Reviewerly/exHarmony huggingface dataset:
Raspberry Pi 4 and Python Based on Speed and Direction of DC Motor

Technology advances at an exponential rate, resulting in a significant improvement in our living standards. It is extremely close to us, which is why we must take advantage of it because these changes imply the ability to continue progressing in any area. This project shows how a Raspberry Pi minicomputer may be used to control a Brushless motor and propeller. Thus, a system has been developed for controlling punctual angular locations, given a specific menu, and for stabilizing said positions even in the presence of external disturbances, by adjusting the rotational speed of a Brushless motor through changes in the percentage of a signal pulse width PWM. The controller and data collecting was programmed using the Thonny development environment, which is based on the Python programming language. The results of the implementation suggest that using a low-cost minicomputer, as a control device is beneficial.
Does having a virtual body make a difference during cinematic vr experiences?

Cinematic VR, either as an in-game cutscene or a standalone 360° 3D movie, is a widespread narration tool for stories unfolding through Immersive Virtual Reality (IVR) experiences. Commonly, users experience cinematics from a 3rd or 1st person perspective, while firmly positioned in specific proxemics zones of the characters or events they are meant to take in, with no interactivity options provided. This study examines whether having a virtual body (VB) amplifies users' emotional response, specifically their anxiety levels, and how dependent those are to the interpersonal distances between the VB and non-player characters.
Simulation of Single-shot Qubit Readout of a 2-Qubit Superconducting System with Noise Analysis

It is desirable to reduce the readout time in a quantum computer so that more operations can be performed before decoherence occurs. In this paper, we propose and study a novel readout scheme of two qubits through simulations with a calibrated noise model. Two resonators with resonant frequencies only ~5MHz apart are designed and coupled to two superconducting qubits, respectively. A single shot readout is performed using one single frequency pulse to read the states of the two qubits. This has the potential to increase the number of qubits for a given bandwidth. The simulator used is written in Matlab which takes S <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">21</inf> calculated by Ansys HFSS as inputs. The know-how of performing repeatable and accurate simulations in HFSS is also discussed.
Collision Avoidance for Multiple Quadrotors Using Elastic Safety Clearance Based Model Predictive Control

When multiple quadrotors fly in a cluttered environment, collision-free flight must be assured. In this paper, we propose a novel elastic safety clearance based model predictive control (ESC-MPC) for multiple maneuverable quadrotors to avoid collisions in the presence of disturbance. This is accomplished through leveraging tube based model predictive control to maintain the quadrotor in a tube of trajectories. Exponential control barrier function (ECBF) is integrated to realize the elastic safety clearance mechanism which offers a dynamic safety margin in maneuverable flight. We validate the superiority of our approach with laboratory experiments.
Resource Consumption for Supporting Federated Learning Enabled Network Edge Intelligence

Federated learning (FL) has recently become one of the hottest focuses in network edge intelligence. In the FL framework, user equipments (UEs) train local machine learning (ML) models and transmit the trained models to an aggregator where a global model is formed and then sent back to UEs, such that FL can enable collaborative model training. In large-scale and dynamic edge networks, both local model training and transmission may not be always successful due to constrained power and computing resources at mobile devices, wireless channel impairments, bandwidth limitations, etc., which directly degrades FL performance in terms of model accuracy and/or training time. On the other hand, we need to quantify the benefits and cost of deploying edge intelligence when we plan to improve network performance by using artificial intelligence (AI) techniques which definitely incur certain cost. Therefore, it is imperative to deeply understand the relationship between the required multiple-dimensional resources and FL performance to facilitate FL enabled edge intelligence. In this paper, we construct an analytical model for investigating the relationship between the accuracy of ML model and consumed network resources in FL enabled edge networks. Based on the analytical model, we can explicitly quantify the trained model accuracy given spatial-temporal domain distribution, available user computing and communication resources. Numerical results validate the effectiveness of our theoretical modeling and analysis. Our analytical model in this paper provides some useful guidelines for appropriately promoting FL enabled edge network intelligence.
A Spatial-Temporal Information based Traffic-Flow Detection Method for video Surveillance

Traditional video-based traffic flow detection algorithms face challenges in extracting multiple vehicle features. They also cannot properly handle situations, such as vehicle occlusion. In this case, an improved vehicle detection algorithm was proposed that integrates time and space features, leading to better accuracy. Specifically, the proposed algorithm uses the normalized values of the cross-correlation between the foreground and background image blocks as the features of the detected vehicles passing by. The spatio-temporal accumulation is applied for processing and analyzing the extracted feature values. Moreover, the deep learning target detection algorithm is introduced to update the background, thereby eliminating missed detection caused by vehicle occlusion. The results show that the proposed method is less affected by the environment than the traditional background subtraction method. Therefore, better accuracy is achieved.
A Novel Container Workload Prediction Method Based on Automatic Classification and Attention-based LSTM

