Reviewerly/exHarmony huggingface dataset:
Robust multifocus deep neural network for progression prediction on patient trajectory data

Purpose Patient treatment trajectory data are used to predict the outcome of the treatment to particular disease that has been carried out in the research. In order to determine the evolving disease on the patient and changes in the health due to treatment has not considered existing methodologies. Hence deep learning models to trajectory data mining can be employed to identify disease prediction with high accuracy and less computation cost. Design/methodology/approach Multifocus deep neural network classifiers has been utilized to detect the novel disease class and comorbidity class to the changes in the genome pattern of the patient trajectory data can be identified on the layers of the architecture. Classifier is employed to learn extracted feature set with activation and weight function and then merged on many aspects to classify the undetermined sequence of diseases as a new variant. The performance of disease progression learning progress utilizes the precision of the constituent classifiers, which usually has larger generalization benefits than those optimized classifiers. Findings Deep learning architecture uses weight function, bias function on input layers and max pooling. Outcome of the input layer has applied to hidden layer to generate the multifocus characteristics of the disease, and multifocus characterized disease is processed in activation function using ReLu function along hyper parameter tuning which produces the effective outcome in the output layer of a fully connected network. Experimental results have proved using cross validation that proposed model outperforms methodologies in terms of computation time and accuracy. Originality/value Proposed evolving classifier represented as a robust architecture on using objective function to map the data sequence into a class distribution of the evolving disease class to the patient trajectory. Then, the generative output layer of the proposed model produces the progression outcome of the disease of the particular patient trajectory. The model tries to produce the accurate prognosis outcomes by employing data conditional probability function. The originality of the work defines 70% and comparisons of the previous methods the method of values are accurate and increased analysis of the predictions.
Correction to: Chatbot Accessibility Guidance: A Review and Way Forward

In the original version of the book, the author names “Ronna ten (First name) Brink (Last name) and Becca Scollan” have been changed to “Ronna (First name) ten Brink (Last name) and Rebecca Scollan” in the Frontmatter, Backmatter and in Chapter 80. The erratum chapter and the book have been updated with the change.
The digital Smart University or University 4.0

The existing international education system is a product and simultaneously an element of the rst industrial revolution, functioning on the basis of the principles of ow-type production established more than a hundred years ago, and solves the problem of maximum penetration of a typical set of knowledge into the masses of the population, rather than maximizing the quality of the obtained knowledge and its individualization.New industrial revolution based on wide digitalization and networking, which we are witnessing, requires modernization of most economic institutions, including education system, and offers for this modernization not only a new set of technologies, but also new principles of exibility, individualization, maximization of ef ciency. The subject of electronic education has been developing in different countries for quite a long period. By now the necessary set of technologies and organizational elements has been created. However, it is during the 2019-2021 pandemic that suf cient conditions have emerged for the non-alternative widespread implementation of a new holistic education system based on remote mode and adaptive digital content. The article considers the modernization of the education system, the creation of the Digital SMART University or University 4.0. The main problems of implementing the new educational system and approaches to their solutions are analyzed. The modernization of the educational system has many parallels with the digital transformation of other sectors of the economy. This makes it possible and necessary to use the experience and knowledge accumulated in other areas of economic activity, their systematization and analysis of applicability.The main tasks in creating a new e-university are the introduction of new processes of design, production, systematization, storage and delivery of educational content, as well as modernization of management and administration of the educational process. Implementation of e-learning is based on integrated organizational and functional structures, IT-architectures with their own rules, responsibilities, people, processes, system of interaction and relationship, i.e. the new business structure, which is built into the overall corporate information system and business architecture of the university system. The paper examines the business processes, services, IT infrastructure of the university, providing a new form of e-learning. The object of the study is the electronic educational environment of the university, which is an electronic educational platform of a new set of educational IT-services. The term «service» de nes a service for the educational business, which is quanti ed by a set of business metrics, the metrics of availability, continuity, security, reproducibility, repeatability and scalability. The result of the implementation of the new University 4.0 should be a progressively developing catalog of electronic educational services of the university, which can be used as a basis for designing specialized educational trajectories and use in various educational institutions.
Media Pembelajaran Digital Berbasis Macromedia Flash untuk Mata Pelajaran Fisika tingkat SMP

