Reviewerly/exHarmony huggingface dataset:
Overview of Controllers of User Interface for Virtual Reality

Abstract Virtual reality has been with us for several decades already, but we are still trying to find the right ways to control it. There are many controllers with various purposes and means of input, each with its advantages and disadvantages, but also with specific ways to be handled. Our hands were the primary means of input for human--computer interaction for a long time. However, now we can use movements of our eyes, our feet, or even our whole body to control the virtual environment, interact with it, or move from one place to another. We can achieve this with various controllers and wearable interfaces, like eye-tracking, haptic suits, or treadmills. There are numerous devices that we can choose from for every category, but sometimes it can be hard to pick the one that matches our intentions best. This article summarizes all types of user interface controllers for virtual reality, with their main pros and cons and their comparison. By combining controllers, the user's feeling of being immersed in a virtual world can be increased. We will cover positional tracking (optical and nonoptical), hand-based controllers, body tracking, wearable controllers, eye-tracking methods, and locomotion systems. New controllers are being invented by lab researchers and companies for gaming or business. We provide a look at numerous controllers, and we offer a reference guide. This guide lists sources for research papers, technical specs, user reviews, and thoughts from outside academia.
AR Hero: Generating Interactive Augmented Reality Guitar Tutorials

We introduce a system capable of generating interactive Aug-mented Reality guitar tutorials by parsing common digital guitar tablature and by capturing the performance of an expert using a multi-camera array. Instructions are presented to the user in an Augmented Reality application using either an abstract visualization, a 3D virtual hand, or a 3D video. To support individual users at different skill levels the system provides full control of the play-back of a tutorial, including its speed and looping behavior, while delivering live feedback on the user&#x0027;s performance.
Explainable Multimedia Feature Fusion for Medical Applications

Due to the exponential growth of medical information in the form of, e.g., text, images, Electrocardiograms (ECGs), X-rays, and multimedia, the management of a patient's data has become a huge challenge. In particular, the extraction of features from various different formats and their representation in a homogeneous way are areas of interest in medical applications. Multimedia Information Retrieval (MMIR) frameworks, like the Generic Multimedia Analysis Framework (GMAF), can contribute to solving this problem, when adapted to special requirements and modalities of medical applications. In this paper, we demonstrate how typical multimedia processing techniques can be extended and adapted to medical applications and how these applications benefit from employing a Multimedia Feature Graph (MMFG) and specialized, efficient indexing structures in the form of Graph Codes. These Graph Codes are transformed to feature relevant Graph Codes by employing a modified Term Frequency Inverse Document Frequency (TFIDF) algorithm, which further supports value ranges and Boolean operations required in the medical context. On this basis, various metrics for the calculation of similarity, recommendations, and automated inferencing and reasoning can be applied supporting the field of diagnostics. Finally, the presentation of these new facilities in the form of explainability is introduced and demonstrated. Thus, in this paper, we show how Graph Codes contribute new querying options for diagnosis and how Explainable Graph Codes can help to readily understand medical multimedia formats.
Using Speech to Visualise Shared Gaze Cues in MR Remote Collaboration

In this paper, we present a 360&#x00B0; panoramic Mixed Reality (MR) sys-tem that visualises shared gaze cues using contextual speech input to improve task coordination. We conducted two studies to evaluate the design of the MR gaze-speech interface exploring the combinations of visualisation style and context control level. Findings from the first study suggest that an explicit visual form that directly connects the collaborators&#x2019; shared gaze to the contextual conversation is preferred. The second study indicates that the gaze-speech modality shortens the coordination time to attend to the shared interest, making the communication more natural and the collaboration more effective. Qualitative feedback also suggest that having a constant joint gaze indicator provides a consistent bi-directional view while establishing a sense of co-presence during task collaboration. We discuss the implications for the design of collaborative MR systems and directions for future research.
On Reporting Performance and Accuracy Bugs for Deep Learning Frameworks: An Exploratory Study from GitHub

