Reviewerly/exHarmony huggingface dataset:
Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation

Deploying machine learning systems in the real world requires both high accuracy on clean data and robustness to naturally occurring corruptions. While architectural advances have led to improved accuracy, building robust models remains challenging. Prior work has argued that there is an inherent trade-off between robustness and accuracy, which is exemplified by standard data augment techniques such as Cutout, which improves clean accuracy but not robustness, and additive Gaussian noise, which improves robustness but hurts accuracy. To overcome this trade-off, we introduce Patch Gaussian, a simple augmentation scheme that adds noise to randomly selected patches in an input image. Models trained with Patch Gaussian achieve state of the art on the CIFAR-10 and ImageNetCommon Corruptions benchmarks while also improving accuracy on clean data. We find that this augmentation leads to reduced sensitivity to high frequency noise(similar to Gaussian) while retaining the ability to take advantage of relevant high frequency information in the image (similar to Cutout). Finally, we show that Patch Gaussian can be used in conjunction with other regularization methods and data augmentation policies such as AutoAugment, and improves performance on the COCO object detection benchmark.
Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training

We introduce a feature scattering-based adversarial training approach for improving model robustness against adversarial attacks. Conventional adversarial training approaches leverage a supervised scheme (either targeted or non-targeted) in generating attacks for training, which typically suffer from issues such as label leaking as noted in recent works. Differently, the proposed approach generates adversarial images for training through feature scattering in the latent space, which is unsupervised in nature and avoids label leaking. More importantly, this new approach generates perturbed images in a collaborative fashion, taking the inter-sample relationships into consideration. We conduct analysis on model robustness and demonstrate the effectiveness of the proposed approach through extensively experiments on different datasets compared with state-of-the-art approaches.
Incremental Few-Shot Learning with Attention Attractor Networks

Machine learning classifiers are often trained to recognize a set of pre-defined classes. However, in many applications, it is often desirable to have the flexibility of learning additional concepts, with limited data and without re-training on the full training set. This paper addresses this problem, incremental few-shot learning, where a regular classification network has already been trained to recognize a set of base classes, and several extra novel classes are being considered, each with only a few labeled examples. After learning the novel classes, the model is then evaluated on the overall classification performance on both base and novel classes. To this end, we propose a meta-learning model, the Attention Attractor Network, which regularizes the learning of novel classes. In each episode, we train a set of new weights to recognize novel classes until they converge, and we show that the technique of recurrent back-propagation can back-propagate through the optimization process and facilitate the learning of these parameters. We demonstrate that the learned attractor network can help recognize novel classes while remembering old classes without the need to review the original training set, outperforming various baselines.
Uncertainty Estimation Using a Single Deep Deterministic Neural Network

We propose a method for training a deterministic deep model that can find and reject out of distribution data points at test time with a single forward pass. Our approach, deterministic uncertainty quantification (DUQ), builds upon ideas of RBF networks. We scale training in these with a novel loss function and centroid updating scheme and match the accuracy of softmax models. By enforcing detectability of changes in the input using a gradient penalty, we are able to reliably detect out of distribution data. Our uncertainty quantification scales well to large datasets, and using a single model, we improve upon or match Deep Ensembles in out of distribution detection on notable difficult dataset pairs such as FashionMNIST vs. MNIST, and CIFAR-10 vs. SVHN.
An Emotion Care Model using Multimodal Textual Analysis on COVID-19

At the dawn of the year 2020, the world was hit by a significant pandemic COVID-19, that traumatized the entire planet. The infectious spread grew in leaps and bounds and forced the policymakers and governments to move towards lockdown. The lockdown further compelled people to stay under house arrest, which further resulted in an outbreak of emotions on social media platforms. Perceiving people's emotional state during these times becomes critically and strategically important for the government and the policymakers. In this regard, a novel emotion care scheme has been proposed in this paper to analyze multimodal textual data contained in real-time tweets related to COVID-19. Moreover, this paper studies 8-scale emotions (Anger, Anticipation, Disgust, Fear, Joy, Sadness, Surprise, and Trust) over multiple categories such as nature, lockdown, health, education, market, and politics. This is the first of its kind linguistic analysis on multiple modes pertaining to the pandemic to the best of our understanding. Taking India as a case study, we inferred from this textual analysis that ‘joy’ has been lesser towards everything (~9-15%) but nature (~17%) due to the apparent fact of lessened pollution. The education system entailed more trust (~29%) due to teachers' fraternity's consistent efforts. The health sector witnessed sadness (~16%) and fear (~18%) as the dominant emotions among the masses as human lives were at stake. Additionally, the state-wise and emotion-wise depiction is also provided. An interactive internet application has also been developed for the same.
A three-dimensional bit-level image encryption algorithm with Rubik’s cube method