The development and improvement of teaching innovation aim to bring an enjoyable, joyful, active, and creative atmosphere for students to increase and improve their achievement. Teaching media that support student's creativity and critical thinking are needed to facilitate teachers presenting the learning material. Information and Communication Technology (ICT) can introduce media to increase students' learning motivation. This program instructs teachers in using Macromedia flash software to create learning media applications for the physics units. It was conducted in SMP Muhammadiyah 6 Dau Malang. An instrument to observe the teaching situation is an interview with the teacher. Information from the interview, students have high motivation during their practice in the lab, but the opposite occurs in the class. The result of this program is an attractive teaching media application created by using Macromedia Flash software.
Improved Naive Bayes Classification for Joint Investment Plan

Large scale investments are mostly done by joint investors in different countries. Most of these investments involve collaboration with financial institutes of different countries. As the aspiration of governments to development their countries, they encourage investments. Financial institutes, at the same time, will set a guideline to decide with whom they will share the investment and collaborate based on profit maximization target. In this paper we are considering individual investors to collaborate with the financial institutes. Naïve Bayes is an ideal approach to aid the approval or rejection of this collaboration by the decision maker. The approach assumes independencies among the variables. However, this assumption may not always be realistic. Hence, this paper uses a method to improve the accuracy of Naïve Bayes approach by using a learning structure of feature variables in the model and apply it to joint investment plan applications. The introduction and use of new applied problem is not only helpful to show the application of the field but also attract researchers from social science to apply and use Bayes based methods which in turn contribute the development of the field with new insights.
Implementation of Particle Swarm Optimization (PSO) to Improve Neural Network Performance in Univariate Time Series Prediction

One of the oldest known predictive analytics techniques is time series prediction. The target in time series prediction is use historical data about a specific quantity to predicts value of the same quantity in the future. Multivariate time series (MTS) data has been widely used in time series prediction research because it is considered better than univariate time series (UTS) data. However, in reality MTS data sets contain various types of information which makes it difficult to extract information to predict the situation. Therefore, UTS data still has a chance to be developed because it is actually simpler than MTS data. UTS prediction treats forecasts as a single variable problem, whereas MTS may employ a large number of time-concurred series to make predictions. Neural Network (NN) model could be built to predict the target variable given the other (predictor) variables. In this study, we used Particle Swarm Optimization (PSO) algorithm to optimize performance of NN on a UTS dataset. Our proposed model is validated using x-validation and and use RMSE to measure its performance. The experimental results show that NN performance after optimization using PSO produces good results compared to classical NN performance. This is evidenced by the value of RMSE = 0.410 which is the smallest RMSE value produced. The smaller the RMSE value, the better the model performance. It can be concluded that the proposed method can improve NN performance on UTS data.
The Analysis of Intelligent Marketing Platform in High-tech Products by Data Mining Algorithm

Under the background of the rapid development in internet technology, the whole marketing is developing towards the direction of intelligence and high technology. The novel social network based on internet technology occupies an important part of the marketing, and has also been widely concerned by the academic community, because the internet makes information data transparent, and the mining, analysis and algorithm research of a large amount of data can provide decision support for marketing and intelligent marketing. Modern data mining analysis mode has become the main solution for data problems. With the development of network technology, business intelligence related to the marketing of high-tech products will become the key component of the future business system, which depends on the overall architecture of the cloud and plays a core role in the process of data analysis and mining.
A Discriminative Deep Neural Network for Recognizing Humor

In humor detection task, taking a context and its title as input, the model outputs the prediction of the humor type. Some datasets contain several classes with different amounts of examples, and in some cases, this difference can be very large. Due to the imbalance of the data classification labels, the model&#x2019;s training progress is significantly influenced by this difference. Under the misleading of the majority classes, the model always treats most of the examples as a few majority labels, and other minority classes cannot be learned. In our work, we propose a neural model with over-sampling and focal loss to solve this imbalanced data problem. The over-sampling method is used for the aspect of data, and the focal loss method is used for the aspect of model training. We conduct experiments on the Wocka joke dataset, and experimental results demonstrate that our model outperforms the baseline model in terms of accuracy.
Rancang Bangun Digital Signage Sebagai Papan Informasi Digital Masjid Di Kota Palangka Raya Berbasis Web Responsive

