Reviewerly/exHarmony huggingface dataset:
Corpus-Driven Resource Recommendation Algorithm for English Online Autonomous Learning

One of the most significant aspects of English teaching, as well as the embodiment of students' comprehensive English skill, is the cultivation of English learning ability. Teachers of English should help students understand the topic's material and be able to convey, describe, and analyze the topic's substance, such as summarizing, subjective judgments analysis, and tale continuation. Students' English learning is restricted by the learning environment, teachers' quality, students' own ability, and other aspects. Furthermore, schools and families do not prioritize English learning, resulting in low teacher expectations for English instruction, as well as a lack of English practice and strategy training among students. In order to improve the inefficiency of English teaching, this paper combines corpus technology with English teaching and proposes an online autonomous learning resource recommendation algorithm. The model is optimized in the aspects of high efficiency, diversity, and timeliness of learning resource recommendation supported by deep learning technology. The model is pretrained through the processed dataset, and the algorithm designed in this study is compared with the classical algorithm to verify the rationality and effectiveness of the algorithm designed in this study. Based on the previous studies, this study attempts to apply the teaching model combining corpus and recommendation algorithm to online teaching, so as to optimize English teaching model and teaching methods.
Drop Loss for Person Attribute Recognition With Imbalanced Noisy-Labeled Samples

Person attribute recognition (PAR) aims to simultaneously predict multiple attributes of a person. Existing deep learning-based PAR methods have achieved impressive performance. Unfortunately, these methods usually ignore the fact that different attributes have an imbalance in the number of noisy-labeled samples in the PAR training datasets, thus leading to suboptimal performance. To address the above problem of imbalanced noisy-labeled samples, we propose a novel and effective loss called drop loss for PAR. In the drop loss, the attributes are treated differently in an easy-to-hard way. In particular, the noisy-labeled candidates, which are identified according to their gradient norms, are dropped with a higher drop rate for the harder attribute. Such a manner adaptively alleviates the adverse effect of imbalanced noisy-labeled samples on model learning. To illustrate the effectiveness of the proposed loss, we train a simple ResNet-50 model based on the drop loss and term it DropNet. Experimental results on two representative PAR tasks (including facial attribute recognition and pedestrian attribute recognition) demonstrate that the proposed DropNet achieves comparable or better performance in terms of both balanced accuracy and classification accuracy over several state-of-the-art PAR methods.
Deep Learning Enabled Social Media Recommendation Based on User Comments

Nowadays, review systems have been developed with social media Recommendation systems (RS).Although research on RS social media is increasing year by year, the comprehensive literature review and classification of this RS research is limited and needs to be improved.The previous method did not find any user reviews within a time, so it gets poor accuracy and doesn't filter the irrelevant comments efficiently.The Recursive Neural Network-based Trust Recommender System (RNN-TRS) is proposed to overcome this method's problem.So it is efficient to analyse the trust comment and remove the irrelevant sentence appropriately.The first step is to collect the data based on the transactional reviews of social media.The second step is pre-processing using Imbalanced Collaborative Filtering (ICF) to remove the null values from the dataset.Extract the features from the pre-processing step using the Maximum Support Grade Scale (MSGS) to extract the maximum number of scaling features in the dataset and grade the weights (length, count, etc.).In the Extracting features for Training and testing method before that in the feature weights evaluating the softmax activation function for calculating the average weights of the features.Finally, In the classification method, the Recursive Neural Network-based Trust Recommender System (RNN-TRS) for User reviews based on the Positive and negative scores is analysed by the system.The simulation results improve the predicting accuracy and reduce time complexity better than previous methods.
Mathematical Model for Image Processing Using Graph Theory

Image segmentation being an important aspect of computer systems, graph theory provides the most elemental way of representing various parts of an image into mathematical structures. There are many applications of image segmentations including face recognition systems, remote sensing, detecting images sent by satellites, optometry, medical image reading, and many more. Bi-partite graphs are useful in determination of cuts in the segmentation process. These structures are analysed by considering each vertex as pixel, and each weight is some aspect of dissimilarity for two vertices connected by an edge with weights. This makes the problem-solving part very flexible, and their computation becomes easy and fast. The problem is usually parted into small subgraphs that are bound under some continuous forms of graphs like spanning trees, cut vertices or edges, shortest paths graphs, and so on. The cluster formation is proved to be one of most commonly used methods in image segmentation.
Multi-Conditional Constraint Generative Adversarial Network-Based MR Imaging from CT Scan Data