This paper proposes a new three-dimensional bit-level image encryption scheme with Rubik’s cube method (3D-BERC). In the permutation process, the Rubik’s cube method and bit-level encryption principle are combined to realize the image scrambling operation in three-dimensional space. Through the contraction mapping principle, the ascending and reducing dimension operation can be implemented effectively. In the diffusion process, we design an improved two-dimensional diffusion structure, which can spread slight change in plain image to the whole cipher image. Experimental results and simulation analysis illustrate the security and the validity of the proposed scheme.
Structure-Measure: A New Way to Evaluate Foreground Maps

Foreground map evaluation is crucial for gauging the progress of object segmentation algorithms, in particular in the field of salient object detection where the purpose is to accurately detect and segment the most salient object in a scene. Several measures (e.g., area-under-the-curve, F1-measure, average precision, etc.) have been used to evaluate the similarity between a foreground map and a ground-truth map. The existing measures are based on pixel-wise errors and often ignore the structural similarities. Behavioral vision studies, however, have shown that the human visual system is highly sensitive to structures in scenes. Here, we propose a novel, efficient (0.005 s per image), and easy to calculate measure known as S-measure (structural measure) to evaluate foreground maps. Our new measure simultaneously evaluates region-aware and object-aware structural similarity between a foreground map and a ground-truth map. We demonstrate superiority of our measure over existing ones using 4 meta-measures on 5 widely-used benchmark datasets. Furthermore, we conduct a behavioral judgment study over a new database. Data from 45 subjects shows that on average they preferred the saliency maps chosen by our measure over the saliency maps chosen by the state-of-the-art measures. Our experimental results offer new insights into foreground map evaluation where current measures fail to truly examine the strengths and weaknesses of models. Code: https://github.com/DengPingFan/S-measure .
Subspace Clustering via Good Neighbors

Finding the informative subspaces of high-dimensional datasets is at the core of numerous applications in computer vision, where spectral-based subspace clustering is arguably the most widely studied method due to its strong empirical performance. Such algorithms first compute an affinity matrix to construct a self-representation for each sample using other samples as a dictionary. Sparsity and connectivity of the self-representation play important roles in effective subspace clustering. However, simultaneous optimization of both factors is difficult due to their conflicting nature, and most existing methods are designed to address only one factor. In this paper, we propose a post-processing technique to optimize both sparsity and connectivity by finding good neighbors. Good neighbors induce key connections among samples within a subspace and not only have large affinity coefficients but are also strongly connected to each other. We reassign the coefficients of the good neighbors and eliminate other entries to generate a new coefficient matrix. We show that the few good neighbors can effectively recover the subspace, and the proposed post-processing step of finding good neighbors is complementary to most existing subspace clustering algorithms. Experiments on five benchmark datasets show that the proposed algorithm performs favorably against the state-of-the-art methods with negligible additional computation cost.
When to use parametric models in reinforcement learning

We examine the question of when and how parametric models are most useful in reinforcement learning. In particular, we look at commonalities and differences between parametric models and experience replay. Replay-based learning algorithms share important traits with model-based approaches, including the ability to plan: to use more computation without additional data to improve predictions and behaviour. We discuss when to expect benefits from either approach, and interpret prior work in this context. We hypothesise that, under suitable conditions, replay-based algorithms should be competitive to or better than model-based algorithms if the model is used only to generate fictional transitions from observed states for an update rule that is otherwise model-free. We validated this hypothesis on Atari 2600 video games. The replay-based algorithm attained state-of-the-art data efficiency, improving over prior results with parametric models. Additionally, we discuss different ways to use models. We show that it can be better to plan backward than to plan forward when using models to perform credit assignment (e.g., to directly learn a value or policy), even though the latter seems more common. Finally, we argue and demonstrate that it can be beneficial to plan forward for immediate behaviour, rather than for credit assignment.
Data Analysis by Web Scraping using Python

