Reviewerly/exHarmony huggingface dataset:
Evaluation of Attention Guidance through Visual Subthreshold Stimulus Using a HMD

Attention guidance is used as a means of conveying important information. Many attention guides use colors or color gradations that stand out from the surrounding environment, such as signs, or directional shapes such as arrows. In the past, some studies have used directional events such as the gaze of others, optical flow, and optical illusions to guide attention. However, such stimulus presentation methods may cause annoyance and discomfort to people because of their high degree of stimulation. For example, when an alert is displayed for a hazard that appears probabilistically when driving a car, it often causes annoyance and discomfort to the driver when the hazard does not appear. In this paper, we propose a new method of alerting the driver by affecting the unconscious region of the brain. In past research, it has been reported that displaying an object in the frame before the visual search task induced attention and shortened the reaction time. In addition, it has been reported that by displaying a short cue during eye movement, the gaze moved in the direction of the cue. The purpose of this study is to evaluate the validity of attentional guidance by presenting such visual subthreshold stimuli. The participants wore a head-mounted display and performed a visual search task in which they had to find and answer one search object different from the others among eight search objects. The time from the start of the visual search task to the finding of the target object and the trajectory of the gaze during the search were used as evaluation indicators. Ten healthy males in their twenties (22.8 years old ± 1.72 years old) with corrected eyesight cooperated in the experiment. The objects to be searched were placed 1 m away from the participants and at 30° to the up, down left, right. A masked image was inserted before the visual search task, and one of the explorers could be seen one frame (Approx. 0.01ms) before the visual search task, which was used as a visual subthreshold stimulus. These cues could be either consistent with or different from the search object to be found. The results of this experiment showed that there was no significant difference in mean reaction time between the three conditions: when the cue matched or differed from the search object to be found, and when the cue was not presented. However, there was a tendency that the position at which the participants first started their search was closer to the location of the cue. This suggests that the threshold stimulus has an effect of inducing attention, but there was no tendency to draw attention strongly. In addition, the speed of recognition was large for some participants, and cues were recognized in some cases. The reason for this result is thought to be the contrast ratio between the cue and the mask image and the presentation position of the cue. Therefore, it is necessary to examine the contrast ratio and the presentation position of the cue for each experimental collaborator.
Blockchain in Smart Healthcare Application

A blockchain is a form of distributed laser technologies, it attracts all stakeholders in many areas, like healthcare application. Its capacity in multi-stakeholders driven sectors such as health is responsible for many investments and studies and implementation. EHR is a system traditionally used to exchange health information between healthcare stakeholders, which exchange has been centralizing power. In this study, an Electronic Health Record (EHR) is an electronic form of clinical history that is kept up by the supplier over the long run and may incorporate the entirety of the key regulatory clinical information applicable to that people care under a specific supplier, including socioeconomics, progress notes, issues, prescriptions. Blockchain is promising to face many HER challenges, among others which is reliable and secure to exchange data between stakeholders. Several blockchain models in healthcare applications have been published, some are prototype, executed. In our study we encourage PRIZMA framework to explore and evaluate various models. The basic design of our work is that we have to study some research papers and we analyze them found that in the healthcare industry how blockchain is an emerging technology and enhance the performance of industry by using EHR model, Functional Distribution and with the help of technical analysis. EHR, functional distribution and technical study were presented in this study. The technical and architectural analysis was detailed for privacy, security, cost and performance.
A SYSTEMATIC APPROACH TO THE STUDY OF THE THEATRICAL CULTURE OF THE RUSSIAN PROVINCE

The article discusses the possibilities of applying a systematic approach to the study and analysis of the theatrical culture of the Russian province. The theoretical justification of the systematic approach in cultural and philosophical studies is given. The author's model of theatrical culture is presented, which includes a set of institutional, classroom, artistic-value and regulatory components, the interrelation of which allows us to consider theatrical culture as a system. The author calls ideology the «core» of the system, as a product of the activity of the regulatory component of theatrical culture. In conclusion, the rationale for the application of a systematic approach to the study of provincial theatrical culture is formulated. The necessity of studying the regional theater as a subsystem of national theatrical culture is proved.
Elementary Dynamic Models: A Strategic Bridge Connecting School and University

