Reviewerly/exHarmony huggingface dataset:
Phantom of Benchmark Dataset: Resolving Label Ambiguity Problem on Image Recognition in the Wild

While deep neural networks achieved supreme accomplishments in image recognition tasks, they conventionally utilize a benchmark dataset that presumes a well-designed label space where each image corresponds to a particular class; we denote these data as obvious samples. However, we claim this assumption is not always Justified in the real world as well as widely-utilized ImageNet. We discover that a label ambiguity problem exists, in which several samples are inherently ambiguous and can be annotated as a particular label. In this study, we propose a series of analyses on the label ambiguity and suggest a solution to resolve it along with the following contributions. First, we define label ambiguity types that exist in conventional image recognition and publicize the corresponding datasets retrieved from ImageNet and the real world. We further reveal that this label ambiguity degrades the classification performance; thus, we Justify the necessity of careful treatment of the label ambiguous samples. Second, we propose Consistent Sample Selector (CSS), a novel framework that solves this label ambiguity problem. Given obvious and ambiguous samples, the proposed CSS learns representations on each label with obvious samples and selects ambiguous samples that embrace semantics consistent with the obvious ones; thus, it aims to update the training set by concatenating obvious samples and selected ambiguous ones. Lastly, we empirically examine our CSS effectively elevates the classification performance and simultaneously improves the inductive bias, similar to how human vision recognizes.
Image Quality Assessment using Semi-Supervised Representation Learning

In this paper, we propose a framework for learning feature representations for Image Quality Assessment (IQA) using contrastive learning. To account for the absence of large-scale IQA dataset, we pretrain an image encoder to cluster images based on the image quality using synthetically distorted versions of pristine unlabeled images. Images of similar quality are grouped closer in embedding space, while simultaneously pushing apart images of dissimilar quality. In addition we show that, augmenting the contrastive learning task with downstream aware joint supervision results in feature representations that are more suitable and easily transferable for IQA specific tasks. We study the effectiveness of the learnt representations in downstream task of image quality prediction and show that our model achieves superior performance on both synthetically and authentically distorted IQA datasets when compared to other deep feature-based IQA methods.
Knowledge-based Visual Context-Aware Framework for Applications in Robotic Services

Recently, context awareness in vision technologies has become essential with the increasing demand for real-world applications, such as surveillance systems and service robots. However, implementing context awareness with an end-to-end learning-based system limits its extensibility and performance because the context varies in scope and type, but related data are mostly rare. To mitigate these limitations, we propose a visual context-aware frame-work composed of independent processes of visual perception and context inference. The framework performs logical inferences using the abstracted visual information of recognized objects and relationships based on our knowledge representation. We demonstrate the scalability and utility of the proposed framework through experimental cases that present stepwise context inferences applied to robotic services in different domains.
RarePlanes Soar Higher: Self-Supervised Pretraining for Resource Constrained and Synthetic Datasets

Self-supervised pretraining has advanced the capabilities of many computer vision tasks without requiring additional labels. One drawback is this technique requires extensive datasets and computational resources. This requirement of large datasets to pretrain with has often precluded the use of smaller, more niche datasets. Recently a method of pretraining has been developed that uses several stages of training, arranging each subsequent pretraining step to a dataset more closely resembling the target labelled data. This Hierarchical PreTraining (HPT) allows small datasets that are significantly different from generalized pretraining datasets (e.g. ImageNet) to build off subsequent knowledge transfers of increasingly focused training. However, there remains computer vision domains that are sufficiently difficult to acquire data that the use of synthetic data to augment their training has become a common convention. This paper examines how Remote Sensing Imagery (RSI) datasets, both augmented with synthetic data and without, still benefit from HPT despite being a niche domain. We show the fine balance that must be maintained when pretraining with these small datasets through a series of experiments focused on isolating various training parameters. We also demonstrate how these techniques lead to model improvements over existing baselines with and without synthetic data. Given that HPT provides a straightforward process to increase performance, and synthetic data is a growing resource for dataset augmentation, these combined methods can enhance a wide variety of current and future computer vision tasks.
Multi-view Target Transformation for Pedestrian Detection

