Reviewerly/exHarmony huggingface dataset:
A Noisy nnU-Net Student for Semi-supervised Abdominal Organ Segmentation

While deep learning methods have shown great potential in the context of medical image segmentation, it remains both time-consuming and expensive to collect sufficient data with expert annotations required to train large neural networks effectively. However, large amounts of unlabeled medical image data is available due to the rapid growth of digital healthcare and the increase in availability of imaging devices. This yields great potential for methods which can exploit large unlabeled image datasets to improve sample efficiency on downstream tasks with limited amounts of labeled data. At the same time, deploying such models in real-world scenarios poses some limitations in terms of model size and required compute resources during inference. The 2022 MICCAI FLARE Challenge tries to address both these aspects in a task where participants can make use of 2000 unlabeled, as well as 50 labeled images, while also measuring inference speed, CPU utilization and GPU memory as part of the evaluation metrics. In the context of this challenge, we propose a simple method to make use of unlabeled data: The noisy nnU-Net student. Here the unlabeled data is exploited through self-training, where a teacher model creates pseudo labels, which in turn are used to improve a student model of the same architecture. We show, based on results in a cross-validation and a separate held-out dataset, that this simple method yields improvements over even a strong baseline (+2 DSC), while simultaneously reducing inference time by an order of magnitude, from an average of over 500 s to roughly 50 s, and peak memory requirements by almost a factor of two.
Uncertainty-aware Mean Teacher Framework with Inception and Squeeze-and-Excitation Block for MICCAI FLARE22 Challenge

Semi-supervised learning has attracted extensive attention in the field of medical image analysis. However, as a fundamental task, semi-supervised segmentation has not been investigated sufficiently in the field of multi-organ segmentation from abdominal CT. Therefore, we propose a novel uncertainty-aware mean teacher framework with inception and squeeze-and-excitation block (UMT-ISE). Specifically, the UMT-ISE consists of a teacher model and a student model, in which the student model learns from the teacher model by minimizing segmentation loss and consistency loss. Additionaly, we adopt an uncertainty-aware algorithm to make the student model learn accurate and reliable targets by making full use of uncertainty information. To capture multi-scale features, the inception and squeeze-and-excitation block are incoporated into the UMT-ISE. It is worth noting that abdominal CT of test cases are first extracted before multi-organ segmentation in the inference phase, which significantly improves segmentation accuracy. We implement experiments on the FLARE22 challenge. Our method achieves mean DSC of 0.7465 on 13 abdominal organ segmentation tasks.
Recommendation Systems

With the increasing amount of data produced daily, it becomes very difficult for users to find resources suitable to their needs. Recommendation systems, which are capable of providing individualized suggestions or guiding the user to interesting or relevant resources within a large data space, are proposed for this purpose. In this article, the authors do a comprehensive assessment of recommendation models, propose a categorization scheme, analyze challenges, and explore unresolved issues. In addition, they highlight new trends and future visions in this field of study, emphasizing the need of merging ontologies and machine-learning algorithms to increase the accuracy and efficiency of the recommender systems.
Chatbot Application to Automate Services in FnB Business Using Seq2Seq LSTM

The relevancy of food and beverage business will never fade. With that in mind, using the tech in order to improve its process will allow the business to thrive and improve its efficiency. Customer service is a fitting role for tech to work, as artificial intelligence might prove to be able to do the job efficiently and effectively. This paper tries to implement a customer service chatbot for restaurants so they may leverage its advantages in order to improve in terms of performance and efficiency. The implementation is done using the LSTM model which has proven to have decent results in chatbot implementation.
Augmented Reality Assisted Healthy Drinking Water Products Promotion and Literacy: A Development and Evaluation

In this study, we develop and evaluate the design of an augmented reality mobile application that allows consumers to get product promotions while increasing health literacy about healthy drinking water. First, we developed an augmented reality mobile application using the Software Development Life Cycle model, namely the v-waterfall model. Next, we implemented the application to a group of target consumers to find out how far the increase in consumer literacy regarding healthy drinking water was after using the application. We use the experimental method by giving tests before and after using the application to the target consumer group. The test results in the application development section show that this application has met ISO/IEC 25010 in functional suitability, performance efficiency, compatibility, and usability. In addition, the results of further research found that there were differences in consumer literacy regarding healthy drinking water before and after being processed (using applications). Through this research, we contribute to the development of the use of augmented reality technology as advertising content for a product while increasing consumer literacy.
Development of Educational Application about Mental Disorders with Augmented Reality for Android