The standard information investigation are built on the root and impact relationship, shaped an example minuscule examination, subjective and quantitative examination, the rationality approach of creating extrapolation examination. The Web Scraper's conniving ethics and procedures are juxtaposed, it explains about the working of how the scraper is premeditated. The technique of it is allocated into three fragments: the web scraper draws the desired links from web, and then the data is extracted to get the data from the source links and finally stowing that data into a csv file. The Python language is implemented for the carrying out. By doing so, linking all these with the moral knowledge of libraries and working know-how, we can have an adequate Scraper in our hand to produce the desired result. Due to an enormous community and library resources for Python and the exquisiteness of coding chic of python language, it is most appropriate one for Scraping desired data from the desired website.
Deep Semi-Supervised Anomaly Detection

Deep approaches to anomaly detection have recently shown promising results over shallow methods on large and complex datasets. Typically anomaly detection is treated as an unsupervised learning problem. In practice however, one may have---in addition to a large set of unlabeled samples---access to a small pool of labeled samples, e.g. a subset verified by some domain expert as being normal or anomalous. Semi-supervised approaches to anomaly detection aim to utilize such labeled samples, but most proposed methods are limited to merely including labeled normal samples. Only a few methods take advantage of labeled anomalies, with existing deep approaches being domain-specific. In this work we present Deep SAD, an end-to-end deep methodology for general semi-supervised anomaly detection. Using an information-theoretic perspective on anomaly detection, we derive a loss motivated by the idea that the entropy of the latent distribution for normal data should be lower than the entropy of the anomalous distribution. We demonstrate in extensive experiments on MNIST, Fashion-MNIST, and CIFAR-10, along with other anomaly detection benchmark datasets, that our method is on par or outperforms shallow, hybrid, and deep competitors, yielding appreciable performance improvements even when provided with only little labeled data.
Adversarial Training and Provable Defenses: Bridging the Gap

We propose a new method to train neural networks based on a novel combination of adversarial training and provable defenses. The key idea is to model training as a procedure which includes both, the verifier and the adversary. In every iteration, the verifier aims to certify the network using convex relaxation while the adversary tries to find inputs inside that convex relaxation which cause verification to fail. We experimentally show that this training method is promising and achieves the best of both worlds – it produces a model with state-of-the-art accuracy (74.8%) and certified robustness (55.9%) on the challenging CIFAR-10 dataset with a 2/255 L-infinity perturbation. This is a significant improvement over the currently known best results of 68.3% accuracy and 53.9% certified robustness, achieved using a 5 times larger network than our work.
Blockchain for industrial transformations: A forward-looking approach with multi-stakeholder engagement for policy advice

Beyond more recognized financial applications of Blockchain, its potential for other sectors has increasingly come to the foreground. Yet, its development still faces questions over impact, added value, or concrete paths for widespread deployment. In this paper we argue for a transdisciplinary forward-looking approach to address such uncertainties, based on the processes and findings of the research project #Blockchain4EU: Blockchain for Industrial Transformations, which was developed inside the European Commission with a focus on multi-stakeholder engagement and co-creation. We invested in a mix of desk research with qualitative methods, including interviews, surveys, or ethnographic explorations, together with participatory workshops for collective vision building and speculative prototyping for policy. Our main findings underline key sociotechnical challenges and opportunities for the development and uptake of Blockchain in specific European industrial and business contexts, taking into account the complexity of policy, economic, social, technical, legal and environmental elements. But, aiming to push the frontiers of what's common practice in advice for policy when looking into early-stage technologies as Blockchain, we also strive to emphasize how our approach can benefit decision-makers through robust methodological and conceptual processes that are simultaneously evidence-based and experimental in their delivery and impact.
An efficient brain tumor image segmentation based on deep residual networks (ResNets)

Automatic segmentation of brain tumor from Magnetic Resonance Images (MRI) is one of the challenging tasks in computer vision. Many proposals investigate the use of Deep Neural Networks (DNN) in image segmentation as they have a high performance in automatic segmentation of brain tumors images. Due to the gradient diffusion problem and complexity, it generally takes a lot of time and extra computational power for training deeper neural networks. In this paper, we present an automatic technique for brain tumor segmentation depending on Deep Residual Learning Network (ResNet) to get over the gradient problem of DNN. ResNets accomplish more accuracy and can make the training process faster compared to their equivalent DNN. To achieve this enhancement, ResNets add a shortcut skip connection parallel to convolutional neural networks layers. Simulation examples have been carried out on dataset BRATS 2015 to verify the superiority of the proposed technique. Results verify that the proposed technique has an improved accuracy of 83%, 90%, and 85% for the complete, core, and enhancing regions, respectively. Moreover, it has an average computation time (3 times) faster than other DNN techniques.
Blockchains

