Reviewerly/exHarmony huggingface dataset:
Exploiting Web Images for Fine-Grained Visual Recognition via Dynamic Loss Correction and Global Sample Selection

To distinguish subtle differences among fine-grained categories, a large amount of well-labeled images are typically required. However, acquiring manual annotations for fine-grained categories is an extremely difficult task as it usually has a high demand for professional knowledge. To this end, directly leveraging web images for learning fine-grained models becomes a natural choice. Nevertheless, due to the existence of label noise, this learning paradigm tends to have a poor performance. In this work, we propose an end-to-end approach by combining dynamic loss correction and global sample selection to alleviate the problem of label noise. Specifically, we leverage the network to predict all samples, record the predictions of recent several epochs, and calculate the uncertainly-based dynamic loss for global sample selection. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our proposed approach. The source code of our approach has been released on the website: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/NUST-Machine-Intelligence-Laboratory/dlc</uri> .
Dependent Modeling of Temporal Sequences of Random Partitions

We consider modeling a dependent sequence of random partitions. It is well known in Bayesian nonparametrics that a random measure of discrete type induces a distribution over random partitions. The community has therefore assumed that the best approach to obtain a dependent sequence of random partitions is through modeling dependent random measures. We argue that this approach is problematic and show that the random partition model induced by dependent Bayesian nonparametric priors exhibits counter-intuitive dependence among partitions even though the dependence for the sequence of random probability measures is intuitive. Because of this, we suggest directly modeling the sequence of random partitions when clustering is of principal interest. To this end, we develop a class of dependent random partition models that explicitly models dependence in a sequence of partitions. We derive conditional and marginal properties of the joint partition model and devise computational strategies when employing the method in Bayesian modeling. In the case of temporal dependence, we demonstrate through simulation how the methodology produces partitions that evolve gently and naturally over time. We further illustrate the utility of the method by applying it to an environmental dataset that exhibits spatio-temporal dependence. Supplemental files for this article are available online.
Adaptive investigation of the optical properties of polymer fibers from mixing noisy phase shifting microinterferograms using deep learning algorithms

In this article, an adaptive denoising method is suggested to accurate investigate the optical and structural features of polymeric fibers from noisy phase shifting microinterferograms. The mixed class of noise that may produce in the phase-shifting interferometric techniques is established. To our knowledge, this is an early study considered the mixing noises that may occur in microinterferograms. The suggested method utilized the convolution neural networks to detect the noise class and then denoising, it according to its class. Four convolution neural networks (Googlenet, VGG-19, Alexnet, and Alexnet-SVM) are refined to perform the automatic classification process for the noise class in the established data set. The network with the highest validation and testing accuracy of these networks is considered to apply the suggested method on realistic noisy microinterferograms for polymeric fibers, polypropylene and antimicrobial polyethylene terephthalate)/titanium dioxide, recoded using interference microscope. Also, the suggested method is applied on noisy microinterferograms include crazing and nanocomposite material. The demodulated phase maps and the three-dimensional birefringence profiles are calculated for tested fibers according to the suggested method. The obtained results are compared with the published data for these fibers and found to be in good agreements.
Detecting and Mitigating Adversarial Examples in Regression Tasks: A Photovoltaic Power Generation Forecasting Case Study

With data collected by Internet of Things sensors, deep learning (DL) models can forecast the generation capacity of photovoltaic (PV) power plants. This functionality is especially relevant for PV power operators and users as PV plants exhibit irregular behavior related to environmental conditions. However, DL models are vulnerable to adversarial examples, which may lead to increased predictive error and wrong operational decisions. This work proposes a new scheme to detect adversarial examples and mitigate their impact on DL forecasting models. This approach is based on one-class classifiers and features extracted from the data inputted to the forecasting models. Tests were performed using data collected from a real-world PV power plant along with adversarial samples generated by the Fast Gradient Sign Method under multiple attack patterns and magnitudes. One-class Support Vector Machine and Local Outlier Factor were evaluated as detectors of attacks to Long-Short Term Memory and Temporal Convolutional Network forecasting models. According to the results, the proposed scheme showed a high capability of detecting adversarial samples with an average F1-score close to 90%. Moreover, the detection and mitigation approach strongly reduced the prediction error increase caused by adversarial samples.
HARTIV: Human Activity Recognition Using Temporal Information in Videos