We present an innovative educational path thought as a link between High School and University studies. The topic is the introduction to dynamic models (both discrete and continuous) which represent a key tool in a wide range of disciplines: sciences, techniques, economics, life sciences and more.
Dual-State Knowledge Tracing Model with Mutual Information Maximization

Knowledge tracing aims to trace students’ knowledge states and predict their future performance based on their historical learning processes. Most existing methods of characterizing a student’s state are not effective enough, using only global representation or knowledge concept level representation. Such representation methods cannot consider the characteristics of knowledge concepts and the relations between concepts at the same time. In this paper, we propose a Dual-State Knowledge Tracing (DSKT) Model with Mutual Information Maximization. DSKT uses dynamic routing to extract knowledge commonalities from original knowledge concepts, updates the knowledge state at the concept and commonality levels, and predicts future performance by fusing two states. In addition, to incorporate the relationship between exercises and knowledge concepts, we use the principle of mutual information maximization to learn their representations. Extensive experimental results show the effectiveness of our model.
PWN

You may be confused by the word “PWN”. “PWN” doesn’t like Web or CRYPTO which represent a specific meaning. In fact, “PWN” is an onomatopoeia that represents the “bang” of a hacker gaining access to a computer through a vulnerability. It is a slang term derived from the verb own. In short, the process of gaining access to a computer through a vulnerability in binary is called PWN.
HematoNet: Expert-Level Classification of Bone Marrow Cytology Morphology in Hematological Malignancy With Deep Learning

There have been few efforts made to automate the cytomorphological categorization of bone marrow cells. For bone marrow cell categorization, deep-learning algorithms have been limited to a small number of samples or disease classifications. In this paper, we proposed a pipeline to classify the bone marrow cells despite these limitations. Data augmentation was used throughout the data to resolve any class imbalances. Then, random transformations such as rotating between 0° to 90°, zooming in/out, flipping horizontally and/or vertically, and translating were performed. The model used in the pipeline was a CoAtNet and that was compared with two baseline models, EfficientNetV2 and ResNext50. We then analyzed the CoAtNet model using SmoothGrad and Grad-CAM, two recently developed algorithms that have been shown to meet the fundamental requirements for explainability methods. After evaluating all three models’ performance for each of the distinct morphological classes, the proposed CoAtNet model was able to outperform the EfficientNetV2 and ResNext50 models due to its attention network property that increased the learning curve for the algorithm which was represented using a precision-recall curve.
Using Genetic Algorithm to Optimize the Elements of College Students’ Physical Training

In this paper, an improved hybrid genetic algorithm with global optimization ability is designed by combining sorting adaptive genetic algorithm with simplex method. The algorithm is used to optimize the teaching and training elements of College Students’ physical strength (based on heart function and lung function), and the experimental research is carried out in college students. The experimental results show that this method has better training effect than the traditional method, and has innovative significance to improve the physical health of college students.
A Light-Weight Network with Multi-Scale Features Fusion And Color Guidance for Stereo Matching

In recent years, convolutional-neural-network based stereo matching methods have achieved significant gains compared to conventional methods in terms of both speed and accuracy. Current state-of-the-art disparity estimation algorithms require many parameters and large amounts of computational resources and are not suited for applications on edge devices. In this paper, we propose an end-to-end light-weight network (LWNet) for fast stereo matching, which consists of an efficient backbone with multi-scale feature fusion for feature extraction, a 3D U-Net aggregation architecture for disparity computation, and color guidance in a 2D convolutional neural network (CNN) for disparity refinement. We adopt MobileNetV2 as an efficient backbone in feature extraction. The channel attention module is applied to improve the representational capacity of features and multi-resolution information is adaptively incorporated into the cost volume via cross-scale connections. In addition, instead of using regular 3D convolutions, we utilize pseudo 3D convolutions in the 3D U-Net architecture to aggregate the cost volume for a better balance between computational cost and accuracy. Further, we introduce a left-right consistency check and color guidance and design a robust disparity refinement network with skip connections and dilated convolutions to capture global context information and further improve disparity-estimation accuracy with little computational cost and memory space. A depth-wise separable convolution is proposed to replace all the standard convolutions in the section of disparity refinement, which can decrease computational complexity and the number of parameters without significant accuracy reduction. Extensive experiments on Scene Flow, KITTI 2015, and KITTI 2012 benchmarks demonstrate that the proposed LWNet achieves competitive accuracy when compared with state-of-the-art stereo matching methods.
Pose Sequence Generation with a GCN and an Initial Pose Generator

