Reviewerly/exHarmony huggingface dataset:
Meta-analysis: Effect of health media types on children's knowledge about balanced nutrition

Imbalance in food intake has become a problem that often arises in nutrition.This can occur in various age groups, including school-age children, and impacts malnutrition or overweight (obesity).Knowledge of nutrition plays an important role in determining the health status of the community, especially children.Health media is one of the solutions to increase knowledge.The types of health media that are often used are lash cards and poster media.To determine the effect of the type of health media on children's knowledge about balanced nutrition.A meta-analysis study using a pre-test-post-test design using a control group.The data was obtained from Google Scholar, PubMed, and the Garuda portal databases.10 Journals were selected according to the inclusion criteria.Statistical test using revman 5.4.The effect off picture card media on children's knowledge was Z = 3.13 p = 0.0025, 95% Conidence Interval (CI) = 0.48-2.07,meanwhile poster media was Z = 1.7 p = 0.09, 95% CI = -0.05-0.71.Picture card media was more effective than the control group.Poster media did not differ in effectiveness compared to the control group.
PERANCANGAN SISTEM INFORMASI PRAKTEK KERJA INDUSTRI (PRAKERIN) PADA SMK NEGERI 1 KOTA SERANG

Industrial work practice or prakerin is an activity carried out by every vocational school student in Class XI. This activity is an implementation of the science of vocational students in the world of work, in addition to being an experience for students when graduating from school. SMK Negeri 1 Serang is one of the schools organizing this activity and the Industrial Relations section is the party that handles prakerin activities. Industrial Relations Department (hubin), handles prakerin activities ranging from finding prakerin places and cooperating with agencies, to monitoring students during prakerin and archiving documents related to prakerin. In this activity, the homeroom teacher is involved in delivering practical information to students in the classroom. Based on the problems found, hubin has difficulties in the placement of prakerin students. In addition, the monitoring process still found obstacles sourced from agencies, as well as document management related to prakerin is still done manually, and making it difficult to see when the data is needed. Some of these problems implement prakerin to be less than the maximum, therefore the author will make the design of Information Systems based prakerin using the waterfall method as a method of system development. System modeling using the unified modeling language (UML), as well as the design of the system view that will provide an overview of the system created. The expectation of the design of the industrial work practice information system (prakerin) web-based is to assist the industrial relations (hubin) in the implementation of prakerin activities at SMK Negeri 1 Serang City.
NEW STUDENT ADMISSION INFORMATION SYSTEM AT AMIK WAHANA MANDIRI

In an increasingly advanced era like today computers have become an inseparable part of human life. The role of computers has been felt in almost all areas of life, especially along with the rapid development of computer network technology, all the information we need can be obtained in just a short time. One of them is through the internet network, there are various information in it. The Internet is a global computer network that is interconnected through telecommunications lines. AMIK Wahana Mandiri, as a formal higher education institution that concentrates on informatics and computer management has the task of improving the competence,activities, and professionalism of students to be able to compete globally after completing their education at AMIK Wahana Mandiri. In this study , we will discuss the web-based information system fornew AMIK Wahana Mandiri students. This system is an application system to manage new student data and as a medium to see various information about new student admissions at AMIK Wahana Mandiri which can be accessed anytime and anywhere (online). The system is designed using the UML (Unified Modelling Language) modeling language and implemented using PHP, Apache Web Server and MySQL Server. The research methods used are literature, observation, interviews, analysis, design, trial and implementation. The conclusion of this study is that it is hoped that a web-based new student admission information system can be built.
Registration of multi-modal images under a complex background combining multiscale features extraction and semantic segmentation

Multi-modal imaging technology has a very broad application value in target recognition and other fields, and image registration is one of its key technologies. In this paper, a multi-modal image registration algorithm that combines multiscale features extraction and semantic segmentation is proposed to achieve accurate registration of polarized images and near-infrared images under complex backgrounds. A classical convolutional neural network ResNet is employed to capture the robust feature descriptors, and a convolutional neural network with an attention mechanism is trained to filter out the irrelevant feature points. Further, the two multi-modal images can be further registered. The experimental results show the feasibility and effectiveness of the proposed method.
Introduction to Artificial Intelligence in Healthcare