Nowadays, the most challenging and important problem of computer vision is to detect human activities and recognize the same with temporal information from video data.The video datasets are generated using cameras available in various devices that can be in a static or dynamic position and are referred to as untrimmed videos.Smarter monitoring is a historical necessity in which commonly occurring, regular, and out-of-the-ordinary activities can be automatically identified using intelligence systems and computer vision technology.In a long video, human activity may be present anywhere in the video.There can be a single or multiple human activities present in such videos.This paper presents a deep learning-based methodology to identify the locally present human activities in the video sequences captured by a single wide-view camera in a sports environment.The recognition process is split into four parts: firstly, the video is divided into different set of frames, then the human body part in a sequence of frames is identified, next process is to identify the human activity using a convolutional neural network and finally the time information of the observed postures for each activity is determined with the help of a deep learning algorithm.The proposed approach has been tested on two different sports datasets including ActivityNet and THUMOS.Three sports activities like swimming, cricket bowling and high jump have been considered in this paper and classified with the temporal information i.e., the start and end time for every activity present in the video.The convolutional neural network and long short-term memory are used for feature extraction of temporal action recognition from video data of sports activity.The outcomes show that the proposed method for activity recognition in the sports domain outperforms the existing methods.
ALOHA: A Unified Platform-Aware Evaluation Method for CNNs Execution on Heterogeneous Systems at the Edge

CNN design and deployment on embedded edge-processing systems is an error-prone and effort-hungry process, that poses the need for accurate and effective automated assisting tools. In such tools, pre-evaluating the platform-aware CNN metrics such as latency, energy cost, and throughput is a key requirement for successfully reaching the implementation goals imposed by use-case constraints. Especially when more complex parallel and heterogeneous computing platforms are considered, currently utilized estimation methods are inaccurate or require a lot of characterization experiments and efforts. In this paper, we propose an alternative method, designed to be flexible, easy to use, and accurate at the same time. Considering a modular platform and execution model that adequately describes the details of the platform and the scheduling of different CNN operators on different platform processing elements, our method captures precisely operations and data transfers and their deployment on computing and communication resources, significantly improving the evaluation accuracy. We have tested our method on more than 2000 CNN layers, targeting an FPGA-based accelerator and a GPU platform as reference example architectures. Results have shown that our evaluation method increases the estimation precision by up to 5× for execution time, and by 2× for energy, compared to other widely used analytical methods. Moreover, we assessed the impact of the improved platform-awareness on a set of neural architecture search experiments, targeting both hardware platforms, and enforcing 2 sets of latency constraints, performing 5 trials on each search space, for a total number of 20 experiments. The predictability is improved by 4×, reaching, with respect to alternatives, selection results clearly more similar to those obtained with on-hardware measurements.
Signal Modulation Recognition Method Based on Differential Privacy Federated Learning

Signal modulation recognition is widely utilized in the field of spectrum detection, channel estimation, and interference recognition. With the development of artificial intelligence, substantial advances in signal recognition utilizing deep learning approaches have been achieved. However, a huge amount of data is required for deep learning. With increasing focus on privacy and security, barriers between data sources are sometimes difficult to break. This limits the data and renders them weak, so that deep learning is not sufficient. Federated learning can be a viable way of solving this challenge. In this article, we will examine the recognition of signal modulation based on federated learning with differential privacy, and the results show that the recognition rate is acceptable while data protection and security are being met.
Philosophy, politics, and economics of cryptocurrency II: The moral landscape of monetary design

Abstract In this article, we identify three key design dimensions along which cryptocurrencies differ – privacy, censorship‐resistance, and consensus procedure. Each raises important normative issues. Our discussion uncovers new ways to approach the question of whether Bitcoin or other cryptocurrencies should be used as money, and new avenues for developing a positive answer to that question. A guiding theme is that progress here requires a mixed approach that integrates philosophical tools with the purely technical results of disciplines like computer science and economics.
Supervised Machine Learning Approaches for Breast Cancer Classification and a high performance Recurrent Neural Network