With the rapid development of cloud native technology, a growing number of applications run in containers. For cloud service providers, accurate container workload prediction can contribute to the improvement of resource utilization and service level agreement. However, containers that support different sorts of businesses have different preferences on resource requirements, and thus the prediction methods via one general model cannot work for all containers. In this paper, we propose a novel workload prediction method to adapt to different classes of containers and obtain more accurate prediction results. We first develop an automatic classifier to classify the containers according to their resource usage records. For each type of container, we train an attention mechanism-based Long Short-Term Memory(LSTM) model to predict the workload. Then a weight optimizer and the trained LSTM models are assembled into an ensemble predictor. The proposed weight optimizer can dynamically update weights according to the real-time container workload. Experiments on the Google cluster-data dataset show that the accuracy of our ensemble predictor is 7% higher than that of other two competitive methods.
Multi-Label Feature Selection with Conditional Mutual Information

Multi-label feature selection plays an irreplaceable role in pattern recognition and data mining. This process can improve the efficiency and accuracy of multi-label classification. However, traditional multi-label feature selection based on mutual information does not fully consider the effect of redundancy among labels. The deficiency may lead to repeated computing of mutual information and leave room to enhance the accuracy of multi-label feature selection. In order to deal with this challenge, this paper proposed a multi-label feature selection based on conditional mutual information among labels (CRMIL). Firstly, we analyze how to reduce the redundancy among features based on existing papers. Secondly, we propose a new approach to diminish the redundancy among labels. This method takes label sets as conditions to calculate the relevance between features and labels. This approach can weaken the impact of the redundancy among labels on feature selection results. Finally, we analyze this algorithm and balance the effects of relevance and redundancy on the evaluation function. For testing CRMIL, we compare CRMIL with the other seven multi-label feature selection algorithms on seven datasets and use four evaluation criteria to examine the results. Experimental results illustrate that CRMIL performs better than the other algorithms.
Virtual reality premedication in dental procedures

The term "virtual" originates from Latin virtus "potency" which gained through ages the meaning from "capable of producing a certain effect, though not actually or in fact" to computer sense of "not physically existing but made appear by software".The term reality derived from Latin res "matter" means "actually existing, having physical existence".Thus, virtual reality is a simulated experience that can be similar to or completely different from the real world.The term "premedication", that means preliminary medication, originates from Latin prae "before" or limen "threshold/limit" and medication "heal/cure", yet its definition is not as simple as just those two words.Traditionally, premedication used pharmaceutical drug with a sedative, analgesic, as well as antibiotic, antiemetic or antihistamine before treatment/surgery.It is commonly associated with local or general anesthesia.Non-drug premedication, supporting the classic one, is including a variety of distraction methodologies, from calming to focusing multimodal stimulations, visual, auditory, tactile or olfactory ones.
On the Effects of Conjunctions in the Solution Set of Multi-adjoint Fuzzy Relation Equations

A multi-adjoint fuzzy relation equation is defined from a sup-composition operator, which combines different conjunctions. The choice of such compositions has a direct impact on the resolution of the equation. This paper presents a first approach to the consequences of modifying the sup-composition associated with a multi-adjoint fuzzy relation equation in its solution set. Firstly, we show that greater conjunctions lead to lower greatest solutions. Then, two counterexamples are presented to highlight that, in general, an existing ordering in the conjunctions does not lead to comparable minimal solutions. Nevertheless, if the minimal solutions are comparable, we show that greater conjunctions lead to lower minimal solutions.
Early Detection of Sepsis Using LSTM and Reinforcement Learning

The delay in predicting sepsis is possibly life-threatening and to procure restrictions in medical resources. The prediction of sepsis in patients having no sepsis and prediction of sepsis in the earliest for patients having sepsis is the concept of predicting the sepsis with good quality acts as the key source of this work. The foremost objective is to create a platform in this digitized environment using the Machine Learning and Deep Learning model to detect the deterioration of sepsis in the patient before it's too late. This work aims to predict sepsis sequentially using Long Short Term Memory (LSTM) an RNN model that helps to learn the order dependencies and clinical detection of sepsis 6 h before using the clinical data. Further Reinforcement Learning is applied to label the data for accurate matching.
Analysis of the Development Trend of Artificial Intelligence Technology Application in the Field of Sports-Based on Patent Measurement

