Reviewerly/exHarmony huggingface dataset:
Zero and Few Shot Learning With Semantic Feature Synthesis and Competitive Learning

Zero-shot learning (ZSL) is made possible by learning a projection function between a feature space and a semantic space (e.g., an attribute space). Key to ZSL is thus to learn a projection that is robust against the often large domain gap between the seen and unseen class domains. In this work, this is achieved by unseen class data synthesis and robust projection function learning. Specifically, a novel semantic data synthesis strategy is proposed, by which semantic class prototypes (e.g., attribute vectors) are used to simply perturb seen class data for generating unseen class ones. As in any data synthesis/hallucination approach, there are ambiguities and uncertainties on how well the synthesised data can capture the targeted unseen class data distribution. To cope with this, the second contribution of this work is a novel projection learning model termed competitive bidirectional projection learning (BPL) designed to best utilise the ambiguous synthesised data. Specifically, we assume that each synthesised data point can belong to any unseen class; and the most likely two class candidates are exploited to learn a robust projection function in a competitive fashion. As a third contribution, we show that the proposed ZSL model can be easily extended to few-shot learning (FSL) by again exploiting semantic (class prototype guided) feature synthesis and competitive BPL. Extensive experiments show that our model achieves the state-of-the-art results on both problems.
McDPC: multi-center density peak clustering

Density peak clustering (DPC) is a recently developed density-based clustering algorithm that achieves competitive performance in a non-iterative manner. DPC is capable of effectively handling clusters with single density peak (single center), i.e., based on DPC's hypothesis, one and only one data point is chosen as the center of any cluster. However, DPC may fail to identify clusters with multiple density peaks (multi-centers) and may not be able to identify natural clusters whose centers have relatively lower local density. To address these limitations, we propose a novel clustering algorithm based on a hierarchical approach, named multi-center density peak clustering (McDPC). Firstly, based on a widely adopted hypothesis that the potential cluster centers are relatively far away from each other. McDPC obtains centers of the initial micro-clusters (named representative data points) whose minimum distance to the other higher-density data points are relatively larger. Secondly, the representative data points are autonomously categorized into different density levels. Finally, McDPC deals with micro-clusters at each level and if necessary, merges the micro-clusters at a specific level into one cluster to identify multi-center clusters. To evaluate the effectiveness of our proposed McDPC algorithm, we conduct experiments on both synthetic and real-world datasets and benchmark the performance of McDPC against other state-of-the-art clustering algorithms. We also apply McDPC to perform image segmentation and facial recognition to further demonstrate its capability in dealing with real-world applications. The experimental results show that our method achieves promising performance.
Convolutional neural networks and extreme learning machines for malware classification

Research in the field of malware classification often relies on machine learning models that are trained on high-level features, such as opcodes, function calls, and control flow graphs. Extracting such features is costly, since disassembly or code execution is generally required. In this paper, we conduct experiments to train and evaluate machine learning models for malware classification, based on features that can be obtained without disassembly or code execution. Specifically, we visualize malware samples as images and employ image analysis techniques using both two-dimensional images and one-dimensional vectors derived from images. We consider two machine learning techniques, namely, convolutional neural networks (CNN) and extreme learning machines (ELM). For images we find that ELMs can achieve accuracies on par with CNNs, yet ELM training requires less than 2% of the time needed to train a comparable CNN. We also find that ELMs and CNNs perform as well when trained on one-dimensional data as when trained on two-dimensional data. In this latter case, ELMs are faster to train than CNNs, but only by a relatively small factor as compared to image-based training.
Evaluating garments in augmented reality when shopping online

Purpose Augmented Reality (AR) integrates computer-generated images to a physical environment in real-time. Online apparel shopping presents some product-related risks, as consumers can neither physically see and touch the products nor try them on. The present study examined whether AR conveys reliable apparel product information in terms of fit, size, and product performance; and how AR affects attitudes toward apparel and purchase intentions when shopping online. Design/methodology/approach This research was designed as a within-subject quasi-experimental study using repeated measures in two conditions: virtual try-on using the AR technology vs. physical try-on. A scenario was developed to help participants imagine themselves shopping online for a specific dress. Findings Results indicated that size and color of dresses were conveyed accurately when utilizing AR as compared to physical try-on. Visual attributes such as style, garment details, and coordination with other items were found to be satisfactorily predicted when AR was employed. Overall, attitudes towards both AR and real dress, and purchase intentions were favorable. Participants with higher telepresence levels were found to have more positive attitudes towards the dress and greater purchase intentions when using AR as compared to the participants with low telepresence levels. Research limitations/implications Our findings implied that AR can provide enough information especially for garment sizes and visual characteristics when making purchase decisions. AR technology can be instrumental in introducing a certain style, building positive attitudes towards products, and driving sales, when the consumers perceive a certain level of “being there”. This study was limited to female students in North America. Also, because a single stimulus was used, the results cannot be generalized to other stimuli. Originality/value Our study findings showed that participants were able to select the right garment size by using AR. The average ratings for visual characteristics such as style and detail were above the neutral level when using AR; indicating that participants can understand visual attributes in AR when shopping online. Moreover, in the AR condition participants with higher telepresence levels had higher attitudes towards the garment and purchase intentions as compared to the participants with low telepresence. AR can be instrumental for online apparel shopping. Retailers need to understand the potentials of these technologies and work with technology developers to enhance consumers' experiences.
Russian University Students about Distance Learning: Assessments and Opportunities

