Collaborative mind mapping in RICOSRE learning model to improve students’ information literacy

Abstract: This research aimed to determine how the reading, identifying problem, constructing solution, solving problem, reviewing and extending problem solution (RICOSRE) learning model integration with collaborative mind mapping affects students’ information literacy in higher education institutions. This quasi-experimental study used a pretest-posttest non-equivalent control group design. There were 40 multiple-choice questions selected to assess student information literacy. All instruments have been validated and are reliable. The data were collected from 100 participants who were college students enrolled in the environmental pollution course of the biology education degree program at Siliwangi University, Indonesia. To analyze the research data, analysis of covariance (ANCOVA) was used at a 5% level of significance, followed by the least significance difference (LSD) test. The ANCOVA results showed that the RICOSRE-CMM learning model impacted students’ information literacy. LSD scores were significantly different in terms of students’ information literacy. It can be concluded that the RICOSRE-CMM model could enhance each component of information literacy as these components have been integrated into the learning model syntax, which involves a series of information discovery and analysis processes during group work to find a solution to a problem.


Challenges encountered by newly arrived Thai students when learning Indonesian as a foreign language

Abstract: This study aimed to determine the challenges faced by Thai international students when they first came to Indonesia to study Indonesian as a foreign language. There were nine Thai students studying in six different cities in Indonesia were recruited as participants. The thematic analysis revealed two major themes: academic challenges and technological issues. Both made it difficult for Thai students to master the Indonesian language effectively. The condition potentially hinders their academic success when studying in Indonesia. However, the researchers found that despite the language barrier, Thai students still tried to speak Indonesian with Indonesian students and residents. It indicates that Thai students in this study were highly determined, friendly, and open to new cultures. Furthermore, the researchers advise teachers and program organizers to pay attention to these challenges to maximize the learning output.


Assessing numeracy skills on flat shapes and scaffolding forms in junior high school

Abstract: Based on The Regulation of The Minister of Education, Culture, Research and Technology No.17 of 2021, cognitive learning outcomes consist of literacy and numeracy as measured through a minimum competency assessment (MCA) with grade 5, 8, and 11. The subjects in this study are 42 students of grade VIII (second grade) from state Junior High School 4 Cirebon and State Junior High School 2 Cirebon, Indonesia. The instruments were tests of numeracy literacy skills, documentation, and interviews. Purposive sampling was used to determine the subject of analysis of test results and interviews. The data were analyzed descriptively through the following stages: data reduction, data presentation, and conclusions drawing. The results showed that the research subjects are still not familiar with numeracy literacy questions which have the characteristics of always being preceded by texts, not understanding what is known and asked in the questions so they tend to blindly guess the answers and needed to be reminded of the prerequisite material. Based on the results of this study, teachers can develop learning media, project-based learning, scaffolding, and teaching materials that accommodate numeracy skills or differentiated learning.


Stakeholders’ awareness and acceptance of the vision, mission, goals, and outcomes statements of a teacher education college

Abstract: This study examined the awareness and acceptance of the stakeholders of a teacher education college in the Philippines of their university vision and mission and the college goals, and outcomes statements (VMGOs). Of the 700 target respondents, 406 stakeholders responded to the survey transcribed in Google Form. They were 42 school personnel, 106 parents, and 258 students; 301 females and 105 males. The five-point rating scales asked the respondents to assess their awareness and acceptance of 16 statements including one vision, one mission, five institutional outcomes (IO), two college goals, and seven program outcomes (PO). The respondents as a whole indicated they were very aware of the VMGOs and these were very acceptable for them. However, the school personnel were consistently more aware than the students and the parents/officials; while the parents/officials consistently indicated the lowest awareness and acceptability scores for all the statements. The results further revealed a significant acceptable relationship between awareness and acceptability scores. The variables share a variance ranging from 51% to 61% confirming that as awareness increases, acceptance rate also increases. Various implications of these findings are discussed.


Portrait of students’ language politeness in elementary school

Abstract: This study described students’ language impoliteness during the Indonesian language learning process taking place at elementary school in the Kediri Regency, East Java Province, Indonesia. This study used a qualitative approach to the type of qualitative descriptive research. The research participant of this study were elementary school educators and students in Kediri, East Java, Indonesia, who have a rough and smooth Javanese language culture when communicating with teachers during the learning process in class. The results of the study showed that all politeness maxims were violated in the learning process. Language politeness must continue to be implemented in elementary schools because it is still being discovered violation of the maxim of wisdom is shown when students give long-winded statements to educators. Violation of the maxim of generosity is shown when students give emotional statements to educators. Violation of the maxim of praise is shown when students express criticism at will, to both educators and friends. Violation of the maxim of politeness is shown when students make statements that demean the speech partner. Violation of the maxim of agreement can be seen when students are giving very harsh statements and ignoring the rules of the speech partners. The violation of the maxim of sympathy is shown when students do not have sympathy for the speech partner who is having difficulty. Language politeness is very important for the world of education as an effort to familiarize students as the nation’s next generation with polite language.


The study of digital literacy components for youths during the COVID-19 pandemic in Bangkok, Thailand

Abstract: This research paper investigates digital literacy components for youths in Bangkok. The first phase used a qualitative method. The key informants were eight experts in information and communication technology (ICT), human resource (HR), and education. The second phase was to develop the measurement of digital literacy components using the confirmatory factor analysis (CFA) approach with a sample consisting of 1,362 youths in Bangkok. Data collection was conducted through a questionnaire with 52 questions. The results revealed that digital literacy criteria for youths in Bangkok consisted of four factors containing 13 indicators. The first factor and its related access components was three subcomponents, which are i) use of tools and devices; ii) data collection in the cloud; and iii) internet connection. The second factor was understanding components consisting of evaluation, ethics, and legal literacy. The third factor was use of components consisting of safeguarding, search, sharing, and innovation. The fourth factor was creating components consisting of creating weblogs/applications, presenting on website/application, and safeguarding. CFA was employed to test the construct validity of the research latent variables that revealed the harmony correlation of empirical data contained in this research model. These results were employed to develop a digital literacy for youth during the COVID-19 pandemic in Bangkok, Thailand.


TLFS23 Tamil Language Fingerspelling Dataset

Abstract: Tamil is one of the oldest existing languages, spoken by around 65 million people across India, Sri Lanka and South-East Asia. Countries such as Fiji and South Africa also have a significant population with Tamil ancestry. Tamil is a complex language and has 247 characters. A labelled dataset for Tamil Fingerspelling named TLFS23 has been created for research related to vision-based Fingerspelling translators for the Speech and hearing Impaired. The dataset would open up avenues to develop automated systems as translators and interpreters for effective communication between fingerspelling language users and non- users, using computer vision and deep learning algorithms. One thousand images representing each unique finger flexion motion for every Tamil character was collected overall constituting a large dataset with 248 classes with a total of 2,55,155 images. The images were contributed by 120 individuals from different age groups. The dataset is made publicly available at : https://data.mendeley.com/datasets/39kzs5pxmk/2


3-D Shape Measurement of Translucent Objects Based on Fringe Projection

Abstract: Fringe projection profilometry (FPP) has been widely used in various fields because of its advantages of non-contact, non-destructive, high-precision, and high-speed. However, due to the subsurface scattering effect, FPP encounters severe challenges when performing three-dimensional (3D) measurements of translucent objects. Based on FPP, this paper proposes a novel 3D measurement method for translucent objects using periodic line-fringe patterns and complementary Gray code patterns. By photographing a set of periodic line-fringe patterns with different phases and performing multi-Gaussian sum fitting on the grayscale values of the same pixel position in different images, the wrapping phase of the fringe corresponding to the maximum fitted grayscale value can be obtained. Furthermore, a novel complementary Gray code method is proposed to solve the absolute phase, which avoids phase unwrapping errors caused by subsurface scattering. Comparative experimental results show that the point cloud of translucent objects obtained using the proposed 3D reconstruct method is more complete, denser, and smoother, and more accuracy. The fitting diameter errors of the reconstruction results of the translucent nylon sphere and PP sphere are 0.025 mm and 0.044 mm respectively. In addition, our method can use the conventional binocular stereo vision system based on grating projection, without modifying the hardware system.


A Tourism Dataset from Historical Transaction for Recommender Systems.

Abstract: The tourism industry has currently grown in various aspects, including the types of attractions, their quantity, and the number of tourist visits in various regions, contributing positively to both regional and global economies. Historical transactions are essential for developing recommender systems, utilizing techniques such as Collaborative Filtering and Demographic Filtering. TripAdvisor is a reputable website providing a wide range of accessible tourism information, including attractions, user profiles, and ratings. However, this unstructured raw data requires processing to create an adequate dataset for recommender systems. This study conducted a series of data processing steps on the raw data, including data restructuring, validation, content addition, integration with Google Maps, normalization, and modeling. This study successfully produced an original dataset comprising User Transaction, Item or Attraction, Attraction Type, Continent, Region, Country, City, and Visiting Mode. It also includes an entity relational model for tourism in Indonesia, particularly in Bali, Malang, and Yogyakarta regions, based on various global user experiences. This dataset is adequate and essential for developing various models of tourism recommender systems such as using Collaborative Filtering.


