Reviewerly/exHarmony huggingface dataset:
Survey of image classification algorithms based on deep learning

The traditional image classification methods have defects, which can not process massive image data, and can not meet the needs of image classification in speed and accuracy. The performance of deep learning in the field of computer vision is better than the traditional machine learning technology, and it has become the mainstream method of image classification. Based on the deep learning method, this paper summarizes the commonly used algorithm models in the field of image classification, analyzes the error rate, architecture design, application scenarios and other aspects of the models, and then compares the differences between the current network models with outstanding classification effect through experiments, and further verifies the advantages and disadvantages of various models. Finally, the development trend of deep learning in image classification is summarized, and the possible research directions in the future are discussed.
Design of a small-scale and failure-resistant IaaS cloud using OpenStack

Purpose Major public cloud providers, such as AWS, Azure or Google, offer seamless experiences for infrastructure as a service (IaaS), platform as a service (PaaS) and software as a service (SaaS). With the emergence of the public cloud's vast usage, administrators must be able to have a reliable method to provide the seamless experience that a public cloud offers on a smaller scale, such as a private cloud. When a smaller deployment or a private cloud is needed, OpenStack can meet the goals without increasing cost or sacrificing data control. Design/methodology/approach To demonstrate these enablement goals of resiliency and elasticity in IaaS and PaaS, the authors design a private distributed system cloud platform using OpenStack and its core services of Nova, Swift, Cinder, Neutron, Keystone, Horizon and Glance on a five-node deployment. Findings Through the demonstration of dynamically adding an IaaS node, pushing the deployment to its physical and logical limits, and eventually crashing the deployment, this paper shows how the PackStack utility facilitates the provisioning of an elastic and resilient OpenStack-based IaaS platform that can be used in production if the deployment is kept within designated boundaries. Originality/value The authors adopt the multinode-capable PackStack utility in favor of an all-in-one OpenStack build for a true demonstration of resiliency, elasticity and scalability in a small-scale IaaS. An all-in-one deployment is generally used for proof-of-concept deployments and is not easily scaled in production across multiple nodes. The authors demonstrate that combining PackStack with the multi-node design is suitable for smaller-scale production IaaS and PaaS deployments.
Secure Authentication using Zero Knowledge Proof

Zero- Knowledge Proof is a cryptographic protocol exercised to render privacy and data security by securing the identity of users and using services anonymously. It finds numerous applications; authentication is one of them. A Zero-Knowledge Proof-based authentication system is discussed in this paper. Advanced Encryption Standard (AES) and Secure Remote Password (SRP) protocol have been used to design and build the ZKP based authentication system. SRP is a broadly used Password Authenticated Key Exchange (PAKE) protocol. The proposed method overcomes several drawbacks of traditional and commonly used authentication systems such as a simple username and plaintext password-based system, multi-factor authentication system and others.
Inferring Multilingual Domain-Specific Word Embeddings From Large Document Corpora

The use of distributed vector representations of words in Natural Language Processing has become established.To tailor general-purpose vector spaces to the context under analysis, several domain adaptation techniques have been proposed.They all require sufficiently large document corpora tailored to the target domains.However, in several cross-lingual NLP domains both large enough domainspecific document corpora and pre-trained domain-specific word vectors are hard to find for languages other than English.This paper aims at tackling the aforesaid issue.It proposes a new methodology to automatically infer aligned domain-specific word embeddings for a target language on the basis of the general-purpose and domain-specific models available for a source language (typically, English).The proposed inference method relies on a two-step process, which first automatically identifies domainspecific words and then opportunistically reuses the non-linear space transformations applied to the word vectors of the source language in order to learn how to tailor the vector space of the target language to the domain of interest.The performance of the proposed method was validated via extrinsic evaluation by addressing the established word retrieval task.To this aim, a new benchmark multilingual dataset, derived from Wikipedia, has been released.The results confirmed the effectiveness and usability of the proposed approach.
An Online Semantic-Enhanced Graphical Model for Evolving Short Text Stream Clustering