Until now, mosques are still often found in providing information such as prayer schedules, study schedules, financial reports, appeals still use wooden boards or triplex so that it can only be known by people who enter or worship at the mosque and the information is still very monotonous. There are still a few mosques that already have information screens in the form of television screens that present prayer times, running texts about study info, but they are still limited and have not been equipped with study pamphlets and mosque cash reports. Most mosques in the city of Palangka Raya and its surroundings still do not have information screens because the cost of the equipment is relatively too expensive, so they only use bulletin boards and paper pasted on the mosque walls to display or inform prayer schedules, routine studies and Friday sermons and other important information. So it is necessary to have a tool application in the form of information screens throughout the mosques in Palangka Raya and its surroundings that can display prayer schedules, regular studies and Friday sermons and other important information in an integrated manner so that people can see the desired mosque information through smartphone media, Besides that, mosque administrators or ta'mir can manage their own content or information media content according to the needs of the mosque by utilizing digital signage so that the form of delivering information through electronic display media is carried out dynamically and attractively. The method used in this research is to use the System Development Life Cycle (SDLC). Software development uses the Waterfall software development model.
Automatic English Proficiency Assessment for Air Traffic Controllers in Emergencies

Computer-assisted language learning (CALL) is particularly effective for addressing language skills such as vocabulary, listening and comprehension. Computer-assisted language learning can significantly reduce the time required in class; however, it does not assess learner’s language skills when they have to react quickly and under stress. Controlling aircrafts is an inherently stressful activity. As a result, controlling an aircraft while communicating across language barriers, deprived of visual cues, negatively affects the aviation safety. In this specific context, this paper proposes an Intelligent Communication System based on automatic speech recognition (ASR). With this new system, the air traffic controller can acquire in-depth knowledge to develop language skills. Using ASR-based CALL technology will have positive consequences in terms of using International Civil Aviation Organization (ICAO) standardized phraseology, objectivity and allowing Air Traffic Controllers to develop calm and neutral intonation, even in an abnormal and emergency situation. We also introduce proficiency indicators to assess the voice interaction of air traffic controller in abnormal and emergency situation.
Study on Temperature Control and Crack Prevention Scheme for Lining Concrete Construction of Spillway Tunnel in Summer

Abstract In view of the characteristics of high strength and thin thickness of spillway tunnel lining concrete, the temperature field and stress field of spillway tunnel lining concrete during summer construction are simulated by using large-scale finite element software SAPTIS, and the risk assessment of lining concrete surface flow maintenance time and internal water passage time on its own cracking during summer construction is analyzed. For a project, the simulation calculation is carried out, and the results show that under the given working conditions, compared with non-flow curing, the surface point stress of surface flow curing for 28 d decreases by 1.34 MPa, and the minimum crack safety factor increases by 0.79 ; the internal middle point stress decreases by 0.75 MPa, and the safety factor increases by 0.31. The cooling time of internal water in lining concrete is longer than the time when the maximum temperature of concrete occurs, which cannot effectively reduce the maximum temperature of internal temperature, but it can reduce the internal tensile stress to a small extent and improve the safety factor. The research results have guiding value for thermal control and crack prevention of lining concrete.
Securing Criminal Records using R-Pi, QR code and Steganography

In today`s digital scenario it has become very essential to maintain secrecy of criminal records otherwise forgery could happen. Using steganography it is possible to provide security for the information which is communicated over the internet from one crime branch to the other. Steganography one of the emerging security fields works to mask the very existence of the message. A wide range of carrier file formats can be utilized, but digital steganography is the extremely beneficial data hiding technique to secure criminal image as well as the crime scene images. Various applications have various prerequisites of the steganography method utilized. In this paper, we proposed CRSS (Criminal Record Security System) an image steganography method with LSB and RSA technique for enhanced security and along with that Raspberry pi and GSM module is used. Thus, for a more secure approach, the proposed method hides the criminal`s confidential records such as criminal`s image, crime scene digital images etc. using LSB steganography and also encrypts the confidential data making use of a private key using RSA algorithm and then sends it to the desired end. The receiver then decrypts the confidential data to get the original criminal information. CRSS is also proposed to send a QR code to the receiver which hides sensitive data and may include criminal`s previous crime history and other written proofs which are scanned at the receiving end reveals the entire criminal record. The entire system is implemented on Raspberry Pi 3 processor and thus a secure transmission of data without traditional desktop dependency in a more economical way could be established.
A Multi-criteria Fuzzy Random Crop Planning Problem using Evolutionary Optimization

In this paper, we deal with fuzzy random objectives in a multi-criteria crop planning problem considered as a multiobjective linear programming problem.These fuzzy random factors are related to decision making processes in practice, especially the uncertainty and synthesized objectives of experts.The problem is transformed into a multi-objective nonlinear programming problem by a step of evaluating expectation value.Instead of using classical methods, we use a multi-objective evolutionary algorithms called NSGA-II to solve the equivalent problem.This helps finding many approximate solutions concurrently with a low time consumption.In computational experiments, we create a specific fuzzy random crop planning problem with the data synthesized from government's reports and show convergence of the algorithm for proposed model.
5 Teenagers and social networking. Twitter as a data source for the study of the language of London teenagers and young adults