Balancing exploration and exploitation: Unleashing the adaptive power of automatic cuckoo search for meta-heuristic optimization

Abstract: Meta-heuristic optimization algorithms are versatile and efficient techniques for solving complex optimization problems. When applied to clustering algorithms, these algorithms offer numerous advantages over traditional optimization methods, including global search capabilities, iterative refinement processes, robustness to initial conditions, and flexibility in handling diverse clustering objectives and constraints. Employing meta-heuristic optimization in clustering algorithms leads to improved accuracy, scalability, robustness, and flexibility in finding optimal or near-optimal clustering solutions. These algorithms generate new individuals iteratively using nature-inspired operations to obtain high-quality results. However, they often suffer from slower convergence and lack guarantees of finding the best solution for every problem, posing ongoing challenges in algorithm development. This study focuses on addressing the issue of premature convergence in metaheuristic algorithms by introducing an automatic cuckoo search (AuCS) algorithm. The AuCS algorithm aims to strike a balance between exploration and exploitation by dynamically updating the step size in each generation, thereby avoiding premature convergence. To evaluate the effectiveness of the proposed algorithm, experiments were conducted on 13 standard benchmark functions and 14 CEC 2005 benchmark functions. In overall performance, AuCS has the best optimum value in 72.22% of cases. This demonstrates the efficacy of the proposed algorithm in achieving improved clustering accuracy and minimizing intra-cluster distance. The proposed AuCS algorithm was applied to data clustering and compared with four swarm optimization algorithms. Here, AuCS outperforms these well-known algorithms in 5 out of 7 datasets. The experimental evaluations in both benchmark functions and clustering problems confirm the promising results of the proposed algorithm, suggesting that AuCS could be considered as a potential improvement over the cuckoo search algorithm.


Learning image representations for anomaly detection: Application to discovery of histological alterations in drug development

Abstract: We present a system for anomaly detection in histopathological images. In histology, normal samples are usually abundant, whereas anomalous (pathological) cases are scarce or not available. Under such settings, one-class classifiers trained on healthy data can detect out-of-distribution anomalous samples. Such approaches combined with pre-trained Convolutional Neural Network (CNN) representations of images were previously employed for anomaly detection (AD). However, pre-trained off-the-shelf CNN representations may not be sensitive to abnormal conditions in tissues, while natural variations of healthy tissue may result in distant representations. To adapt representations to relevant details in healthy tissue we propose training a CNN on an auxiliary task that discriminates healthy tissue of different species, organs, and staining reagents. Almost no additional labeling workload is required, since healthy samples come automatically with aforementioned labels. During training we enforce compact image representations with a center-loss term, which further improves representations for AD. The proposed system outperforms established AD methods on a published dataset of liver anomalies. Moreover, it provided comparable results to conventional methods specifically tailored for quantification of liver anomalies. We show that our approach can be used for toxicity assessment of candidate drugs at early development stages and thereby may reduce expensive late-stage drug attrition.


Genetic Programming for Document Classification: A Transductive Transfer Learning System

Abstract: Document classification is a challenging task to the data being high-dimensional and sparse. Many transfer learning methods have been investigated for improving the classification performance by effectively transferring knowledge from a source domain to a target domain, which is similar to but different from the source domain. However, most of the existing methods cannot handle the case that the training data of the target domain does not have labels. In this study, we propose a transductive transfer learning system, utilizing solutions evolved by genetic programming (GP) on a source domain to automatically pseudolabel the training data in the target domain in order to train classifiers. Different from many other transfer learning techniques, the proposed system pseudolabels target-domain training data to retrains classifiers using all target-domain features. The proposed method is examined on nine transfer learning tasks, and the results show that the proposed transductive GP system has better prediction accuracy on the test data in the target domain than existing transfer learning approaches including subspace alignment-domain adaptation methods, feature-level-domain adaptation methods, and one latest pseudolabeling strategy-based method.


Updating of higher education curriculum for future engineers during the COVID-19 pandemic

Abstract: Higher education is immersed in unpredictable environments and is facing challenges during the Coronavirus Disease 2019 (COVID-19) pandemic. It needs to adapt to external changes constantly, especially to the level of development of science and technology as the most important component of education courses for future engineers are facing development difficulties within achieving their goals as the core of higher education. Curriculum serves as basis for realization of goals of higher education and curriculum for future engineers needs updating. The paper analyzes dilemma of updating of higher education curriculum for future engineers within its content and structure. During the learning process students participated in curriculum renewal and accelerated the upgrading of curriculum content and structure. Finishing the course, students conducted reflection, improved their understanding during the reflection process continuously that helped them to develop the habit of lifelong learning. The authors propose to expand teaching methods for the courses, to adapt engineering courses to the needs of economic development during the COVID-19 pandemic, to strengthen students’ interaction and participation, to supply multiple participation in curriculum “customization” countermeasures to update higher education curriculum for future engineers.


The role of gamification implementation in improving quality and intention in software engineering learning

Abstract: Gamification can make learning more fun and engaging for students. Software engineering can utilize gamification to help students learn and improve their skills from the complexity of software engineering. This study used quantitative research to examines perceived ease of use, student satisfaction, and perceived usefulness to measure gamification quality, which can have an impact on software engineering intention, namely intention, loyalty, and participation in following and understanding software engineering materials. The data was collected based on an online questionnaire survey, 90 data were collected and then measured and analyzed using SmartPLS 3. The results showed that perceived ease of use, student satisfaction, and perceived usefulness have a significant influence on gamification quality, which also leads to a positive impact on software engineering intention. This research guides teachers and educational institutions that gamification is very successful as a learning medium to simplify complex information to be more interactive.


Corrections to “Model-Based Design of the COAST Guidewire Robot for Large Deflection”

Abstract: Presents corrections to the article “Model-Based Design of the COAST Guidewire Robot for Large Deflection.


TestFit: A plug-and-play one-pass test time method for medical image segmentation

Abstract: Deep learning (DL) based methods have been extensively studied for medical image segmentation, mostly emphasizing the design and training of DL networks. Only few attempts were made on developing methods for applying DL models in test time. In this paper, we study whether a given off-the-shelf segmentation network can be stably improved on-the-fly during test time in an online processing-and-learning fashion. We propose a new online test-time method, called TestFit, to improve results of a given off-the-shelf DL segmentation model in test time by actively fitting the test data distribution. TestFit first creates a supplementary network (SuppNet) from the given trained off-the-shelf segmentation network (this original network is referred to as OGNet) and applies SuppNet together with OGNet for test time inference. OGNet keeps its hypothesis derived from the original training set to prevent the model from collapsing, while SuppNet seeks to fit the test data distribution. Segmentation results and supervision signals (for updating SuppNet) are generated by combining the outputs of OGNet and SuppNet on the fly. TestFit needs only one pass on each test sample—the same as the traditional test model pipeline—and requires no training time preparation. Since it is challenging to look at only one test sample and no manual annotation for model update each time, we develop a series of technical treatments for improving the stability and effectiveness of our proposed online test-time training method. TestFit works in a plug-and-play fashion, requires minimal hyper-parameter tuning, and is easy to use in practice. Experiments on a large collection of 2D and 3D datasets demonstrate the capability of our TestFit method.


Heterogeneous Sensor Fault Detection for Networked Systems Based on a Graph Transformer

Abstract: Networked systems like fuel, petrochemical, and water supply systems use various sensors for monitoring and control. Due to harsh environmental conditions, sensors are prone to failure and pose a significant threat to the stability of the system, making the accurate identification of such anomalies crucial. In recent years, data-driven sensor fault detection methods utilized sensor data correlations, spatial and temporal features, but they haven’t fully harnessed the physical structure relation of sensors in real system. Our study introduces a novel sensor fault detection method that integrates graph neural networks and transformers. The model efficiently uses the interdependence among various sensors for information transmission and aggregation, relying on the physical struction of networked system. To enhance the information exchange among identical sensor, the model devides the graph neural network into subgraphs based on sensor types. Within each subgraph, the attention mechanism of transformers is employed to capture sensor features and distributions, thereby improving the learning of the sensor node representation vector. Finally, a decoder with reconstructed input is utilized for sensor fault detection. The experiments demonstrate better fault detection accuracy in both real networked systems and four heterogeneous sensor datasets. Across all experimental cases, the F1 score of our method is 95.79%, and the AUC value is 98.83%. Finally, through ablation study, the effectiveness of each component of the model is confirmed.


Application of Augmented Reality in Manufacturing

Abstract: AR is a distinctive interface between humans and machines that overlays virtual information generated by computers onto a practical application environment. In the past few years, it has been discovered that AR has the potential to be used in various fields, such as military training, maintenance, assembly, product design, and other production processes. AR is now playing a key role in overcoming the problems of integrating technology to speed up Industry 4.0. It enables workers to convert from mass production to mass customization. AR technology is gaining popularity because of the ease with which it can be implemented in the production and wide accessibility of hardware devices, including smartphones and tablets, that are capable of supporting it. This chapter provides a comprehensive evaluation of the latest AR applications and advancements in the manufacturing industry. Efforts are also made to understand the major challenges in AR applications and the scope for overcoming them in the future.


Causal Inference in Recommender Systems: A Survey and Future Directions

Abstract: Recommender systems have become crucial in information filtering nowadays. Existing recommender systems extract user preferences based on the correlation in data, such as behavioral correlation in collaborative filtering, feature-feature, or feature-behavior correlation in click-through rate prediction. However, unfortunately, the real world is driven by causality , not just correlation, and correlation does not imply causation. For instance, recommender systems might recommend a battery charger to a user after buying a phone, where the latter can serve as the cause of the former; such a causal relation cannot be reversed. Recently, to address this, researchers in recommender systems have begun utilizing causal inference to extract causality, thereby enhancing the recommender system. In this survey, we offer a comprehensive review of the literature on causal inference-based recommendation. Initially, we introduce the fundamental concepts of both recommender system and causal inference as the foundation for subsequent content. We then highlight the typical issues faced by non-causality recommender system. Following that, we thoroughly review the existing work on causal inference-based recommender systems, based on a taxonomy of three-aspect challenges that causal inference can address. Finally, we discuss the open problems in this critical research area and suggest important potential future works.


