Reviewerly/exHarmony huggingface dataset:
Learning robust features for indoor scene recognition

As digital image acquisition becomes ubiquitous in recent years, the need for indoor scene recognition becomes more pronounced. Existing methods leverage the features of composing objects in a scene and overlook the adverse impacts of the common objects reoccurring in other scenes. This drawback decreases the feature discrimination between scenes (e.g., living room, dining room, and bedroom) due to reoccurring objects (e.g., tables, chairs, and toys). We propose a method of training convolutional networks by punishing or discounting the local object representations’ predictive ability and encouraging the network to learn global scene layout representations. To retain more vital information for the scene feature representation, we introduce an activation function (with unbounded above, bounded below, smooth, and non-monotonic properties) to allow more low-negative values to flow through the network, discarding high negative values. We evaluate the proposed methods on MIT Indoor 67 and Scene 15 datasets. The experiment findings show that the proposed methods capture global scene concepts and improve performance.
Determining the Production Amount of Tofu Using Mamdani Method (Case Study: UD. Eko Jaya Pasuruan Tahu Factory)

Lots of problems that contain uncertainty arise in this world. Fuzzy logic is one of the methods created to analyze systems that contain these uncertainties. In this study, the author uses the Mamdani method or often also known as the Min-Max method which is an application of the fuzzy method. The purpose of this study was to determine the amount of tofu production at UD. Eko Jaya Pasuruan by using the MinMax (Mamdani) method. The data used is the number of requests and the amount of product inventory at UD. Eko Jaya in June 2022. After that, data analysis was carried out using the Mamdani fuzzy method to determine the amount of production. The design of the system to obtain the output is carried out in several stages, including (a) forming fuzzy sets, (b) application of implication functions, (c) forming rules, and (d) affirmation (defuzzification). In this study, defuzzification was carried out using the centroid method. From the results of data processing that has been carried out, using input variables in June 2022, namely the number of requests of 14,000 pcs and the amount of inventory of 3000 kg each producing an output of 13,808 pcs.
A Bayesian Network-Based Semi-automated Injury Classification System

Injury surveillance plays an important role in understanding the underlying factors leading to different types of injuries and accordingly developing effective preventionPrevention strategies. Multi-dimensional injury-related data are captured in computerized systems at hospital emergencyEmergencies rooms in various fields such as the presenting problem/triage text (or injury narrativeNarratives), injury diagnosis, location of the injury, external cause of injury and activity at the time of the accident. The external cause of injury code (E-code), which plays a significant role in injury surveillance, is typically assigned by human coders based on the case information collected at hospitals. Machine learning models trained on manually coded past data can predict E-codes based on the injury information captured in fields mentioned above and thus help human coders in the form of a semi-automated decision support system. Previous studies have examined the prediction performance of various machine learning models for predicting E-codes based on injury narrativesNarratives, but they have not studied the performance of Bayesian Networks to predict E-codes based on the collective information available in different fields. In this study, we examined the prediction performance of multiple Bayesian Networks developed using various fields of injury-related data. Our results indicate that (a) the Bayesian Networks combining the information available from the narrativeNarratives text with other fields results in better prediction performance as compared to prediction based on narrativeNarratives alone, and (b) the prediction performance of Bayesian Network that used coder assigned values to the fields Mechanism of Injury (MEI) and Major Injury Factor (MIF) was better than the Bayesian Network that used narrativeNarratives-based predicted values of MEI and MIF.
Human-Automation Interaction in Hospitality and Tourism: Toward a Frictionless Experience

Technology development is changing the traditional human-to-human interaction model in service delivery in the hospitality and tourism industry. Service automation has been increasingly adopted by companies to enhance their services. However, consumer experience during human-automationHuman automation interaction (HAI) requires further investigation, especially regarding the potential negative effects of automation technology. This chapter introduces the concept of cognitive friction in HAI and proposes a framework to examine the factors that may negatively impact the delivery of automated services in hospitality and tourism. Two major categories of friction-triggers are proposed in this framework: HAI value belief and HAI experience perception. This chapter contributes to a different perspective towards understanding the consumer experiences during HAI in hospitality and tourism and sets the stage for further empirical examinations in this area.
Factors influencing the performance of virtual reality in urban planning: Evidence from a View corridor Virtual Reality project, Beijing