5 Teenagers and social networking. Twitter as a data source for the study of the language of London teenagers and young adults was published in Global Perspectives on Youth Language Practices on page 85.
Implementasi Algoritma Naive Bayes Untuk Mengetahui Minat Beli Konsumen Terhadap Sarang Burung Walet

Implementation of the Naive Bayes Algorithm to determine consumer buying interest in swallow nests. This study implements the Naive Bayes algorithm for managing data analysis which is used to classify new case data based on samples from training data by calculating the probability or probability value of each class. The number of samples used in this analysis is 25 training data records that are calculated manually or using the rapidminer application. The results of the calculation by applying the nave Bayes algorithm by multiplying the probability value or the probability value of the cases taken from the training data can be seen that P(X|Classification=”Interest”) gets greater results than P(X|Classification='No” ) with a value of interest = 0.233739 and a value of 0.00078. In addition, the results of data processing with the rapidminer application show that the new case data in the training data is included in the "Interest" classification category with the prediction results of interest with an interest confidence number of 0.983 with 24 consumers and 0.990 for 25 consumers with a comparison of confidence results not 0.017 and 0.010. So it can be concluded that the two new case data in the training data are included in the "Interest" classification category. This shows that the results of processing with the Naive Bayes algorithm are very accurate.
Mammoth Scale: CRISPR Dx Company Partners with MilliporeSigma, Hamilton

GEN EdgeVol. 2, No. 1 VirologyMammoth Scale: CRISPR Dx Company Partners with MilliporeSigma, HamiltonTest developer focuses on supercharging capacity of commercial labs multi-foldAlex PhilippidisAlex PhilippidisSearch for more papers by this authorPublished Online:3 Nov 2020https://doi.org/10.1089/genedge.2.1.55AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 2Issue 1Jan 2020 Information© 2020 by GEN PublishingTo cite this article:Alex Philippidis.Mammoth Scale: CRISPR Dx Company Partners with MilliporeSigma, Hamilton.GEN Edge.Jan 2020.325-330.http://doi.org/10.1089/genedge.2.1.55Published in Volume: 2 Issue 1: November 3, 2020PDF download
Design of Intelligent Drip Infusion Control System Based on Huawei Internet of Things Platform

Abstract In order to solve the problems of heavy labor burden, cumbersome work, patients can not get comfortable care and low medical efficiency, an intelligent drip infusion control system based on Huawei Internet of things platform is designed. The system first describes the system implementation technical principle, hardware design idea, Huawei cloud platform upper management application solution idea, using NB-IoT to communicate with Huawei cloud platform, Huawei cloud server design, web back-end, web front-end and mobile app design process. The test results show that the system has the functions of web page and mobile app remote centralized monitoring, remote speed regulation and remote run out alarm, which reduces the burden of nurses, reduces the contact time between nurses and patients, and improves the medical efficiency.
D <scp>etect</scp> S <scp>ec</scp> : Evaluating the robustness of object detection models to adversarial attacks

Despite their tremendous success in various machine learning tasks, deep neural networks (DNNs) are inherently vulnerable to adversarial examples, which are maliciously crafted inputs to cause DNNs to misbehave. Intensive research has been conducted on this phenomenon in simple tasks (e.g., image classification). However, little is known about this adversarial vulnerability for object detection, a much more complicated task, which often requires specialized DNNs and multiple additional components. In this paper, we present DetectSec, a uniform platform for robustness analysis of object detection models. Currently, DetectSec implements 13 representative adversarial attacks with 7 utility metrics and 13 defenses on 18 standard object detection models. Leveraging DetectSec, we conduct the first rigorous evaluation of adversarial attacks on the state-of-the-art object detection models. We analyze the impact of the factors including DNN architecture and capacity on the model robustness. We show that many conclusions about adversarial attacks and defenses in image classification tasks do not transfer to object detection tasks, for example, the targeted attack is stronger than the untargeted attack for two-stage detectors. Our findings will aid future efforts in understanding and defending against adversarial attacks in complicated tasks. In addition, we compare the robustness of different detection models and discuss their relative strengths and weaknesses. The platform DetectSec will be open source as a unique facility for further research on adversarial attacks and defenses in object detection tasks.