A New Algorithm for Earthquake Prediction Using Machine Learning Methods

Abstract: Seismic tremors are among the foremost perilous normal fiascos individuals confront due to their event without earlier caution and their effect on their lives and properties. In expansion, to consider future disaster prevention measures for major earthquakes, it is necessary to predict earthquakes using Neural Networks (NN). A machine learning technique has developed a technology to predict earthquakes from ground controller data by measuring ground vibration and transmitting data by a sensor network. Devices to process this data and record it in a catalog of seismic data from 1900-2019 for Iraq and neighboring regions, then divide this data into 80% training data and 20% test data. It gave better results than other prediction algorithms, where the NN model performs better Seismic prediction than other machine learning methods.


Data on investigation of potential gold mineralization sites in Tchollire and Environs (North Cameroon)

Abstract: Fresh and altered rock samples were collected and analyzed during field and laboratory studies in Tchollire and Environs. This approach aimed at the delineation of hydrothermal alteration minerals which according to the geological and mining settings of Tchollire and Environs, may be associated with gold mineralizations sites. Field investigations were achieved during the dry season to ensure the representativeness and reliability of our samples whose collections were constrained by earlier remote sensing and geophysical studies as expressed in [1]. An optical microscope both in reflected and transmitted light was used for petrographic analyses of thin and polished sections of rock samples. Other rock samples were prepared for spectral measurements which were achieved using an analytical spectral device spectrometer. The data presented here are further interpreted and discussed in [1].


Improving the accuracy of predicted first-arrival times using deep learning

Abstract: Recently, the task of first-arrival time picking for seismic shot gathers has been treated as an image segmentation problem, and deep-learning (DL) algorithms have been successfully used to predict first-arrival times. Currently, researchers mainly focus on leveraging cutting-edge DL algorithms to improve the performance of DL in first-arrival picking. There are few publications addressing the quality control of the results predicted by DL. We develop a three-step workflow to improve the accuracy of first-arrival time detection computed using DL algorithms. First, we obtain three predicted results (generation I) by applying the holistically nested U-net (HU-net) to seismic shot gathers, the envelope of seismic shot gathers, and the cosine of the instantaneous phase of seismic shot gathers. Subsequently, we obtain generation II picking by statistically analyzing the predicted generation I picking. Finally, we treat the first-arrival picking task as a constrained path search problem and the generation II picking function as the constraints. The developed workflow is applied to real seismic surveys to demonstrate its effectiveness.


Analysis of Effective Strategies and Teacher Motivation in Improving Literacy and Numeracy at State Elementary School Besowo 5 Kediri: Findings and Implications

Abstract: State Elementary School Besowo 5, Kepung District, Kediri, is an elementary school that succeeded in achieving high literacy and numeracy results on the education report card and achieved recognition as the Best BOSP Performance in Kediri Regency, even though the school experienced infrastructure challenges such as limited road access and limited cellular signal. The aim of this research is to explore the challenges, strategies and collaborative efforts of teachers in improving students' literacy and numeracy skills. The method in this research uses case studies through observation, interviews and document analysis. The analysis technique of this research uses triangulation techniques. Through the use of educational report cards as a basis, SDN Besowo 5 teachers provide special additional hours by utilizing a variety of literacy and numeracy materials, implementing various learning strategies, including guidance on finding keywords, Polya steps, using innovative worksheet, involving students as peer tutors, and provide treatment according to students' abilities. Apart from that, teachers also collaborate. Teachers' motivation for collaboration includes, among other things, improving school achievement, developing knowledge, and strengthening the school's positive image in the eyes of the community.


Amortized Simulation-Based Frequentist Inference for Tractable and Intractable Likelihoods

Abstract: Abstract High-fidelity simulators that connect theoretical models with observations are indispensable tools in many sciences. If the likelihood is known, inference can proceed using standard techniques. However, when the likelihood is intractable or unknown, a simulator makes it possible to infer the parameters of a theoretical model directly from real and simulated observations when coupled with machine learning. We introduce an extension of the recently proposed likelihood-free frequentist inference ( LF2I ) approach that makes it possible to construct confidence sets with the p -value function and to use the same function to check the coverage explicitly at any given parameter point. Like LF2I , this extension yields provably valid confidence sets in parameter inference problems for which a high-fidelity simulator is available. The utility of our algorithm is illustrated by applying it to three pedagogically interesting examples: the first is from cosmology, the second from high-energy physics and astronomy, both with tractable likelihoods, while the third, with an intractable likelihood, is from epidemiology 3 3 Code to reproduce all of our results is available on https://github.com/AliAlkadhim/ALFFI . .


EXPLORING FACTORS ASSOCIATED WITH SEROLOGICAL RESPONSE TO SARS-COV-2 VACCINES IN INDIVIDUALS WITH INFLAMMATORY BOWEL DISEASE: INTERACTIVE VISUALIZATION METHODS FOR MULTIDIMENSIONAL DATA

Abstract: SARS-CoV-2 vaccine response may be reduced in seniors (age 65+) with inflammatory bowel disease (IBD) and those on anti-TNF therapy. A challenge of the COVID-19 era has been conveying rapid-evolving health information, especially for groups with higher risk of poor outcomes of COVID-19 such as seniors and immunocompromised individuals.


Edge Perception Temporal Data Anomaly Detection Method Based on BiLSTM-Attention in Smart City Big Data Environment

Abstract: The improvement of edge perception layer anomaly detection performance has an immeasurable driving effect on the development of smart cities. However, many existing anomaly detection methods often suffer from problems such as ignoring the correlation between multiple source temporal sequences and losing key features of a single temporal sequence. Therefore, a new anomaly detection method using BiLSTM and attention mechanism is proposed. First, a fusion algorithm TCDCD was formed by combining Data Correlation Detection (DCD) and Temporal Continuity Detection (TCD) to preprocess Edge Perception Data (EPD). Then, BiLSTM is employed to gather deep-level features of EPD, and the attention mechanism is utilized to enhance important features that contribute to anomaly detection. Ultimately, the SoftMax classifier is employed to categorize abnormal data. The experimental findings from the SWaT and WADI datasets demonstrate that the suggested method achieves better performance than other newer anomaly detection methods. Among them, the accuracy, precision, recall and F1 of the proposed method on the SWaT dataset were 96.62%, 94.32%, 96.02% and 94.30%, respectively. In terms of performance, it is superior to traditional EPD anomaly detection models, and has good representational and generalization capabilities.


Towards Effective Solid Waste Management: A Mobile Application for Coordinated Waste Collection and User-official Interaction

Abstract: Solid Waste Management is an especially important task related to human health and the environment. Due to ineffective scheduled date & time, poor communication between waste collecting institutions and local house owners, people are compelled to throw waste on streets which is not good. Even if there is a routine, people tend to miss the schedule. Our aim is to develop an application for mobile phones, which consists of two parties- the user and waste management officials, where the second one acts as reminders. Officials will send a notification to the user, signaling that they are at a certain checkpoint near the user and the user can now throw waste properly and not on the streets. An incremental model was used throughout our project; basic requirements are fulfilled first and then iterated to create the final product. The proposed application includes two portals for whether you are user or waste management personnel. This application helps to improve the coordination between clients and collectors and determines whether the waste in an area has been collected or not. The survey conducted in this study involved consulting the Environment and Agricultural Department of Kathmandu Metropolitan City, which highlighted the significance of a notifying application. This application addresses the issue of uncoordinated waste disposal by providing users with information about collection schedules, leading to better waste management practices and reduced unsystematic garbage disposal.


Comparative Study: Performance of MVC Frameworks on RDBMS

Abstract: The regular utilization of web-based applications is crucial in our everyday life. The Model View Controller (MVC) architecture serves as a structured programming design that developers utilize to create user interfaces. This pattern is commonly applied by application software developers to construct web-based applications. The use of a MVC framework of PHP Scripting language is often essential for application software development. There is a significant argument regarding the most suitable PHP MVC such as Codeigniter & Laravel and Phalcon frameworks since not all frameworks cater to everyone's needs. It's a fact that not all MVC frameworks are created equal and different frameworks can be combined for specific scenarios. Selecting the appropriate MVC framework can pose a challenge at times. In this context, our paper focuses on conducting a comparative analysis of different PHP frameworks. The widely used PHP MVC frameworks are picked to compare the performance on basic Operation of Relational databases and different type of Application software to calculate execution time. In this experiment a large (Big Data) dataset was used. The Mean values of insert operation in MySQL database of Codeigniter, Laravel, Phalcon were 149.64, 149.99, 145.48 and PostgreSQL database`s 48.259, 49.39, 45.87 respectively. The Mean values of Update operation in MySQL database of Codeigniter, Laravel, Phalcon were 149.64, 158.39, 207.82 and PostgreSQL database`s 48.24, 49.39, 46.64 respectively. The Mean values of Select operation in MySQL database of Codeigniter, Laravel, Phalcon were 1.60, 3.23, 0.98 and PostgreSQL database`s 1.95, 4.57, 2.36 respectively. The Mean values of Delete operation in MySQL database of Codeigniter, Laravel, Phalcon were 150.27, 156.99, 149.63 and PostgreSQL database`s 42.95, 48.25, 42.07 respectively. The findings from our experiment can be advantageous for web application developers to choose proper MVC frameworks with their integrated development environment (IDE). This result will be helpful for small, medium & large-scale organization in choosing the appropriate PHP Framework.


