Reviewerly/exHarmony huggingface dataset:
Empathy building ‘in the wild’ - a reflection on an avoidance of the emotional engagement

In 2019 we were asked to prepare a VR stand providing an opportunity to experience applications with social impact during an open event. Every visitor of our stand could choose among several applications (therapeutic, relaxing vs educational vs perspective-taking &#x0026; empathy-building) and after testing was asked (optionally) to fill in the shortened immersion questionnaire <tex>$(\mathrm{N}=44, \text{age}\ 8\ \text{to}\ 62,\ \text{mean}: 31)$</tex>. We have noticed that although people declared they appreciate this kind of experiences, they avoided them (probably because of the potential emotional labor). In this article we share insights and conclusions on differences in conducting research on VR social applications in the controlled laboratory conditions, and in the field testing. Taking into account advantages and disadvantages of this approach as well as methodological challenges, we also advocate for taking this kind of research as a supplementary for typical laboratory research.
Interactive Mixed Reality Rendering on Holographic Pyramid

Currently, ray tracing and image-based lighting (IBL) have shortcomings when rendering the metallic virtual object displayed in the holographic pyramid in mixed reality. Ray tracing can hardly achieve the interactive frame rates, and IBL cannot accurately render the reflection result of the foreground near the virtual object. In this paper, we propose a mixed reality rendering method to render glossy and specular reflection effects on metallic virtual objects displayed in the holographic pyramid based on the surrounding real environment at interactive frame rates. First, we acquire the real environment data with four RGBD cameras and a panoramic camera; then, we introduce a foreground point cloud generation method to extract a temporally stable foreground point cloud from RGBD videos captured in real time; after this, we propose an efficient ray tracing method to render the dynamic glossy and specular reflections on the virtual objects that are displayed in the holographic pyramid. We test our method on several real and synthetic scenes. Compared with IBL and screen-space ray tracing, our method can generate the rendering results closer to the ground truth at the same time cost. Compared with Monte Carlo path tracing, our method is 2.5-4.5&#x00D7; faster in generating rendering results of the comparable quality.
Design and Implementation of WeChat Public Platform Service for Career Counseling in Colleges and Universities in Development Mode

In order to improve the quality and efficiency of career counseling in colleges and universities, the hot news of career counseling, career counseling resource library and other query functions are embedded into WeChat application based on the development mode of WeChat public platform and using the message receiving and reply interface provided by it, so that students can conveniently use platform services and resources in the social network environment through instruction interaction by configuring service environment preparation, basic language of program development and design, database and front-back linkage design of WeChat public platform. This application can enrich the forms of career education, improve efficiency and strengthen pertinence.
LIPSHOK: LIARA Portable Smart Home Kit

Several smart home architecture implementations have been proposed in the last decade. These architectures are mostly deployed in laboratories or inside real habitations built for research purposes to enable the use of ambient intelligence using a wide variety of sensors, actuators and machine learning algorithms. However, the major issues for most related smart home architectures are their price, proprietary hardware requirements and the need for highly specialized personnel to deploy such systems. To tackle these challenges, lighter forms of smart home architectures known as smart homes in a box (SHiB) have been proposed. While SHiB remain an encouraging first step towards lightweight yet affordable solutions, they still suffer from few drawbacks. Indeed, some of these kits lack hardware support for some technologies, and others do not include enough sensors and actuators to cover most smart homes' requirements. Thus, this paper introduces the LIARA Portable Smart Home Kit (LIPSHOK). It has been designed to provide an affordable SHiB solution that anyone is able to install in an existing home. Moreover, LIPSHOK is a generic kit that includes a total of four specialized sensor modules that were introduced independently, as our laboratory has been working on their development over the last few years. This paper first provides a summary of each of these modules and their respective benefits within a smart home context. Then, it mainly focus on the introduction of the LIPSHOK architecture that provides a framework to unify the use of the proposed sensors thanks to a common modular infrastructure capable of managing heterogeneous technologies. Finally, we compare our work to the existing SHiB kit solutions and outline that it offers a more affordable, extensible and scalable solution whose resources are distributed under an open-source license.
Art and aesthetic education in the Reggio Emilia Approach

in the educational experience of Reggio Emilia, art education is held to be a central element in children's knowledge building processes and the aesthetic experience is considered an activator of learning process. In the Reggio educational philosophy, the space dedicated to aesthetic education and the exploration of artistic languages and techniques is called the "Atelier". The Atelier is a place closely connected to the other school environments, aimed at fostering children's agency by allowing them to explore visual and expressive languages in close synergy with verbal, body and logical ones. This paper addresses the main characteristics and values of the Reggio Emilia educational philosophy that contribute to fostering children's agency, by focusing on the aspects that are most connected to the aesthetic experience. From such a perspective, the visual and artistic languages are considered to be a means of inquiry regarding the world and as media that support children in building bridges and relationships between cognitive and expressive processes . Finally, the paper problematises such characteristics by drawing on recent developments and interpretations of Malafouris' material engagement theory Montani, P. 2020. "The Imagination and Its Technological Destiny." Open Philosophy 3 (1): 187–201.).
Accelerating DIN Model for Online CTR Prediction with Data Compression