Virtual reality (VR) technologies, as new forms of planning support systems (PSSs), are becoming increasingly vital to planning. However, there is a lack of empirical research on factors influencing VR’s supportive role from a user perspective, thus producing barriers to VR’s advancement in planning practices. This neglect motivates the focus of this study, in which we adapt relevant PSS theory to build a conceptual framework that examines factors influencing VR performance in an experience-based environment. Empirical data are gathered predominantly through students’ experiences with and evaluation of the “Viewing the Western Hills at Yinding Bridge” ( Yinding Guanshan) VR Project—a VR system developed to optimize the view corridor of Beijing’s Western Hills. The results show large variability in factors influencing VR performance. In general, the effects of six factors are significant, including 3D visualization, simulating real-world scenes, user-friendliness, interactivity, inspiring participants’ enthusiasm, and inspiring creative thinking. These factors are attributed to the functionality, usability and innovativeness dimensions of VR systems. This study concludes that to realize the supportive and useful role of VR in planning, at minimum, these six factors should be explicitly taken into account.
Salient object detection based on edge‐interior feature fusion

Recently, existing FCNs-based methods have shown their advantages in processing object boundaries. However, these methods still suffer from false object interference, which appears in saliency predictions. To solve this problem, an edge-interior feature fusion (EIFF) framework is proposed, which consists of an internal-boundary decoupled generation structure with receptive field enlargement and attention mechanism enhancement, and a salient feature refinement module. Specifically, the framework first learns edge features and interior features through an internal-boundary decoupling generation network, which is supervised by labels obtained by decoupling ground-truth through an image erosion algorithm. Then, feature refinement module (FRM) is designed to purify the coarse prediction by focusing on the ambiguous regions through a mining strategy to generate the final saliency map. To compensate for shortcomings of the BCE and IU loss, we also introduce a weighted loss to guide our model to focus more on the error-prone parts. Experimental results on five benchmark datasets demonstrate that the proposed method performs favorably against 19 state-of-the-art approaches under four standard metrics.
Role of Augmented Reality and Internet of Things in Education Sector

This study outlines the role of augmented reality and the IoT in education systems. The research process on the role of augmented reality in education systems is relatively early, and the IoT is expected to improve the quality of education through the development of innovative applications. Using this technology, you can gain insights into why it is part of everyday learning and teaching methods. The right approach is to integrate IoT and AR to make AR scalable, allowing for more perceptual coverage of a wider range of educational systems and interactions via IoT around the world. This study can explain the use of augmented reality in the IoT and show how augmented reality applications provide contextual information to students and teachers in the education sector. This could be the best choice for integration into the field of education so that students can learn everything with interest rather than study pressure.
Reproduction operators in solving LABS problem using EMAS meta-heuristic with various local optimization techniques

Agent-based evolutionary, computational systems have been proven to be an efficient concept for solving complex computational problems. This paper is an extension of [Biełaszek, S., Piętak, K., & Kisiel-Dorohinicki, M. (2021). New extensions of reproduction operators in solving LABS problem using EMAS meta-heuristic. Springer, cop. 2021. – Lecture Notes in Artificial Intelligence, Computational collective intelligence 12876 304-316. 13th International Conference, ICCCI 2021: Rhodes, Greece, September 29ŰOctober 1, 2021.] where we proposed new variants of reproduction operators together with new heuristics for the generation of initial population, dedicated to LABS – a hard discrete optimization problem. In this research, we verify if the proposed recombination operators improve EMAS efficiency also with different local optimization techniques such as Tabu Search and Self-avoiding walk, and therefore can be seen as better recombination operators dedicated to LABS problem in general. This paper recalls the definition of new recombination variants dedicated to LABS and verify if they can be successfully used in many different evolutionary configurations.
Low-cost real-time motion capturing system using inertial measurement units

&lt;p&gt;Human movement modeling - also referred to as motion-capture - is a rapidly expanding field of interest for medical rehabilitation, sports training, and entertainment. Motion capture devices are used to provide a virtual 3-dimensional reconstruction of human physical activities - employing either optical or inertial sensors. Utilizing inertial measurement units and digital signal processing techniques offers a better alternative in terms of portability and immunity to visual perturbations when compared to conventional optical solutions.&lt;br /&gt;In this paper, a cable-free, low-cost motion-capture solution based on inertial measurement units is proposed. The goal of the proposed solution is to apply motion capture to the fields that, because of cost problems, did not take enough benefit of such technology. An example, for instance, could be a gym. According to this goal, the necessary requirement for the proposed system is to be low-cost. Therefore, all the considerations and all the solutions provided in this work have been done according to this main requirement.&lt;/p&gt;
Verb conjugation errors by learners of Korean