Breast Cancer is among the most widespread forms of cancer that affects hundreds of thousands of women across the world each year. Early detection of this cancer can really help, in that it cannot only make the treatment easier, but also significantly improve survival chances. It is thus imperative that the machine learning algorithm being deployed for real world use be reliable and accurate. This paper not only compared the performance of several classifiers on the given dataset on breast cancer from UCI with data from close to 600 cases, but also used the Recurrent Neural Network (RNN) for classification, which to the best of the authors' knowledge has never been used before for breast cancer classification. Also, the performances of these classifiers are compared with the same already implemented before in this domain and discuss about the improvements made in the proposed work. The proposed experimental results showed that, RNN has yielded best performance with the highest accuracy of 98.49%, followed by the linear kernel SVM classifier with an accuracy of 98.24% and XGBoost at 97.36%. This research work has trained and tested a total of 9 classifiers and analyzed their performance by considering accuracy as the key metric. Although, to the best of the authors' knowledge, the Recurrent Neural Network has not been implemented in this domain before and is thus novel. Since our domain is mission critical, the proposed research work highly emphasizes on accuracy, while ensuring that the every classifier is trained and tested accurately.
Prior to Segment: Foreground Cues for Weakly Annotated Classes in Partially Supervised Instance Segmentation

Instance segmentation methods require large datasets with expensive and thus limited instance-level mask labels. Partially supervised instance segmentation aims to improve mask prediction with limited mask labels by utilizing the more abundant weak box labels. In this work, we show that a class agnostic mask head, commonly used in partially supervised instance segmentation, has difficulties learning a general concept of foreground for the weakly annotated classes using box supervision only. To resolve this problem, we introduce an object mask prior (OMP) that provides the mask head with the general concept of foreground implicitly learned by the box classification head under the supervision of all classes. This helps the class agnostic mask head to focus on the primary object in a region of interest (RoI) and improves generalization to the weakly annotated classes. We test our approach on the COCO dataset using different splits of strongly and weakly supervised classes. Our approach significantly improves over the Mask R-CNN baseline and obtains competitive performance with the state-of-the-art, while offering a much simpler architecture. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
The widening rift between aesthetics and ethics in the design of computational things

Abstract In the face of massively increased technological complexity, it is striking that so many of today’s computational and networked things follow design ideals honed decades ago in a much different context. These strong ideals prescribe a presentation of things as useful tools through design and a withdrawal of aspects of their functionality and complexity. Beginning in the mid-twentieth century, we trace this ‘withdrawal program’ as it has persisted in the face of increasing computational complexity. Currently, design is in a dilemma where computational products can be seen as brilliantly designed and engaging to use yet can also be considered very problematic in how they support hidden agendas and often seem less than trustworthy. In this article, we analyse factors shaping this emergent ethical dilemma and reveal the concept of a widening rift between what computational things actually are and do and the ways in which they are presented as things for use . Against this backdrop, we argue that there is a need for a new orientation in design programs to adequately address this deepening rupture between the aesthetics and ethics in the design of computational things.
Consensus cubature filtering based on Gaussian process for distributed sensor network with model uncertainty

In this paper, we consider a distributed sensor network without accurate knowledge of the state and observation models or even the form of system functions. Traditionally, consensus filtering approaches are widely used for distributed state estimation, but they require an accurate parametric model of the system. We propose a novel consensus cubature filtering algorithm based on Gaussian process (CCF-GP) for distributed state estimation with model uncertainty. The parameters in distributed sensor networks can be obtained from training data by Gaussian process regression (GPR). The approximate parametric model can be incorporated into Gaussian process (GP) to enhance the distributed estimation performance. In the Bayesian filtering frameworks, we derive the prediction and update steps of the hybrid consensus on measurements and consensus on information (HCMCI) method based on GPR. And then every node in the distributed network cooperates the information pair with its neighbors. For nonlinear systems, we derive the GP-based consensus filtering based on the spherical-radial cubature rule. Comparing with the standard nonlinear consensus filtering methods, the validity of our proposed algorithm is confirmed by a series of simulations.
Using Deep Learning Technology to Realize the Automatic Control Program of Robot Arm Based on Hand Gesture Recognition

In this study, the robot arm control, computer vision, and deep learning technologies are combined to realize an automatic control program. There are three functional modules in this program, i.e., the hand gesture recognition module, the robot arm control module, and the communication module. The hand gesture recognition module records the user’s hand gesture images to recognize the gestures’ features using the YOLOv4 algorithm. The recognition results are transmitted to the robot arm control module by the communication module. Finally, the received hand gesture commands are analyzed and executed by the robot arm control module. With the proposed program, engineers can interact with the robot arm through hand gestures, teach the robot arm to record the trajectory by simple hand movements, and call different scripts to satisfy robot motion requirements in the actual production environment.
A conditional-weight joint relevance metric for feature relevancy term

