The proceedings contain 59 papers. The special focus in this conference is on HCI in Games. The topics include: Research on the Quantization of User Experience of Spectator Mode in Moba Games;computer-Aided Games-Base...
ISBN:
(纸本)9783030774134
The proceedings contain 59 papers. The special focus in this conference is on HCI in Games. The topics include: Research on the Quantization of User Experience of Spectator Mode in Moba Games;computer-Aided Games-Based Learning for Children with Autism;analysis of the Competitiveness of Asymmetric Games in the Market;a Systematic Review of the Effect of Gamification on Adherence Across Disciplines;an Exploration of the Fear of Attack Strategy in Chess and Its Influence on Class-A Players of Different Chess Personalities: An Exploration Using Virtual Humans;the Factorial Structure and Underlying Contributors of Parents’ Behavioral Involvement in Children’s Video Game Use;in-Game Virtual Consumption and Online Video Game Addiction: A Conceptual Model;Player Types and Game Element Preferences: Investigating the Relationship with the Gamification User Types HEXAD Scale;the Foundations and Frontiers of Research on the Effect of Video Games on Child Development: A Scientometrics and Knowledge-Mapping Analysis Based on CiteSpace;a Specific Measurable Model: How Can Test Results be Influenced by Interactive Prototypes and Design Manuscripts?;using Neural-Network-Driven Image recognition Software to Detect Emotional Reactions in the Face of a Player While Playing a Horror Video Game;exploratory and Confirmatory Factor Analysis of the Chinese Young Children’s Video-Gaming Questionnaire;using Multiple Data Streams in Executive Function Training Games to Optimize Outcomes for Neurodiverse Populations;gameplay as Network: Understanding the Consequences of Automation on Play and Use;hitboxes: A Survey About Collision Detection in Video Games;adaptive Gamification and Its Impact on Performance;analyzing and Prioritizing Usability Issues in Games.
Tight sandstone reservoir is very important in oil and gas exploration in China. Tight reservoirs classification and evaluation are a frontier research field. There are many indexes involved in reservoirs classificati...
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In any patternrecognition challenge, extracted features play a vital role. It is important to extract useful features and then proceed with classification. Especially in applications like mechanical fault diagnosis o...
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To maintain a healthy world, the emission of Greenhouse Gases (GHG) should be minimized. Due to the overall economic crisis in the last few years, the fuel cost for running an automobile, power generation, and operati...
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ISBN:
(数字)9798350375190
ISBN:
(纸本)9798350375206
To maintain a healthy world, the emission of Greenhouse Gases (GHG) should be minimized. Due to the overall economic crisis in the last few years, the fuel cost for running an automobile, power generation, and operating industries become more complicated. To overcome these issues, new technologies should be adopted. Utilizing Electric Vehicle is one of the most important ways to minimize such effects for increasing overall global warming. When such EV Charging S tations (EVCS) are integrated with the grid, it affects most of the parameters and leads to difficult problems like overvoltage, overloading, harmonics, and instability. This research study deals with analyzing the behavior of such EVCS with the grid and renewable energy integrated system, the IEEE14 bus system with the effective utilization of Electrical Transient and Analysis Program (ETAP).
Vehicle retrieval has become crucial for public transportation and intelligent transportation systems due to the exponential development of large-scale transportation videos. Vehicle re-identification and vehicle trac...
Vehicle retrieval has become crucial for public transportation and intelligent transportation systems due to the exponential development of large-scale transportation videos. Vehicle re-identification and vehicle tracking are the main components of most vision-based vehicle recovery systems. Unfortunately, the limited amount of information provided by traffic video feeds limits the vision-based vehicle retrieval algorithm’s efficacy. Therefore, this article proposes a contrastive cross-modal vehicle retrieval approach to maximize the complementarity of natural language and visual representations. An efficient method to fuse multiple input image features is also proposed to extract comprehensive information from various vehicles along with pseudo labeling and efficient post-processing techniques to enhance retrieval accuracy. The proposed method achieved the 3rd ranking of Mean Reciprocal Rank (MRR) score of 0.4795 on the test set for the Challenge Track 2: Tracked-Vehicle Retrieval by Natural Language Descriptions 2023. Source code for the proposed approaches is openly accessible at https://***/anminhhung/AICity-2023-Track2.