Occlusion is one of the most challenging problems in single-view pedestrian detection. To alleviate the occlusion problem, multi-view systems have been exploited to fully acquire and recognize blocked targets. Most often, methods from the literature exploit perspective transformation to aggregate different sensing view angles of the scene, but projection distortion issues cause spatial structure break and prevent these methods from fully exploring the projected features. In this paper, we propose a novel approach, Multi-view Target Transformation (MVTT), to address the distortion problem inherent in multi-view aggregation by encoding the full target features and limiting the area of interest of the projected features. Experiment results show that the performance of our proposed method compares favorably against recent relevant methods on public datasets. The ablation studies also confirm the effectiveness of the proposed components.
Application of Recurrent Neural-Network based Kalman Filter for Uncertain Target Models

For various target tracking applications, it is well known that the Kalman filter is the optimal estimator(in the minimum mean-square sense) to predict and estimate the state(position and/or velocity) of linear dynamical systems driven by Gaussian stochastic noise. In the case of nonlinear systems, Extended Kalman filter(EKF) and/or Unscented Kalman filter(UKF) are widely used, which can be viewed as approximations of the(linear) Kalman filter in the sense of the conditional expectation. However, to implement EKF and UKF, the exact dynamical model information and the statistical information of noise are still required. In this paper, we propose the recurrent neural-network based Kalman filter, where its Kalman gain is obtained via the proposed GRU-LSTM based neural-network framework that does not need the precise model information as well as the noise covariance information. By the proposed neural-network based Kalman filter, the state estimation performance is enhanced in terms of the tracking error, which is verified through various linear and nonlinear tracking problems with incomplete model and statistical covariance information.
How to Compensate After a Data Breach?

Data breach recovery is often likened to service failure recovery. Like service failure recovery, compensation is a major factor toward data breach recovery. However, compensation involves different types, which may have varying effects on data breach recovery. This study investigates the effect of compensation types on customer's repatronage intentions to organizations that have suffered a data breach. Three compensation types were identified in this study: monetary, service, and psychological compensation. The findings of this study indicate that only service and psychological compensation have a significant effect on customers' perceptions of being treated fairly by the organization following a data breach. Furthermore, service compensation impacts the trust a customer regains in the company. Monetary compensation did not play a statistically significant role in the data breach recovery process. In turn, both perceived fairness and regained trust influences customer repatronage intentions. This study contributes to the information privacy field by identifying the types of compensation that are impactful to re-engage individuals who suffered PII related data breach.
Self-Supervised Effective Resolution Estimation with Adversarial Augmentations

High-resolution, high-quality images of human faces are desired as training data and output for many modern applications, such as avatar generation, face super-resolution, and face swapping. The terms high-resolution and high-quality are often used interchangeably; however, the two concepts are not equivalent, and high-resolution does not always imply high-quality. To address this, we motivate and precisely define the concept of effective resolution in this paper. We thereby draw connections to signal and information theory and show why baselines based on frequency analysis or compression fail. Instead, we propose a novel self-supervised learning scheme to train a neural network for effective resolution estimation without human-labeled data. It leverages adversarial augmentations to bridge the domain gap between synthetic and real, authentic degradations - thus allowing us to train on domains, such as hu-man faces, for which no or only few human labels exist. Finally, we demonstrate that our method outperforms state-of-the-art image quality assessment methods in estimating the sharpness of real and generated human faces, despite using only unlabeled data during training.
Attentive Sensing for Long-Range Face Recognition

To be effective, a social robot must reliably detect and recognize people in all visual directions and in both near and far fields. A major challenge is the resolution/field-of-view tradeoff; here we propose and evaluate a novel attentive sensing solution. Panoramic low-resolution pre-attentive sensing is provided by an array of wide-angle cameras, while attentive sensing is achieved with a high-resolution, narrow field-of-view camera and a mirror-based gaze deflection system. Quantitative evaluation on a novel dataset shows that this attentive sensing strategy can yield good panoramic face recognition performance in the wild out to distances of ~35m.
A Transformer-based Late-Fusion Mechanism for Fine-Grained Object Recognition in Videos

Fine-grained image classification is limited by only considering a single view while in many cases, like surveillance, a whole video exists which provides multiple perspectives. However, the potential of videos is mostly considered in the context of action recognition while fine-grained object recognition is rarely considered as an application for video classification. This leads to recent video classification architectures being inappropriate for the task of fine-grained object recognition. We propose a novel, Transformer-based late-fusion mechanism for fine-grained video classification. Our approach achieves superior results to both early-fusion mechanisms, like the Video Swin Transformer, and a simple consensus-based late-fusion baseline with a modern Swin Transformer backbone. Additionally, we achieve improved efficiency, as our results show a high increase in accuracy with only a slight increase in computational complexity. Code is available at: https://github.com/wolfstefan/tlf.
Benefits of Synthetically Pre-trained Depth-Prediction Networks for Indoor/Outdoor Image Classification

