Reviewerly/exHarmony huggingface dataset:
How Watching Live Streams Creates Connection and Enhances Enjoyment

Peer-to-peer live streaming is a rapidly growing phenomenon: in 2020, users spent over 27 billion hours viewing live streams online, nearly doubling the hours spent in 2019. We examine the viewing experiences of over 2,700 consumers in both naturalistic and carefully controlled environments and find consistent positive effects of viewing online live streams (versus identical pre-recorded videos) on feelings of social connection. We develop a comprehensive conceptual model of the routes by which viewing live streams may facilitate feelings of connection. We find evidence that liveness itself enhances feelings of connection to the broadcaster, and that this benefit of viewing live streams is driven by an elevated sense of presence, or "being there," in events that are viewed in real-time. We also find that increased salience of other viewers enhances feelings of connection to others who are watching the same events at the same time. The social connection afforded by viewing live streams enhances consumers' enjoyment and increases their propensity to continue watching similar content. In a world where people increasingly turn to technology to satisfy their social needs, live streams present a novel opportunity for consumers to feel connected and for content creators to facilitate feelings of connection.
Bayesian Gate Mechanism for Multi-task Scale Learning

Multi-task learning demonstrates excellent performance in multiple domains of deep learning. Nevertheless, how to obtain knowledge beneficial to each task from the shared representation is a long-term challenge of multi-task deep learning. Simultaneously, insufficient information communication is a current issue in multi-task scale deep networks. In this paper, we propose a novel multi-task communication network on pixel-level prediction. Specifically, to realize the joint learning of high-scale and low-scale features in network decoding, we design a multi-scale multi-task knowledge communication module in the shared network decoder. A multi-task learning unit pool is utilized for communicating the constructed shared features among multiple tasks. Furthermore, we design the Bayesian knowledge gating mechanism to filter the information flow. By introducing the Bayesian deep learning method, uncertain shared features are gated in different communication stages of the shared network. Our experiments show that the proposed model can effectively improve the prediction performance in multiple vision tasks.
Linear Arrangement of Spherical Markers for Absolute Position Tracking of a Passive Stylus

We present a simple and novel method for the absolute position tracking of a passive stylus tip -or any hand-held tool. We use a linear arrangement of two or more spherical markers on the back of the stylus, and a stereo camera to track the positions of the two markers to find the stylus tip location by extending the line connecting the markers. Our compact design allows the stylus to be simple, light-weighted, and easy to hold. We show that our method can provide accurate position tracking across a range of distances and orientations.
The Nonlinear Connectedness Among Cryptocurrencies Using Markov-Switching VAR Model

This paper aims to examine the regime-dependent dynamic relations among the leading cryptocurrencies using the Markov Switching Vector Autoregressive model. Our findings suggest evidence in favor of regime-switching properties in the cryptocurrency market. Furthermore, these findings provide strong evidence in favor of nonlinear connectedness among cryptocurrencies; and, thus, it is necessary to employ the MS-VAR model to determine the dynamic nonlinear connectedness between cryptocurrencies. Moreover, we find that the degree of connectedness and volatility spillover is different in both regimes and, based on the transition probability matrix, the low volatility regime is more lengthy than the high volatility regime.
Equations as a Tool for Hypothesis Formulation in Physics

Propositional logic is a subfield of logic that deals with propositions and their connection via so-called junctors. The starting points are unstructured elementary statements (atoms), which are assigned a truth value (“true” or “false”). The truth value of a compound statement can then be determined from the truth values of its partial statements without additional information.
MultiYOLO: Learning New YOLO Categories Without Full Retraining

An important problem that arises when trying to add new classes to a pretrained object detection system is that full retraining is needed with an augmented dataset (where images of the new object category have been added) and an extended network architecture with additional outputs for the new class. We propose a solution to the above problem called multiYOLO, which is a new methodology developed and tested using the YOLO real-time object detector. In the proposed approach, the original YOLO is left intact, and a second YOLO is trained to accommodate the new category. A subtle issue is that it is possible for the original model to confuse the new category with some old ones. After training the second model, object detection is performed by taking into account the decisions of both models in an appropriate manner. Since in the multiYOLO approach only the second network is trained on few categories, the training procedure to accommodate the new class is significantly accelerated compared to full retraining of a single model with all classes.
Continuously Learning from User Feedback