The existing methods on video synthesis have succeeded in generating higher quality videos by using guide information such as human pose skeletons, segmentation masks and optical flows as auxiliary information. Some existing video generation methods on human motion adopts a two-step video generation consisting of generation of pose sequences and video generation from pose sequences. In this paper, we focus on the first stage, the generation of pose sequences, in the whole processing of video generation of human motion. We incorporate a Graph Convolutional Network (GCN) and an initial pose generator into the model to model poses more explicitly and to generate pose sequences naturally. The experimental results show that the proposed method can generate better quality pose sequences than the conventional methods by improving the initial pose generation and introducing GCN.
Ambidextrous leadership and project-based organisational innovation performance: the mediating role of innovative climate and moderating role of organisational learning

The objective of this paper is to examine the impact of ambidextrous leadership on project-based organisations' innovation performance with the mediation of innovative climate and moderation of organisational learning. Hence, the prevalent objective of the existing study is the instigation of a mediating and moderation model to inquest the efficiency of ambidextrous leadership in the presence of organisational learning and innovative climate. For this research paper, data had been collected by following a quantitative deductive approach of positivist research philosophy. Among 1,500 questionnaires distributed to software project employees based in Pakistan registered under P@sha, 749 useable responses were received. Later, data was analysed through SPSS 26 and Smart PLS 3. The outcomes of the research indicate that ambidextrous leadership significantly influence project-based organisational innovation performance. Furthermore, there is the support that the manifestation of organisational learning tends to positively moderate the impact of ambidextrous leadership on project-based organisational innovation performance, whereas innovative climate plays an integral role as a catalyst for enhancing project-based organisational innovation performance through ambidextrous leadership. These outcomes will help developing countries' software firms like in Pakistan that want to improve performance through organisational learning and innovative culture.
An Integrated Approach to Reduce Class Imbalance

The most challenging task in the software development life cycle is to defect management as early as possible to increase reliability. Most of the available defect predicting methods suffer from class imbalance problem. In this paper, classification is done after resolving the class imbalance by balancing defects and non-defect records. Our proposed algorithm class imbalance reduction using K-nearest neighbor (CIRKNN) algorithm is contrast to synthetic minority oversampling technique (SMOTE) which is considered as base and most popular machine learning algorithm for handling class imbalance problem. The basic concept behind SMOTE is that it tries to balance the majority and minority classes by picking the random sample from minority class. But, drawback is that randomly selecting initial sample may not be appropriate. So, our proposed algorithm CIRKNN integrates both centroid and K-nearest neighbor (KNN) concepts to balance the datasets. To evaluate performance from the repository of PRedict or Models in Software Engineering (PROMISE), open-source software defective datasets of about forty were collected and passed to CIRKNN for balancing; after balancing, these were passed to eight different classifiers, and six measures of performance were also done. By cross-examining the forty-eight accuracy values generated by CIRKNN and SMOTE, we observed that thirty-eight values of CIRKNN are showing good and improved performance, and ten are showing equal performance. Finally, our algorithm outstands in overall performance and showed good accuracy than SMOTE.
Selecting and Training Young Cyber Talent: A Recurrent European Cyber Security Challenge Case Study

Cyber security is a big challenge nowadays. However, the lack of qualified individuals and awareness is making the current situation more problematic. One way to address this problem is through National cyber security competitions. Such competitions provide large-scale awareness of cyber security issues and motivate individuals to join the cyber security workforce. ENISA (European Network and Information Security Argent) motivated European countries to organize national-level cyber security competitions in early 2014, similar to national-level football competitions. After that, ENISA organized a European-level competition similar to UEFA football league in which multiple countries participated with their national teams. This cyber security competition is known as a ECSC (European Cyber Security Challenge). Individuals aged between 14–25 participate and try to solve different cyber security challenges related to web, forensics, crypto, OSINT (Open Source Intelligence), and reversing. To select individuals and to teach those skills, different countries apply different strategies. In this study, we focus on how different countries select cyber talent and train them for national competitions and how they impact their overall cyber security ecosystem to produce skilled individuals for a cyber security workforce.
Analyzing Random Forest, Naive Bayes, and SVM to Filter Spam Emails Across Multiple Datasets