Blockchain technology has gained tremendous popularity both in practice and academia. The goal of this article is to develop a coherent overview of the state of the art in blockchain technology, using a systematic (i.e., protocol-based, replicable), multivocal (i.e., featuring both white and grey literature alike) literature review to (1) define blockchain technology, (2) elaborate on its architecture options and (3) tradeoffs, as well as to understand (4) the current applications and challenges, as evident from the state of the art. We derive a systematic definition of blockchain technology, based on a formal concept analysis. Further, we flesh out an overview of blockchain technology elaborated by means of Grounded-Theory.
An Asymmetric Image Encryption Algorithm Based on a Fractional-Order Chaotic System and the RSA Public-Key Cryptosystem

Herein, an asymmetric image encryption algorithm based on RSA cryptosystem and a fractional-order chaotic system is proposed. Its security depends on RSA algorithm. First, a pair of public and private keys is generated by RSA algorithm. Subsequently, a random message shown as plaintext key information is encrypted by the public key and RSA to achieve ciphertext key information. Next, a new transformation map is established to generate the initial key according to the ciphertext key information. Subsequently, the initial key is substituted into a fractional hyperchaotic system equation to calculate the keystream. Finally, permutation and diffusion operations are employed to encrypt a plain image to obtain the final cipher image. In the proposed algorithm, different keys for encryption and decryption are designed under an asymmetric architecture. The RSA algorithm and fractional chaotic system are combined to encrypt images; in particular, a fast algorithm for computing power multiplication is employed, which significantly improves the encryption effect and enhances the security. Simulation results show that the proposed algorithm is effective and applicable to image protection.
Intellectual Property in the New Technological Age: 2023

Rapid advances in digital and life sciences technology continue to spur the evolution of intellectual property law. As professors and practitioners in this field know all too well, Congress and the courts continue to develop intellectual property law and jurisprudence at a rapid pace. For that reason, we have significantly augmented and revised Intellectual Property in the New Technological Age.
Model-Based Active Exploration

Efficient exploration is an unsolved problem in Reinforcement Learning which is usually addressed by reactively rewarding the agent for fortuitously encountering novel situations. This paper introduces an efficient active exploration algorithm, Model-Based Active eXploration (MAX), which uses an ensemble of forward models to plan to observe novel events. This is carried out by optimizing agent behaviour with respect to a measure of novelty derived from the Bayesian perspective of exploration, which is estimated using the disagreement between the futures predicted by the ensemble members. We show empirically that in semi-random discrete environments where directed exploration is critical to make progress, MAX is at least an order of magnitude more efficient than strong baselines. MAX scales to high-dimensional continuous environments where it builds task-agnostic models that can be used for any downstream task.
Exploiting Implicit Influence From Information Propagation for Social Recommendation

Social recommender systems have attracted a lot of attention from academia and industry. On social media, users' ratings and reviews can be observed by all users, and have implicit influence on their future ratings. When these users make subsequent decisions about an item, they may be affected by existing ratings on the item. Thus, implicit influence propagates among the users who rated the same items, and it has significant impact on users' ratings. However, implicit influence propagation and its effect on recommendation rarely have been studied. In this article, we propose an information propagation-based social recommendation method (SoInp) and model the implicit user influence from the perspective of information propagation. The implicit influence is inferred from ratings on the same items. We investigate the concrete effect of implicit user influence in the propagation process and introduce it into recommender systems. Furthermore, we incorporate the implicit user influence and explicit trust information in the matrix factorization framework. To demonstrate the performance, we conduct comprehensive experiments on real-world datasets to compare the proposed method with the state-of-the-art models. The results indicate that SoInp makes notable improvements in rating prediction.
Making reference solar forecasts with climatology, persistence, and their optimal convex combination

Climatology and persistence are the two most popular naïve reference methods for benchmarking deterministic solar forecasts. Depending on the forecast horizon, the preferred reference methods differ. This brief note derives the general relationship among climatology, persistence, and their optimal convex combination. The value of the lag-h autocorrelation is shown to be the main indicator of choice for the standard of reference.