As the key task of recommender systems, the click-through rate(CTR) prediction is to predict the probability of users clicking on a specific product. It is often costly due to the big data sets. In this paper, we apply some data compression technology to accelerate CTR prediction. By casting the data format to some more memory-efficient format, we can significantly improve a popular recommender method online.
Double information preserving canonical correlation analysis

The methods based on canonical correlation analysis (CCA-based methods) are typical and effective methods for unsupervised dimensionality reduction of multi-view data. However, the traditional CCA-based methods ignore the dissimilarity information while considering the similarity information of samples, which will make the heterogeneous samples in the subspace cannot be well separated. In this paper, we propose a novel unsupervised multi-view dimensionality reduction method: Double Information Preserving Canonical Correlation Analysis (DIPCCA). DIPCCA aims at finding two projection matrices by integrating two cross double weight graphs with CCA to explore the similarity and dissimilarity information of data in cross views. Furthermore, on the basis of consistency and complementarity, DIPCCA uses the similarity information to maintain the local structure, and the dissimilarity information to disperse embedded samples of distinct clusters, so as to extract more discriminative features. Moreover, CCA and a new locality-preserving CCA are two special cases of DIPCCA when parameters take special values. In order to better extract the features of nonlinear data and more than two views, DIPCCA is extended to Double Information Preserving Kernel Canonical Correlation Analysis and Double Information Preserving Multiple Canonical Correlation Analysis, respectively. Experiments on an artificial dataset and three real multi-view datasets show our proposed methods have better performance than the traditional CCA-based methods.
Application and Optimization of Algorithms for Pressure Wave Evaluation Based on Measurement Data

Leakages can occur in a district heating network, resulting in high economical damage. The propagating pressure wave resulting from large, spontaneous leakages reaches sensors at different locations in the network. This leads to pressure drops registered at each sensor at a different point in time. The time differences help to localize the leakage. Different algorithms are presented and applied in this paper to estimate the pressure drop time points based on non-uniform, time-discrete sensor signals. Five of the nine algorithms are self-developed with, e.g., parts of linear regression, whereas the other four algorithms have already been described in the literature, such as change-point detection. In this paper, several recorded events were investigated, and the algorithms were applied to real measurement data. After detection, leakage localization was performed to determine the affected exclusion area. A performance criterion was used as a measure to compare the algorithms. For practical application, the best-performing algorithm was identified. Furthermore, the events were classified according to how well they could be evaluated.
Opinion Mining System For Domain Specific Mobile Application Development

Opinion Mining and Sentiment Analysis are fields of Computer Science that has applications in data-driven sub-fields like Information Retrieval and Processing, Business Analytics, and Review Mining. Opinion Mining, a field of Information Retrieval and Text Processing has applications in domains like financial analytics, political analytics, business analytics, and psychological analytics. In an ongoing research-oriented project in the field of Opinion Mining, the authors of this paper are developing an Opinion Mining System for a Mobile Application that facilitates locality based online procurement of medicines. Initial research findings in the field of Opinion Mining and its application in Mobile App development is presented in this paper. An Opinion Mining System for a Mobile-Application that facilitates sentiment-based procurement of medicine is presented in this paper.
A Sport Monitoring System Based on the Optimized Adaptive Fuzzy PID Control Algorithm in OneNet Internet of Things and Cloud Platform