The tremendous success of Deep Learning (DL) has significantly boosted the number of open-sourced DL frameworks hosted on GitHub. Among others, performance and accuracy bugs are critical factors that affect the reputation of these DL frameworks, therefore understanding the practice of discovering and investigating them for DL is important. In this paper, we conduct an exploratory study on the nature of reporting performance and accuracy bugs for DL frameworks, aiming to improve our knowledge on this topic. Our study covers 10 most popular open-sourced DL frameworks on GitHub (e.g., TensorFlow, Keras, and PyTorch), based on which we sample 664 representative performance and accuracy bug reports out of a total population of 22,522. Through systematic analysis, we found that: (1) low speed is the primary reason that a performance bug related report is submitted but we see no consistent pattern for accuracy related ones; (2) most of the reports are about issues encountered in the training stage; (3) only a small proportion of the reports provide insufficient information to investigate; (4) the majority of the performance and accuracy bug reports (from 69% to 100%) are not related to the actual bug or regarded as unclassified; (5) around 50% of the performance and accuracy bug reports, which indeed reveal bugs, are not resolved by direct patches. Deriving from the above, we discuss a set of actionable implications to the researchers, maintainers, and report submitters. To promote open science, the labeled dataset has been made publicly available at https://zenodo.org/record/6371676 .
Classification of Imbalanced Offensive Dataset – Sentence Generation for Minority Class with LSTM

The classification of documents is one of the problems studied since ancient times and still continues to be studied. With the social media becoming a part of daily life and its misuse, the importance of text classification has started to increase. This paper investigates the effect of data augmentation with sentence generation on classification performance in an imbalanced dataset. We propose an LSTM based sentence generation method, Term Frequency-Inverse Document Frequency (TF-IDF) and Word2vec and apply Logistic Regression (LR), Support Vector Machine (SVM), K Nearest Neighbour (KNN), Multilayer Perceptron (MLP), Extremly Randomized Trees (Extra tree), Random Forest, eXtreme Gradient Boosting (Xgboost), Adaptive Boosting (AdaBoost) and Bagging. Our experiment results on imbalanced Offensive Language Identification Dataset (OLID) that machine learning with sentence generation significantly outperforms.
Multi-level News Recommendation via Modeling Candidate Interactions

Due to the information explosion on the Internet, news recommendation, which helps users quickly find the news they are interested in, has become an essential issue for online news services. Previous research work usually adopts collaborative filtering or content-based methods which extract features and measure the similarities between users and each candidate news independently. However, candidate news often competes with each other for user attention, and modeling the interactions of multiple candidate news helps distinguish them better for news recommendation. In this paper, we propose a multi-level news recommendation method via modeling the interactions of multiple candidate news explicitly. Specifically, we design a Candidate Interaction Module (CIM) to generate interaction-enhanced candidate news representations. For each candidate news, the interaction-enhanced news representation contains information from other candidate news displayed to the user at the same time. Furthermore, in order to identify the connections between candidate news and user preferences at different semantic levels, we add a Multi-level Prediction Module (MPM) to exploit the category and subcategory information of news. Experimental results demonstrate that our proposed model achieves the state-of-the-art performance on two real-world benchmark datasets.
From eye movements to scanpath networks: A method for studying individual differences in expository text reading

Eye movements have been examined as an index of attention and comprehension during reading in the literature for over 30 years. Although eye-movement measurements are acknowledged as reliable indicators of readers' comprehension skill, few studies have analyzed eye-movement patterns using network science. In this study, we offer a new approach to analyze eye-movement data. Specifically, we recorded visual scanpaths when participants were reading expository science text, and used these to construct scanpath networks that reflect readers' processing of the text. Results showed that low ability and high ability readers' scanpath networks exhibited distinctive properties, which are reflected in different network metrics including density, centrality, small-worldness, transitivity, and global efficiency. Such patterns provide a new way to show how skilled readers, as compared with less skilled readers, process information more efficiently. Implications of our analyses are discussed in light of current theories of reading comprehension.
Radiology Text Analysis System (RadText): Architecture and Evaluation

Analyzing radiology reports is a time-consuming and error-prone task, which raises the need for an efficient automated radiology report analysis system to alleviate the workloads of radiologists and encourage precise diagnosis. In this work, we present RadText, a high-performance open-source Python radiology text analysis system. RadText offers an easy-to-use text analysis pipeline, including de-identification, section segmentation, sentence split and word tokenization, named entity recognition, parsing, and negation detection. Superior to existing widely used toolkits, RadText features a hybrid text processing schema, supports raw text processing and local processing, which enables higher accuracy, better usability and improved data privacy. RadText adopts BioC as the unified interface, and also standardizes the output into a structured representation that is compatible with Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), which allows for a more systematic approach to observational research across multiple, disparate data sources. We evaluated RadText on the MIMIC-CXR dataset, with five new disease labels that we annotated for this work. RadText demonstrates highly accurate classification performances, with a 0.91 average precision, 0.94 average recall and 0.92 average F-1 score. We also annotated a test set for the five new disease labels to facilitate future research or applications. We have made our code, documentations, examples and the test set available at https://github.com/bionlplab/radtext.
Leveraging Dignity Theory to Understand Bullying, Cyberbullying, and Children’s Rights