Healthcare is one of the most critical sectors in modern human society. From patient to hospital management, fast and secure services with people-friendly and intelligent systems are in constant demand. Artificial intelligence has made inroads into almost every aspect of human life, and is used in the prediction, diagnosis and treatment of disease, in drug development and patient monitoring. Artificial intelligence algorithms for the diagnosis and prevention of disease are used for analyzing large amounts of data through electronic health records. Early and accurate diagnosis is at the heart of health support systems. Most of the data available for medical diagnosis is in the form of images. AI plays an important role in the four major processes involved in making predictions using medical images: pre-processing, segmentation, registration, and visualization. This chapter presents a comprehensive study of these four vital aspects of medical imaging.
Coherent robust <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msup><mml:mi>H</mml:mi><mml:mi>∞</mml:mi></mml:msup></mml:math> control of uncertain linear quantum systems with direct and indirect couplings

In this paper, a robust H∞ analysis method is presented for a class of uncertain quantum systems where uncertainties may exist in the system and interaction Hamiltonians, and the coupling operators. We provide a sufficient condition to guarantee this class of uncertain systems robust strictly bounded real with respect to a given disturbance attenuation. Moreover, we propose a robust H∞ control method for quantum systems with uncertain system Hamiltonian and uncertain coupling operators. The controller and the quantum plant are connected via direct and indirect couplings. This robust H∞ control problem is shown to have a connection with a scaled H∞ problem. We propose a numerical procedure to find the corresponding coefficients of the desired H∞ controller by using an LMI formulation and multi-step optimization.
Information Security Challenges and Solutions in Smart Nations

In the study, the author analyzed the basic conceptual environment of smart nations, the information security challenges, threats and dangers inherent in it. In the second half of the research, he seeks to answer the question of what are the most commonly used protection solutions against the threats that arise. To do these, the author has analyzed the available relevant literature and peer-reviewed reports to identify the areas most affected by potential threats, such as networks, clients and users, and the associated potential protection solutions. The analysis has produced a matrix illustrating which vulnerable areas could potentially benefit from protection solutions against typical attack vectors.
Critical Threat of Critical Infrastructures: The Human Factor

While critical infrastructures have always been the most important parts of a nation’s economy, the threats that they are facing, when it comes to safety and security issues, has changed radically in the past decades owing to the rapid development of the applied technologies. Though processes are speeding up and the machinery is improving at a rapid pace the human factor seems to be the weakest link. Hence, present paper endeavours to address this very aspect of organisational safety and security, in order to call attention to its crucial role. The paper first presents the role of personnel in critical infrastructures, then enumerates traditional and more novel ways of improving the expertise and decreasing the vulnerability of the human factor. While the topic to be tackled is a complex one by nature, a simple model is also introduced, on the basis of which managers of critical infrastructures are able to make personnel decisions. Last but not least, present paper intends to call attention to the fact, that the basic processes, and the way the everyday tasks of the employees are defined may also influence the safety and security of critical infrastructures; hence the issue regarding the human factor cannot be solved solely through training and motivation, but the reorganisation of processes might also be necessary.
Can Trust Be a Factor of Organisational Safety and Security?

Safety and security are of increasing importance to all business ventures, but first and foremost for organisations operating as parts of critical infrastructure. Kinetic and cyber tools are being developed and employed, in order to decrease security risks that – in case of critical infrastructure organisations – would have far-fetched consequences not only on a company but on a national level as well. In present paper we endeavour to address the human side of this problem, namely the control function of managers in critical infrastructure organisations. Trust will be addressed as a proxy for external locus of control and described, how a strong and stable superior-subordinate relation can substantially contribute not only to the wellbeing of the employees but to organisational – and in the long run national – safety and security. Since trusting, safety-oriented cultures affect individual decisions not only on a normative but on a subconscious level as well, they can increase not only the willingness of employees to perceive, recognise and tackle safety and security threats, but trigger their eagerness to make good decisions that – through their trusting relations – do not only benefit the individual decision maker, but the organisation and its safety and security as well.
Interconnected Drone Systems for Monitoring Regional Safety Issues in Field Agriculture

The potential impact of the climate change is less clear at regional scales, but it is likely that climate variability and change will increase food insecurity in areas currently vulnerable to hunger and undernutrition. The situation is severe but with optimization in the agriculture changes can happen. Via less fuel used by machinery farms can reduce the carbon-dioxide emission, also with optimal pesticide and herbicide pulverization the amount of chemicals can be reduced, which has direct impact on the quality of food, and the quality of life. This paper foresees a cooperative method for global warning of farmers about spreading diseases or swarming insects on the fields. Drones with autonomous flights can reduce the time spent for inspection, also can gather big amount of precise data from the terrain. The data can be used for precise interventions, for global defense against threads, and to get a better view of the agrar world.
Machine Learning Aspects of Internet Firewall Data