Differences in student-AI interaction process on a drawing task: Focusing on students’ attitude towards AI and the level of drawing skills

Abstract: Recent advances and applications of artificial intelligence (AI) have increased the opportunities for students to interact with AI in their learning tasks. Although various fields of scholarly research have investigated human-AI collaboration, the underlying processes of how students collaborate with AI in a student-AI teaming scenario have been scarcely investigated. To develop effective AI applications in education, it is necessary to understand differences in the student-AI interaction (SAI) process depending on students' characteristics. The present study attempts to fill this gap by exploring the differences in the SAI process amongst students with varying drawing proficiencies and attitudes towards AI in performing a public advertisement drawing task. Based on the empirical evidence obtained from the think-aloud protocols of 20 Korean undergraduate students, the study first conducted a lag sequential analysis to identify statistically significant linear patterns of each group and then chronologically incorporated them into the SAI duration via coded activity alignment series to distinguish the overall SAI process of each group. The study revealed the distinctive differences in SAI processes of students with different attitudes towards AI and drawing skills. To better facilitate student-AI teams for learning, a range of implications of educational AI development and instructional design is discussed. Implications for practice or policy: Educational AI should not be limited to performing a specific task and solving well-defined problems. It should be designed with a holistic view of the end-to-end student-AI process, interconnected to different learning activities in the learning process. Educational AI should be capable of increasing students’ metacognition and emotional engagement. An educational AI system architect team inclusive of diverse stakeholders should be formed to collaboratively design the AI system.


Virtual/Augmented Reality Applications in Education & Life Long Learning

Abstract: Recent advancements in educational technology highlight the significance of disruptive technologies in enhancing Learning Experiences (LX) in a more targeted, personalized, and engaging context [...]


Wireless body area sensor networks based human activity recognition using deep learning

Abstract: Abstract In the healthcare sector, the health status and biological, and physical activity of the patient are monitored among different sensors that collect the required information about these activities using Wireless body area network (WBAN) architecture. Sensor-based human activity recognition (HAR), which offers remarkable qualities of ease and privacy, has drawn increasing attention from researchers with the growth of the Internet of Things (IoT) and wearable technology. Deep learning has the ability to extract high-dimensional information automatically, making end-to-end learning. The most significant obstacles to computer vision, particularly convolutional neural networks (CNNs), are the effect of the environment background, camera shielding, and other variables. This paper aims to propose and develop a new HAR system in WBAN dependence on the Gramian angular field (GAF) and DenseNet. Once the necessary signals are obtained, the input signals undergo pre-processing through artifact removal and median filtering. In the initial stage, the time series data captured by the sensors undergoes a conversion process, transforming it into 2-dimensional images by using the GAF algorithm. Then, DenseNet automatically makes the processes and integrates the data collected from diverse sensors. The experiment results show that the proposed method achieves the best outcomes in which it achieves 97.83% accuracy, 97.83% F-measure, and 97.64 Matthews correlation coefficient (MCC).


Neural networks in libraries: A new development in bibliographic services

Abstract: Based on the review of professional literature and selected monitoring of Russian and foreign library websites, the author introduces readers to the Artificial Intelligence (AI) technologies for bibliographic services. She focuses on artificial neural networks as one of the most popular AI instruments. In particular, she discusses practical experiences of using AI in bibliographic services and supporting library and information areas, including information indexing. The author provides examples to demonstrate the AI potential for higher quality and performance in bibliographic and user services, e. g. interaction with users through chat bots, advanced bibliographic search, references, personalized information awareness services, etc. The AI instruments including neural networks are also used for metadata extraction and bibliographic records generation. Today, library chat bots and text intellectual analysis systems are the most developed services. The findings of the study demonstrate that AI technologies enable to accomplish user bibliographic services, though not all AI benefits and drawbacks have been revealed yet. The author suggests that expanding AI sphere would enable to improve quality and efficiency of bibliographic services, competences of bibliographers, and to introduce new types and forms of bibliographic services. The article is prepared within the framework of R&D “The model of reference and bibliographic services for the national libraries” (reg. No. 122021800339-7) of the Russian State Library.


Specific aspects of the Russian federal and scientific libraries' support of the libraries in the RF new entities

Abstract: The author focuses on the book charity drives to support the libraries in Donetsk and Lugansk republics, Kherson and Zaporozhye regions, and examines the contribution of state and non-government organizations. The experience of the Great Patriotic War (WWII) in restoration of the libraries in cleared areas and the possibilities to use this experience today, is explored. The need for coordination center for developing collections in the new territories is substantiated. The challenges of these libraries’ entering the RF digital space are discussed. The author analyzes the role of federal libraries in professional retraining of the librarians in the new regions, and the content of the programs offered by leading library and information centers for the librarians in Donetsk and Lugansk republics, Kherson and Zaporozhye regions. The author argues that the substantive side of the libraries in the new RF areas has to be reformatted in recognition of the hostile Ukrainian policy that the above libraries had to communicate for several decades.


Implementasi Model Digital Forensik Procces Untuk Sosial Media Investigation Dengan Tools Hunchly

Abstract: Perkembangan teknologi yang semakin pesat dapat menjadi dampak posistif dan dapat negatif. Dampak positifnya adalah proses penyebaran informasi yang semakin cepat, namun dampak negatifnya adalah banyaknya informasi hoax dan hate space yang terjadi di media sosial akibat tidak adanya filter dan pengecekan keabsahan informasi. Hal ini juga mengakibatkan banyaknya tindak kejahatan yang terjadi di media sosial. Salah satu pemecahan masalah dengan banyaknya kasus yang terjadi akibat penyalahgunaan media sosial adalah dengan melakukan proses invesitgasi forensic pada social media. Proses investigasi forensik ini dilakukan dengan metode live investigation menggunakan metode digital forensik proses yang terdiri dari proses Collection, Examination, Analysis dan Reporting. Proses pengumpulan barang bukti dengan menggunakan tools hunchly sehingga dapat dilakukan proses investigasi secara langsung. Hasil dari penelitian ini adalah dengan memanfaatkan metode digital forensik proses dan penggunaan tools hunchly untuk melakukan teknik live investigation untuk melakukan pengumpulan bukti digital dari akun-akun media sosial yang melakukan tindak kejahatan di media sosial sehingga dapat di proses sesuai dengan peraturan hukum yang telah berlaku Kata kunci: Bukti Digital, Media Sosial, Digital Forensik, Hunchly ------------------------------------------------------------------- The increasingly rapid development of technology can have both positive and negatif impacts. The positive impact is that the process of disseminating information is getting faster, but the negatif impact is the large amount of hoax information and hate space that occurs on social media due to the absence of filters and checking the validity of the information. This also results in many crimes occurring on social media. One solution to the problem with the many cases that occur due to misuse of social media is to carry out a forensic investigation process on social media. This forensic investigation process is carried out using the live investigation method using a digital forensic process method consisting of Collection, Examination, Analysis and Reporting processes. The process of collecting evidence uses powerful tools so that the investigation process can be carried out directly. The results of this research are by utilizing digital forensic process methods and using hunchly tools to carry out live investigation techniques to collect digital evidence from social media accounts that commit crimes on social media so that it can be processed in accordance with applicable legal regulations. Keywords: Sosial Media, Digital Forensics, Hunchly


OPTIMISASI PEMILIHAN FITUR UNTUK PREDIKSI GAGAL JANTUNG: FUSION RANDOM FOREST DAN PARTICLE SWARM OPTIMIZATION

Abstract: Heart failure is a serious, life-threatening cardiovascular disease that increases with age and unhealthy lifestyles. Early prediction is essential to provide timely treatment and reduce mortality. The use of machine learning techniques, especially the Random forest (RF) method, for predicting heart failure has been previously researched, so the problem that occurs is that the RF method does not have maximum results because of irrelevant features. Selection of relevant features is a key step in building an accurate prediction model. Particle Swarm Optimization (PSO) is used to improve feature selection by searching for optimal combinations. The aim of the research is to reduce the mortality rate by improving the RF method with relevant features so as to increase the accuracy of predictions with Fusion RF and PSO. The results show an increase in accuracy of 02.78% to 87.33% with PSO, although the AUC decreased by 0.031%. The advantage of PSO is a significant increase in accuracy, but the disadvantage is a slight decrease in AUC. Future developments could explore how to address AUC degradation without compromising accuracy and transmitting additional relevant features.


whiteheadi metafizika megjelenése három fallácia fényében

Abstract: Whitehead and the method How can we interpret the emergence of Whitehead's metaphysical and cosmological project on the map of 20th-century  philosophy after Kant's critical turn? How can this project be related to Whitehead's contemporary philosophical tendencies, such as the pragmatist movement that was developing in America? This paper assumes a fundamental link between the two questions, namely that it is necessary to clarify the methodological reflections that define their philosophies in interpreting Whitehead and the pragmatists, including William James. In Whitehead’s case, this also means that the goal of metaphysics and cosmology can only be understood as a systematic elaboration of the method. And for Whitehead, this constructing process (of the method) is the main task of speculative philosophy.