The forced shift to remote learning in universities in March 2020 raised the complex issues of organizing lifelong learning for students, conducting academic assessments and maintaining the stability of the educational institution in general for educational systems of all countries of the world. This paper examines peculiarities and general problems of distance learning in the higher education system. It is based on the materials of the sociological survey “Opinions of Russian universities’ students on the forced distance learning”, conducted in May-June 2020. 31,423 university students in all federal subjects of Russia were interviewed. Analysis of research data covers the following areas: organizational problems of transition to distance learning; resources used, organization of lectures and seminars, assessment of the positive and negative aspects of distance learning; perspectives, limitations and trajectories of distance learning implementation in Russian universities. The article also provides comparative data on the analysis of the issues of distant learning of Russian and foreign researchers. The authors propose future-oriented measures for organizing distance learning in universities in the case of the introduction of quarantine restrictions, consider possible forms of combining distance and full-time education, the prospects for the introduction of digital technologies in the organization of training in universities.
The Use of Virtual Reality to Influence Motivation, Affect, Enjoyment, and Engagement During Exercise: A Scoping Review

Many adults are physically inactive. While the reasons are complex, inactivity is, in part, influenced by the presence of negative feelings and low enjoyment during exercise. While virtual reality (VR) has been proposed as a way to improve engagement with exercise (e.g., choosing to undertake exercise), how VR is currently used to influence experiences during exercise is largely unknown. Here we aimed to summarize the existing literature evaluating the use of VR to influence motivation, affect, enjoyment, and engagement during exercise. A Population (clinical, and healthy), Concept (the extent and nature of research about VR in exercise, including underpinning theories), and Context (any setting, demographic, social context) framework was used. A systematic search of Medline, Scopus, Embase, PsycINFO, and Google Scholar was completed by two independent reviewers. Of 970 studies identified, 25 unique studies were included ( n = 994 participants), with most (68%) evaluating VR influences on motivation, affect, enjoyment, and engagement during exercise in healthy populations ( n = 8 studies evaluating clinical populations). Two VR strategies were prominent – the use of immersion and the use of virtual avatars and agents/trainers. All studies but one used virtual agents/trainers, suggesting that we know little about the influence of virtual avatars on experiences during exercise. Generally, highly immersive VR had more beneficial effects than low immersive VR or exercise without VR. The interaction between VR strategy and the specific exercise outcome appeared important (e.g., virtual avatars/agents were more influential in positively changing motivation and engagement during exercise, whereas immersion more positively influenced enjoyment during exercise). Presently, the knowledge base is insufficient to provide definitive recommendations for use of specific VR strategies to target specific exercise outcomes, particularly given the numerous null findings. Regardless, these preliminary findings support the idea that VR may influence experiences during exercise via multiple mechanistic pathways. Understanding these underlying mechanisms may be important to heighten effects targeted to specific exercise outcomes during exercise. Future research requires purposeful integration of exercise-relevant theories into VR investigation, and careful consideration of VR definitions (including delineation between virtual avatars and virtual agents), software possibilities, and nuanced extension to clinical populations.
Offline Signature Recognition Using Image Processing Techniques and Back Propagation Neuron Network System

As the technology improves, there are many new innovations which give the security frameworks with different methods that are used to identify a person. Signature recognition is one of methods used to identify person. In this paper, offline signature recognition using back propagation neuron network system and image processing techniques has been proposed. Preprocessing of signature can be done using image processing techniques which involves RGB2Gray conversion, filtering, adjusting, thresholding followed by canny edge detection and at the last image scaling applied to reduce the processing time. Processed image feature is extracted using back propagation neuron network system with defined number of neurons and hidden layers. Similarly, data set images undergo preprocessing operation and features are extracted. Based on number of layers that are hidden and neuron, better recognition rate is obtained. The proposed method shows that the experimental result has more success rate.
What Will it Take to Fix Benchmarking in Natural Language Understanding?