Abstract This study aims to identify patterns of verb conjugation errors that learners of Korean manifest and the factors that influence these errors through an analysis of an error-annotated learner corpus. For this purpose, a paradigmatic relations-based description of language acquisition was proposed. The predictability of each conjugation class was estimated by way of entropy, a tool for measuring predictability. Using entropy allowed us to compare the regularity of each class in detail. The results showed that there are 332 verb conjugation errors that can be classified into three types of errors: errors with vowel endings, errors with lower entropy, and errors with higher entropy. The frequency of the first two types suggests that learners make errors when producing frequently used conjugated forms and with more predictable classes. Considering this study’s reproducibility and the reliability of its procedures and the results, its findings are expected to make a substantial contribution to the study of error analysis using error-annotated learner corpora.
Developing a Prototype of Mobile-based Miko and Mila Animation Series Application Using the ADDIE Method

In a previous research and development study, Dini et al. had produced six 2D animated series (Miko and Mila) for educating children on how to deal with child violence. The series need to be disseminated in various media. One of the media that is often used by children is an interactive mobile application. The objectives of the current research were to complete the initial three stages – specification, initiation, and design – using the ADDIE method for developing an interactive mobile application for the Miko and Mila animation series. At the initiation stage, material testing was carried out on the animated media, and the average validation score was 93.33% by media experts and 89% by material experts. The results of this stage will be used for further research in the development stage.&#x0D; Keywords: mobile application, animated series, ADDIE
Perception in Krashen's Monitor Utilization and the Learners' English-Speaking Performance

Speaking of oral communication has frequently been labeled unfavorable to people because it requires confidence and courage. Thus, it appears to be one of the most challenging learning skills, requiring practice and exposure. This study aimed to investigate the relationship between Senior High School learners' level of performance in spoken discourse and their dominant monitor performance. The study employed a descriptive-correlational research design with 156 respondents selected through stratified random sampling. Recognizing and understanding Senior High School students' monitor performance was expected to motivate teachers to address the root cause of the problem, resulting in more proficient and communicative second language use. The findings disclosed that monitoring performance is crucial in determining the level of performance of learners in spoken discourse. A firm grasp of the English language, including its grammar and vocabulary components, effective discourse management, pronunciation, and interactive communication, are critical components in developing learners' speaking abilities. Additionally, this study revealed that those enrolled in the academic track are proficient at communicating, which explains why most are labeled optimal monitor users. In this regard, they use their information appropriately to strike a balance between self-correction and fluency, ensuring that error repair does not become a barrier to communication. Future recommendations were also discussed in this paper.
Meta-artistic immersion in digital exhibitions. History – mobilization – spectatorship

In the recent decade, the works of canonical visual artists have been presented as immersive multimedia attractions in venues worldwide. Performed in all-encompassing screen spaces and put into motion, this otherwise static and spatially confined imagery has been turned into mobilizing and seemingly boundless visual experiences. What are the aesthetic-receptive characteristics of such immersive digital exhibitions (IDE) centered around art history? To give an answer, this article takes a closer look at one of the IDE’s prominent predecessors, the Panorama, in order to identify moments of continuity and difference. Through a comparative analysis, I gain a nuanced understanding of the IDE’s experiential premises: its tactics and techniques of mobilization, its relation to its classical art historical content matter as well as its aesthetic core—a meta-artistic immersion that moves artistic practice and identity into the center of the experience.
Inverse Imaging: Reconstructing High-Resolution Images from Degraded Images

Inverse problems such as image denoising, super-resolution and image reconstruction are based on complex unsupervised machine learning techniques. We propose a supervised deep learning approach for image denoising and high resolution. Our proposed system simply follows the U-Net architecture which takes images with noise as input. In this work, we show that U-Net architecture consisting of convolutional and de-convolutional or transpose convolutional neural networks does a pretty good job of removing noise from the image and producing high-resolution images. The task belongs to a general class of inverse problems that fall under the mathematical branch of problems based on the posterior probability distribution which is the probability of the parameter theta given the evidence X: Pr(theta|X). We show that supervised learning algorithms based on deep learning are enough capable to produce equivalent or better results than the unsupervised machine learning approach and maintain the complexity even if the generality is extended.
Age Estimation in Social Network Using Machine Learning Algorithm