One of the numerous implementations of neural networks (NN), as part of machine learning, is the modeling of network firewall rules. For this purpose, a suitable dataset containing standard traffic attributes as well as the ability of Weka software package for modeling and testing multilayer perceptrons (MLP) was used. The aim of this paper was to create and examine an NN model of Internet firewall and optimize its parameters that best simulates the operation of rules. It was found that the number of neurons in hidden layers, the learning rate, momentum, and number of epochs affect the accuracy, while the impact of percentage split and batch size can be ignored. Also, it performed an evaluation of losses of different activation functions in NN environment, with previously determined optimal parameters. Moreover, it has been shown that the following algorithms provided the highest accuracy in solving classification problems for a firewall dataset: Random Forest, J48 and MLP. From the aspect of the possibility of clustering firewall data, the paper found that the k-means algorithm showed greater accuracy and speed than the EM and DBSCAN algorithms.
Tasks of the Implementation of Physical Security System of the CBRN Laboratories

The state of our future security can be eroded, besides many definite factors, releasing of dangerous materials used for peaceful industrial aims, or the chemical, biological or radiological components of research that are not ‘guarded’ properly and can be used for criminal aims. Unauthorized access to this materials and the prevention of their theft, their defense is of highly importance. The lab staff in certain cases has to face risks posed by chemical, biological, radiological hazard. However, these risks can be significantly diminished by a properly created defense concept. For the reason that the employees and the users of dangerous materials would not be harmed by the everyday working conditions and also that by appropriation of dangerous materials and this way, the danger posed by these would not get beyond of the controlled areas. Therefore, the creation of the physical protection of the mentioned areas and equipment is extremely important. After the accomplishment of the defense concept, with the planning of the complex security system, the work on the proper rate establishment of security subsystems needs serious analyzing and evaluation.
UAV Route Planning in Urban and Suburban Surveillance Scenarios

Critical infrastructure protection has a significant role in the modern world. The critical infrastructure can be found in many fields, but the roads and road infrastructure surely are one of the very important factors in this domain. This paper targets the issue of utilization of Unmanned Aerial Vehicles (UAVs) for road infrastructure monitoring. The contribution of this paper is the methodology proposed for UAV path planning in urban and suburban scenarios. The purpose of UAVs is the city road condition monitoring, and the proposed methodology tends to optimize this process by usage of a minimal number of UAVs to monitor a maximal number of locations, by randomly chosen locations, and dynamically calculated paths. The methodology uses Traveling salesman Problem (TSP) with Genetic Algorithm (GA) support. The TSP solution gives the shortest direct path between randomly selected locations. To enable the following of the road TSP is expanded with Dijkstra’s shortest path algorithm giving, as a result, the calculated path which follows the city roads. The addition of Dijkstra’s shortest path algorithm does not significantly increase the length of the path, comparing to TSP calculated path.
Improving Noised Gradient Penalty with Synchronized Activation Function for Generative Adversarial Networks

Generative Adversarial Networks (GANs) are one of the most successful learning principles of generative models and were wildly applied to many generation tasks. In the beginning, the gradient penalty (GP) was applied to enforce the discriminator in GANs to satisfy Lipschitz continuity in Wasserstein GAN. Although the vanilla version of the gradient penalty was further modified for different purposes, seeking a better equilibrium and higher generation quality in adversarial learning remains challenging. Recently, DRAGAN was proposed to achieve the local linearity in a surrounding data manifold by applying the noised gradient penalty to promote the local convexity in model optimization. However, we show that their approach will impose a burden on satisfying Lipschitz continuity for the discriminator. Such conflict between Lipschitz continuity and local linearity in DRAGAN will result in poor equilibrium, and thus the generation quality is far from ideal. To this end, we propose a novel approach to benefit both local linearity and Lipschitz continuity for reaching a better equilibrium without conflict. In detail, we apply our synchronized activation function in the discriminator to receive a particular form of noised gradient penalty for achieving local linearity without losing the property of Lipschitz continuity in the discriminator. Experimental results show that our method can reach the superior quality of images and outperforms WGAN-GP, DiracGAN, and DRAGAN in terms of Inception Score and Fréchet Inception Distance on real-world datasets.
Utilization of Volumetric Data in Stage performance1