The goal of developing this MentalAR application is to educate people about the existence of mental illness because the authors feels that this topic is rarely discussed and rarely gets the public spotlight, but according to a survey data the number of people with mental disorder is high. The development method that the author uses is Waterfall method where the author must collect requirements, design applications from the collected needs, build applications according to the designs made, test applications using black box and U AT, and refine applications according to user input. The result of this project is an educational application that is easy to use, easily accessible and can explain mental illness to the public.
Cyber Threats and Scams in FinTech Organizations: A brief overview of financial fraud cases, future challenges, and recommended solutions in Bangladesh

The idea of financial systems has changed with the touch of applications based on information technology and came up with a new terminology called ‘FinTech’ (Financial Technology). With the rising technology, Fintech has become a modern phenomenon. Financial organizations deal with highly confidential and sensitive information, including personal and financial data, all of which are the primary target of cybercriminals. Users and other stakeholders are massive in number, and many are not concerned about security, so they often find themselves as victims. The result of the study shows that from the perspective of Bangladesh, most of the attacks on the fintech industry are generated using ransomware and social engineering methods. It also shows that app-based Mobile Financial System (MFS) is the most affected sector in the financial system. The study provides a comprehensive framework, FinSec, which refers to the financial Security Framework to protect from cyber-attacks targeting any financial organization. It covers recommendations for regular end-users and anybody working in the financial sector. It also provides an architecture based on Consortium Blockchain, Hyper-ledger Fabric in a hybrid cloud to ensure a high level of security at the application level. Additionally, the framework proposes newer Three-Way Authentication (3WA) and Gamification to protect end-users. The research emphasizes ensuring a minimum level of training, as even after ensuring everything, massive damage can occur for the simplest mistake of the individuals related to the industry. To protect the financial system, from end-users to employees and user applications to the whole infrastructure, everything, and everyone should be secured. The framework hence recommends three subunits – Action, Knowledge, and Simulation Unit. These subunits protect the respective sector and, finally, end up protecting the fintech organizations.
Automatic Modulation Classification using Graph Convolutional Neural Networks for Time-frequency Representation

Recognition of the radio signal's modulating scheme is becoming increasingly important in civil and military applications. It can potentially alleviate the electromagnetic signal congestion in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$5\mathrm{G}$</tex> networks by utilization of dynamic spectrum access or perform friend/foe identification in electronic military warfare as well as to support the detection of cyber-security related attacks. The recent advances in graph-convolutional networks (GCN) reveal a potential for usage in applications such as automatic modulation classification (AMC). Considering the structure of the modulated signal in time and frequency, this work proposes GCN architecture for AMC in various signal to noise (SNR) levels. The experimental results reveal that such approach delivers comparable results to other approaches published in the literature.
UI Pattern Analysis in Obtaining Highest Success Rate in University Admission Website

The university admissions website has a fairly important role. The website is not only useful as a place of information for prospective new students but it also can be a way for prospective students to register, other than come directly to the location of the university. Each University has a different website, but with the same objectives, those are providing information and make web visitors feel easier to register and get the information that visitors are looking. Visitors may leave the website when they find it difficult. Each website provides various user interface design in providing information regarding new student admissions, tuition fees, registration flow, information and study programs, registration requirements, as well as registration menus for prospective students who want to register. The user interface designed is quite varied, it impacts in the possibility of website visitors will feel helped, or vice versa. Interface design is the first door for new students to register, so it is necessary to research which design variation is the best. Researchers grouped 15 existing interface variations of university admission websites into 6 patterns. Each pattern has its success rate. The success rate in achieving the main objectives of the website is the main thing that will be sought in this research. The task success rate analysis is the method used in the research. Success rate obtained from observing 30 participants that has completed tasks. The results of this study are 3 patterns get the highest success rate, namely patterns 2, 3, and 6. The pattern with the highest success rate has an average user time that is closer to the standard time compared to other patterns. Patterns 2, 3, and 6 have similar characteristics in interface components, which other patterns do not have. The patterns with the best success rates have a Call To Action component that is designed as button, the button is on the homepage, and the website has a clear visual hierarchy.
Implementation of Haversine Algorithm and Geolocation for travel recommendations on Smart Applications for Backpackers in Bali