Abstract WP178: Evaluation of Low NIHSS Subitems Association With LVO Detection in Large Telestroke Database

Abstract: Background: The benefits of mechanical thrombectomy in low NIHSS score (<6) is unclear and undergoing investigation. Current acute stroke guidelines do not address the utilization of computed tomography angiography (CTA) in low NIHSS largely due to a paucity of data leading to great variation in clinical practice. Understanding the predictive value of NIHSS subitems for LVO may help guide imaging selection. Methods: Acute stroke consultations seen in the emergency department in 227 facilities (27 states) from July 1, 2021 to December 31, 2021 were extracted from the TeleCare by TeleSpecialists TM database. The encounters with CTA performed were reviewed for LVO detected, and NIHSS score with individual subitems. Logistic regression analysis was performed on the NIHSS subitems in total and at NIHSS cutoff points of ≤6, ≤4, and ≤2. Results: A total of 12,668 patients had complete NIHSS subitems available with 1,459 (11.5%) having an LVO and 11,209 without an LVO were extracted. There is a significant difference in the ORs for each subitem. The ORs for patients with NIHSS ≤6 subitems: Level of Consciousness 1.11 (p=0.626), Consciousness Questions 1.36 (p<0.001), Consciousness Commands 1.94 (p<0.001), Best Gaze 1.61 (p=0.061), Visual Field 1.65 (p<0.001), Facial palsy 1.71 (p <0.001), Motor Arm-left 1.43 (p <0.001), Motor arm-right 1.03 (p=0.840), Motor leg-left 0.97 (p=0.758), Motor leg-right 0.83 (p=0.103), Limb ataxia 1.09 (p=0.545), Sensory 0.67 (p=0.001), Language 1.92 (p<0.001), Dysarthria 1.81 (p<0.001), and Neglect 5.78 (p<0.001). Trends in subitems with each lower cutoff were increased odds of LVO with Consciousness Commands, Visual Fields, Language, and Neglect, and lower odds of LVO with Sensation. Conclusions: This analysis shows the limitations of the NIHSS subitems to predict LVO. Despite the trend of lower odds of LVO for Sensation, even with the NIHSS cutoff of ≤2 there would have been 7 LVOs missed. There are 6 subitems showing statistical significance at an NIHSS cutoff of ≤2 which suggests that an NIHSS cutoff, even with these identified subpoints, is not a good CTA candidate selection tool for the detection of all LVOs.


Interaction of Digital Trends and Sustainable Development: The role of Contemporary Art

Abstract:
The purpose of the work is to analyze the impact of digital technologies on the sustainable development of contemporary art - an overview of the main trends that have emerged as a result of the integration of new media into artistic practices. The article methodologically classifies various genres of digital art by means of a critical qualitative analysis of practices, artworks, installations, exhibitions, collectives and events at the forefront of artistic innovation with new technologies. The scientific novelty lies in a comprehensive analysis of digital art through the prism of the concept of sustainable development. The work considers digital art not just as another technological innovation, but as a full-fledged artistic phenomenon of our time. The systematization of artistic practices associated with the use of the latest technologies is carried out, their common conceptual foundations are revealed. In addition, the article deals with issues of environmental sustainability, social responsibility, inclusiveness of digital practices. The dilemmas around the balance of technological innovation and artistic freedom from the perspective of sustainability ethics are outlined. The conclusions provide a conceptual understanding of optimal pathways for developing digital art in relation to societal sustainability.
 Keywords: digital art, sustainable development, new media art, network art, generative art, interactive art, AI, VR, AR, digital culture


Sentiment Analysis Using Convolutional Neural Network (CNN) and Particle Swarm Optimization on Twitter

Abstract: Over time, social media has always changed quickly. People can voice their ideas on various topics and communicate with each other through social media. One social media platform that allows users to express their ideas through tweets is Twitter. Sentiment is the route via which each person can express their ideas on a variety of subjects. The sentiment can be positive or negative. Sentiment analysis can be used to determine how Twitter users feel about particular subjects. Sentiment analysis on popular subjects in 2023, specifically the 2024 presidential contenders, will be done in this research. The dataset used in this research consists of 37,391 entries with 5 keywords. The research aims to understand how Twitter users respond and express their sentiments towards the presidential candidate through the use of deep learning classification techniques with Convolutional Neural Network (CNN), feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF) method, and feature expansion with Word2Vec. Furthermore, this study employs Particle Swarm Optimization as an optimization technique to enhance the sentiment analysis model's performance. The test's results demonstrate a high degree of accuracy, offering a comprehensive picture of Twitter users' sentiments and perspectives toward the 2024 presidential contenders. This research helps to understand the dynamics of public opinion in the political context. Based on the evaluation results of the research, it yielded an accuracy of 78.2%, showcasing an improvement of 10.07% compared to the baseline.


Analisis Instrumen Model Mitigasi Banjir di Indonesia

Abstract: One of measures to reduce the flood impact in Indonesia has been done by flood emergency response action plan. The action plan is generated from decision making process assisted by instrument such computer models which have capability in producing scenarios and decision to be taken. This research is conducted to identify how the written procedures (delivered by international and national agencies) related to flood emergency response, being accommodated by those (computer) models. By using qualitative analysis, the element procedures of an effective flood emergency response model have been reviewed. The result shows that the detailed information such human and material resources, facilities information, and warning time-lag, have not been properly and entirely accommodated by the (computer) model. Highlights are also given to propose more effective instrument to improve the management of flood crisis.


Perbedaan Nilai Indeks Eritrosit Pada Sampel Darah Vena Dengan Pengambilan Posisi Duduk Dan Posisi Berbaring

Abstract: The erythrocyte index or Mean Cospuscular Value is an average value that can provide information about the average erythrocyte and the amount of hemoglobin per-erythrocyte. The erythrocyte index consists of Mean Corpuscular Volume (MCV) or the average volume of an erythrocyte, Mean Corpusculara Hemoglobin (MCH) or the amount of hemoglobin per-erythrocyte, Mean Corpuscular Hemoglobin Concentration (MCHC) or the concentration of hemoglobin obtained per-erythrocyte. Erythrocyte index examination is more specifically used to determine what type of anemia is suffered by someone who experiences it. This study aims to determine the difference in the results of the examination of erythrocyte index values in venous blood sampling by taking sitting and lying positions. This research method is Crossectional, research conducted at Bunda Hospital Palembang. The population taken was female students of the S.Tr study program. Medical Laboratory Technology Muhammadiyah Palembang, Samples used in this study amounted to 31 samples using Purposive Sampling. The study obtained the results of MCV sitting position 84.88 and lying position 84.34 fl, MCH obtained an average value in sitting position 28.09 and lying position 28.05 pg / cell and MCHC examination obtained an average value in sitting position 33.05 and lying position 33.22 g / dl which means there are differences in erythrocyte index examination in venous blood sampling with sitting position and lying position. It can be concluded that venous blood sampling in the sitting position is not recommended for erythrocyte index examination, because the examination results are different in the sitting position.


The Development of Flip Chart Media in the Indonesian Language Subject to Enhance Students' Reading Comprehension Skills at Elementary School

Abstract: The objectives of this research are as follows: 1) To explore the development of Flip Chart Media in the Indonesian language subject to improve reading comprehension skills at SDN 1 Sukarara; 2) To analyze the product of the development of Flip Chart Media in the Indonesian language subject in enhancing students' reading comprehension skills at SDN 1 Sukarara; 3) To identify the obstacles faced by teachers and students in implementing Flip Chart Media in the Indonesian language subject at SDN 1 Sukarara. The methods of this research utilized a research and development approach, referring to the Research and Development (R&D) model by Borg and Gall. The population in this study consists of the entire number of male and female students at SDN 1 Sukarara, totaling 251 students, and the research sample was the fourth-grade class, comprising 30 students. This research was conducted from October to December 2023. The product trial included the tested product, trial subjects, and the implementation of the trial. Data collection techniques employed were interviews, questionnaires, written tests, and documentation. Research instruments consisted of: a) Instruments for Subject Matter Experts; b) Instruments for Media Experts; c) Questionnaire Instruments for Students; and d) Student Evaluation Test Instruments. Data analysis was conducted through validation and trial steps by calculating the obtained scores. The data collected in this research were both qualitative and quantitative. The research findings are as follows: 1) Based on validation results, revisions, and evaluations, there was an improvement in the assessment of the developed product, specifically showing positive outcomes for the Interactive Indonesian Flip Chart Media. The development of this Flip Chart media used the Borg and Gall development model, simplified into 7 steps: needs analysis, design, prototype creation, product revision, field trials, final revision, and utilization; 2) Based on the analysis of expert team validation regarding the quality of the developed product, the Interactive Indonesian Flip Chart Media falls under the "good" category. Observations during the trial process also indicated that students were enthusiastic about learning when using the developed Interactive Indonesian Flip Chart Media. The delivery of material using Flip Chart media was easily understood by students. In terms of learning outcomes, the use of Interactive Indonesian Flip Chart Media also had an impact on students' reading comprehension skills in the Indonesian language. Based on the post-test results given to 30 students, 26 students (87%) passed and 4 students did not pass (23%), classified as "good" learning achievement. This indicates that the development of Interactive Indonesian Flip Chart Media has the potential to facilitate students in learning; and 3) Obstacles in implementing Flip Chart Media in the Indonesian language subject at SDN 1 Sukarara, including 1) Limited Resources, 2) Teacher Readiness Level; 3) Technical Constraints in Using Flip Chart; 4) Varied Student Participation Levels; 5) Lack of Administrative Support; 6) Resistance to Change; and 7) Limited Classroom Physical Conditions.


