Reviewerly/exHarmony huggingface dataset:
Adapting smart supportive technology for hospital use: A case report

Purpose Persons living with dementia (PLWD) often have demanding needs, placing burden on healthcare systems and care partners (GBD, 2019).Smart supportive technology is emerging as a solution to improve PLWD's quality of life/wellbeing, increasing interest in ambient assisted living (Gettel, 2021).However, there is limited research surrounding the customization of systems to each user's needs, which may lead to higher rates of acceptance (Fares, 2021).This case study explores the personalization of a smart technology system adapted to a hospital setting.Method A male patient in his late 60s, who is ambulatory and relatively independent, residing at Greystone Village Retirement, Ottawa, Ontario, Canada, participated in this study.The patient lives with dementia and a significant psychiatric diagnosis.A bed pressure sensor and motion sensors were installed in the room.The bed pressure sensor was placed under the mattress to indicate bed occupancy.One motion sensor was placed on the bedroom wall to detect movement in the room.A contact sensor for the doorway was intended to be installed to indicate room exits.Starting at 10pm, alerts would be sent to a cellphone in the nursing station if the patient was detected to be out of bed for a defined period of time (risk of fall) or had left the room during the night (risk of wandering).Weekly meetings with facility staff assessed their satisfaction with the technology.The facility staff kept reports on each alert that was received, the action(s) taken, and noted if there were any false alerts.Results & Discussion Since this patient leaves his door open overnight, the door contact sensor would not indicate room exits.Instead, a second motion sensor was placed in the closet.As the patient was relatively independent, the second sensor would sense when they would attempt to access the closet to get ready for their day, presumably prior to leaving the room.The system was first set up to alert if the patient did not return to bed within 8 minutes.However, this patient would typically leave the bed to head directly to the nurses' station, taking less than 8 minutes and rendering the alert useless.Consequently, the alert was adjusted to notify immediately after a bed exit, with a maximum frequency of one every 20 minutes.After a month, the system was readjusted to alert when the patient did not return to bed after 12 minutes.Staff reported satisfaction and are integrating the technology into their workflow.For example, staff would instinctively go to the medicine room when the alert went off, as the patient typically required as-needed medication at that time.The increased success and acceptance of the system after adjusting it to the user suggests that adaptation of smart technology to a patient's unique behavioural considerations and staff care needs is important for clinical use.Future digital solutions should aim to customize their systems to the user, rather than implementing a 'one size fits all' approach.
Study Of Learning Opportunities And Challenges Based On Digital Pandemic Era

This article will discuss digital-based learning across subjects at all levels of educational institutions as a learning media tool. The discussion of digital-based learning is a very fundamental part to be understood by all educators, both at the school level and at the university level. This article is a literature review with a qualitative analysis knife method that explores in depth related to digital-based learning across subjects at various levels of education. Furthermore, digital-based learning for educators holistically is an obligation in mastering digitalization in the learning process because all materials or subjects at all levels of education are very in tune with technology. Based on the findings from the literature review results in various subjects and levels of education, digitalization in learning has increased student learning achievement. On average, educators who use digital-based learning across subjects encourage student achievement. But on the other hand, a phenomenon was also found, including students experiencing digital addiction for those who use cellphones, some teachers still have difficulty in digital-based learning, facilities for digital learners have not become full attention, internet access is limited, digital media innovation has not been maximized. Therefore, through this article, it is hoped that all educators such as teachers and lecturers will be more creative and explore the use of digital-based learning media that are more sophisticated and attractive so that students are more motivated in the learning process, so that student achievement continues to increase.
House Prices Segmentation Using Gaussian Mixture Model-Based Clustering

House is a place for humans to live and a main necessity for humans. For years, the need for houses is increasing and varied so that it affects the selling price of the house. Therefore, more research is needed to learn about the selling price of houses. This research is only focusing on house price segmentation in DKI Jakarta using the Gaussian Mixture Model-Based Clustering Method with the Expectation-Maximization algorithm. The goal of this research is to make a house price segmentation model so that we can obtain useful information for the potential buyer. Clustering with GMM utilize the log-likelihood function to optimize the GMM parameters. The result of this research is houses in DKI Jakarta can be segmented into 3 different clusters. The first cluster is for the low-profile houses. The second cluster is for the mid-profile houses. The third cluster is for the high-profile houses. The silhouette score that was produced by the clustering method is 0.60866 meaning that this score is quite good because it’s close to a value of 1.&#x0D;
Ultra-Fast Machine Learning Inference through C Code Generation for Tangled Program Graphs