Machine Learning has been evolving rapidly over the past years, with new algorithms and approaches being devised to solve the challenges that the new properties of data pose. Specifically, algorithms must now learn continuously and in real time, from very large and possibly distributed sets of data. In this paper we describe a learning system that tackles some of these novel challenges. It learns and adapts in real-time by continuously incorporating user feedback, in a fully autonomous way. Moreover, it allows for users to manage features (e.g. add, edit, remove), reflecting these changes on-the-fly in the Machine Learning pipeline. The paper describes some of the main functionalities of the system, which despite being of general-purpose, is being developed in the context of a project in the domain of financial fraud detection.
Developing soft skills of youth: regional experience

This article is devoted to the problem of developing soft skills in the youth of Chelyabinsk region (the Russian Federation). The young generation requires possessing moral and ethical qualities and values, the leading tool for the formation of which is education. The aim of this study is to analyze and generalize the soft skills of young people in the region and develop educational technologies at the university aimed at developing these skills. The structural components of soft skills include: emotional intelligence (the ability to recognize and manage the feelings and emotions, the ability to understand and influence the emotions and feelings of other people, empathy, rapport); communicative skills (interpersonal skills, teamwork skills, collaboration, organizational skills, negotiation skills and public speaking skills). The system of developing soft skills of youth implemented at the university, implies: 1) the inclusion of students into the activities of volunteer organizations and public associations; 2) implementation of social activities at the university; 3) modeling of extreme pedagogical situations in the educational process of the university.
The Research of Performance of Crossing Obstacles for the Tracked Emergency Disposal Robot

With the development of artificial intelligence technology, the application field of robot becomes more and more extensive. Aiming at the emergency disposal requirements of the complex environments in the field and buildings, this paper calculates and analyses the performance of crossing obstacles of the tracked robot, meanwhile, the theoretical model of climbing and obstacle surmounting is established. The height and attitude of the robot over obstacles are calculated by theoretical analysis method. The slope angle that the robot can climb has a close relationship with the friction coefficient between the robot’s main track and the ground. It can be found that the height of a robot crossing an obstacle is related to its own structure. The nonlinear relationship between the height over obstacles and the tilt angle and swing arm angle of the robot is analyzed, which provides a theoretical basis for the design of the tracked robot in the complex environment.
Improving Binary Semantic Scene Segmentation for Robotics Applications

Robotics applications are accompanied by particular computational restrictions, i.e., operation at sufficient speed, on embedded low power GPUs, and also for high-resolution input. Semantic scene segmentation performs an important role in a broad spectrum of robotics applications, e.g., autonomous driving. In this paper, we focus on binary segmentation problems, considering the specific requirements of the robotics applications. To this aim, we utilize the BiseNet model, which achieves significant performance considering the speed-segmentation accuracy trade-off. The target of this work is two-fold. Firstly, we propose a lightweight version of BiseNet model, providing significant speed improvements. Secondly, we explore different losses for enhancing the segmentation accuracy of the proposed lightweight version of BiseNet on binary segmentation problems. The experiments conducted on various high and low power GPUs, utilizing two binary segmentation datasets validated the effectiveness of the proposed method.
One Step Beyond: Keyword Extraction in German Utilising Surprisal from Topic Contexts

This paper describes a study on keyword extraction in German with a model that utilises Shannon information as a lexical feature. Lexical information content was derived from large, extra-sentential semantic contexts of words in the framework of the novel Topic Context Model. We observed that lexical information content increased the performance of a Recurrent Neural Network in keyword extraction, outperforming TexTRank and other two models, i.e., Named Entity Recognition and Latent Dirichlet Allocation used comparatively in this study.
Targeting IMPACT: A New Psychological Model of User Experience

User experience (UX) models present, in broad terms, the domains of influence affecting users’ experience of a given technology. Since the number of variables that could affect a user’s experience are manifold and context specific, UX models tend to specify broad categories of influence. Prominent models highlight technology form, content, context, business goals and individual differences as key domains which all affect how a user will experience a technology product or service. These high-level conceptualisations often miss identification of key psychological variables affecting UX. Identifying psychological factors could support user-centred design by designing for user goals, critiquing why different design choices are effective and for guiding evaluation choices. To address this gap, this paper presents a new psychological model of UX, The IMPACT model, meaning Interesting, Meaningful, Personalised, Affective, Collective and Transportive dimensions of experience. The model was developed by reconceptualising an evaluation framework of technology impact and undertaking a literature review. In this paper, we apply the model to a user journey of gameplay, demonstrating the model’s use for assessing which strategies build interest, enjoyment and engagement throughout the user journey. Whilst it is not a fully comprehensive psychological model of UX, we suggest hypotheses to empirically test the model for different user types. We offer these next steps as a framework to support gaining new knowledge about gamers and gaming.
Adaptive Approach of Credit Card Fraud Detection Using Machine Learning Algorithms