In recent years, it has become common in the entertainment industry to use 3D and 4D data (data with time- sequential information added to 3D data) of performers for live stage production. The emergence of various application and devices in addition to the establishment of various methods for acquisition of 4D data with color information such as Photogrammetry, Grid Pattern Projection, and Time of Flight (TOF) makes it easier for those creators without special engineering skills to use those dataset, which leads to more frequent encounter with video works made with those technologies than before.
Risk Modeling and Analysis

Securing the supply chain of information and communications technology (ICT) has recently emerged as a critical concern for national security and integrity. With the proliferation of Internet of ThingsInternet of Things (IoT) devices and their increasing role in controlling real world infrastructure, there is a need to analyze risks in networked systems beyond established security analyses. Existing methods in literature typically leverage attack and fault trees to analyze malicious activity and its impact. In this chapter, we develop a security risk assessment framework borrowing from system reliability theory to incorporate the supply chain. We also analyze the impact of grouping within suppliers that may pose hidden risks to the systems from malicious Supply chain actorssupply chain actors. The results show that the proposed analysis is able to reveal hidden threats posed to the IoT ecosystem from potential supplier collusion.
Studying the association of diabetes and healthcare cost on distributed data from the Maastricht Study and Statistics Netherlands using a privacy-preserving federated learning infrastructure

The mining of personal data collected by multiple organizations remains challenging in the presence of technical barriers, privacy concerns, and legal and/or organizational restrictions. While a number of privacy-preserving and data mining frameworks have recently emerged, much remains to show their practical utility. In this study, we implement and utilize a secure infrastructure using data from Statistics Netherlands and the Maastricht Study to learn the association between Type 2 Diabetes Mellitus (T2DM) and healthcare expenses considering the impact of lifestyle, physical activities, and complications of T2DM. Through experiments using real-world distributed personal data, we present the feasibility and effectiveness of the secure infrastructure for practical use cases of linking and analyzing vertically partitioned data across multiple organizations. We discovered that individuals diagnosed with T2DM had significantly higher expenses than those with prediabetes, while participants with prediabetes spent more than those without T2DM in all the included healthcare categories to different degrees. We further discuss a joint effort from technical, ethical-legal, and domain-specific experts that is highly valued for applying such a secure infrastructure to real-life use cases to protect data privacy.
Toxicity detection sensitive to conversational context

User posts whose perceived toxicity depends on conversational context are rare in current toxicity detection datasets. Hence, toxicity detectors trained on existing datasets will also tend to disregard context, making the detection of context-sensitive toxicity harder when it does occur. We construct and publicly release a dataset of 10,000 posts with two kinds of toxicity labels: (i) annotators considered each post with the previous one as context; and (ii) annotators had no additional context. Based on this, we introduce a new task, context sensitivity estimation, which aims to identify posts whose perceived toxicity changes if the context (previous post) is also considered. We then evaluate machine learning systems on this task, showing that classifiers of practical quality can be developed, and we show that data augmentation with knowledge distillation can improve performance further. Such systems could be used to enhance toxicity detection datasets with more context-dependent posts, or to suggest when moderators should consider parent posts, which often may be unnecessary and may otherwise introduce significant additional costs.
Sentiment Analysis From Bengali Social Media Posts Using Hybridized Feature Extraction Approach

Now-a-days, Social media platforms like Facebook, Twitter etc. are becoming more and more popular to express feelings and thoughts. People not only share their happy moments on these platforms but also share their feelings when they are extremely depressed. Analyzing these social media posts, one’s mental condition can be detected whether he/she is happy, sad or angry at a particular time using sentiment analysis in natural language processing. Most of the research in this field is based on English language and the accuracy of sentiment analysis from Bengali language is not very high. So, our purpose is to work on this field using Bengali dataset collected from different social media posts and make the sentiment detection more accurate so that this work can be used to build a system that can be used in the mental health sector of our country. In this research, we have first collected social media data. After applying different data preprocessing techniques, we have made a number of feature selection and extraction combinations. We have applied some basic and advanced machine learning and deep learning algorithms on each of these to find the best combination and algorithm by monitoring the highest achieved accuracy.