The patents related to the application of AI technology in sports from 1963 to 2021 included in the Derwent patent database were used as the data source. With the help of the ISS platform and CiteSpace visual analysis software, the patent data were analyzed in terms of annual application volume, geographical distribution, major patent owners, and hot technologies. The study found that: after 2015, the number of patent applications proliferated. The patents are mainly distributed in the United States, China, Japan, and South Korea, with the number of patents in China and South Korea growing the most. The primary patent owners are multinational giants such as Tencent, Microsoft, and IBM. The main technology areas of patents are distributed in database application technology, computer processing of video games, gaming, sports, and training equipment, and software products. The frontier hot technology areas are concentrated in developing emerging sports and leisure projects, sports e-learning equipment development, and deep learning algorithms.
Cloud Computing Infrastructure in Healthcare Industry

Cloud Computing has become a default infrastructure in the healthcare industry. In UK, there is a nationwide backbone of Electronic Health Record (EHR). Whether it is a General Practice (GP) or a Primary Care Unit or Hospital or Diagnostic Center, every establishment needs to access the EHR over the Cloud Infrastructure. Patient records are stored in the NHS Cloud over internet. In North America, especially in the US, while there is no nationwide EHR, most of the Independent Practice Association (IPA) uses software solutions available over cloud, utilizing a Software As A Service (SAAS) model. Large hospitals use private clouds to manage different units and systems such as Emergency, inpatient and outpatient services, Hospital ERP, and Revenue Cycle Management (RCM). Most of the Business Intelligence (BI) systems are available over the cloud infrastructure. In this chapter we will address different cloud system infrastructure and models.
Management of Uncertain Data in Event Graphs

We consider graphs to model uncertain facts as edges, linking involved entities, with weights reflecting uncertainty degree. Rules are used to create new edges from the existing ones, and methods to propagate uncertainty measures are defined using a suitable theoretical framework. We also consider new rules, mined from graphs containing uncertain information and answer sets obtained using such rules. We then use Argument Graphs and Possibility Networks to evaluate the conclusions that can be drawn from the facts, taking into account their uncertainty. Finally, information revision is discussed for cases when a new piece of information is added to the graph.
An Observation of Different Clustering Algorithms and Clustering Evaluation Criteria for a Feature Selection Based on Linear Discriminant Analysis

Linear discriminant analysis (LDA) is a very popular method for dimensionality reduction in machine learning. Yet, the LDA cannot be implemented directly on unsupervised data as it requires the presence of class labels to train the algorithm. Thus, a clustering algorithm is needed to predict the class labels before the LDA can be utilized. However, different clustering algorithms have different parameters that need to be specified. The objective of this paper is to investigate how the parameters behave with a measurement criterion for feature selection, that is, the total error reduction ratio (TERR). The k-means and the Gaussian mixture distribution were adopted as the clustering algorithms and each algorithm was tested on four datasets with four distinct clustering evaluation criteria: Calinski-Harabasz, Davies-Bouldin, Gap and Silhouette. Overall, the k-means outperforms the Gaussian mixture distribution in selecting smaller feature subsets. It was found that if a certain threshold value of the TERR is set and the k-means algorithm is applied, the Calinski-Harabasz, Davies-Bouldin, and Silhouette criteria yield the same number of selected features, less than the feature subset size given by the Gap criterion. When the Gaussian mixture distribution algorithm is adopted, none of the criteria can consistently select features with the least number. The higher the TERR threshold value is set, the more the feature subset size will be, regardless of the type of clustering algorithm and the clustering evaluation criterion are used. These results are essential for future work direction in designing a robust unsupervised feature selection based on LDA.
Defining Requirements for the Development of Useful and Usable Chatbots: An Analysis of Quality Attributes from Academy and Industry

Chatbots are conversational interfaces that enable human-like dialogue and can be designed in a textual chat format or a graphical interface with voice and embedding options. In the last year, there has been a significant growth in the emergence of chatbots in the market and this popularization has attracted the efforts of researchers to this area. Despite the existence of techniques to evaluate these tools, there is an urgent need to propose solutions that also support the chatbot design process. Currently, there is no knowledge of a specific list of requirements capable of supporting development teams in the process of designing these tools. In view of this, this directed study proposes a literature review aiming at deepening the knowledge about these tools and identifying important quality attributes in academic and industry sources. As a result, this directed study presents a list composed of 82 requirements related to Usefulness, Ease of Use and Presence to aid the design of these tools. These requirements presented in this study are useful to guide developers in the process of building quality chatbots, making this task less challenging and for researchers who aim to propose technologies that contribute to the development of better and better chatbots.
Digital Museum Visualization Digital System Based on Big Data Technology

