Reviewerly/exHarmony huggingface dataset:
Fixing Localization Errors to Improve Image Classification

Deep neural networks are generally considered black-box models that offer less interpretability for their decision process. To address this limitation, Class Activation Map (CAM) provides an attractive solution that visualizes class-specific discriminative regions in an input image. The remarkable ability of CAMs to locate class discriminating regions has been exploited in weakly-supervised segmentation and localization tasks. In this work, we explore a new direction towards the possible use of CAM in deep network learning process. We note that such visualizations lend insights into the workings of deep CNNs and could be leveraged to introduce additional constraints during the learning stage. Specifically, the CAMs for negative classes (negative CAMs) often have false activations even though those classes are absent from an image. Thereby, we propose a loss function that seeks to minimize peaks within the negative CAMs, called ‘Homogeneous Negative CAM’ loss. This way, in an effort to fix localization errors, our loss provides an extra supervisory signal that helps the model to better discriminate between similar classes. Our designed loss function is easy to implement and can be readily integrated into existing DNNs. We evaluate it on a number of classification tasks including large-scale recognition, multi-label classification and fine-grained recognition. Our loss provides better performance compared to other loss functions across the studied tasks. Additionally, we show that the proposed loss function provides higher robustness against adversarial attacks and noisy labels.
A Comparison of Nonlinear Filters for Underwater Geomagnetic Navigation

The present article focuses on the use of Bayesian Filters for Geophysical Navigation (GN) of autonomous underwater vehicles (AUVs) using geomagnetic data. Due to the nonlinear non-Gaussian nature of the problem, three classical Bayesian nonlinear filtering algorithms are considered: the Extended Kalman Filter (EKF), the Unscented Kalman Filter (UKF), and the Particle Filter (PF). Extensive Monte Carlo (MC) simulations using real data acquired with an autonomous marine vehicle of the Medusa class were done to assess the robustness and efficacy of the proposed solutions. For a fair evaluation of the estimation performance, the following scenarios were considered: different initial covariances and kidnapped vehicle. Simulation results showcase the robustness of the PF solution for the geomagnetic navigation problem. However, EKF and UKF are computationally less expensive and may be viable solutions in certain scenarios.
BERT-IAN Model for Aspect-based Sentiment Analysis

Aspect-based sentiment analysis is different from document-level and sentence-level sentiment analysis, which aims to predict the sentiment polarity of a certain aspect in a sentence. The accuracy of the existing aspect-based sentiment analysis model still needs to be improved. A BERT-IAN sentiment analysis model that improves the Interactive Attention Networks (IAN) model is proposed to further improve the accuracy of the aspect-based sentiment analysis. First use the BERT pre-training model to encode aspects and context respectively. Then use a transformer encoder with interactive attention to interactively learn the attention of the aspect and context, and generate a final representation. Finally, through the sentiment classification layer, the aspect corresponding sentiment are analyzed. The experimental results on Restaurant and Laptop datasets show the effectiveness and superiority of the BERT-IAN model.
A Black-Box Approach to Post-Quantum Zero-Knowledge in Constant Rounds

In a recent seminal work, Bitansky and Shmueli (STOC '20) gave the first construction of a constant round zero-knowledge argument for $$\mathbf {NP}$$ secure against quantum attacks. However, their construction has several drawbacks compared to the classical counterparts. Specifically, their construction only achieves computational soundness, requires strong assumptions of quantum hardness of learning with errors (QLWE assumption) and the existence of quantum fully homomorphic encryption (QFHE), and relies on non-black-box simulation. In this paper, we resolve these issues at the cost of weakening the notion of zero-knowledge to what is called $$\epsilon $$ -zero-knowledge. Concretely, we construct the following protocols: – We construct a constant round interactive proof for $$\mathbf {NP}$$ that satisfies statistical soundness and black-box $$\epsilon $$ -zero-knowledge against quantum attacks assuming the existence of collapsing hash functions, which is a quantum counterpart of collision-resistant hash functions. Interestingly, this construction is just an adapted version of the classical protocol by Goldreich and Kahan (JoC '96) though the proof of $$\epsilon $$ -zero-knowledge property against quantum adversaries requires novel ideas. – We construct a constant round interactive argument for $$\mathbf {NP}$$ that satisfies computational soundness and black-box $$\epsilon $$ -zero-knowledge against quantum attacks only assuming the existence of post-quantum one-way functions. At the heart of our results is a new quantum rewinding technique that enables a simulator to extract a committed message of a malicious verifier while simulating verifier's internal state in an appropriate sense.
A Novel Multi-Task Optimization Algorithm Based on the Brainstorming Process