Evaluation for many natural language understanding (NLU) tasks is broken: Unreliable and biased systems score so highly on standard benchmarks that there is little room for researchers who develop better systems to demonstrate their improvements.The recent trend to abandon IID benchmarks in favor of adversarially-constructed, out-of-distribution test sets ensures that current models will perform poorly, but ultimately only obscures the abilities that we want our benchmarks to measure.In this position paper, we lay out four criteria that we argue NLU benchmarks should meet.We argue most current benchmarks fail at these criteria, and that adversarial data collection does not meaningfully address the causes of these failures.Instead, restoring a healthy evaluation ecosystem will require significant progress in the design of benchmark datasets, the reliability with which they are annotated, their size, and the ways they handle social bias.
Robustness Gym: Unifying the NLP Evaluation Landscape

Despite impressive performance on standard benchmarks, natural language processing (NLP) models are often brittle when deployed in real-world systems.In this work, we identify challenges with evaluating NLP systems and propose a solution in the form of Robustness Gym (RG), 1 a simple and extensible evaluation toolkit that unifies 4 standard evaluation paradigms: subpopulations, transformations, evaluation sets, and adversarial attacks.By providing a common platform for evaluation, RG enables practitioners to compare results from disparate evaluation paradigms with a single click, and to easily develop and share novel evaluation methods using a built-in set of abstractions.Robustness Gym is under active development and we welcome feedback & contributions from the community.
Machine Learning in Detection and Classification of Leukemia Using Smear Blood Images: A Systematic Review

Introduction. The early detection and diagnosis of leukemia, i.e., the precise differentiation of malignant leukocytes with minimum costs in the early stages of the disease, is a major problem in the domain of disease diagnosis. Despite the high prevalence of leukemia, there is a shortage of flow cytometry equipment, and the methods available at laboratory diagnostic centers are time-consuming. Motivated by the capabilities of machine learning (machine learning (ML)) in disease diagnosis, the present systematic review was conducted to review the studies aiming to discover and classify leukemia by using machine learning. Methods. A systematic search in four databases (PubMed, Scopus, Web of Science, and ScienceDirect) and Google Scholar was performed via a search strategy using Machine Learning (ML), leukemia, peripheral blood smear (PBS) image, detection, diagnosis, and classification as the keywords. Initially, 116 articles were retrieved. After applying the inclusion and exclusion criteria, 16 articles remained as the population of the study. Results. This review study presents a comprehensive and systematic view of the status of all published ML-based leukemia detection and classification models that process PBS images. The average accuracy of the ML methods applied in PBS image analysis to detect leukemia was &gt;97%, indicating that the use of ML could lead to extraordinary outcomes in leukemia detection from PBS images. Among all ML techniques, deep learning (DL) achieved higher precision and sensitivity in detecting different cases of leukemia, compared to its precedents. ML has many applications in analyzing different types of leukemia images, but the use of ML algorithms to detect acute lymphoblastic leukemia (ALL) has attracted the greatest attention in the fields of hematology and artificial intelligence. Conclusion. Using the ML method to process leukemia smear images can improve accuracy, reduce diagnosis time, and provide faster, cheaper, and safer diagnostic services. In addition to the current diagnostic methods, clinical and laboratory experts can also adopt ML methods in laboratory applications and tools.
Image Restoration for Remote Sensing: Overview and toolbox

Remote sensing provides valuable information about objects and areas from a distance in either active (e.g., radar and lidar) or passive (e.g., multispectral and hyperspectral) modes. The quality of data acquired by remotely sensed imaging sensors (active and passive) is often degraded by a variety of noise types and artifacts. Image restoration, which is a vibrant field of research in the remote sensing community, is the task of recovering a true unknown image from a degraded observed one. Each imaging sensor induces unique noise types and artifacts into the observed image. This fact has led to the expansion of restoration techniques along different paths according to sensor type.
When do AI chatbots lead to higher customer satisfaction than human frontline employees in online shopping assistance? Considering product attribute type

The increasing adoption of AI chatbots in online shopping assistance, as a complement or substitute for human frontline employees (HFLEs), leads to the question whether HFLEs perform better than AI service robots and why. From the perspective of product attribute type (experiential/functional) and focusing on customer satisfaction, this study explores how the impact of service agent on customer satisfaction varies along with product attribute type. A scenario-based experiment was designed and completed by 567 participants. Although HFLEs lead to higher customer satisfaction when the product attribute is experiential, AI chatbots perform better than HFLEs when the product attribute is functional. We make use of perceived information quality, perceived waiting time, and positive emotions, three determinants of customer satisfaction, to explain the variation of the role of different service agent types. The findings offer useful implications for companies when selecting service agent types in online shopping assistance.
Ordinal Constraint Binary Coding for Approximate Nearest Neighbor Search