Ground truth depth information is necessary for many computer vision tasks. Collecting this information is chal-lenging, especially for outdoor scenes. In this work, we propose utilizing single-view depth prediction neural networks pre-trained on synthetic scenes to generate relative depth, which we call pseudo-depth. This approach is a less expen-sive option as the pre-trained neural network obtains ac-curate depth information from synthetic scenes, which does not require any expensive sensor equipment and takes less time. We measure the usefulness of pseudo-depth from pre-trained neural networks by training indoor/outdoor binary classifiers with and without it. We also compare the difference in accuracy between using pseudo-depth and ground truth depth. We experimentally show that adding pseudo-depth to training achieves a 4.4% performance boost over the non-depth baseline model on DIODE, a large stan-dard test dataset, retaining 63.8% of the performance boost achieved from training a classifier on RGB and ground truth depth. It also boosts performance by 1.3% on another dataset, SUN397, for which ground truth depth is not avail-able. Our result shows that it is possible to take information obtained from a model pre-trained on synthetic scenes and successfully apply it beyond the synthetic domain to real-world data.
Is Meta-Learning Always Necessary?: A Practical ML Framework Solving Novel Tasks at Large-scale Car Sharing Platform

While the deep neural networks achieved superior performance in various tasks under the supervised regime, the ML practitioners in the real world frequently encounter a novel task that cannot acquire the labeled dataset shortly. Even if they have become available in acquiring the target samples from the unlabeled dataset, conventional labeling procedures require the practitioners to invest in resource consumption. Pursuing an effective solution to these problems, our study proposes a practical ML framework that efficiently enables practitioners to solve novel tasks. Our ML framework consists of two solutions consisting of early and mature stages. First, the early stage solution lets the practitioners solve the novel task under the few-shot classification setting. Second, the mature stage solution enhances the labeling efficiency by retrieving samples that seem relevant to the target. Upon these solutions, acquiring a qualified representation power is the most important job. Under the public benchmark datasets and image recognition tasks in a large-scale car-sharing platform, we examined that the paradigm of supervised learning, surprisingly not meta-learning, produces the most beneficial representation power to solve novel tasks. We further scrutinized the supremacy of supervised representation derives from broader, nourished high-level representations in the neural networks. We highly expect our analyses can be a concrete benchmark to the ML practitioners who solve novel tasks in their domain.
Social Networks and Language Applications, Other Means of Learning? Students of English at Tlemcen University

In recent times, at the turn of the 21st century, there have been significant developments in the use of technological advances for teaching languages. Following the use of traditional language laboratories, and then the emergence of computer-assisted forms of language teaching, language-oriented social networks such as YouTube and Facebook and other types of language applications have been used for that purpose, particularly in teaching English. Learners’ attitudes are aroused in significantly different ways compared to feelings about traditional forms of language teaching, including learner-centered approaches. One aim of this paper is to elicit learners’ interest in Internet use and to consider the degree to which social media and language applications enhance in-class formal learning. This study brings forth learners’ digital experience in the use of social media and we argue that it will raise awareness as to the importance of these media in improving their language skills. We thus attempt to explore the extent to which language learners benefit from their out-of-class Internet use. A small-scale survey, based on an online questionnaire addressed to Algerian students of English at Tlemcen University, reveals increasing interest in using such applications and social networks outside the class and their positive attitudes towards English in virtual spaces, particularly when having the possibility of discussing with native speakers. The results also show that communicating with others allows them to share intercultural aspects and mutual understanding. Indeed, respondents’ positive attitudes towards mixed-culture conversation are attention-grabbing and significantly encouraging. However, it is believed that such informal, non-systematic methods of teaching/ learning a foreign language can only complement formal teaching in class with non-virtual teachers.
Implementing a Specifications Grading System in a Nursing Course