Evolutionary multi-task optimization (EMTO) is an emerging research topic in the field of evolutionary computation, which aims to simultaneously optimize several component tasks within a problem and output the best solution for each task.Since EMTO has widespread applications in solving real-world multi-task optimization problems, in recent years, some EMTO algorithms have been proposed.However, most of which are based on the multifactorial evolution framework which has difficulties in independently controlling the optimization of each component task and implementing parallel computing.To tackle this problem and enrich the EMTO algorithms' family, this paper firstly designs a novel EMTO framework inspired by the brainstorming process of human beings when they solve multi-task problems.Under this framework, a novel EMTO algorithm, named as brain storm multi-task optimization (BSMTO), is presented, where the optimization for each component task and the knowledge transfer between different tasks are both implemented by the proposed brainstorming operations.Furthermore, through investigating the knowledge transfer process in the proposed algorithm, an enhanced BSMTO algorithm named as BSMTO-II is further proposed, where the knowledge transfer in each component task can be managed and controlled by our newly designed scheme.Finally, the proposed two algorithms are tested on benchmark problems.Experimental results show that BSMTO-II has a competitive performance compared with both classical and state-of-the-art algorithms.Moreover, the effectiveness of the proposed EMTO framework and the knowledge transfer control scheme is proved through experiments, and the key parameters settings and the algorithmic complexity are also discussed at last.
Heavy-Tailed Self-Similarity Modeling for Single Image Super Resolution

Self-similarity is a prominent characteristic of natural images that can play a major role when it comes to their denoising, restoration or compression. In this paper, we propose a novel probabilistic model that is based on the concept of image patch similarity and applied to the problem of Single Image Super Resolution. Based on this model, we derive a Variational Bayes algorithm, which super resolves low-resolution images, where the assumed distribution for the quantified similarity between two image patches is heavy-tailed. Moreover, we prove mathematically that the proposed algorithm is both an extended and superior version of the probabilistic Non-Local Means (NLM). Its prime advantage remains though, which is that it requires no training. A comparison of the proposed approach with state-of-the-art methods, using various quantitative metrics shows that it is almost on par, for images depicting rural themes and in terms of the Structural Similarity Index (SSIM) with the best performing methods that rely on trained deep learning models. On the other hand, it is clearly inferior to them, for urban themed images and in terms of all metrics, especially for the Mean-Squared-Error (MSE). In addition, qualitative evaluation of the proposed approach is performed using the Perceptual Index metric, which has been introduced to better mimic the human perception of the image quality. This evaluation favors our approach when compared to the best performing method that requires no training, even if they perform equally in qualitative terms, reinforcing the argument that MSE is not always an accurate metric for image quality.
Improving Security of Web-Based Application Using ModSecurity and Reverse Proxy in Web Application Firewall

The use of web applications has been undergoing rapid increase. Many individuals, groups, organizations or governments use web applications as a means to exchange information or support business-related tasks. Despite the increased adoption, web applications use is however directly associated with comparable threats and attacks. With the increasing threats and attacks on web applications, organizations require a more effective concept of web application security. Web Application Firewall (WAF) is a security concept that can be used to prevent various threats and attacks on web applications. WAF has the ability to filter packets, block dangerous HTTP requests, and also do logging. This paper demonstrates and proposes the implementation of WAF on a web-based application using ModSecurity and the Reverse Proxy method. From the tests carried out e.g. cross-site scripting, SQL injection and unauthorized vulnerability web scanning, all threats were successfully thwarted by ModSecurity and reverse proxy method implemented in the WAF.
Behavior detection method of OpenPose combined with Yolo network

The essence of human behavior recognition is the problem of locating key points of the human body. The key points of different parts are connected to form an effective limb skeleton, and then the human behavior is determined by the change of the skeleton. However, the positioning of key points of human body has the following problems: the complexity of human body actions leads to many kinds of joint positions, small joint points that are difficult to see, fuzzy or covered joint points. At present, the mainstream algorithms mainly judge the location of key points according to the context. In this paper, it is difficult to detect the human pose of fuzzy and remote micro key points by combining OpenPose network and Yolo network. Firstly, by modifying the CSPdrarknet53 feature extraction network, two different distance feature extraction methods are used. For the close human body, the OpenPose Part Confidence Maps (PCM) and Part Affinity Fields (PAFs) method is used to detect the posture. For the long-distance human body actions that cannot be detected, the PANet enhancement is used and then the NMS is used to filter the candidate frame. Compared with experiments, the algorithm can get more than 95% accuracy through training, which has better real-time performance.
Towards Byzantine-Resilient Federated Learning via Group-Wise Robust Aggregation