As Emails are one of the most growing and important modes of communication, it is more vulnerable to be exploited. This paper addresses the several types of machine learning classification models for filtering spam emails using multiple email datasets, such as Random Forest, Naive Bayes, and SVM algorithms that are implemented in python with Scikit-learn library. Email Dataset, Enron Dataset, and Ling Spam Dataset are the datasets used for classifying emails into spam (indicated as 1) or ham (indicated as 0). The result shows that Random Forest and Naive Bayes does extremely well than Support Vector Machine.
Cluster-based Convolutional Baseline for Multi-Camera Vehicle Re-identification

Increased traffic-related problems like traffic rule violations, traffic congestions, road accidents, etc., have resulted in huge demands for smart traffic management systems worldwide. Vehicle re-identification is a key component of such systems where the goal is to identify vehicles across images that have been captured by different cameras. In this work, we propose a Cluster-based Convolutional Baseline (CCB) model that extracts part-level features from the vehicle mages using a partitioning method based on K-means clustering. The extracted part-level features provide fine-grained information present in the images that is necessary to differentiate between two very similar looking vehicle images and helps to overcome some of the biggest challenges of vehicle re-identification like intra-class differences i.e. when two images of the same vehicle (captured from different viewpoints) look very different and inter-class similarities in which two different vehicles look very similar to each other. We evaluated our model using mean average precision (mAP) and cumulative characteristics curve (CMC) evaluation metrics and achieved an mAP of 84.5%, Rank@1 and Rank@5 accuracy of 96.7% and 98.7% respectively, beating the existing state-of-the-art models on VeRi-776 dataset. Code is available at https://github.com/Rajat-Mehta/Vehicle_Reidentification .
Does Economic Development Lead to the Dematerialization of Our Economies?

Over the last decades, industrializing countries have experienced economic growth, which coincided with an increase in the use of materials. In a finite world in terms of materials, the consumption of resources and the link with economic development is a challenge for economies. While most of literature relative to Material Kuznets Curve (MKC) and Environmental Kuznets Curve refers to the work of Grossman and Krueger (1991), the theory of inverted U-shaped consumption is older and can be traced from the work of Malenbaum (1978). Using a new indicator, the material footprint (MF), we want to estimate the material footprint-GDP per capita elasticity. Thus, comparing for the first-time four different methodologies and two classes of econometric models (robust to reverse/simultaneous causality) in the same empirical study, we investigate this nexus in order to provide a robust recommendation for policy makers. Our main results find that all the models lead in a similar direction, seeming to indicate a strong link between development and raw material consumption: There is neither sign of absolute decoupling nor a clear evidence of demand saturation. Results stay consistent for the MF for fossil fuels, non-metallic minerals, metal and biomass.
Labeling Quality Problem for Large-Scale Image Recognition

Most CNN models trained on the popular ImageNet dataset are created under the assumption that a single label is used per training image. These models realize remarkable performance on the ImageNet benchmark (with top-1 scores over 90%). Despite this, recognition of several categories is not reliable, as models for these categories can be easily attacked by natural adversarial examples. We show that this effect is related to ambiguous, single labels assigned to training and testing data for these categories. The CNN models tend to learn representations based on parts of an image not related to the label/category. We analyze the labeling scheme used to annotate the popular ImageNet benchmark dataset and compare it with two recent annotation schemes - CloudVision and Real labeling schemes, which are both crowd-sourced annotation efforts. We show that these two schemes lead to a very different granularity of annotations; we also argue that new annotations schemes should not rely on the accuracy on current ImageNet benchmarks as the hint for their correctness (at the Real scheme does).
Augmented Reality-based Programming of Robot Arms

Programming of production systems depends on highly specialized personnel and proprietary development environments. This can hinder the evolution towards flexible production. In this paper we present a concept for an intuitive way of human-machine interaction by utilizing the augmented reality technology. This involves a method of programming a machine using an augmented reality headset. All desired machine operations can be predefined via a virtual user interface and by arbitrary placement of virtual working points. We further show how the referencing of the coordinate systems of the augmented reality environment and the robot can be accomplished. We prototypically apply the concept to a robotic arm with 6 degrees of freedom and employ a Microsoft HoloLens 2 optical head-mounted display to interact with robotic arm in the virtual world.