Feature selection is an important preprocessing operation in the fields of machine learning and data mining. Information theory is widely used in feature selection methods because it can measure linear and nonlinear correlations among variables. Traditional information theory-based feature selection methods intend to maximize feature relevancy while minimizing feature redundancy. However, previous feature selection methods focus on either the effect of candidate features or the effect of already-selected features on the feature relevancy. In fact, both candidate features and already-selected features offer important classification information in the design of feature relevancy term. To avoid this problem, we extract useful classification information from joint mutual information to design a novel feature relevancy term named Conditional-Weight Joint Relevance (CWJR). Based on CWJR, we propose a novel feature selection method named Feature Selection considering Conditional-Weight Joint Relevance (CWJR-FS). Additionally, to distinguish the differences between our method and previous methods, we divide information theory-based feature selection methods into two categories: linear-based feature selection methods and nonlinear-based feature selection methods. Finally, our method is compared to seven linear-based methods and four nonlinear-based methods on 19 benchmark data sets. The experimental results demonstrate that CWJR-FS outperforms the compared methods in terms of the average classification accuracy, AUC and F1 score.
Generalized transitionless quantum driving for open quantum systems

A general approach for transitionless quantum driving in open quantum systems is introduced. Under the assumption of adiabatic evolution for time-local master equations, we derive the generalized transitionless Lindbladian required to implement a shortcut to adiabaticity in an open system scenario. The general counter-diabatic Lindbladian obtained accounts for a phase freedom, which translates into a set of free parameters throughout the dynamics. We then discuss how our generalized approach allows us to recover the transitionless Lindbladian introduced by G. Vacanti et al. [New J. Phys. 16, 053017 (2014)]. We then show how to engineer time-independent master equations that provide the same dynamics as the time-dependent master equation provided by the standard transitionless quantum driving in open systems. We illustrate our results by applying them both to the adiabatic Deutsch algorithm under dephasing and to the Landau-Zener Hamiltonian under bit-phase-flip.
Performance Comparison of Benchmark Activation Function ReLU, Swish and Mish for Facial Mask Detection Using Convolutional Neural Network

This work presents a convolutional neural network (CNN) based approach to compare three separate activation functions, namely Rectified Linear Unit (ReLU), Swish and Mish, in developing a binary facial recognition system for face detection covered with masks or without masks in any orientation against COVID-19 using CNN. The rudimentary function of the model is to extract human facial features like nose, eyes, mouth, etc. using Haar cascade classifier from images and predict the class: with a mask or without a mask. After obtaining the feature of the dense layer and softmax function, it is optimized upon utilizing the gradient-descent method and loss functions exerted is binary cross-entropy. After a detailed comparison of this particular problem, it is found that, for this particular problem, ReLU stays unbeaten with 95.3544% accuracy whereas Swish with an accuracy of 89.7912% is beaten by Mish, which gives 92.5326% accuracy) lines before and after the abstract. This document is in the required format.
Adaptive Label Noise Cleaning with Meta-Supervision for Deep Face Recognition

The training of a deep face recognition system usually faces the interference of label noise in the training data. However, it is difficult to obtain a high-precision cleaning model to remove these noises. In this paper, we propose an adaptive label noise cleaning algorithm based on meta-learning for face recognition datasets, which can learn the distribution of the data to be cleaned and make automatic adjustments based on class differences. It first learns re-liable cleaning knowledge from well-labeled noisy data, then gradually transfers it to the target data with meta-supervision to improve performance. A threshold adapter module is also proposed to address the drift problem in transfer learning methods. Extensive experiments clean two noisy in-the-wild face recognition datasets and show the effectiveness of the proposed method to reach state-of-the-art performance on the IJB-C face recognition benchmark.
Geometric Deep Neural Network using Rigid and Non-Rigid Transformations for Human Action Recognition

Deep Learning architectures, albeit successful in most computer vision tasks, were designed for data with an underlying Euclidean structure, which is not usually fulfilled since pre-processed data may lie on a non-linear space. In this paper, we propose a geometry aware deep learning approach using rigid and non rigid transformation optimization for skeleton-based action recognition. Skeleton sequences are first modeled as trajectories on Kendall’s shape space and then mapped to the linear tangent space. The resulting structured data are then fed to a deep learning architecture, which includes a layer that optimizes over rigid and non rigid transformations of the 3D skeletons, followed by a CNN-LSTM network. The assessment on two large scale skeleton datasets, namely NTU-RGB+D and NTU-RGB+D 120, has proven that the proposed approach outperforms existing geometric deep learning methods and exceeds recently published approaches with respect to the majority of configurations.