Binary code learning, <i>a.k.a.</i> hashing, has been successfully applied to the approximate nearest neighbor search in large-scale image collections. The key challenge lies in reducing the quantization error from the original real-valued feature space to a discrete Hamming space. Recent advances in unsupervised hashing advocate the preservation of ranking information, which is achieved by constraining the binary code learning to be correlated with pairwise similarity. However, few unsupervised methods consider the preservation of <i>ordinal relations</i> in the learning process, which serves as a more basic cue to learn optimal binary codes. In this paper, we propose a novel hashing scheme, termed Ordinal Constraint Hashing (OCH), which embeds the ordinal relation among data points to preserve ranking into binary codes. The core idea is to construct an ordinal graph via tensor product, and then train the hash function over this graph to preserve the permutation relations among data points in the Hamming space. Subsequently, an in-depth acceleration scheme, termed Ordinal Constraint Projection (OCP), is introduced, which approximates the <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> -pair ordinal graph by <inline-formula><tex-math notation="LaTeX">$L$</tex-math></inline-formula> -pair anchor-based ordinal graph, and reduce the corresponding complexity from <inline-formula><tex-math notation="LaTeX">$O(n^4)$</tex-math></inline-formula> to <inline-formula><tex-math notation="LaTeX">$O(L^3)$</tex-math></inline-formula> ( <inline-formula><tex-math notation="LaTeX">$L\ll n$</tex-math></inline-formula> ). Finally, to make the optimization tractable, we further relax the discrete constrains and design a customized stochastic gradient decent algorithm on the Stiefel manifold. Experimental results on serval large-scale benchmarks demonstrate that the proposed OCH method can achieve superior performance over the state-of-the-art approaches.
Visibility Graphs for Image Processing

The family of image visibility graphs (IVG/IHVGs) have been recently introduced as simple algorithms by which scalar fields can be mapped into graphs. Here we explore the usefulness of such\an operator in the scenario of image processing and image classification. We demonstrate that the link architecture of the image visibility graphs encapsulates relevant information on the structure of the images and we explore their potential as image filters. We introduce several graph features, including the novel concept of Visibility Patches, and show through several examples that these features are highly informative, computationally efficient and universally applicable for general pattern recognition and image classification tasks.
The hematoxylin and eosin stain in anatomic pathology—An often-neglected focus of quality assurance in the laboratory

Accuracy in morphological diagnosis is the cornerstone of anatomic pathology. Proficiency with the microscope offers values to the health care system that cannot be overestimated. However, that skill is only possible if high-quality histological substrates are available for assessment, particularly focusing on hematoxylin and eosin (H&E)-stained slides. This brief review considers the several steps that are necessary to control in the preparation of high-quality H&E sections, including those dealing with fixation, embedding, microtomy, histochemical staining, and coverslipping. A table for the troubleshooting of problem slides is also included.
“The Enemy Among Us”

Offensive or antagonistic language targeted at individuals and social groups based on their personal characteristics (also known as cyber hate speech or cyberhate) has been frequently posted and widely circulated via the World Wide Web. This can be considered as a key risk factor for individual and societal tension surrounding regional instability. Automated Web-based cyberhate detection is important for observing and understanding community and regional societal tension—especially in online social networks where posts can be rapidly and widely viewed and disseminated. While previous work has involved using lexicons, bags-of-words, or probabilistic language parsing approaches, they often suffer from a similar issue, which is that cyberhate can be subtle and indirect—thus, depending on the occurrence of individual words or phrases, can lead to a significant number of false negatives, providing inaccurate representation of the trends in cyberhate. This problem motivated us to challenge thinking around the representation of subtle language use, such as references to perceived threats from “the other” including immigration or job prosperity in a hateful context. We propose a novel “othering” feature set that utilizes language use around the concept of “othering” and intergroup threat theory to identify these subtleties, and we implement a wide range of classification methods using embedding learning to compute semantic distances between parts of speech considered to be part of an “othering” narrative. To validate our approach, we conducted two sets of experiments. The first involved comparing the results of our novel method with state-of-the-art baseline models from the literature. Our approach outperformed all existing methods. The second tested the best performing models from the first phase on unseen datasets for different types of cyberhate, namely religion, disability, race, and sexual orientation. The results showed F-measure scores for classifying hateful instances obtained through applying our model of 0.81, 0.71, 0.89, and 0.72, respectively, demonstrating the ability of the “othering” narrative to be an important part of model generalization.