Tangled Program Graph (TPG) is a Reinforcement Learning (RL) technique based on genetic programming concepts. On state-of-the-art learning environments, TPGs have been shown to offer comparable competence with Deep Neural Networks (DNNs), for a fraction of their computational and storage cost. The contribution of this paper focuses on accelerating the inference of pre-trained TPGs, through the generation of standalone C code. While the training process of TPGs, based on genetic evolution principles, requires the use of flexible data structures supporting random mutations, this flexibility is no longer needed when focusing on the inference process. Evaluation of the proposed approach on four computing platforms, including embedded CPUs, produces an acceleration of the TPG inference by a factor 50 compared to state-of-the-art implementations. The inference performance obtained within a complex RL environment range between hundreds of nano-seconds to micro-seconds, making this approach highly competitive for edge Artificial Intelligence (AI).
Poisoning Attacks Against Machine Learning: Can Machine Learning Be Trustworthy?

Many practical applications benefit from machine learning and artificial intelligence technologies, but their security needs to be studied in more depth. We discuss the risk of poisoning attacks against the training stage of machine learning and challenges of defending against them.
Fuzzy c-Means Clustering untuk Pengenalan Pola Studi kasus Data Saham

Fuzzy Clustering is one of the five roles used by data mining experts to transform large amounts of data into useful information, and one method that is often and widely used is Fuzzy c-Means (FCM) Clustering. FCM is a data clustering technique where the existence of each data point in the cluster is based on the degree of membership. This study aims to see the pattern of data samples or data categories using FCM clustering. The analyzed data is stock data on Jakarta Stock Exchange (BEJ) in the Property and Real Estate sector (issuer group). The data mining processes comply Cross Industry Standard Process Model for Data mining Process (Crisp-DM), with several stages, starting with the stage of getting to know the business process (Business Understanding) then studying the data (Data Understanding), continuing with the Data Preparation stage, Modeling stage, Evaluation stage and finally the Deployment stage. In the modeling stage, the FCM model is used. FCM clustering model data mining can analyze data in large databases with many variables and complicated, especially to get patterns from the data. Then a Fuzzy Inference System (FIS) was built based on a known pattern for simulating input data into output data based on fuzzy logic. Keywords: Fuzzy c-Means Clustering, Pattern Recognition
Nondeterministic Deformation Analysis Using Quasiconformal Geometry

Deformation analysis is crucial in many applications, especially in medical image analysis. Analyzing the deformation pattern of anatomical structures provides important information for disease analysis. With degraded images or uncertainties, getting a deterministic solution of the deformation is challenging. In some cases, there may also be multiple solutions with different probabilities. As such, it is important to analyze the probability distribution of deformations, given data information with uncertainty. In this work, we propose to use computational Quasiconformal (QC) Teichmuller theories to parameterize the space of deformations. A distribution over the space of special features, called the QC features, can be computed and applied for deformation analysis. Extensive experiments are carried out on both synthetic data and real medical images, which demonstrate the efficacy of the proposed framework.
Optics manufacturing technician apprenticeships

The Optics, Photonics, and Imaging industry is responsible for remarkable innovations that have revolutionized our world—and improve our lives, every single day. The industry is growing at an exponential rate and is suffering from a worldwide shortage of skilled optics technicians. For several decades, our society and education system have emphasized the value of a 4-year college education at the expense of workforce education, and our nation is suffering because of it. The registered Optics Manufacturing Technician apprenticeship is a structured “earn and learn” solution that combines on-the-job learning with related technical instruction, with benefits for employer and employee.
Application of binary classification of magnetic resonance therapy images to detect the threat of the disease due to COVID-19

The paper considers the use of a neural network for the binary classification of magnetic resonance imaging images to establish a possible disease of COVID-19. Processing of input data and their reduction to one format is provided, and neural network software for binary classification is developed and its training based on about 60,000 open patient images. The using of developed software is universal for medical doctors or ordinary Internet users and does not require additional training.
A Crowdsourcing Data Annotation System For Vietnamese Scene Text Detection

Most of the recent breakthroughs in AI-related research and applications are data driven. Annotated data plays the main role in improving AI/ML-based solutions. The lack of high-quality annotated data is one of the main obstacles to AI adoption, especially in Vietnamese scene text detection. Additionally, manual annotation is expensive yet not scalable. This project aims to tackle the problem by developing a crowdsourcing data annotation system at word-level annotations, vCaptcha, in the form of an anti-bot widget to integrate to some web pages. After that, results can be derived from the workers prior to evaluating and aggregating the crowdsourced labels. Also, we propose a novel human-in-the-loop approach for incorporating the state-of-the-art scene-text detection models and crowdsourcing system so that in the end, a fully annotated dataset with optimal and high quality can be generated with significant cost and time savings. Usage document to integrate vCaptcha to any web pages is available at https://bichvan2810.github.io/vCaptcha-end-user-info.
Work-in-Progress: Toward a Robust, Reconfigurable Hardware Accelerator for Tree-Based Genetic Programming