Due to the popularity of social media and online fora, such as Twitter, Reddit, Facebook, and Wechat, short text stream clustering has gained significant attention in recent years. However, most existing short text stream clustering approaches usually work on static data and tend to cause a "term ambiguity" problem due to the sparse word representation. Beyond, they often exploit short text streams in a batch way and are difficult to find evolving topics in term-changing subspaces. In this article, we propose an online semantic-enhanced graphical model for evolving short text stream clustering (OSGM), by exploiting the word-occurrence semantic information and dynamically maintaining evolving active topics in term-changing subspaces in an online way. Compared to the existing approaches, our online model is not only free of determining the optimal batch size but also lends itself to handling large-scale data streams efficiently. It is also able to handle the "term ambiguity" problem without incorporating features from external resources. More importantly, to the best of our knowledge, it is the first work to extract evolving topics in term-changing subspaces automatically in an online way. Extensive experiments demonstrate that the proposed model yields better performance compared to many state-of-the-art algorithms on both synthetic and real-world datasets.
A New Method for Stance Detection Based on Feature Selection Techniques and Ensembles of Classifiers

Stance detection is one of the promising areas of computational linguistics, the task of which is to automatically recognize the author's viewpoint on the target object.In our study, to detect the stance, we propose the Ensemble-based Stance Detection method (ESD).First, we calculate the optimal number of features that are most relevant to the given domain based on the function approximating the dependence of F1-score on the number of features.Then we form a relevant feature set using the homogeneous ensemble of feature selection methods.At last, we build the optimal composition of classifiers using the crossvalidation procedure.Furthermore, we study the impact of various feature types on the performance in the stance detection task.The proposed ESD method is evaluated on the SemEval-2016 text corpus of tweets and the UKP Sentential Argument Mining corpus, and it outperforms the state-of-the-art systems.
Deep Multi-view Subspace Clustering Network with Exclusive Constraint

Multi-view subspace clustering aims to find the inherent structure of data as much as possible by fusing complementary information of multiple views to achieve better clustering results. However, most of the traditional multi-view subspace clustering algorithms are only shallow clustering algorithms, which does not capture the deep information of the data well, and does not conduct in-depth research at the self-representation level of the data. To this end, this paper proposes a novel deep multi-view subspace clustering model that introduces exclusive constraints. A deep autoencoder is used to perform nonlinear low-dimensional subspace mapping for each view to learn the deep structure of the original data. To better retain multiple views’ local structure and better reflect the complementarity, the exclusive constraints are introduced into the self-representation matrix which located in the middle layer of the deep autoencoder. The multi-view consensus self-representation matrix is used to capture the consistency information between the multi-view data. The update of autoencoder parameters and clustering parameters are iteratively optimized under the same learning framework to improve the clustering performance. Experiments on multi-view data sets prove that this method can better dig out the inherent complementary structure of multi-view data, which reflects the superiority of this method.
Towards a Teachers’ Augmented Reality Competencies (TARC) Framework

Augmented reality, the technology that augments real environments with virtual components, is constantly evolving. Augmented reality (AR) has the potential to offer immersive, authentic, and meaningful learning experiences to students and therefore enhance learning. However, the effective integration of augmented reality into teaching requires from teachers to master a special set of digital competencies. The current study proposes a framework that defines the augmented reality competencies that teachers should have in order to effectively integrate augmented reality into their teaching. The framework comprises four dimensions: basic augmented reality literacies, create, use, and manage augmented reality learning resources. Based on the proposed framework, the study introduces also the Teachers’ Augmented Reality Competencies (TARC) questionnaire that can help educators to self-assess and develop their AR competencies in order to integrate augmented reality in their practice.
Long-Range Multi-Object Tracking at Traffic Intersections on Low-Power Devices

The next generation of intelligent traffic signal control systems needs multi-object tracking (MOT) algorithms that can track vehicles hundreds of meters away from traffic intersections. To facilitate the integration of long-range MOT into existing traffic infrastructure, the tracker must achieve a good balance of cost-effectiveness, accuracy, and efficiency. Although much progress has been made on deep-learning-based MOT for video, these approaches have limited applicability for edge deployment since deep neural networks typically require power-hungry hardware accelerators to achieve real-time performance. Furthermore, traffic cameras have a field of view limited to near the intersection. To address these shortcomings, we introduce a practical MOT framework that fuses tracks from a novel video MOT neural architecture designed for low-power edge devices with tracks from a commercially available traffic radar. The proposed neural architecture achieves high efficiency by using depthwise separable convolutions to jointly predict object detections alongside a dense grid of features at a single scale for spatiotemporal object re-identification. A simple and effective late fusion strategy is also presented where tracks of distant vehicles from a traffic radar are handed over to the video tracker within a region where the sensor fields of view overlap. Our video tracker is empirically validated on the UA-DETRAC video MOT benchmark for traffic intersections and the multi-sensor tracker is evaluated on video and radar data collected and labeled by the authors at an instrumented traffic intersection.
Speech Command Recognition in Computationally Constrained Environments with a Quadratic Self-Organized Operational Layer