Nurse educators are charged with the challenging task of evaluating student learning and abilities effectively using traditional student assessment methods, which come with several inherent shortcomings. New guidelines for nurse educators now present a more competency-based approach to nursing education that calls for objective-driven assessments.Traditional grading practices lack the ability to connect student learning to their final course grades. However, specifications grading, which directly links grades to learning objectives, has not been not adequately explored in the nursing literature.A specifications grading system was designed and implemented in an online undergraduate nursing research course. Student and faculty feedback on the system were reviewed.Overall, students and faculty approved of the specifications grading system. Recommendations are presented for adopting specifications grading in nursing courses. Research is needed to explore student perceptions of the grading system and its effects on student learning and performance.
Kidney Stone Feature Extraction from the Selected Kidney Region Using Rapid Haralick Polygonal and Surface Algorithm

Extraction and selection from medical images are rich with different techniques. These include exhaustive searches such as exponential methods, sequential or random search methods, heuristic methods, and meta-heuristic methods. An exhaustive search methods, all combinations of subsets were evaluated. These are Image Acquisition Image Pre-processing Image Feature Extraction. Methods give an optimal solution but searching optimal subset is an NP-Hard problem. Moreover, there is a limitation for the stone developed in the ureter and bladder because the systems reported kidney stone extraction from the selected kidney region. This research work aims to develop kidney stone detection in the urinary without limiting the stone location to overcome this limitation via Rapid Haralick Polygonal and Surface (RHPS).Feature Extraction algorithm.
Detecting Cryptography Misuses With Machine Learning: Graph Embeddings, Transfer Learning and Data Augmentation in Source Code Related Tasks

Cryptography is a ubiquitous tool in secure software development in order to guarantee security requirements in general. However, software developers have scarce knowledge about cryptography and rely on limited support tools that cannot properly detect bad uses of cryptography, thus generating vulnerabilities in software. In this work, we extend the scarcely use of machine learning to detect cryptography misuse in source code by using a state of the art deep learning model (i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">code2vec</i> ) through transfer learning to generate features that feed machine learning models. In addition, we compare this approach to previous ones in different types of binary models. Also, we adapt code obfuscation to serve as data augmentation in machine learning source code related tasks. Finally, we show that through transfer learning <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">code2vec</i> can be a competitive feature generator for cryptography misuse detection and simple code obfuscation can be used to generate data to enhance machine learning models training in source code related tasks.
CRmix: A regularization by clipping images and replacing mixed samples for imbalanced classification

Deep convolutional neural networks (CNNs) often show inferior classification performance when training samples are seriously class-imbalanced. In this paper, we present a new Mixup-based regularization method, which loosens the constraint of only using synthetic samples to train the model. Specifically, we choose to cut and paste patches between the training images to generate samples, and restore corresponding minority image samples in the synthetic samples which contain a large proportion of minority image patches. At the same time, we restrict the location of patch generation, so as to intercept more image areas of training samples. By doing so, we increase the number of minority samples for the training set. The proposed method performs good generalization on minority classes by training the classifier to push the decision boundary toward majority classes. Besides, our approach integrates well with conventional rebalancing techniques such as re-weighting or re-sampling. We validate our method on the imbalanced datasets created by CIFAR-10, CIFAR-100, CINIC-10 and Tiny-ImageNet. The results demonstrate that our method is superior to some other Mixup-based regularization techniques (e.g., Mixup, Remix and Cutmix). For example, on the long-tailed version of CIFAR-10 (CIFAR-100) with an imbalance ratio of 100, our proposed method achieves a 4.90% (4.06%) improvement in classification accuracy compared with the classical Remix method.
The Pricing of American Options on the Quantum Computer

An American option valuation and finding the optimal stopping time are among the most critical problems in option pricing theory and derivatives valuation. The classical approaches use the dynamic programming principle, so parallelizing is hard. Because we need to store the continuation values for all paths to do one dynamic programming step, we are limited in the number of simulations that can be processed by the memory size available on the classical computer.This paper proposes a novel approach to pricing American options on quantum computers. Before the article, only one method for that purpose was proposed. The novel algorithm combines Quantum Binomial Tree and Quantum Machine Learning to implement the direct method for pricing American options on a quantum computer. It utilizes the quantum amplitude estimation algorithm with a quadratic speed-up over the classical Monte Carlo. This method allows us to exploit the exponential growth in the state vector on quantum computers and, by this, to overcome the limitations on memory on classical techniques.