Teachers’ Strategies and Challenges to Promote Students’ Active English Learning in SMK Muhammadiyah 1 Palu

Abstract: Student engagement has an important aspect in achieving excellent learning outcomes for learners and further improving their abilities. So teachers must have strategies to encourage student involvement. The aim of this research was to investigate the strategies the teachers use in activating students active learning in teaching and learning process and the challenges the teachers face in promoting students active learning. The researcher used qualitative research. The place of the research was in SMKS Muhammdiyah 1 Palu that located in Letjend Seoprapto. The Subject of the research were all English teachers at SMK Muhammadiyah 1 Palu that consist of four teachers. The data collection techniques that wil be used are: In depth Interview, Observation, and Documentation. The data will be analyzed in 3 steps, are Data reduction, data display, and conclusion. After conducting a research in SMK Muhammadiyah 1 Palu, it is found that in stimulating students’ active English learning, the teachers use six strategies; Questionning & discussion, visual based instruction, rolep lay, game, project based learning, and discovery based learning.From those six strategies the teachers used in the previous explanation, there are four strategies that are used dominantly; questioning & discussion, visual based instruction, project based learning, and discovery based learning.researcher proposes that future researchers interested in the same issue might do in-depth research on teacher ways to encourage students' active learning in English. For instance, the specific strategy that can stimulate the specific skill.


Evaluating the Performance of Mixed Zero-Inflated Poisson Regression Models with Time-dependent and Time-independent Covariates

Abstract: One of the issues that researchers may encounter in count data is having many zeros. One of the solutions to model these data is using zero-inflated Poisson (ZIP) regression models. Recently, researchers have started to model longitudinal count data with time-dependent covariates. However, it has not been considered whether a model with time-dependent covariates provides a better fit than a model with time-independent covariates. In this paper, the fit between a mixed ZIP model with time-dependent covariates and a mixed ZIP model with time-independent covariates is compared using simulation. Using the deviance information criterion as a measure of fit, we found that the model with time-dependent covariates exhibits a better fit than the model with time-independent covariates. KEYWORDS correlated data, count data, excess zeros, longitudinal, mixed models, model fit


THE EFFECT OF IMMERSIVE TECHNOLOGY ON ENHANCING STUDENT LEARNING:

Abstract: Education is one of the fundamental factors influencing national development. Educators are required to constantly explore the newest and creative strategies to deliver topics to students and promote students’ engagement in the learning process. One technology that has numerous potentials to facilitate the education process is immersive technology. Immersive technology has the potential to improve student achievement by enabling better learning outcomes. This study aims (1) to analyze the trends and impact of immersive technology usage in education; (2) to identify immersive technology that supports students’ learning processes in the pandemic era; and (3) to identify the usage of learning theories in educational application development that implement immersive technology to enhance students’ learning outcomes. This study examined seventeen selected studies after adopting the selection process of the systematic literature review process by Kitchenham. The selected studies were published between 2018 and 2022 by Emerald Insight, IEEE Xplore, and Scopus. There are three kinds of immersive technology identified in this study: virtual reality, augmented reality, and mixed reality. The study showed the nine types of students’ learning outcomes, and the results showed that using immersive technology significantly improved student learning compared to traditional methods. Furthermore, thirteen learning theories were adopted as the basis for developing educational applications. Future research directions are also suggested to continue developing promising and limited new technologies to enhance the variety of education-oriented applications. Immersive technologies designed for learning are still on a long journey, including considerations of readiness and potential effects that may arise for users such as privacy and security concern.


Abstract WP37: Way Beyond the Call: An Extended Collaborative Feedback Project With EMS

Abstract: Background/Purpose: Emergency Medical Services (EMS) providers assess, treat and transport patients to destinations that provide the most appropriate emergency care. EMS provides the first crucial link in the chain of survival for patients suffering from life-threatening conditions. Stroke is a time-sensitive emergent condition which requires urgent evaluation, treatment, and expeditious transport to stroke-specialty centers. We noticed that EMS was not providing us with important information that could impact our initial stroke care. Our 21-month collaborative feedback project was created to provide EMS personnel who frequent our Emergency Department (ED) the tools necessary to administer high-quality, expeditious care. Our goal was to improve information provided to the stroke team and ED upon patient arrival by improving and sustaining three metrics by 20% within a 21-month period, by providing feedback to EMS, which include last known well time (LKWT), caregiver contact information (CCI), and mLAPSS documented (mLAPSS). Methods/Implementation: We collaborated with the Beverly Hills Fire Department (BHFD) educators from July of 2021 through March of 2023, providing a continual feedback loop for all EMS runs with an adjudication of stroke and TIA. Our feedback tool included quality metrics with benchmarks determined by the Los Angeles County EMS Agency, in conjunction with the BHFD educators and our team. We performed chart reviews and extracted data pertinent to each run. We summarized the hospital course and disposition for each patient and the feedback tool was distributed to the EMS providers for review. Data was collected and analyzed over seven quarters (Q3 and Q4 of 2021, Q1-Q4 of 2022, and Q1 of 2023, respectively). Results/Conclusions: Sustained improvements were noted in documentation. We achieved 20% improvement in LKWT (from 78% in Q3-Q4 2021, 98% in Q1-4 2022, 98% in Q1 2023), CCI collected (from 29% in Q3-Q4 2021, 75% in Q1-4 2022, 100% in Q1 2023), and mLAPSS completion was 8% improvement (from 89% in Q3-Q4 2021, 97% in Q1-4 2022, 100% in Q1 2023). An unanticipated result of this project was a halo effect of improved destination decisions from EMS. We will continue to track and trend these metrics and work closely with our colleagues in EMS.


Unravelling the tectonic phases: The impact of the South Caspian Block on Late Cenozoic deformation in the Central Alborz, Iran

Abstract: Paleostress reconstruction through polyphase fault-slip data of a multi-deformed region suffered collisional tectonics can lead to detecting the stress phases. Based on earthquakes’ focal mechanisms and morphotectonic features, multiple deformation phases model has been proposed for the Alborz Mountains located in the collision zone between the Arabian (Central Iran) and Eurasian (South Caspian block) plates during the Late Cenozoic. In this study, paleostress analysis has been carried out in an area bounded by two (Kandovan and Taleghan) regional faults in the Central Alborz Mountains using fault-slip data. This analysis resulted in the identification of three main tectonic phases. The first, compressional phase is proposed to cause the inversion of the Alborz Mountains’ major initial normal faults to reverse faults during the convergence of the Arabian and Eurasian plates. The second, transpressional phase is offered as a factor for the reactivation of the hidden Alborz basement faults to form the NE-striking left-lateral strike-slip faults on the sedimentary cover. The third, transtensional phase is suggested to be responsible for the development of the NNE–SSW left-lateral transtensional faults. It is proposed that the second and third paleostress phases are affected mainly by the indentation of the South Caspian Block into the Alborz Mountains during the Late Cenozoic.


Implementation of Association Rules Algorithm to Identify Popular Topping Combinations in Orders

Abstract: Association rule is a data mining technique to find associative rules between a combination of items. This research aims to apply association rules algorithm in identifying popular topping combinations in food orders. This application aims to help restaurant owners or food businesses understand their customers' preferences and optimize their menu offerings. Data obtained from kaggle, the association rules algorithm is applied to this dataset to identify patterns or combinations of toppings that often appear together in orders. The results of this study show toppings with chocolate as a popular item in orders. These findings can provide valuable insights for food business owners in structuring their menus and determining attractive offers for customers. This study also applied a comparison between the apriori, fp- growth and eclat algorithms, with the result that the best item transaction rule was found: a combination of dill & unicorn toppings with chocolate with 60% confidence. Overall, the application of eclat algorithm in this study provides the best performance with higher execution speed, thus providing insight into customer preferences regarding topping combinations in food orders. Despite the shortcomings of the data form from this study, it is expected to help business owners in optimizing their offerings, increasing customer satisfaction, and improving their business performance.


Comparison of K-Means, BIRCH and Hierarchical Clustering Algorithms in Clustering OCD Symptom Data

Abstract: The hallmarks of Obsessive-Compulsive Disorder (OCD) are intrusive, anxiety-inducing thoughts (called obsessions) and associated repeated activities (called compulsions). To understand the patterns and relationships between OCD data that have been obtained, data will be grouped (clustering). In clustering using several clustering algorithms, namely K-Means, BIRCH, In this work, hierarchical clustering was used to identify the optimal cluster value comparison, and the Davies Bouldin Index (DBI) was used to confirm the results. Then the results of the best cluster value in processing OCD data are using the BIRCH algorithm in the K10 experiment which gets a value of 1.3. While the K-Means algorithm obtained the best cluster at K10 with a value obtained of 1.36 and the Hierarchical clustering algorithm also at the K10 value of 2.03. Thus in this study, the comparison results of the application of 3 clustering algorithms obtained results, namely the BIRCH algorithm shows the value of the resulting cluster is the best in clustering OCD data. This means that the BIRCH algorithm can be used to cluster OCD data more accurately and efficiently.