With the development of various electronic and computer technologies, augmented reality With the establishment of (AR) technology, virtual reality technology (VR), artificial intelligence (AI), big data technology, and the concept of meta universe, it is imperative to deepen the cooperation between electronic information technology and various industries. Therefore, in the era of big data, how to manage and use big data scientifically and efficiently has become an issue of great concern to all walks of life. This article attempts to overcome some of the shortcomings of the current traditional museum display design in the modern and contemporary digital era, and proposes a digital museum visualization digital system that includes the concept of a museum display design model based on digital technology and big data technology. The system will fully integrate computer technology, such as big data platform, virtual reality technology, and multimedia technology, and will perceive, collect, process, analyze and store data in various links of the museum according to the characteristics of museum big data. Based on this, the system will also use advanced visualization technology to present the processing results and analysis results of museum big data to users in various forms, involving three parts: the client, the content management server, and the database. Through preliminary investigation and related experiments, the results show that the system can accelerate the transformation and upgrading of traditional museums, and to a certain extent can improve the economic and social benefits of museums.
Design Implications for a Gamified Recycling House

This paper encircles explorative design research in a multiple stakeholder triple helix project concerning circular economy and household recycling. Design ethnography was employed to find implications for outlining a gamification artifact that would facilitate recycling behaviors. We collected our data during 27 weeks by attending two field sites: Site A, project stakeholder meetings and a participatory design workshop, and Site B, semi-structured interviews in the household stakeholders' residences. Our thematic analysis of the sites' collected ethnographic record extrapolated two specific categories: Stakeholder requirements and Gamification ruleset, together enfolding five key-themes and various sub-themes that could be used to inform the design of a gamification artifact aimed at recycling. Also, based on our research, we propose two research propositions regarding storytelling and understanding for further gamification design researchers to investigate.
Dissemination of São Tomé and Príncipe Culture Through Virtual Reality: Comparative UX Study Between Potential Tourists from Portugal and Santomean Inhabitants

Context analysis is an essential condition in the development of a product or service. Understanding the context in which the system is used (characteristics of users, tasks, technical and physical environment), but also the cultural context (physical environment, historical, social, and cultural factors), are part of the activities of the Human-Centered Design process. Nowadays, due to the accessibility and improvement of technology, experience based on virtual reality, can be used to provide immersive experiences that absorbs and awakens the user's interest in cultural aspects in a playful but also didactic way. In this context, the present study sought to assess and compare the user experience through the development of an immersive virtual reality game experience. The goal of this study is to evaluate the user experience of participants from Portugal (13 potential tourists) and from São Tomé Island (18 local inhabitants) in a 3D VR environment prototype of a traditional hostel, with a beach landscape view. For interaction in VR, we measure participants through a presence questionnaire. For emotional responses, we used a scale of valence and arousal from Self-Assessment Manikin (SAM). The compared results reveal that the VR prototype provided a high level of immersion in both samples. However, the data from control factors reveal that the sample from São Tomé had more interaction problems. Regarding the emotional reactions (SAM), the compare results reveal that the user experience was emotionally positive with high values of pleasure for both samples. The data from arousal, indicate that the participants from Portugal were calm, and from São Tomé are very excited (Arousal average Portugal = 4.5; Arousal average São Tomé = 7.3) In conclusion, the 3D VR environment prototype was able to engage the participants with high levels of pleasure and provide interactive experiences. However, the control factor and arousal data reveal differences between the two samples that should be considered and analyzed to provide a better experience in future studies.
Detection and Mitigation Method of Bottlenecks in Data Stream Based on Program Net

In this paper, we proposed a method to detect and improve bottlenecks in the data flow of IoT systems. Various IoT devices such as sensors and actuators are used in IoT systems, and millions of data flow through the edge nodes to be processed. Data exchange between edge routers is processed in buffers in sorted order of the event-driven flow. However, because buffers in edge routers are limited, link interference and power instability always reduce the latency of the system. Even a small misalignment in data flow may cause minor incidents that will later cause a chain of breakdown. In this study, we model the data flow with program nets to capture the stagnation of data tokens in order to detect bottlenecks. The data flow is modeled with Program net, and we can visualize the flow of data represented by tokens. We also proposed a mitigation method to tackle the bottleneck problem. Then, we demonstrate the effectiveness of our approach by quantitative evaluation. The evaluation result showed that the proposed method could detect bottlenecks in IoT systems and reduce the buffer to below the 80% of the threshold value.