Abstract This article outlines how dignity theory could be used to better understand bullying behaviors. Dignity is defined here as the inherent worth of every human being and it allows us to trace the motivations behind bullying behaviors to broader social values that are rarely the primary focus of bullying research, as well as prevention and intervention efforts. In this manner, the theory could elucidate the cultural patterns which contribute to not only child bullying and cyberbullying, but to workplace bullying, and to similar abusive behaviors among adults. We give special attention to cyberbullying and illustrate how dignity theory can clarify why this behavior is not only about online safety but about relational issues, which are reflective of social values. We argue that seeing cyberbullying through the lens of online safety can limit the scope of artificial intelligence–based solutions whose development is gaining momentum at this time. We provide suggestions about dignity-based considerations that collaborations between computer and social scientists could take into account in order to pave the way towards innovation that upholds dignity and children’s rights.
Fraud App Detection of Google Play Store Apps Using Decision Tree

Along the rise in the various mobile applications which are used in daily life, it&#x0027;s more necessary than ever to stay on top of things to decide which are safe and which don&#x0027;t. It is impossible to pass judgment. Our system is based on four parameter that include ratings, reviews, in app purchases and Contains ad to predict. Our system compares three models Decision Tree classifier, Logistic Regression and Na&#x00EF;ve Bayes. These models were further analyzed on four parameters of F1 score, Recall, Precision and Accuracy. A good F1 score should be greater than 0.7 and a recall score greater than 0.5 is considered to be good with higher precision and accuracy. On analysis we found Decision tree model as a good model with accuracy of 85&#x0025;, F1score of 0.815, Recall value of 0.85 and precision of 0.87
Classifying multi-level product categories using dynamic masking and transformer models

In an online shopping platform, a detailed categorization of the products greatly enhances user navigation. Online retailers also benefit from well-defined product categories as various sales and marketing operations such as special discounts and promotions can be easily done over a set of product categories. Furthermore, incorrect and subjective product categories suggested by an operator can be more easily identified thanks to an automated classification system. In this study, we investigate the task of classifying grocery product categories using product titles. We employ a wide variety of text classification models for this task, including traditional machine learning and deep learning models as well as state-of-the-art transformer models. In our analysis, we specifically focus on the generalizability of the trained classification models to the products of other online retailers, the dynamic masking of infeasible subcategories for pretrained language models, and the impact of incorporating different word embeddings. We observe that the deep learning models and the transformers significantly outperform traditional text classification methods such as XGBoost and SVM, and achieve excellent prediction performance exceeding 90% accuracy and F1-score values. We lastly explore the failure cases where a product is misclassified, and make recommendations for future studies to improve the prediction performance.
Geometric invariant features for the detection and analysis of vehicle

The intelligent traffic management system (ITS) is one of the active research areas. Vehicle detection is a major role in traffic analysis. In the paper, analysis of detecting vehicles is proposed based on the features posed by the vehicle. The foreground pixels from image are extracted by histogram based foreground segmentation. After segmenting, Hu-Moments and Eigen values features are extracted and normalized. The classifiers are trained with the extracted Hu-Moments and Eigen values. The experiments are conducted on different benchmark datasets, and results are analysed considering the overall classification accuracy. Results of the algorithm are satisfactory and acceptable in real time.
Apache Spark based analysis on word count application in Big Data

The rise in the volume of data, as well as the type of data and the rate at which data is produced, has led to the development of novel processing methods capable of working with such massive amounts of data, termed as Big Data. The most difficult aspects of managing Big Data include its collection and storage, as well as search, sharing, analysis, and visualizations. In this paper explain the characteristics, processing, application of bigdata and its challenges. Also explained how big data support Internet of Things and Cloud computing. By combining big data, IOT, Cloud provides the new architecture.
Feature selection for multi-label text data: An ensemble approach using geometric mean aggregation