With the evolution of the electronic payment systems, fraudsters always find an illegal way to steal people's money. Credit card fraud has become a critical issue for businesses and individuals as all online transactions can be easily done by just entering credit card information’s. For this reason, fraud detection is of utmost importance for all financial institutions. Different techniques and approaches are used to secure online transactions, as well as rule-based fraud detection methods, EMV technology, 3D-secure protocol. However, fraud rates still increasing in card not present transactions. Researchers started using different machine learning methods to detect and prevent frauds in online transactions as well as Logistic Regression (LR), Naïve bayes (NB), Random Forest (RF) and Multilayer perceptron (MLP) algorithms. In this paper, the aim objective is to elaborate a comparative study of credit card fraud detection methods. For this objective, we used Google-Colab as an experimentation platform and studied the performance of each machine learning techniques in term of accuracy, Forecast Error and time of prediction using the European cardholder’s dataset that we have balanced by applying SMOTE method. Finally, basing in this comparison study, we proposed and discussed an adaptive approach for credit card detection system.
Audio Driven Artificial Video Face Synthesis Using GAN and Machine Learning Approaches

Now-a-days a large number of people share their opinion in either audio or video format through internet. Some of them are real videos and some are fake. So, we need to find out the differences between these two with the help of digital forensics. In this paper, the authors will discuss the different types of artificial face synthesis methods and after that authors will analyze the deepfake videos using machine learning methods. In artificial face synthesis, based on an incoming audio stream in any language, a face image or source video of a single person is animated with full lip synchronization and synthesized expression. For full lip synchronization, GAN can also be used to train the generative models.
Towards Security Awareness of Mobile Applications using Semantic-based Sentiment Analysis

With the rapid increase of smartphones and the growing interest in their applications, e.g., Google Play Apps, it becomes necessary to analyze users' reviews whether they are expressed as ratings or comments.This is because recent studies reported that users' reviews could provide us with useful clues and valuable features that can help in understanding the broad opinion about some applications in term of security awareness.Several techniques have been developed for this crucial task and significant progress have been achieved such as Semantic and Sentiment Analysis, Topic Modelling, and Clustering.The majority of the existing methods are mainly based on representing reviews' words in a Bag-Of-Words vector space with Stringmatched approaches without considering the common polysemy and synonymy problems of words.This is true due to the fact that users who make use of these applications are often from a diverse background and thus, different vocabulary.This paper proposes a new approach to classifying security opinions about applications from users' reviews while considering special features of synonymous and polysemous words.To achieve this task, the proposed model makes use of word embedding, topic modelling, Bi-LSTM, and n-grams approach.For the proposed model, a new dataset is built that contains reviews about 18 popular applications.The application's selection was primarily governed by making the dataset diverse in its domain.The experiment results showed that the proposed ensemble model which combines the prediction of the extracted features, which in turn captures synonymy, polysemy, and dependency of wordsis significantly useful, and it achieves better results with an accuracy approaching 90% compared to the use of each technique separately.The model could contribute in preventing mobile users from unsafe applications.
HTML

The Web was originally created to serve HTML documents. Now it is used to serve all sorts of documents as well as data of different kinds. Nevertheless, HTML is still the main document type delivered over the Web.
Integration of Immersive Learning Simulation and Augmented Reality for Healthcare Training: Towards a Transmedia Solution to Address the Opioid Crisis

Immersive Learning Simulation (ILS) and Augmented Reality (AR) technologies hold tremendous potential for transforming training and education for healthcare professionals and patients. While both ILS and AR have demonstrated an unprecedented level of success in a variety of healthcare training and education applications, particularly in the last few years, combining the two approaches remains a relatively uncharted territory. This is largely due to the relative lack of evidence-based methods, strategies, and practical guidance for the design and implementation of mixed reality and transmedia learning environments. This paper presentation will discuss new integration strategies for bringing together the ILS and AR technologies to provide a mixed transmedia learning experience where the virtual and physical components seamlessly co-exist to create a new level of sophistication for education, training, and human performance support. The ILS/AR integration strategies will be presented within the context of an ongoing federally-sponsored research effort that aims to design, develop, and evaluate an innovative learning solution to help address the national opioid crisis with an ultimate goal of improving outcomes for people at risk for opioid misuse and overdose. Focusing on the training needs of healthcare professionals, first responders, and ordinary people (family members, caregivers, etc.) who are on the frontlines of the ongoing opioid crisis, the presented integrated ILS/AR solution illustrates how this transmedia approach can be used for training diverse learner audiences. Based on this research effort, the presenters will share practical insights, lessons learned, and best practices for the design of comprehensive integrated ILS/AR-based solutions that can be easily applied to create effective and engaging learning experiences for a broad spectrum of training, education, and human performance support needs.
A Framework for Segmentation of Characters and Words from In-Air Handwritten Assamese Text