Federated learning (FL) is a distributed machine learning approach where many participants collaboratively train a model while keeping the training data decentralized. The distributed setting makes FL vulnerable to Byzantine failures and malicious participants, leading to unusable models or compromised model with backdoor. Although several robust statistics based methods have been proposed to address the robustness of FL against Byzantine failures, recent works has shown the insufficiency of their defenses because that non-i.i.d data distribution and high variance of gradients among participants in practical FL settings disrupt their common assumptions. To address this problem, we propose a simple but efficient group-wise robust aggregation framework, which clusters model parameters into different groups and applies robust aggregation with-in each cluster. We apply our framework with a number of popular Byzantine-robust aggregation methods, and evaluate its resiliency against the attack that can successfully circumvent these methods in their original settings. Our experimental results demonstrate that the group-wise robust aggregation effectively improves the robustness against Byzantine failures and highlights the effect of clustering for addressing the gap between practical FL and theoretical assumptions of robust statistics based defense.
A Bayesian approach on the two-piece scale mixtures of normal homoscedastic nonlinear regression models

In this application note paper, we propose and examine the performance of a Bayesian approach for a homoscedastic nonlinear regression (NLR) model assuming errors with two-piece scale mixtures of normal (TP-SMN) distributions. The TP-SMN is a large family of distributions, covering both symmetrical/ asymmetrical distributions as well as light/heavy tailed distributions, and provides an alternative to another well-known family of distributions, called scale mixtures of skew-normal distributions. The proposed family and Bayesian approach provides considerable flexibility and advantages for NLR modelling in different practical settings. We examine the performance of the approach using simulated and real data.
Parallel Multi-Path Age Distinguish Network for Cross-Age Face Recognition

Cross Age Face Recognition (CAFR) is a challenging task in the field of face recognition. There still exist some limitations in mainstream CAFR methods. On one hand, some methods need synthesizing multiple groups of features and fusing the results under different ages, where the similarity score in some age groups may affect final classification results. On the other hand, many other methods takes a facial image as a linear combination of identity information and age information, which treats age factor as a value independent of identity information, but may be inconsistent with the aging pattern of many individuals. And these methods require both age labels and identity labels in training, which is limited by the scale of existing CAFR datasets. To address the above limitations, this work proposes the Parallel Multi-path Age Distinguish Network (PMADN) model. Specifically, our model consists of two cascading networks, an Age Distinguish Mapping Network (ADMN) and a Cross-Age Feature Recombination Network (CFRN). Firstly, the face features are mapped into different age groups by parallel multi-path full connected layers in ADMN, which can better extract the identity features in a small age span. Secondly, CFRN nonlinearly recombines the mapped features to extract the age robust features that are beneficial to identity classification, which can avoid the simple linear combination of identity factor and age factor in the existing methods. What's more, our algorithm is combined with transfer learning and only uses the age label and a pre-trained ordinary face recognition network for training, which can make use of a larger aging face dataset for training. Extensive CAFR experiments performed on the benchmark MORPH Album2, CACD-VS and Cross Age LFW databases demonstrate the effectiveness and superiority of our method.
A Comparison Framework of Machine Learning Algorithms for Mixed-Type Variables Datasets: A Case Study on Tire-Performances Prediction

Many engineering applications in the automotive, aeronautic, rubber, mechanics, and manufacturing industries collect multiple datasets measuring physical relations between input variables and performances for modeling purposes.The challenge relies on that such data is often highly dimensional, non-linear and contain mixed variables, i.e., numerical and categorical features, requiring specific algorithms and encoding schemes to perform regression task efficiently.Moreover, defining an appropriated similarity criterion for mixed-type data is a non-trivial task, especially when it is meant to be used in regression problems.This paper discusses the use of different machine learning algorithms for regression problems, involving mixed-type variables across multiple datasets.We use tire-related datasets as a case study to perform a rigorous, statistically founded comparison of different machine learning algorithms with encoding schemes to handle mixed variables in the prediction of tire-performances across multiple tirerelated datasets.Friedman's statistic and Nemenyi post-hoc tests are used to test the significance of performance differences between techniques and encoding strategies.Our contributions come as a series of recommendations for handling efficiently mixed-type variables while achieving high performances on regression tasks over multiple datasets.Furthermore, we provide a flexible and efficient similarity function between tires useful for tire comparison, prediction, and retrieval tasks.
PeerBFT: Making Hyperledger Fabric’s Ordering Service Withstand Byzantine Faults