Human countenances, as fundamental visual signs, pass on a lot of non-verbal data, taking into account genuine human-to-human discussion. Therefore, present day insightful frameworks are relied upon to have the option to perceive and comprehend human faces precisely continuously. Personality, age, sexual orientation, look, and ethnic beginning are extremely significant elements in certifiable facial picture studies, like media correspondence, human-PC cooperation (HCI), and security. Law requirement associations can utilize face mug shot recovery to distinguish imminent suspects in criminal examinations. Regardless of the broad review on human recognizable proof from facial photographs, there is just a limited quantity of exploration on the best way to successfully evaluate and utilize segment proof like age, sex, and society present in facial photos. Despite the fact that programmed picture-based age valuation is a huge strategy complex in some genuine applications, assessing human ages from face photographs stays a troublesome point. Precisely assessing human age utilizing facial picture examination offers a wide scope of genuine uses in Internet-based interpersonal interaction applications. For a long time, online interpersonal organizations (OSN) have been a huge stage for people to draw in with each other and offer data at different stages. Presently, billions of mechanics use OSN to impart, and they are significant scenes for (in addition to other things) content and assessment transmission. Also, every informal organization has an age limitation for signing in. Be that as it may, it stays a troublesome issue to tackle continuously. In this review, we will see how to decide age from face datasets utilizing a few face highlight extraction calculations. The main motivation is to restrict the person to register the social network whose age greater than the predefined threshold using human facial feature.
Development of a middle school science Q&amp;A chatbot using Doc2Vec and analysis of student’s queries

In this study, a method of constructing a science Q&amp;A chatbot developed by a teacher for students at a specific middle school was introduced, and implications were obtained by analyzing student's queries. The chatbot built its own server using a Korean morpheme analyzer and a document classification model based on Doc2Vec.&#x0D; As a result of operating the chatbot service for three semesters, half of the queries were related to the subject, and the other half were non-curricular, such as small talk. The students used the chatbot a lot to prepare for the test, and they used it more after school by taking advantage of the merit of all day operation. In order to increase the usage of educational chatbots, it is necessary to build a dataset based on students' actual queries and improve the search algorithm to increase the probability of getting appropriate answers that match the students' intentions. Even if a chatbot is created for the purpose of Q&amp;A in science subjects, it should be able to cover both subject and non-curricular areas according to the needs of students.
Present NLP Status for Dogri Language

As a result of the recent information boom, the contents of the Internet are increasing in the multilingual form and mostly in the form of natural languages. Therefore, the research on Natural Language Processing (NLP) tasks of regional languages is very important. Dogri, the state language of Jammu and Kashmir (J&K), has remained as a low-resourced language in the field of NLP. In this survey, the contributions of various researchers for Dogri NLP are reviewed and discussed. Sources of Dogri dataset and tools for processing Dogri are explored. Challenges such as the complex linguistic tasks and lack of digital resources are also discussed. The future scope and basic requirements for improving various Dogri language processing tasks are highlighted.
An Integrated Framework for Multi-State Driver Monitoring Using Heterogeneous Loss and Attention-Based Feature Decoupling

Multi-state driver monitoring is a key technique in building human-centric intelligent driving systems. This paper presents an integrated visual-based multi-state driver monitoring framework that incorporates head rotation, gaze, blinking, and yawning. To solve the challenge of head pose and gaze estimation, this paper proposes a unified network architecture that tackles these estimations as soft classification tasks. A feature decoupling module was developed to decouple the extracted features from different axis domains. Furthermore, a cascade cross-entropy was designed to restrict large deviations during the training phase, which was combined with the other features to form a heterogeneous loss function. In addition, gaze consistency was used to optimize its estimation, which also informed the model architecture design of the gaze estimation task. Finally, the proposed method was verified on several widely used benchmark datasets. Comprehensive experiments were conducted to evaluate the proposed method and the experimental results showed that the proposed method could achieve a state-of-the-art performance compared to other methods.
Improving Cluster-Based Index Structure for Approximate Nearest Neighbor Graph Search by Deep Learning-Based Hill-Climbing

This study presents a novel approach to archive an excellent tradeoff between search accuracy and computation cost in approximate nearest neighbor search. Usually, the k-nearest neighbor (kNN) graph and hill-climbing algorithm are adopted to accelerate the search process. However, using random seeds in the original hill-climbing is inefficient as they initiate an unsuitable search with inappropriate sources. Instead, we propose a neural network model to generate high-quality seeds that can boost query assignment efficiency. We evaluated the experiment on the benchmarks of SIFT1M and GIST1M datasets and showed the proposed seed prediction model effectively improves the search performance.