Perbandingan Hasil Recovery Tools Mobile Forensic Di Smartphone Android Menggunakan Metode National Institute Of Justice (NIJ)

Abstract: Penggunaan smartphone di Indonesia mencapai 192,15 juta pengguna aktif pada tahun 2022, menjadikannya pasar terbesar keempat di dunia. Namun, pertumbuhan ini juga berdampak pada meningkatnya tindak kejahatan siber, tercatat sebanyak 8,831 kasus selama tahun tersebut. Penelitian tentang efektivitas alat pemulihan mobile forensic pada smartphone Android dapat menjadi acuan pemilihan alat uji mobile forensic baik untuk kebutuhan penyelidikan maupun akademis. Pada penelitian ini dilakukan pengujian terhadap dua tools mobile forensic, yaitu Andriller yang merupakan aplikasi open source, dan MOBILedit Forensic Express Pro yang merupakan aplikasi berbayar, untuk mengetahui seberapa tingkat recovery yang mampu dilakukan oleh kedua tools tersebut. Penelitian dengan metode National Institute of Justice (NIJ) menemukan bahwa kedua software yang digunakan dapat digunakan untuk analisis mobile forensic tanpa akses root ke smartphone. Hasil pengujian menunjukkan MOBILedit Forensic Express Pro memiliki tingkat pemulihan data yang lebih tinggi (83,33%) dibandingkan dengan Andriller (50%). Dari hasil ini diharapkan dapat membantu dalam memilih alat atau software mobile forensic dalam proses penyelidikan maupun kebutuhan akademis. Kata kunci: digital forensik, mobile forensic, national institute of justice ------------------------------------------------------------------------- The usage of smartphones in Indonesia reached 192.15 million active users in 2022, making it the fourth largest market in the world. However, this growth has also led to an increase in cybercrime, with 8,831 cases reported during that year. A research on the effectiveness of mobile forensic recovery tools on Android smartphones, can be a reference for selecting mobile forensic testing tools for both investigative and academic needs. In this research, two mobile forensic tools were tested, namely Andriller, which is an open source application, and MOBILedit Forensic Express Pro, which is a paid application, to find out what level of recovery these two tools are capable of. Research using the National Institute of Justice (NIJ) method found that the two software used can be used for mobile forensic analysis without root access to a smartphone. The test results indicated that MOBILedit Forensic Express Pro achieved a higher data recovery rate (83.33%) compared to Andriller (50%). These findings are expected to aid in selecting suitable mobile forensic tools for investigative and academic purposes. Keywords: digital forensik, mobile forensic, national institute of justice


Real-Time Traffic Light Recognition with Lightweight State Recognition and Ratio-Preserving Zero Padding

Abstract: As online shopping is becoming mainstream, driven by the social impact of Coronavirus disease-2019 (COVID-19) as well as the development of Internet services, the demand for autonomous delivery mobile robots is rapidly increasing. This trend has brought the autonomous mobile robot market to a new turning point, with expectations that numerous mobile robots will be driving on roads with traffic. To achieve these expectations, autonomous mobile robots should precisely perceive the situation on roads with traffic. In this paper, we revisit and implement a real-time traffic light recognition system with a proposed lightweight state recognition network and ratio-preserving zero padding, which is a two-stage system consisting of a traffic light detection (TLD) module and a traffic light status recognition (TLSR) module. For the TLSR module, this work proposes a lightweight state recognition network with a small number of weight parameters, because the TLD module needs more weight parameters to find the exact location of traffic lights. Then, the proposed effective and lightweight network architecture is constructed by using skip connection, multifeature maps with different sizes, and kernels of appropriately tuned sizes. Therefore, the network has a negligible impact on the overall processing time and minimal weight parameters while maintaining high performance. We also propose to utilize a ratio-preserving zero padding method for data preprocessing for the TLSR module to enhance recognition accuracy. For the TLD module, extensive evaluations with varying input sizes and backbone network types are conducted, and then appropriate values for those factors are determined, which strikes a balance between detection performance and processing time. Finally, we demonstrate that our traffic light recognition system, utilizing the TLD module’s determined parameters, the proposed network architecture for the TLSR module, and the ratio-preserving zero padding method can reliably detect the location and state of traffic lights in real-world videos recorded in Gumi and Deagu, Korea, while maintaining at least 30 frames per second for real-time operation.


Problematika Kemampuan Literasi Membaca Siswa Sekolah Dasar

Abstract: Reading is an activity that has the aim of understanding the content of what is written and is also a skill that elementary school students must master because with good reading skills students can learn a lot of knowledge. The purpose of writing this journal is to describe the problematic factors of low reading ability in elementary school students. This type of research is qualitative research using the library study method, with data collection techniques from various reading sources in the form of articles on various types of problems in elementary schools. The research results show that 1) The first problematic cause of low reading ability is students' low interest in reading, books that are less interesting to students, inadequate libraries, short reading time of only 15 minutes. 2) The role of parents in increasing literacy, is providing motivation and encouragement, 3) The role of teachers in increasing literacy, providing media and methods that attract students' interest, teachers providing reading corners in the classroom.


Evaluating the Usefulness of Least Squares Regression in Petrophysics Interpretation

Abstract: This paper is to update the information provided in the author’s previous papers on evaluating the uncertainty in least squares results. It is prompted by new information that shows that the usefulness of any least squares result cannot be guaranteed by conventional statistics (such as R-squared, F-statistic, or standard error of the regression, sigma). A new method based on the singular value decomposition (SVD) of a matrix, when accompanied by a Relative Error Bound (REB) on the estimated parameters, provides the user with a tool that can better assess the usefulness of any least squares result. Another important aspect of the REB is that it provides the user of the SVD method with a powerful tool for judging which is the best among several candidate solutions. In addition, it provides the user with a numerically stable method of computing the data ordinarily provided by principal component regression by eliminating the need to perform an eigenvector-eigenvalue analysis of an ATA matrix. This is of particular interest because forming the ATA matrix is often accompanied by a loss of data. The new method also provides the user with an improved method for selection of which principal components should be retained for a given problem.


Copy-Move Forgery Detection and Localization Framework for Images Using Stationary Wavelet Transform and Hybrid Dilated Adaptive VGG16 with Optimization Strategy

Abstract: Due to the availability of low-cost electronic devices and advanced image editing tools, changing the semantic meaning of a particular image has become straightforward by employing various image manipulation techniques like image copy-move, image splicing and removal operations. The tampered images with this sophisticated software are rich in visualization, making the modifications invisible to the naked eye. Detecting these image alterations is laborious, time-consuming, and often yields inappropriate results. The current techniques use conventional square, slide regular, and artifacts procedures to identify image deviations to combat image forgery practices. Still, these techniques exhibit problems related to generalization, training and testing, and model complexity. So, in this paper, a novel image forgery detection and localization framework is implemented using stationary wavelet transform (SWT), and a Hybrid Dilated Adaptive VGG16 model with optimization is introduced to classify forgery images and localize the forgery regions present in an image. Initially, the proposed framework processes the input image with SWT to decompose an image into different subband and further divide it into patches. After that, the hybrid dilated adaptive VGG16 Network (HDA-VGG16Net) is built to extract the deep image features from the patches. Later, the Hybridized Tuna Swarm with Bald Eagle Search Optimization (HTS-BESO) technique is applied to optimize the VGG16 parameters. Finally, feature matching is formed using multi-similarity searching to recognize whether the input image is forged or original by locating forgery regions. The evaluation results are compared with existing forgery detection approaches to ensure the efficiency of the developed model by considering multiple performance measures.


When Handcrafted Features Meet Deep Features: An Empirical Study on Component-Level Image Classification

Abstract: Scene text detection from natural images has been a prime focus from last few decades. Classification of foreground object components is an essential task in many scene text detection approaches under uncontrollable environment. As it heavily relies upon robust and discriminating features, several features have been engineered for component-level text non-text classification. Competency of such feature descriptors particularly in respect of deep features needs to be examined. In this paper, we present prospective feature descriptors applicable to component-level text non-text classification and examine their performance along with convolutional neural network based deep features. Series of experiments have been carried out on publicly available benchmark dataset(s) of multi-script document-type, scene-type, and combined text vs. non-text components. Interestingly, feature combination is found to put well-demonstrated deep features into tough competition on most datasets under consideration. For instance, on the combined text non-text classification problem, CNN based deep features yield 97.6%, whereas aggregated features produce an accuracy of 98.4%. Similar findings are obtained on other experiments as well. Along with the quantitative figures, results have been analyzed and insightful discussion is made to ascertain the conjectures drawn herein. This study may cater the need of leveraging potentially strong handcrafted feature descriptors.


Non-Gaussianity detection of single-mode rotationally symmetric quantum states via cumulant method

Abstract: Abstract The non-Gaussianity of quantum states incarnates an important resource for improving the performance of continuous-variable quantum information protocols. We propose a novel criterion of non-Gaussianity for single-mode rotationally symmetric quantum states via the squared Frobenius norm of higher-order cumulant matrix for the quadrature distribution function. As an application, we study the non-Gaussianities of three classes of single-mode symmetric non-Gaussian states: a mixture of vacuum and Fock states, single-photon added thermal states, and even/odd Schrödinger cat states. It is shown that such a criterion is a faithful and effective one for revealing non-Gaussianity. We further extend this criterion to two cases of symmetric multi-mode non-Gaussian states and non-symmetric single-mode non-Gaussian states, respectively.


Field-enriched A* search algorithm for robot motion in path planning

Abstract: Path planning in unknown or complex environments is a central issue in the fields of automation and robotics. To address this global path planning problem while maintaining the smoothness of robot motion, as well as to ensure safety by keeping away from obstacles, this paper propose an innovative method called Field-Enriched A* Planner (FEAP), which incorporates the repulsive potential field into the heuristic computation of the A* search algorithm. The research modified the heuristic mechanism of the A* algorithm by incorporating both attraction and repulsion factors from the Artificial Potential Field method, thereby enhancing the influence of obstacles in the decision-making process. The repulsive potential field enhances obstacle avoidance, ensuring smoother robot motion during the path planning process. The simulation experiments are conducted on the proposed method and compare it with common path planning algorithms. Results from the experimental trials demonstrate that the new method can effectively enhance the smoothness of robot motion while ensuring a safe path is identified.