Automatic classification of speech commands has revolutionized human computer interactions in robotic applications. However, employed recognition models usually follow the methodology of deep learning with complicated networks which are memory and energy hungry. So, there is a need to either squeeze these complicated models or use more efficient lightweight models in order to be able to implement the resulting classifiers on embedded devices. In this paper, we pick the second approach and propose a network layer to enhance the speech command recognition capability of a lightweight network and demonstrate the result via experiments. The employed method borrows the ideas of Taylor expansion and quadratic forms to construct a better representation of features in both input and hidden layers. This richer representation results in recognition accuracy improvement as shown by extensive experiments on Google speech commands (GSC) and synthetic speech commands (SSC) datasets.
Online signature verification using signature down-sampling and signer-dependent sampling frequency

Abstract Online signature verification considers signatures as time sequences of different measurements of the signing instrument. These signals are captured on digital devices and therefore consist of a discrete number of samples. To enrich or simplify this information, several verifiers employ resampling and interpolation as a preprocessing step to improve their results; however, their design decisions may be difficult to generalize. This study investigates the direct effect of the sampling rate of the input signals on the accuracy of online signature verification systems without using interpolation techniques and proposes a novel online signature verification system based on a signer-dependent sampling frequency. Twenty verifier configurations were created for five different public signature databases and a variety of popular preprocessing approaches and evaluated for 20–40 different sampling rates. Our results show that there is an optimal range for the sampling frequency and the number of sample points that minimizes the error rate of a verifier. A sampling frequency range of 15–50 Hz and a signature point count of 60–240 provided the best accuracies in our experiments. As expected, lower ranges showed inaccurate results; interestingly, however, higher frequencies often decreased the verification accuracy. The results show that one can achieve better or at least the same verification accuracies faster by down-sampling the online signatures before further processing. The proposed system achieved competitive results to state-of-the-art systems for different databases by using the optimal sampling frequency. We also studied the effect of choosing individual sampling frequencies for each signer and proposed a signature verification system based on signer-dependent sampling frequency. The proposed system was tested using 500 different verification methods and improved the accuracy in 92% of the test cases compared to the usage of the original frequency.
Understanding individual differences: factors affecting secure computer behaviour

Understanding users' individual differences may provide clues to help identify computer users who are prone to act insecurely. We examine factors that impact home users' reported computer security behaviour. We conducted two online surveys with a total of 650 participants to investigate the relationship between self-reported security behaviour and users' knowledge, motivation, confidence, risk propensity and sex-typed characteristics. We found that all of these factors impacted security behaviour, with knowledge as the most important predictor. We further show that a user's affinity to feminine or masculine characteristics is a better determinant of security behaviour than using binary male/female descriptors. Our study enabled us to confirm earlier results in the literature in a non-organisational setting, and to extend the literature by studying additional factors and by comparing the relative importance of each factor as a predictor of security behaviour.
Dynamic camera configuration learning for high-confidence active object detection

The performance of object detection is closely related to the quality of input images. However, the current image acquisition is purely guided by human visual perception, and such camera imaging process ignores the subsequent application. In this context, detection performance is impacted by imaging configuration and dynamic camera motion. To address the above problems, an active object detection framework is proposed in this paper, which aims to build the bridge between imaging configuration and object detection task. Within the proposed framework, a dynamic camera configuration learning approach is presented based on deep reinforcement learning, where the camera is actively controlled to maximize the detection performance. Through iterated interactions between imaging, control and object detection, the deep gap between perception and cognition in the object detection system is eliminated, and the transformation from physical imaging to purposeful imaging is realized. The effectiveness and advantages of the proposed framework are demonstrated in three dynamic environments.
An Adaptive Energy-Aware Stochastic Task Execution Algorithm in Virtualized Networked Datacenters