Genetic programming (GP) is a general, broadly effective procedure by which computable solutions are constructed from high-level objectives. As with other machine-learning endeavors, one continual trend for GP is to exploit ever-larger amounts of parallelism. In this paper, we explore the possibility of accelerating GP by way of modern field-programmable gate arrays (FPGAs), which is motivated by the fact that FPGAs can sometimes leverage larger amounts of both function and data parallelism—common characteristics of GP— when compared to CPUs and GPUs. As a first step towards more general acceleration, we present a preliminary accelerator for the evaluation phase of "tree-based GP"—the original, and still popular, flavor of GP—for which the FPGA dynamically compiles programs of varying shapes and sizes onto a reconfigurable function tree pipeline. Overall, when compared to a recent open-source GPU solution implemented on a modern 8nm process node, our accelerator implemented on an older 20nm FPGA achieves an average speedup of 9.7×. Although our accelerator is 7.9× slower than most examples of a state-of-the-art CPU solution implemented on a recent 7nm process node, we describe future extensions that can make FPGA acceleration provide attractive Pareto-optimal tradeoffs.
Pendampingan Pembuatan Konten dan Pengelolaan Website Desa Kiringan Boyolali

The development of information technology is so rapid, of course it provides benefits to the community, one of which is the use of websites. Desa Kiran in using the website is still not optimal, as a result there is a lack of content published on the website. The existence of these problems assisting content creation and website management is important to be followed up in order to help Admin Staff and Volunteer Workers (TKS). The purpose of providing assistance is to assist Admin and TKS staff in creating content and managing websites. The method used is the application of science and technology in the form of assistance in content creation and website management. The implementation of mentoring is done through lectures, questions and answers, and direct practice. The result of this assistance is that the Admin and TKS staff can create content and manage the website well.
Service classification of high-speed network traffic based on Two-Stage Clustering

Service classification of high-speed network traffic is critical for Internet Service Providers (ISPs) to ensure network Quality of Service (QoS). As high-speed network transmission accelerates, ISPs can only obtain unlabeled and sampled traffic from high-speed networks, making supervised learning methods difficult to apply. Some existing methods use unsupervised learning to classify services to reduce the need for labeled data. However, when these methods are applied, fluctuations in the feature vector lead to a certain percentage of the same class of services being grouped into different clusters. We proposes a practical method for classifying traffic services in high-speed networks. Specifically, we propose a method called Two-Stage Clustering (TSC), which automatically implements merging clusters of the same service. Validation experiments on publicly available datasets show that our classifier achieves an accuracy of 90.07% and a recall of 91.81% even with a sampling rate of 1:64, which is higher than the classification methods that also use unsupervised learning.
Construction Method of Overlay Network for Cyber-Physical System

The real-time requirements of network communication in cyber-physical systems are high, and the network topology is complex, which makes the communication efficiency in the system low in real-time. However, the traditional routing algorithm has been unable to adapt to the increasingly complex intelligent communication network in today's society. In order to improve the network communication efficiency of the cyber-physical system, a cyber-physical fusion system overlay network construction method based on an improved variable neighborhood search algorithm is proposed. Reduce the communication time overhead of cyber-physical systems. In the network node selection method of the cyber physical system, the neighborhood structure and the dithering method of the variable neighborhood search algorithm are optimized. The objective function is used to further optimize the selection strategy of the covering node set. Finally, the network time cost, communication delay and algorithm stability of the proposed method are analyzed on a large number of random discrete node sets. The experimental results show that the optimized variable domain search algorithm can effectively reduce the network delay and time overhead, and improve the communication efficiency of the cyber-physical system.
Exam Question Recommendation System Based on the Inclusion-Based TOPSIS Method With Interval-Valued Intuitionistic Hesitant Fuzzy

This study presents the recommendation system to produce intelligent and efficient exam questions. The teaching team chose the questions that were the most pertinent based on certain criteria that had been established in advance. The Interval-Valued Intuitionistic Hesitant Fuzzy (IVIHF) and the Inclusion-based TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) hybrid approaches were combined in order to make it possible for test sheet generation. For the purposes of this study, five exam questions were chosen as the candidates to appear on a test sheet. The four criteria (difficulty, discrimination, reliability, and cognitive level) have been used by the teaching team consists of three lecturers to evaluate the alternatives. According to the findings of this study, the questions with the numbers five, four, and three will most likely be found on the examination sheet.
LINE Chatbot For Covid-19 Wellness Support using Sentence Similarity Measurement