Magnetic resonance (MR) imaging is an important computer-aided diagnosis technique with rich pathological information. The factor of physical and physiological constraint seriously affects the applicability of that technique. Thus, computed tomography (CT)-based radiotherapy is more popular on account of its imaging rapidity and environmental simplicity. Therefore, it is of great theoretical and practical significance to design a method that can construct an MR image from the corresponding CT image. In this paper, we treat MR imaging as a machine vision problem and propose a multi-conditional constraint generative adversarial network (GAN) for MR imaging from CT scan data. Considering reversibility of GAN, both generator and reverse generator are designed for MR and CT imaging, respectively, which can constrain each other and improve consistency between features of CT and MR images. In addition, we innovatively treat the real and generated MR image discrimination as object re-identification; cosine error fusing with original GAN loss is designed to enhance verisimilitude and textural features of the MR image. The experimental results with the challenging public CT-MR image dataset show distinct performance improvement over other GANs utilized in medical imaging and demonstrate the effect of our method for medical image modal transformation.
Optimal Deep Convolutional Neural Network with Pose Estimation for Human Activity Recognition

Human Action Recognition (HAR) and pose estimation from videos have gained significant attention among research communities due to its application in several areas namely intelligent surveillance, human robot interaction, robot vision, etc. Though considerable improvements have been made in recent days, design of an effective and accurate action recognition model is yet a difficult process owing to the existence of different obstacles such as variations in camera angle, occlusion, background, movement speed, and so on. From the literature, it is observed that hard to deal with the temporal dimension in the action recognition process. Convolutional neural network (CNN) models could be used widely to solve this. With this motivation, this study designs a novel key point extraction with deep convolutional neural networks based pose estimation (KPE-DCNN) model for activity recognition. The KPE-DCNN technique initially converts the input video into a sequence of frames followed by a three stage process namely key point extraction, hyperparameter tuning, and pose estimation. In the keypoint extraction process an OpenPose model is designed to compute the accurate keypoints in the human pose. Then, an optimal DCNN model is developed to classify the human activities label based on the extracted key points. For improving the training process of the DCNN technique, RMSProp optimizer is used to optimally adjust the hyperparameters such as learning rate, batch size, and epoch count. The experimental results tested using benchmark dataset like UCF sports dataset showed that KPE-DCNN technique is able to achieve good results compared with benchmark algorithms like CNN, DBN, SVM, STAL, T-CNN and so on.
Laser transverse dual differential confocal radius measurement with high efficiency and high precision

To meet the need for rapid, high-precision, and non-contact measurement of the radius of curvature (ROC) for large quantities of spherical optics, a radius measurement method based on transverse dual differential confocal (TDDC) detection is proposed in this study. First, a template S0 with a known ROC, R0, is axially scanned on its confocal position to obtain the fitted linear function lTDDC(z) using TDDC. Second, the template S0 is replaced by Sn, which is one of the test sample in large quantities, then the single point TDDC intensity ITDDC(Δzn) is captured without scan, which will be applied to obtain the defocus Δzn according to the linear function lTDDC(z). Finally, the ROC Rn under test is calculated using Δzn and R0. Simulation and experiments show that the measurement accuracy can achieve 8.0 ppm, and the measurement efficiency is 60 times higher than that of the traditional differential confocal scanning measurement. Measurement based on TDDC only requires scanning once and replacing Sn N times to realize the fast, high-precision, non-contact ROC detection of N pieces of spherical optics, which enables the high-efficiency and high-precision measurement of large quantities of spherical optics.
Between People and Paper: Inhabiting Experiment in a Journal Club

In 2015, the Open Science Collaboration reported in the journal Science that a disturbingly large proportion of psychological studies cannot be replicated (Open Science Collaboration, 2015). The ensuing ‘reproducibility crisis’ became a lightning rod for contesting what counts as legitimate research, and for negotiating the relationship between communication infrastructures and research practice. In the psychological and cognitive sciences, the Open Science community has advocated widespread reforms to incentivize transparency, encourage replication, and detect and discourage questionable research practices. The model of ‘openness’ underlying mainstream Open Science centers on sharing information to increase science’s self-correcting capacity. Against the backdrop of broad-scale transformations in Open Science, this case study depicts how scientists read. By examining the activity of a group of researchers ‘virtually witnessing’ an experiment together, this study reveals reading as a non-trivial process that matters for how research is apprehended and for how science is moved through time and space. The case complicates a disembodied, information-centric ‘openness’ pursued by mainstream Open Science reforms and advocates integrating situated and embodied resources into methods reforms, beginning with practices of reading.
Visual bias could impede diagnostic accuracy of breast cancer calcifications