Aiming at some problems existing in the existing sports monitoring system, based on the joint action of OneNet Internet of Things (IoT) and cloud platform, an optimized adaptive fuzzy PID control algorithm is adopted to monitor and analyze sports. Finally, the accuracy of the optimization model is verified through the comparison of different models, and the algorithm is used to predict and analyze sports. The research shows that (1) as the index calculation of cloud platform shows, with the increase of iteration time, the change curve of relevant indexes can be divided into four different stages, namely, rapid fluctuation stage, slow decline stage, slow fluctuation stage, and rapid decline stage. (2) The conventional calculation method (CPID) cannot well describe the change rule of the test data in the early stage of settlement. The fuzzy adaptive calculation method (NPID) also exposes some errors in the fitting and description of the test curve in the calculation process, while the improved adaptive calculation method (GPID) can describe the change characteristics and rules of the test curve well for different stages. (3) Compared with the original model, the optimization model can better describe the first and second stages of index change, indicating the accuracy of the optimization model. And the algorithm can be used to predict and analyze the changes of indicators and sports monitoring better, and the analysis results can provide relevant guidance for sports monitoring. This optimization scheme provides basis and theoretical support for the application of OneNet IoT and cloud platform.
A New Approach to Estimate Concentration Levels with Filtered Neural Nets for Online Learning

The COVID-19 pandemic heavily influenced human life by constricting human social activity. Following the spread of the pandemic, humans did not have a choice but to change their lifestyles. There has been much change in the field of education, which has led to schools hosting online classes as an alternative to face-to-face classes. However, the concentration level is lowered in the online learning class, and the student’s learning rate decreases. We devise a framework for recognizing and estimating students’ concentration levels to help lecturers. Previous studies have a limitation in that they classified attention levels using only discrete states. Due to the partial information from discrete states, the concentration levels could not be recognized well. This research aims to estimate more subtle levels as specified states by using a minimum amount of body movement data. The deep neural network is used to continuously recognize the human concentration model, and the concentration levels can be predicted and estimated by the Kalman filter. Using our framework, we successfully extracted the concentration levels, which can aid lecturers and can be expanded to other areas. To implement the framework, we recruited participants to take online classes. Data were collected and preprocessed using pose points, and an accuracy of 90.62 <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mo>%</mo> </math> was calculated by predicting the concentration level using the framework. Furthermore, the concentration level was approximated based on the Kalman filter. We found that webcams can be used to quantitatively measure student concentration when conducting online classes. Our framework is a great help for instructors to measure concentration levels, which can increase the learning efficiency. As a future work of this study, if emotion data and skin thermal data are comprehensively considered, a student’s concentration level can be measured more precisely.
A Framework to Assist in Didactic Planning at Undergraduate Level

In the teaching-learning process under the competency-based educational model, the instructor is a facilitator and seeks to generate a flexible and adaptable environment for student learning. One of the first tasks of the facilitator is the structuring of didactic planning. Didactic planning includes strategies for teaching and learning, evidence gathering, and choice of evaluation instruments. In this paper, we propose a framework based on natural language processing techniques with the support of an ontology grounded in the experience of instructors and university level course plans in the information systems area. We employ Bloom’s taxonomy in the ontology design, producing an ascending structure for didactic planning, which allows the student to learn gradually. The developed framework can analyze the key elements that a didactic plan must contain and identify inter-related areas. Evaluation results with Cohen’s kappa coefficient between expert judgement and our framework show that is possible to assist instructors in structuring their didactic planning. Out of the nine processes analyzed with the framework, an almost perfect kappa level was achieved in five processes, a substantial level in three processes, and a moderate level for one process.
Evaluation the Efficienty of Forecasting Sports Events Using a Cascade of Artificial Neural Networks Based on FPGA

Well-known implementations of software systems for predicting sports events based on artificial neural networks do not provide sufficiently accurate results in individual sports. The main difficulty in this case is the fact that various types and formats of data act as source information: numeric, symbolic, interval. The authors propose an approach based on cascading of several types of neural networks. A specialized training sample is prepared for each module in accordance with the type of model and the selected cascade structure. The results of information processing on each layer serve as data sets for modules of subsequent tiers. Initially, the system was implemented using the MATLAB package. The experiments carried out confirm the effectiveness of cascading modules. To significantly increase the performance of the system, for example, when used in betting shops, a cascade of neural networks is implemented on the basis of Altera Cyclone III FPGA using Quartus CAD.
BCSimMP: A Blockchain Simulator with Mining Pool