The development of Mobile Applications in Indonesia is proliferating, especially in the development of Information Technology (IT) through social media and Instant Messaging (IM). The chatbot feature in some IMs is used daily to obtain information, one of which is LINE Messenger. Based on a survey conducted by authors to 221 respondents, the average number of respondents was 75.1% who were LINE users. In addition, the Covid-19 outbreak has hit the world. Covid-19 is a pandemic that is happening in many countries around the world. Therefore, many people are worried about the impact, and the authority must be ready to overcome the problem caused by this. Various research and collaborative efforts for Covid-19 wellness support by utilizing IT were conducted. This paper reports the use of LINE Chatbot as a platform to support the Covid-19 wellness program with the Sentence Similarity Measurement method. In addition to the chatbot, we created a website to make it easier for users to access the chatbot and obtain information generated from user queries. The accuracy rate of the SSM method for the Covid-19 chatbot is 85.71% in responding to user natural language questions.
LA Novel Proposal of Cooperation and Reputation Mechanisms for IoT-based Tourism Solutions

The Internet of Things (IoT), which links intelligent items to the Internet to improve our daily lives and give them a sense of autonomy and intelligence, is transforming a number of societal sectors. Tourism-related IoT solutions are already available to speed up procedures like locating places, getting recommendations based on context and location, and processing payments. These solutions struggle to be widely used and to provide a level of service and adoption that is favourable due to the variety of techniques. In order to increase resource sharing in a sensor network and route optimization, this paper makes a novel suggestion for IoT-based solutions that makes use of reputation and cooperation mechanisms. This study proposal presents its conceptual design and IoT architecture, which includes the organization of devices that request collaboration using a Fog/Edge layer capable of handling large amounts of data produced by IoT, as well as latency and opportunities to bring the user closer to the cloud’s services. This IoT work proposal relates to tourism. To increase connectivity and communication between devices, the system examines a number of factors, including reputation, trust, and incentive models. By incorporating this suggestion into any IoT-based tourism solution, other processes and duties that have an impact on visitors and organizations can also be automated, saving time and money.
The Solution for Creating a 2D Top-View Map of the Pedestrian Positions Around the Vehicle

Currently, one of the fastest-growing industries in the world is the automotive industry. More and more vehicles are encountered on the road that have Advanced Driver-Assistance Systems (ADAS) installed to perform complex operations while driving. They constantly monitor the environment around the vehicle and reduce the risk of traffic accidents. In this paper, the ADAS solution for estimating the distance of detected pedestrians and drawing a 2D top-view map of their positions in the vicinity of the vehicle is proposed. The solution processes frames simultaneously captured by four cameras located on different sides of the vehicle. It is developed using the C programming language and finally implemented on a real ADAS embedded platform. The proposed solution achieved high performance when tested on video sequences captured by real automotive cameras. Furthermore, it is capable of processing 15 frames per second when operating with state-of-the-art automotive camera resolutions.
Federated-Learning-Assisted Failure-Cause Identification in Microwave Networks

Machine Learning (ML) adoption for automated failure management is becoming pervasive in today’s communication networks. However, ML-based failure management typically requires that monitoring data is exchanged between network devices, where data is collected, and centralized locations, e.g., servers in data centers, where data is processed. ML algorithms in this centralized location are then trained to learn mappings between collected data and desired outputs, e.g., whether a failure exists, its cause, location, etc. This paradigm poses several challenges to network operators in terms of privacy as well as in terms of computational and communication resource usage, as a massive amount of sensible failure data is transmitted over the network. To overcome such limitations, Federated Learning (FL) can be adopted, which consists of training multiple distributed ML models at multiple decentralized locations (called “clients”) using a limited amount of locally-collected data, and of sharing these trained models to a centralized location (called “server”), where these models are aggregated and shared again with clients. FL reduces data exchange between clients and a server and improves algorithms’ performance thanks to sharing knowledge among different domains (i.e., clients), leveraging different sources of local information in a collaborative environment. In this paper, we focus on applying FL to perform failure-cause identification in microwave networks. The problem is modeled as a multi-class ML classification problem with six pre-defined failure causes. Specifically, using real failure data from an operational microwave network composed of more than 10000 microwave links, we emulate a multi-operator scenario in which one operator has partial knowledge of failure causes during the training phase. Thanks to knowledge sharing, numerical results show that FL achieves up to 72% precision in identifying an unknown particular class concerning traditional ML (non- FL) approaches where training is performed without knowledge sharing.