Purpose: Diagnosing breast cancer based on the distribution of calcifications is a visual task and thus prone to visual biases. We tested whether a recently discovered visual bias that has implications for breast cancer diagnosis would be present in expert radiologists, thereby validating the concern of this bias for accurate diagnoses. Approach: We ran a vision experiment with expert radiologists and untrained observers to test the presence of visual bias when judging the spread of dots that resembled calcifications and when judging the spread of line orientations. We calculated visual bias scores for both groups for both tasks. Results: Participants overestimated the spread of the dots and the spread of the line orientations. This bias, referred to as the variability overestimation effect, was of similar magnitudes in both expert radiologists and untrained observers. Even though the radiologists were better at both tasks, they were similarly biased compared with the untrained observers. Conclusions: The results justify the concern of the variability overestimation effect for accurate diagnoses based on breast calcifications. Specifically, the bias is likely to lead to an increased number of false-negative results, thereby leading to delayed treatments.
Real-Time Resource Monitoring Framework in a Heterogeneous Kubernetes Cluster

Container-based virtualization is quickly becoming the standard method for developing and deploying applications. Kubernetes is a well-known open-source project that provides a platform for containerized application orchestration. In such a container-based environment, it is critical that Kubernetes can monitor the cluster computing resource as well as the resource requirements of the running pods. The goal of this paper is to introduce a generic framework which is named as Kube-Monitor for real-time monitoring of a heterogeneous Kubernetes cluster's resources, which include traditional data center devices and raspberry pi devices, as well as monitoring pod resource requirements. Kube-Monitor will collect 15 different cluster resource metrics and update their values every five seconds. As a result, the Kubernetes operator can use the Kube-monitor framework to view a comprehensive set of continuously updated resource metrics.
Machine learning approach for phishing website detection : A literature survey

The past year saw our world afflicted by COVID-19 undergo a digital transformation which led to a majority of people and organizations gravitate towards the internet. A remote working environment complicated the pre-existent crisis of phishing where the vulnerable population incurred huge losses at the hands of internet miscreants. A phishing attack comprises an attacker that creates fake websites to fool users and steal client-sensitive data which may be in form of login, password, or credit card details. Timely detection of phishing attacks has become more crucial than ever. Hence in this paper, we provide a thorough literature survey of the various machine learning methods used for phishing detection. This thesis will discuss in detail, different approaches used by various authors over the past few years. This survey aims to identify and narrow down the best machine learning algorithms that can be adopted to develop a hybrid model which can be implemented to detect whether a website is legitimate or phishing in nature.
The Avatar’s Gist: How to Transfer Affective Components From Dynamic Walking to Static Body Postures

Dynamic virtual representations of the human being can communicate a broad range of affective states through body movements, thus effectively studying emotion perception. However, the possibility of modeling static body postures preserving affective information is still fundamental in a broad spectrum of experimental settings exploring time-locked cognitive processes. We propose a novel automatic method for creating virtual affective body postures starting from kinematics data. Exploiting body features related to postural cues and movement velocity, we transferred the affective components from dynamic walking to static body postures of male and female virtual avatars. Results of two online experiments showed that participants coherently judged different valence and arousal levels in the avatar's body posture, highlighting the reliability of the proposed methodology. In addition, esthetic and postural cues made women more emotionally expressive than men. Overall, we provided a valid methodology to create affective body postures of virtual avatars, which can be used within different virtual scenarios to understand better the way we perceive the affective state of others.
Formation of digital competencies among students of economic specialties

The rapid advancement of digital technology is altering the skillsets required of specialists. In today’s world, a competitive analyst working in the field of economics must possess not only professional skills, but also digital competence, which encompasses not only digital skills, but also a body of knowledge and perspectives on the nature and role of information technologies and opportunities, as well as relevant legal and ethical principles. The article examines modern theoretical foundations and approaches to the development of digital literacy in lifelong learning processes: specifically, one of the tools for determining the level of formation of digital competence using the Tsifrogram test (which is based on the conceptual model of digital competence of citizens DigComp - The Digital Competence Framework for Citizens) is described and analyzed in depth. The study was conducted using competence-based, personality-activity, and communication techniques. The economic analyst training program featured active and interactive teaching methods and practices. The findings of a study on the level of digital competence formation among university students of economic specializations are provided. The most important suggestions for improving students’ digital competence are studied, selected, and highlighted. It is proposed to build a roadmap for boosting students’ digital competency during their university studies in the future.
A novel hashing scheme via image feature map and 2D PCA