A blockchain network is a collection of nodes that are interconnected with each other. One of the toughest tasks on these nodes is as a mining node. Playing an active role as a node miner in a blockchain network requires enormous resources, especially on blockchain networks that implement Proof-of-work consensus. Solo miners will get an enormous reward but require hardware resources with very high computing capabilities. Therefore, the pool miner approach or miners who work in groups will have more benefit because the group will jointly support the very high computational needs. However, the rewards that each miner node will obtain will be adjusted based on the contribution made. This paper presents a mining pool simulator for blockchain applications called BCSimMP (a Blockchain Simulator with Mining Pool). BCSimMP simulates using Proof-of-Work consensus and also simulates a group of miners (pool miners) working together to solve the existing puzzle to generate a block. The simulator is using the multiprocessing mechanism in Python. In addition, leading-zero is used as a puzzle to solve during block-time generation. The share reward for each participant node miner and a special reward for a miner that can solve the puzzle. Our application shows the mining process carried out by miners in groups compare to a solo miner. The user can initially set the number of node users, the number of tasks/transactions per block, the number of node miners, and the difficulty level for the target puzzle. The reward given for successfully generating blocks on this application is distributed proportionally to all miners in the group. With this BCSimMP, users can learn how the mining pool works and the mechanism of the rewards obtained before deciding to join as a miner in the blockchain network.
User Experience Evaluation of HTTP/3 in Real-World Deployment Scenarios

QUIC, a standard internet protocol, was developed to perform better in high loss and latency networks. HTTP/3 takes the real benefits of QUIC. In this paper, empirical study of Quality of Experience (QoE) under realistic network scenarios along with the impact of local connectivity, server locations and server software is explored between HTTP/3 and HTTP/2 protocols. To explore the QoE, this paper presents the use of throughput and First Contentful Paint (FCP) metric of the Lighthouse, an open source automated tool by Google to measure the user experience and capture performance of the network protocol. The findings indicate that HTTP/3 performs better than HTTP/2 in more challenging network conditions. The experiments also show that while throughput strongly correlates with FCP for HTTP/2, HTTP/3 throughput is not a good predictor of FCP.
Information security framework for cloud and virtualization security

In recent years, researchers have struggled to build a utility computing model. Cloud computing enables the delivery of information technology on demand. Many organizations do not take into account the security of virtual servers, which may result in the potential loss of data from internal and external threats. Cloud Computing Service Providers (CSPs) allocate resources through virtualization. The application of these techniques, however, goes with the handling of the final control data to a third party. Moreover recently, virtualization at all levels (system storage and networking) became important again as a means of improving security, reliability and availability; lowering costs; and providing greater flexibility. The information security framework aims to provide a secure cloud-based platform to the organization. The proposed framework will use existing traditional IT infrastructure and combine it with Cloud Computing to give secured, reliable and scalable IT services to the organization so that performance and availability of resources may increase. In this chapter, we also discussed the virtualization security requirements and provided an analysis of various security issues related to virtualization, existing solutions, and risk prevention techniques.
Circular Jaccard distance based multi-solution optimization for traveling salesman problems

Traveling salesman problem is a widely studied NP-hard problem in the field of combinatorial optimization. Many and various heuristics and approximation algorithms have been developed to address the problem. However, few studies were conducted on the multi-solution optimization for traveling salesman problem so far. In this article, we propose a circular Jaccard distance based multi-solution optimization (CJD-MSO) algorithm based on ant colony optimization to find multiple solutions for the traveling salesman problem. The CJD-MSO algorithm incorporates "distancing" niching technique with circular Jaccard distance metric which are both proposed in this paper for the first time. Experimental results verify that the proposed algorithm achieves good performance on both quality and diversity of the optimal solutions.
Bouncing Seat: An Immersive Virtual Locomotion Interface with LSTM Based Body Gesture Estimation

A pilot study of the bouncing seat system is presented. This system consists of an air cushion and gravicorder. The former ena-bles users to move easier and feel lively motion. The latter and its computing units detect users&#x0027; body sway and recognize its gestures. Four commands (walk, right turn, left turn, and jump) and associated intuitive body gestures were designed. The estimator based on the multi-timescale LSTM was trained using newly cre-ated body gesture dataset and achieved 88&#x0025; inference accuracy. Results suggest that our system has potential for providing a high-er sense of immersion and enjoyment than the joysticks.
Designing WindCage- Unpacking the Thinking and Prototyping a Propeller-Based Haptic Unit

The topic of Augmented Reality Enabling Superhuman Sports (ARES) highlights the necessity of applying tangible game objects to achieve rich haptic feedback and realistic interactions. In this paper, we propose WindCage, a tetrakaidecahedron shape propeller-based haptic unit, and its two usages to provide different ways to experience &#x201C;holding the wind.&#x201D; Users are required to execute physical movements and activities during the Superhuman Sports and Serious Game (SSSG) content. However, it is foreseeable that users&#x0027; physical and cognitive condition varies. Therefore, we iteratively developed the system according to the Inclusive Design methodology to enable the maximum number of users with different abilities to adopt it in various scenarios. We aim to utilize the results to contribute insights into designing game objects in the Superhuman Sports and Serious Game community.