Prediction of CBR by Deep Artificial Neural Networks with Hyperparameter Optimization by Simulated Annealing

Abstract: Abstract The construction of pavements requires the complete identification of the soils in place and of the added materials. This identification consists in determining the class of the soils and in evaluating their bearing capacity through the California bearing ratio (CBR) index. Obtaining the CBR index is very costly in terms of time and financial resources, especially when it is a large-scale project. It thus leaves prospects of obtaining it by simpler processes; hence, it arises the need to find simpler processes compared to classical processes. This study develops models for predicting the CBR index from physical properties that are less complex to obtain, based on deep neural networks. To achieve this, three databases were used. A first database consists of the proportion of fines, the Atterberg limits and the Proctor references of the soils. A second database uses the methylene blue value instead of the Atterberg limits, and a third database uses only the proportion of fines and the Proctor soil reference. On each of the databases, a deep neural network model was developed using dense layers, regularization layers, residual blocks and parallelization in TensorFlow to predict the CBR value. Each model was formed by combining several deep neural networks developed according to specific architectures. To expedite training, the simulated annealing method was employed to optimize hyperparameters and define the optimal configuration for each network. The predictions obtained are correlated with the true values from 83.6 to 96.5%. In terms of performance, the models have a mean deviation ranging from 3.74 to 5.96%, a maximum deviation ranging from 12.43 to 16.2% and a squared deviation ranging from 0.781 to 2.189. The results suggest that the variable VBS has a negative impact on the accuracy of the networks in predicting the CBR index. The developed models respect the confidence threshold (± 10%) and can be used to set up a local or regional geotechnical platform.


Machine Learning for Web Vulnerability Detection: The Case of Cross-Site Request Forgery

Abstract: Cross-site request forgery (CSRF) vulnerabilities pose a significant threat to web application security, enabling attackers to execute unauthorized actions on behalf of authenticated users. Conventional CSRF detection methods, such as manual code review and static analysis, are often time-consuming, error-prone, and inefficient. Proposes Mitch, a novel machine learning (ML)-based solution for the black-box detection of CSRF vulnerabilities. Mitch employs supervised learning, trained on a comprehensive dataset of HTTP requests and responses, to effectively identify security-sensitive HTTP requests and uncover CSRF vulnerabilities within them. Rigorous evaluations on a diverse set of real-world web applications demonstrate Mitch's remarkable ability to detect CSRF vulnerabilities with high accuracy, outperforming traditional methods. Mitch's automated nature eliminates the need for manual code review and static analysis, saving time and effort while reducing the risk of human error. Additionally, Mitch's scalability allows seamless integration into continuous integration and continuous delivery (CI/CD) pipelines, enabling continuous security monitoring and vulnerability detection. Mitch's efficacy extends beyond detecting known CSRF vulnerabilities. Its ability to identify patterns and relationships enables it to uncover obscure CSRF vulnerabilities that may have been overlooked by traditional methods, including zero-day vulnerabilities. In conclusion, Mitch emerges as a powerful tool for enhancing web application security, offering a comprehensive and automated solution for detecting CSRF vulnerabilities. Its ability to handle complex web applications, uncover hidden CSRF vulnerabilities, and integrate into CI/CD pipelines makes it an indispensable tool for web security professionals. Mitch's adoption has the potential to significantly reduce the risk of CSRF attacks and safeguard sensitive user data. We propose a methodology to leverage machine learning (ML) for the detection of web application vulnerabilities. We use it in the design of Mitch, the first ML solution for the black-box detection of cross-site request forgery vulnerabilities. Finally, we show the effectiveness of Mitch on real software. In this project, we propose a methodology to leverage Machine Learning (ML) for the detection of web application vulnerabilities. Web applications are particularly challenging to analyses, due to their diversity and the widespread adoption of custom programming practices. ML is thus very helpful for web application security it can take advantage of manually labeled data to bring the human understanding of the web application semantics into automated analysis toolsMitch allowed us to identify 35 new CSRFs on 20 major websites and 3 new CSRFs on production software. Keywords: Mitch, CSRF, CI/CD pipelines, Security Token Service (STS), Same-Origin Policy (SOP).


On Time-based Exploration of LMS Data and Prediction of Student Performance

Abstract: Abstract Learning Management Systems (LMS) gather extensive amounts of data about students' progression through courses. Such data is then made available via APIs for data exploration and utilization. A body of research has investigated using such data to predict student performance based on data collected earlier in a course. The driving question in such efforts has been whether student performance can be accurately predicted early enough to intervene and provide needed help. Two main issues have been pointed out in these attempts: the portability and robustness of these predictions. In this work-in-progress study, we introduce a new approach to exploring LMS data. Such an approach looks at the data as a set of time series each representing the progress of a student within a course. This study explores how students advance through courses over time and the variability of student performance between any two time points. It utilizes the LMS data (Canvas in this case) obtained from multiple Computer Science courses taught by different instructors in different formats (online and face-to-face) over three years in a public four-year university. Ways for summarizing and visualizing such data are discussed, and useful predictor features are extracted and used to build and evaluate predictive models of student performance at any time point. The study explores questions such as: How does the progress of passed and failed students differ in these courses? How early can student performance be accurately predicted? Can data collected from one course be used to predict the performance of students in another course by the same or a different instructor? Are student journeys through courses unique, or are there patterns that transcend students and courses?


Knowledge and Instance Mapping: architecture for premeditated interoperability of disparate data for materials

Abstract: Abstract Predicting and elucidating the impacts of materials on human health and the environment is an unending task that has taken on special significance in the context of nanomaterials research over the last two decades. The properties of materials in environmental and physiological media are dynamic, reflecting the complex interactions between materials and these media. This dynamic behavior requires special consideration in the design of databases and data curation that allow for subsequent comparability and interrogation of the data from potentially diverse sources. We present two data processing methods that can be integrated into the experimental process to encourage pre-mediated interoperability of disparate material data: Knowledge Mapping and Instance Mapping. Originally developed as a framework for the NanoInformatics Knowledge Commons (NIKC) database, this architecture and associated methods can be used independently of the NIKC and applied across multiple subfields of nanotechnology and material science.


Lab Modules for Developing Practical Skills in a Python Scripting Course

Abstract: Abstract To implement recommendations from our Industry Advisory Board, we switched from PHP to Python Scripting Language in the course Scripting for Information Technology. Python is easy to learn, easy to use and powerful. It is widely used in software development (Dev) and IT operations (Ops), artificial intelligence (AI), big data and cloud computing. Our students are eager to learn hands-on skills of using Python in real-world scenarios, which are not covered adequately in the textbook. In this project, we developed five supplemental lab modules and an online, virtual environment, which can be used by both face-to-face students and distance education students, to meet their increasing demand for hands-on exercises. The lab modules include 1) Python in Linux Automation (Ansible), 2) Using Python with NoSQL database system MongoDB, 3) Web Programming Environment with Python, 4) Using Python with Podman RESTful API and 5) Simulating an Internet-Connected Weather Sensor Using Python. A Canvas shell was created to host lab manuals, lab quizzes, and supporting material such as demo video and tutorial. The Linux-based virtual environment is portable and can be accessed by students anytime and anywhere to do labs. Some tasks in the labs are automatically graded on Canvas. Other tasks are manually graded. The lab modules were deployed in two sections taught by different instructors in spring 2022. An anonymous survey was conducted at the end of the semester to collect feedback from students. The instructors also provided their evaluations. We believe that the hands-on approach will help students better understand course topics and improve their practical skills of using Python in the real world.


Quantum Computing and Artificial Intelligence: Synergies and Challenges

Abstract: Due to the explosive rise of quantum computing, there has been intense competition in business and academics in the field of quantum optics in recent decades. The current invention's overall scalability in quantum computing has surpassed many orders of magnitude, whereas ubiquitous quantum computers can support up to hundreds of quantum bits, or thousands of qubits. Strong machines continue to be developed. As a result, ethnicity has served as the inspiration for a huge number of studies and reports. This essay offers an introduction for everyone who would truly like to understand more about the ideas of quant communication and computing from a machine learning standpoint. It starts with such an educational approach and goes on to cover important turning points and the latest advancements in quantum computing. In this research, these fundamental characteristics of such a virtual network are divided into four major challenges, each of which has been thoroughly examined. correspondingly, A, B, C, and D stand for quantum physics, networking, security, and algorithms. The main issues, important areas of research, and most recent advancements are discussed as the article comes to a close.


Collider VR: Using Virtual Reality for Immersive Theatre and the Performing Arts

Abstract: This MRP explores the future of immersive theatre in virtual reality by examining the production Collider VR as a case study, produced by Single Thread Theatre and presented at the 2021 Festival of Live Digital Art and the 2021 rEvolver Festival. Collider VR utilized concepts from traditional theatre-making and applied them in a virtual environment to investigate four specific research questions. First, the project explored the use of the avatar as mask and the possibilities of embodiment in virtual space with hand gesture and head control. Second, it examined the potential for an ensemble to move as a collective in virtual space, as a chorus, and the applicability of the concept of a coryphaeus in VR. Third, the project examines how the logic of vector math, necessary to the structure of a virtual setting, can become integrated into the rehearsal lexicon and what effects changes to the virtual world during a performance can have. Finally, Collider VR explores the audience experience of virtual space and how designers can incorporate realities of that usage into immersive design. In addition to documenting the process of producing Collider VR and the conclusions reached in each of these gyms, this paper will contextualize the use of virtual reality technology in theatrical performance over the technology’s history to better forecast the future of virtual theatre.


Tuning atom-field interaction via phase shaping

Abstract: A coherent electromagnetic field can be described by its amplitude, frequency, and phase. All these properties can influence the interaction between the field and an atom. Here we demonstrate the phase shaping of microwaves that are scattered by a superconducting artificial atom coupled to the end of a semi-infinite one-dimensional transmission line. In particular, we input a weak exponentially rising pulse with phase modulation to a transmon qubit. We observe that atom-field interaction can be tuned from a nearly full interaction (interaction efficiency, i.e., amount of the field energy interacting with the atom, of 94.5 %) to effectively no interaction (interaction efficiency of 3.5 %).