In-air handwriting is a popular human–computer interaction platform that offers users with a natural and intriguing means of communication through air-written hand or finger gestures. The complexity of in-air handwriting stems from the fact that it is executed in a single stroke, which leads to irrelevant connecting movements called ligatures between adjoining character strokes or consecutive characters and words in a sentence. So, detection of relevant character or word components from air-written text is an intricate process that requires special consideration. In this paper, we present a multistage heuristic-based text segmentation framework that explores certain statistical and geometrical attributes to extract the significant character and word components from in-air handwritten text (IAHT) lines. Experimental evaluation on an IAHT data set consisting of Assamese sentences indicates that our proposed methodology offers a success rate of around 95% establishing its capability in spotting characters and words from different variations of continuous text lines.
Commonality and Diversity: Both Need Explanation

Five characteristics have been discussed in previous chapters that are common amongst many deposits, but other geological features show diversity within and between deposits. Commonality and diversity are both informative about the way deposits form and need to be addressed in any viable genetic model.
Classification of Paralinguistic Information in Children's Speech Using a Database with Low Inter-Annotator Agreement and Strongly Unbalanced Classes

Paralinguistics comprises elements that accompany verbal communication, qualify it, expand it, and send nonlinguistic signals that carry additional information. Affective aspects are a clear example of information transmitted paralinguistically. How affective aspects, such as emotions, are perceived by listeners can vary, which implies that their evaluation is subjective. This element of subjectivity implies an important challenge in the generation of quality resources for training recognition models of paralinguistic aspects. Another problem in generating related resources is achieving a diverse and sufficient representation of considered categories. This study describes a dataset that is oriented to the automatic classification of paralinguistic aspects in Spanish-speaking Mexican children of elementary school age. This dataset presents a strong imbalance in all labeled aspects and a low agreement between labelers. A method based on the combination of data balancing algorithms and semisupervised learning is proposed to improve the classification performance of the trained models
Long Distance Measurement-Device-Independent Multiparty Quantum Key Agreement

Key agreement is a basic cryptographic primitive, which plays an important role in private communications. In this work, we propose a measurementdevice-independent multiparty quantum key agreement protocol and then analyze its security, which shows that it can guarantee both the security and fairness for key agreement. Furthermore, this protocol is based on Greenberger-Horne-Zeilinger states while only Bell state measurements and single-particle state measurements are required, and therefore it is experimentally feasible with current technology. More importantly, this protocol can double the transmission distance by introducing untrusted relays in the middle compared with prior work.
An Efficient Lucas Wavelet Galerkin Method for Solving Time-Delay Optimal Control Problems

Here, we present a numerical scheme to solve optimal control problems with time-varying delay system. This method is based on Lucas wavelets and Galerkin method. Operational matrices of integration and delay for Lucas wavelets are proposed. Then, Galerkin method is used to solve the mentioned problems . Numerical results are included to demonstrate the efficiency of the present technique.
Deep Learning in Vehicle Detection Using ResUNet-a Architecture

Vehicle detection is still a challenge in object detection. Although there are many related research achievements, there is still a room for improvement. In this context, this paper presents a method that utilizes the ResUNet-a architecture – that is characterized by its high accuracy - to extract features for improved vehicle detection performance. Edge detection is used on these features to reduce the number of calculations. The removal of shadows by combining color and contour features - for increased detection accuracy - is one of the advantages of the proposed method and it is a critical step in improving vehicle detection. The obtained results show that the proposed method can detect vehicles with an accuracy of 92.3%. This - in addition to the obtained F-measure and η values of 0.9264 and 0.8854, respectively - clearly state that the proposed method - which is based on deep learning and edge detection - creates a reasonable balance between speed and accuracy.