Bali is one of the islands known as a tourist destination with the main purpose of traveling to Bali to enjoy the beauty of nature. Various tourist activities in Bali are well known both nationally and internationally. With so many types of tours available in Bali, travelers, especially first-time visitors, may need a guide on the tourist attractions they want to visit in Bali. However, some tourists, especially backpackers, backpackers are known to explore tourist attractions independently, freely, and with as little budget as possible. Concerning this statement and the decline in tourists to Bali due to the impact of the Covid-19 pandemic, the author decided to use the Haversine formula to develop a geographic information system for smart backpacker travel recommendations. with this problem. This Android application helps tourists travel from registration to tour guides. Implementing the Haversine formula in your app is equivalent to up to 88% of the distance reported by the Google Maps API. These results are used as a recommendation function for the selection of attractions by showing the distance between the previous backpacker's location selection and attractions.
Improving Education Quality in Cloud Education Environment

This electronic document is a “live” template and already defines the components of your paper [title, text, heads, etc.] in its style sheet. *CRITICAL: Do Not Use Symbols, Special Characters, Footnotes, or Math in Paper Title or Abstract. (Abstract)sing cloud computing for education is arising in this era, because of the advantages and characteristics of cloud computing itself. Many studies show how cloud computing can be adapted to many fields of life such education, trading, training, and so on. The research on cloud computing is about the concept, cloud computing model, users' acceptance of the cloud, energy consumption, security, cost, and so on. This study discussed how to use cloud computing's advantages and characteristics can help in achieving 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> century learning skills and to understand its environment will help to improve education quality. As a result of this study, we propose a framework and mechanism for cloud education that can improve education quality.
Energy Saving Method in an Illumination System for Brawijaya Museum Display

One of the most important aspects to consider is the lighting in the room and the lighting on display in the museum because if both lightings are adequate, people will be interested in the collection. The museum's illumination is determined by selecting a lightbulb with suitable illuminance needs and the appropriate corresponding color temperature (CCT). Insufficient lighting can have several consequences, such as visitors refusing to come because the room is too dark or excessive illumination of artifacts blinding visitors' eyes. The unsuitable lighting style can also cause harm to the artifact and cause it to require more electricity, resulting in increased costs. The author employed the single case study method of creating a prototype display in the shape of Vitrin with double illumination in this research. The primary lighting source is indirect light from the LED strip material, which illuminates the object. The second illumination uses LED spotlights to provide a detailed effect on the artifact. This spotlight is only activated when a visitor approaches the display at less than 60cm and stands a minimum of two seconds. The author utilizes an Arduino UNO board and an ultrasonic sensor at the bottom of the display to manage this automation. According to the results of trials with various circumstances, the prototype works to save energy in lighting museum exhibits. In addition to gaining energy-saving, choosing the suitable lamp material can help protect the collection from the damage caused by light-emitting.
A User-Centered Design of Natural Language Processing for Maternal Monitoring Chatbot System

A self-monitoring device and remote counseling system for pregnant mothers using interactive chat is an effective method due to the reduction of maternal care visits caused during the pandemic. The employment of an artificial intelligence (AI) based system using natural language processing (NLP) for decision support has prospectively enhanced the conversational access of patient to improve health awareness and knowledge. This research was conducted to develop an AI-based system which focuses on education and monitoring with regard to danger signs during pregnancy using NLP. The Telegram chatbot was used to develop the system after investigating user needs based on the danger sign monitoring guideline from WHO and the Ministry of Health of the Republic of Indonesia. The inputs from users were recognized by NLP and forwarded to the testing data for decision system. Furthermore, the analysis result was sent to the user which provides educational information and a personalized monitoring result. System Usability Scale (SUS) was undertaken to assess the user ability to use the application. The SUS score average for the chatbot system was 62.3 which was classified as “OK” for the adjective ratings. The implication of the maternal monitoring using a chatbot system is the improvement of maternal care with regard to early detection of danger signs and relevant suggestions using an effective and interactive system which could be very promising especially in a limited healthcare resource environment.
Bayesian Neural Networks for 6G CONASENSE Services