Virtualized networked datacenters (VNDCs) are gaining considerable attention for stochastic task execution under real-time constraints. However, the problem of efficiently minimizing the high energy consumption while ensuring high quality of service (QoS) in VNDCs has not been fully addressed. Although many solutions have been proposed to address this challenge, they are not efficient and only consider one or two of the energy consuming resources of VNDCs. To this end, an adaptive energy-aware algorithm, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MCEC</i> , that efficiently reduces the energy consumption of VNDCs while ensuring high QoS is proposed. Different from the existing approaches, the MCEC algorithm considers energy consumed by computing resources, virtual machine (VM) reconfiguration, communication resources and storage media resources while meeting user QoS requirements defined in the service level agreement (SLA). To validate the effectiveness of our algorithm, we carried out extensive experiments and compared the performance of our algorithm with existing baseline algorithms. The results of the experiments show that our algorithm substantially outperforms the baseline algorithms with respect to reducing energy consumption while respecting the service level agreement.
A Survey: Methodologies used for Fraud Detection in Digital Transactions

A Transaction plays a vital role in our daily routine life. It can be done through either Physical or Digital. Mostly people were travelled with digital transactions to make their activities in easy and convenient way in recent days. Digital Transactions are transactions in which the customer authorizes the transfer of money through an electronic mode. Generally Digital Transactions are also called E-Transactions. The main aim of our Indian system is to make India as Digital India. Many technologies were introduced to identify the frauds in E-Transactions. As a result, scams are also having increased alongside. According to information provided by the CERT (Indian Computer Emergency Response Team), the total number of cybercrimes was increased from the years 2018 to 2020 was 1,59,761;2,46,514 and 2,90,445 respectively [1]. Many authors surveyed about classification techniques for detecting frauds like data mining, a Machine Learning. This survey paper has related to how to detect frauds among the transactions with the help of machine learning and deep learning algorithms for a classification and the prediction, which gives more accuracy and works efficiently by considering different datasets. A brief discussion of future research developments is discussed in this article.
An efficient medical image super resolution based on piecewise linear regression strategy using domain transform filtering

Summary High quality medical images are expected for the detailed analysis in medical diagnostic system. However, spatial resolution of medical images mostly suffers from the factors such as medical equipment and time constraints. Despite these limitations, a well‐designed super‐resolution (SR) algorithm will help to improve the resolution of the medical images for medical diagnosis. When comparing to upgrade medical equipment, the adaptation of SR algorithms as a post‐processing method after medical imaging gives the benefits of lower cost and superior performance. This article proposes a piecewise linear regression system for learning image specific low‐to‐high resolution mapping via domain transform filtering and weighted least squares (WLS) optimization framework. We initially employed a WLS optimization framework for gradually coarsen the original input images and extract the multi‐scale information by constructing edge‐preserving multi‐scale decompositions. Then, with the aim of adequately preserving the edges of the medical images, we utilize a recursive filtering in transform domain. The Hadamard patterns generated from low‐resolution training patches are then used to classify the LR–HR training patch pairs into different classes. In the end, the piecewise linear regression system is utilized to learn the mapping relationship between LR space to HR space for each class, which is subsequently utilized to obtain desired HR image. In the context of quantitative metrices and qualitative analysis, our proposed method generates high‐quality medical images as compares to other existing state‐of‐the‐art SR methods.
Standard vs. non-standard cross-validation: evaluation of performance in a space with structured distribution of datapoints

Cross-validation is a popularly used approach to evaluation of performance for classifiers. It relies on random selection of independent samples for training and testing, and assumes that if any similarities among samples exist, they do not lead to known grouping of datapoints in the input space. If these conditions are violated, as it may happen for datasets with some structure of samples included, standard cross-validation can return biased results even for many folds. In the paper the research on cross-validation was reported for application to stylometric datasets, describing a task of authorship attribution. The comparison of standard and non-standard processing was presented. In the latter case, selected subsets of examples were swapped over between training and test sets several times. The experiments with three popular classifiers showed that standard cross-validation tended to give over-optimistic results, whereas non-standard processing was more guarded, and by that more reliable. To avoid high computational costs involved, evaluation based on averaged predictions for limited numbers of test sets can be considered as a reasonable compromise.