Hashing scheme is a high-efficiency technique for processing massive images. Two critical metrics of the hashing scheme are discrimination and robustness, but most schemes do not get satisfied classification performance between them. This paper proposes a novel hashing scheme via image feature map and 2D PCA. First, the proposed scheme extracts local phase quantization (LPQ) features in the frequency domain and local ternary pattern (LTP) features in the spatial domain, and combines them to construct an image feature map. Second, the proposed scheme conducts dimension reduction via 2D PCA for learning features from the image feature map. Last, the learned features are compressed to generate the hash sequence. Performances are tested on open image datasets. The results demonstrate that the proposed scheme can make a good balance between discrimination and robustness. In addition, the classification and copy detection of the proposed scheme are both superior to those of some famous hashing schemes.
Risk-Based Authentication

The rapid internet adoption has heralded the era of connected devices and services. Applications and systems are now internet-centric. The bulk of transactions, communications, and even healthcare data are transmitted online. Internet of things (IoT) are being used extensively across domains. With the increasing digital and online traffic, the risk window grows as well. Evolving threat vectors have prompted the need for multi-factor authentication (MFA) systems that utilize multiple modalities to strengthen security. With enterprises opting for bring-your-own-device (BYOD), static MFA proves insufficient. This brought about risk-based authentication (RBA) that follows an adaptive and continuous authentication strategy. With contextual attributes of the user factored in, RBA is more robust and can withstand multiple attacks. NIST, US and NCSC, UK have recommended RBA. This chapter covers authentication methodologies and their evolution. The need for risk-based authentication, with an analysis of current RBA methodologies, their future outlook, and challenges are covered.
BLOCKLIB: Blockchain enabled library resource sharing

Being developed from traditional construction to smart resource sharing system, libraries are committed to create a sound content sharing infrastructure beyond the walls. Blockchain supports the certainty of resources ownership and access. This paper presents a blockchain based smart library management system for online sharing of data. The system eradicates the chances of tampering as well as unauthorized accessing the data by introducing a unique and secure accessing mechanism with blockchain technology. The proposed framework is able to provide the secure access to database without any load and access from any location.
Nonreciprocal quantum correlations and dense coding

We compute different quantifiers to analyze the spontaneous generation of entanglement for a unidirectional (nonreciprocal) two-two level system. We illustrate this approach by deriving the Markovian master equation in the Lindblad form. The numerical analysis of the unidirectional master equation enabled to obtain the steady-state concurrence. We explore the quantum dense coding between two spatially separated dipoles taking into account the collective damping effect. Our results reveal the sharp decline and then gradual increase in the dense coding capacity to reach a stable value. This procedure opens the possibility to implement this scheme for further applications in the field of quantum information theory.
VITALflow: Visual Interactive Traffic Analysis with NetFlow

Traffic analysis in large enterprise networks has become a vital task for network experts, as understanding application and user traffic is the basis for proper network management with respect to planning, formulating intents, or analyzing causes for implausible behavior. In such networks, NetFlow provides input to network monitoring systems that typically show time series visualizations along different data dimensions. We studied tasks and requirements of network experts and derived a visual analytics approach that improves their analytic workflow as it enables for exploration of large time spans quickly in a multidimensional manner. Our approach guides users and improves the scalability of analyses through a novel combination of a clustered time series view and filtering in interactive parallel sets into a coherent visual analysis framework. Clustering reveals typical patterns and deviations from the daily norm and serves as entry point to exploring, filtering and comparing multiple dimensions in the parallel sets view. In addition, we briefly discuss the feedback we received on two case studies with network experts.
Scattered linear sets in a finite projective line and translation planes

In [15], Lunardon and Polverino construct a translation plane starting from a scattered linear set of pseudoregulus type in PG(1,qt). In this paper a similar construction of a translation plane Af obtained from any scattered linearized polynomial f(x) in Fqt[x] is described and investigated. A class of quasifields giving rise to such planes is defined. Denote by Uf the Fq-subspace of Fqt2 associated with f(x). If f(x) and f′(x) are scattered, then Af and Af′ are isomorphic if and only if Uf and Uf′ belong to the same orbit under the action of ΓL(2,qt). This gives rise to the same number of distinct translation planes as the number of inequivalent scattered linearized polynomials. In particular, for any scattered linear set L of maximum rank in PG(1,qt) there are cΓ(L) pairwise non-isomorphic translation planes, where cΓ(L) denotes the ΓL-class of L, as defined in [5] by Csajbók, Marino and Polverino. A result by Jha and Johnson [8] allows to describe the automorphism groups of the planes obtained from the linear sets not of pseudoregulus type defined in [15].