The purpose of this study is to develop an intelligent automatic scoring system of Tai Chi 12 GONG FA based on machine learning to assist the teaching, training and promotion of 12 GONG FA. On the basis of obtaining t...
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ISBN:
(数字)9781510647381
ISBN:
(纸本)9781510647381;9781510647374
The purpose of this study is to develop an intelligent automatic scoring system of Tai Chi 12 GONG FA based on machine learning to assist the teaching, training and promotion of 12 GONG FA. On the basis of obtaining the action videos of the practitioners with different exercise levels, the two-dimensional human skeleton key point recognition technology was used to extract the different behavior patterns of fine and bad actions by analyzing the complex periodic changes of the spatial distance of different key points in the time domain;And the artificial neural network was used to reduce the dimension of features predict whether the actions conform to the norms. The system only uses monocular RGB video as input, and by the proposed algorithm of recognizing fine action and bad action, 12 kinds of action can be well identified. The intelligent scoring system based on key point detection achieves similar results with the experts' score.
Support vector machines, or SVMs, have become a really big deal in machine learning because of how good they are at classification and regression problems. This article explores in-depth knowledge about SVMs in ML alg...
Support vector machines, or SVMs, have become a really big deal in machine learning because of how good they are at classification and regression problems. This article explores in-depth knowledge about SVMs in ML algorithms. First from the history of SVMs, starting with when they were first thought up and addresses some important stuff different researchers have done with them over time. Next, get into the math and theory behind how SVMs work things like margins support vectors, and optimization problems and also discuss different ways SVMs have been tweaked and changed, like versions for multiple classes, support vector regression one-class SVMs, and twin SVMs. Another key part of SVMs is kernel functions here we spend a bunch of time breaking those down and explaining what they do to transform data so SVMs can work with it better. Further on, we look at some real-world uses for SVMs, like in image recognition natural language processing, bioinformatics, and finance. Even though SVMs have been around for a while they’re still super relevant today, so here summarize the most important discoveries about them and think about ways SVMs might keep evolving or being used differently as machine learning keeps moving fast. Overall, the goal here is to give you a deep dive into Support Vector Machines - where they began, the technical details, how they are applied, and where they might go in the future helpful in better understanding the SVM in ML Algorithm.
Automation offers a new way of driving, but often the human error (HE) in the process of take-over results in adverse effects of unrecognized risks. Hence, the impact of HE in safety of automated driving remains a maj...
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ISBN:
(纸本)9783030783570;9783030783587
Automation offers a new way of driving, but often the human error (HE) in the process of take-over results in adverse effects of unrecognized risks. Hence, the impact of HE in safety of automated driving remains a major problem. This paper proposed a Human error analysis method based on analysis of the root cause of HEs events to understand the process of take-over and identify root cause of take-over failure in automated driving. Simulated driving practice with videos and questionnaire were conducted to identify the main factors leading to HEs in take-over. Human factors events diagram was used to better understand take-over as a human factor event and to provide information for root cause of takeover failure recognition. The results reveal that the most common failure mode in take-over is cognition error caused by driver poor mental state such as driver fatigue and reaction ability, followed by control error caused by inappropriate take-over request (TOR). Determination of these failure modes provide evidence for increasing or repairing barriers in the process of take-over. The suggested cognition-corresponding model of take-over showed that take-over is a complex human-machine interaction process, thus the causes of HEs should be discussed from a multi-dimensional perspective, and explored through empirical research.
Machine Learning is a part of Artificial Intelligence. A branch of artificial Intelligence(AI), that offers the capability to the system by learning on their own and work better from experience without human intervent...
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Evaluation is at the heart of reproducibility in research, and the related but distinct concept of replicability. The difference between the two is whether the determination is based on the original author’s source c...
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