Text datasets have many terms, which decrease the classification accuracy. According to the high-dimensional text data, there are more challenges for these methods. Each classification method has strengths and weaknesses in its feature selection function. Therefore, ensembling should be used for better classifications and exploitation of strengths. In this paper, for the first time, we have presented an ensemble multi-label (ML) feature selection method for the text datasets using the Geometric-Mean (GM) aggregation approach. For this purpose, we have used four multi-label feature selection (MLFS) algorithms with different structures to achieve a good result. Then, the performance and results obtained by the GM method are compared with the four algorithms and based on the six classification criteria on three ML datasets with text domains. According to the obtained outputs, it is possible to realize the ability of the GMA (proposed method) method in using strengths and ignoring weaknesses in the path of feature selection, and be more accurate. Finally, according to experiments and obtained results, superiority of the GMA over other methods can be well seen.
Correlation filters based on spatial-temporal Gaussion scale mixture modelling for visual tracking

Correlation Filters (CFs) have shown outstanding performance in tracking, but are subject to unwanted boundary effects. Spatial regularization (SR) is widely used as an efficient method to alleviate the boundary effects. However, spatial regularization is almost handcrafted and fixed during tracking process, which cannot handle the diversity of objects and the complexity of motion. Furthermore, the rich spatio-temporal correlations among multiple targets of interest cannot be fully exploited. Herein, we propose a spatio-temporal Gaussian scale mixture model (ST-GSM) for correlation-filter-based visual tracking. In our Gaussian scale mixture (GSM) model, each correlation filter coefficient is decomposed into the product of a positive scalar multiplier with sparsity and a Gaussian random variable. The reliable components of the Gaussian random variable can be adaptively selected based on the positive multipliers, aiming at alleviating the notorious boundary effects. To exploit the temporal consistency between adjacent frames, nonzero-means GSM models are developed to characterize the temporal correlations. Specifically, the filter coefficient obtained in the previous frame is used as the mean prior for the current frame. The spatial correlations among filter coefficients have been considered in the structured GSM model, thereby further improving the tracking performance. Experimental results show that the proposed model can significantly improve the performance of CF-based trackers.
The Covid 19 Pandemic and its Effects on Agile Software Development

Companies worldwide have enabled their employees to work remotely as a consequence of the Covid 19 pandemic. Software development is a human-centered discipline and thrives on teamwork. Agile methods are focusing on several social aspects of software development. Software development teams in Germany were mainly co-located before the pandemic. This paper aims to validate the findings of existing studies by expanding on an existing multiple-case study. Therefore, we collected data by conducting semi-structured interviews, observing agile practices, and viewing project documents in three cases. Based on the results, we can confirm the following findings: 1) The teams rapidly adapted the agile practices and roles, 2) communication is more objective within the teams, 3) decreased social exchange between team members, 4) the expectation of a combined approach of remote and onsite work after the pandemic, 5) stable or increased (perceived) performance and 6) stable or increased well-being of team members.
Facial Action Units Detection to Identify Interest Emotion: An Application of Deep Learning

For human beings, facial expression is one of the most powerful and natural ways to communicate their emotions and intentions. A human being is able to recognize facial expressions effortlessly, but for a machine, this task is very difficult. Today, facial expression recognition is proving to be one of the most relevant applications in many fields such as human–computer interaction, medicine, security, education, etc. Facial Action Coding System (FACS) is a method that describes face movements. This later became a main description tool used in the studies concerned with facial expression. In this paper, we propose an action units recognition system which reflects interest emotion using deep learning, particularly the Convolutional Neural Network (CNN) architecture “MobileNetV2”. Our choice of this system is motivated by its success in image classification. Our system allows detecting action units which define interest emotion from an input image. In other words, our classifier differentiates interest facial movements from other affective states. This identification is very useful in several areas, particularly in e-learning: For example, knowing whether a distance learner is interested or not in the course certainly influences the quality of learning and reduces the dropout rate. Our proposed approach presented a very satisfactory recognition rate despite the absence of a large database.
Synchronous and Asynchronous Manipulation Switching of Multiple Robotic Embodiment Using EMG and Eye Gaze

Through the use of multiple avatars and robots, the construction of an alter ego whose senses and movements are synchronized with those of the operator has been explored. There are two possible states of operation when manipulating those bodies as synchronous operation: multiple bodies are synchronized simultaneously, and asynchronous operation: a specific body is selectively operated. We propose a system that allows intuitive switching between the active robot and the operation state by Eye gaze and EMG. A two-stage pick-and-place task was performed with the proposed system, and task performance and subjective evaluation of embodiment were analyzed. To consider the coordinates of the tracker in the asynchronous operation of the robot arm, we performed a similar study for a coordinate condition: the absolute coordinate condition, where the coordinates of the tracker are stored in space, and the relative coordinate condition, where only the motion of the tracker is added.