CONASENSE is the research area that aims to foster the integration of communications, navigation, sense, and services. The existing independence of each CONASENSE area is the main challenge for its full integration. Therefore a chain of rules to model CONASENSE is needed to offer precision in controlling these areas with the aid of Artificial Intelligence (AI). The objectives are to create a causality model expressing the CONASENSE Networks (CNSS). Furthermore, CNSS must be mathematically and graphically fit to enable seamless functionality amongst its nodes. Simulating the plethora of probabilistic patterns that may occur in the complexity of uncertainties generated in these four areas is crucial. Thus, applying probabilistic distribution that tracks a dynamic variety of events conditional to each CNSS node in time progression is a tremendous advantage for efficiently designing and controlling its Quality of Service (QoS). In this perspective, belief networks, also known as the Bayesian Networks (Bnets), can fit very well to assist the proposed acyclic CNSS graphic model here suggested. It will also help comprehend how to accelerate such integration by 2030 and support the 6G roadmap. Then, the authors proposed a CONASENSE Bayesian Neural Network (CBNN) model to enable the realization of CNSS in the future 6G Cognitive Radio.
Factors that influence the acquisition, application and protection of knowledge through m-learning

The main goal of this research is to determine what factors affect how people learn, use, and protect their knowledge based on how they use the m-learning system, which is the link between how easy it seems to use and how people plan to use it in terms of technological acceptance - TAM. The way the study was done was like a non-experimental investigation. A questionnaire was given to 235 university students who were chosen at random. Through structural equation modeling, exploratory and confirmatory factor analyses were used to test the questionnaire's validity and reliability. The results have shown that the acquisition of knowledge positively influences the application of knowledge, thus enabling the protection of knowledge by the student through the use of learning strategies for the level of university higher education in the generation of knowledge as a result of strategies based on the TAM model and mobile learning m-learning.
Reinforcement Learning for Estimating Student Proficiency in Math Word Problems

Math Word Problems (MWPs) allow preparing students to apply mathematical skills in everyday life. Teaching MWPs is challenging because the mathematics difficulties of individual students vary across students within a class. In this paper, it is proposed a reinforcement learning algorithm based on Q-learning for estimating individual student proficiency according to the basic arithmetic operations: addition, subtraction, multiplication, and division. The proposal is analyzed by simulating interactions with hand-coded users but tested by simulating interactions with data-driven users. In addition, results are compared with those in a common Cognitive Diagnosis Model (CDM). Results show that our approach allows estimating the individual student proficiency in around 5 episodes. Thus, since reinforcement learning usually is applied to induce instructional policies in tutoring systems, this paper shows that it can be used for estimating student proficiency as well.
Improving Frequency Estimation via Sequential Bayesian Filtering with Firefly Algorithm

Frequency estimation is considered as one of the most challenging problems in signal processing. Sequential Bayesian filtering has been utilized to the problem extensively due to its versatility for various applications in science and engineering. With the advances of Bayesian filtering, it also has been implemented for frequency estimation extensively with satisfactory results. However, Bayesian filter, particle filter, in particular, has a particle degradation problem of the conventional sequential importance sampling. In this work, we introduce firefly algorithm that will be used instead of the re-sampling in the traditional particle filter to tackle the particle depletion during the execution of the conventional particle filter framework. Computed from the short-time Fourier transforms (STFTs), the signal is analyzed in the frequency domain in this work which is considered as a measurement/observation model of the PF framework. Experimental results revealed the advantage the replacement of standard particle filter by firefly algorithm. Frequency estimates from the proposed filter are better than those obtained from the standard filter in terms of low uncertainty in the frequency trajectories.
Holographic Telepresence in Knowledge Transfer - Potential and Challenges in the Implementation

The development of virtual reality and extended reality technologies provide higher education institutions with an opportunity to develop new training practices. Recently there is ongoing research on the significance of new digital and virtual reality technologies in the process of enhancing knowledge transfer, as well as the efficient transfer of Intellectual Property to industry and businesses, and society. In this paper we focus on the potential and challenges of the implementation of Holographic Telepresence Communications for improving the collaboration between higher education institutions and knowledge sharing.
Ease of use and perceived usefulness and its influence on motivation, collaboration and behavioral intention in university students in times of Covid-19

The purpose of this study was to test the relationship of influence of ease of use and perceived usefulness on motivation, collaboration, and behavioral intention in university students in times of Covid-19. The methodology used for the study corresponds to a non-experimental investigation, a questionnaire was applied to a convenience sample of 530 university students (n=19; α=0.944 ω=0.946), using Factor Exploratory and Confirmatory Analysis as tests of validity and reliability, through the Modeling of Structural Equations of Partial Least Squares PLS-SEM. The results have shown that there is a positive effect of causality of perceived ease of use on student collaboration, student motivation and perceived usefulness; in the same way, there exists a causal relationship between the perceived usefulness and the student's collaboration, the intention of the behavior and the student's motivation. Contrarily, there would not be an influence relationship between perceived ease of use on behavioral intention in university students in a context of Covid-19.