In a blockchain system, there is an entity called an ordering service or miner that is responsible for ordering transactions, building a new block, and broadcasting the new block to all the blockchain peers. In a distributed system, the safety property is violated when an ordering service sends inconsistent blocks to peers and the liveness property is not guaranteed when the ordering service stops sending blocks to peers. Therefore, the ordering service must behave correctly to guarantee safety and liveness in blockchain operations. Hyperledger Fabric, a permissioned blockchain platform assumes the correct behavior of the ordering service. It also adopts an endorsement policy to deal with malicious behaviors of peers. However, a malicious ordering service can easily make the endorsement policy ineffective. Therefore, Hyperledger Fabric is not reliable in an environment where an ordering service may be hacked. In this paper, we propose PeerBFT that enables peers to handle Byzantine faults in the ordering service of Hyperledger Fabric, assuming that n ≥ 3f + 1, where n is the total number of peers andf is the maximum number of malicious peers. In PeerBFT, each peer audits the behavior of the ordering service. When a group of peers detects any incorrect operations in the ordering service, they collectively migrate to a new ordering service. We have built a prototype of PeerBFT in Hyperledger Fabric 1.2. It is shown that PeerBFT can handle the Byzantine faults of the ordering service. The experimental evaluation shows that PeerBFT achieves approximately 90.8% of TPS (Transactions Per Second) of Hyperledger Fabric with Solo ordering service.
Pairwise Similarity Knowledge Transfer for Weakly Supervised Object Localization

Weakly Supervised Object Localization (WSOL) methods only require image level labels as opposed to expensive bounding box annotations required by fully supervised algorithms. We study the problem of learning localization model on target classes with weakly supervised image labels, helped by a fully annotated source dataset. Typically, a WSOL model is first trained to predict class generic objectness scores on an off-the-shelf fully supervised source dataset and then it is progressively adapted to learn the objects in the weakly supervised target dataset. In this work, we argue that learning only an objectness function is a weak form of knowledge transfer and propose to learn a classwise pairwise similarity function that directly compares two input proposals as well. The combined localization model and the estimated object annotations are jointly learned in an alternating optimization paradigm as is typically done in standard WSOL methods. In contrast to the existing work that learns pairwise similarities, our approach optimizes a unified objective with convergence guarantee and it is computationally efficient for large-scale applications. Experiments on the COCO and ILSVRC 2013 detection datasets show that the performance of the localization model improves significantly with the inclusion of pairwise similarity function. For instance, in the ILSVRC dataset, the Correct Localization (CorLoc) performance improves from $$72.8\%$$ to $$78.2\%$$ which is a new state-of-the-art for WSOL task in the context of knowledge transfer.
Information-Directed Random Walk for Rare Event Detection in Hierarchical Processes

The problem of detecting a few anomalous processes among a large number of data streams is considered. At each time, aggregated observations can be taken from a chosen subset of the processes, where the chosen subset conforms to a given tree structure. The random observations are drawn from a general distribution that may depend on the size of the chosen subset and the number of anomalous processes in the subset. We propose a sequential search strategy by devising an information-directed random walk on the tree-structured observation hierarchy. The proposed policy is shown to be asymptotically optimal with respect to the detection accuracy and order-optimal with respect to the size of the search space. Effectively localizing the data processing to small subsets of the search space, the proposed strategy is also efficient in terms of computation and memory requirement.
Rectifying the Mischaracterization of Logic by Mental Model Theorists

Abstract Khemlani et al. (2018) mischaracterize logic in the course of seeking to show that mental model theory (MMT) can accommodate a form of inference ( , let us label it) they find in a high percentage of their subjects. We reveal their mischaracterization and, in so doing, lay a landscape for future modeling by cognitive scientists who may wonder whether human reasoning is consistent with, or perhaps even capturable by, reasoning in a logic or family thereof. Along the way, we note that the properties touted by Khemlani et al. as innovative aspects of MMT‐based modeling (e.g., nonmonotonicity) have for decades been, in logic, acknowledged and rigorously specified by families of (implemented) logics. Khemlani et al. (2018) further declare that is “invalid in any modal logic.” We demonstrate this to be false by our introduction (Appendix A) of a new propositional modal logic (within a family of such logics) in which is provably valid, and by the implementation of this logic. A second appendix, B, partially answers the two‐part question, “What is a formal logic, and what is it for one to capture empirical phenomena?”
Just Another Quantum Assembly Language (Jaqal)

The Quantum Scientific Computing Open User Testbed (QSCOUT) is a trapped-ion quantum computer testbed realized at Sandia National Laboratories on behalf of the Department of Energy's Office of Science and its Advanced Scientific Computing (ASCR) program. Here we describe Jaqal, for Just another quantum assembly language, the programming language we invented to specify programs executed on QSCOUT. Jaqal is useful beyond QSCOUT-it can support mutliple hardware targets because it offloads gate names and their pulse-sequence definitions to external files. We describe the capabilities of the Jaqal language, our approach in designing it, and the reasons for its creation. To learn more about QSCOUT, Jaqal, or JaqalPaq, the metaprogramming Python package we developed for Jaqal, please visit qscout.sandia.gov, gitlab.com/jaqal, or send an e-mail to qscout@sandia.gov.