiveness of the proposed method. In particular, this method is expected to be tested on a reconnaissance satellite launched by the Shanghai Institute of Satellite Engineering in March 2021.