Reviewerly/exHarmony huggingface dataset:
Attribute Selection in Naive Bayes Algorithm Using Genetic Algorithms and Bagging for Prediction of Liver Disease

Liver disease is an inflammatory disease of the liver and can cause the liver to be unable to function as usual and even cause death. According to WHO (World Health Organization) data, almost 1.2 million people per year, especially in Southeast Asia and Africa, have died from liver disease. The problem that usually occurs is the difficulty of recognizing liver disease early on, even when the disease has spread. This study aims to compare and evaluate Naive Bayes algorithm as a selected algorithm and Naive Bayes algorithm based on Genetic Algorithm (GA) and Bagging to find out which algorithm has a higher accuracy in predicting liver disease by processing a dataset taken from the UCI Machine Learning Repository database (GA). University of California Invene). From the results of testing by evaluating both the confusion matrix and the ROC curve, it was proven that the testing carried out by the Naive Bayes Optimization algorithm using Algortima Genetics and Bagging has a higher accuracy value than only using the Naive Bayes algorithm. The accuracy value for the Naive Bayes algorithm model is 66.66% and the accuracy value for the Naive Bayes model with attribute selection using Genetic Algorithms and Bagging is 72.02%. Based on this value, the difference in accuracy is 5.36%.Keywords: Liver Disease, NaÃ¯ve Bayes, Genetic Agorithms, Bagging.
Web-Augmentation, Design Thinking, and Collaboration Engineering to Foster Innovation in DSS for Agriculture: A Case Study

Decision making in agriculture increasingly relies on software, for example to gather important information or to weight alternatives. Information systems innovation in agriculture is a challenging and very active area. Existing software products, frequently implemented as web applications, are found to lack functionality, for example to support collaboration. Augmenting the web is a widely adopted technique for enhancing existing applications with new features which are not available out-of-the-shelf. Design thinking has proven to be an effective tool to support innovation on many domains. Collaboration Engineering is an effective means to reuse design experience of collaboration strategies. This work presents an approach to involve end-users in enhancing exiting web software to produce incremental innovations. The approach relies on Design Thinking, Web Augmentation and Collaboration Engineering. The approach was successfully tried in a case study letting end-users add collaboration support to a system that did not provide it.
Machine Learning Based Anomaly Detection as an Emerging Trend in Telecommunications

Research Question: This paper investigates into how machine learning can be applied for the purpose of detecting anomalies in the data describing transport component within the cellular network. Motivation: In the field of telecommunications, terabytes of data are generated each hour. This makes the manual analysis almost impossible to perform. There are thousands of components whose behaviour needs to be monitored, since anomalous behaviour could indicate a possible failure that can lead to network degradation, huge maintenance costs, and finally – a bad user experience. Our goal is to try to catch anomalous behaviour automatically, and thus help domain experts when performing drill down analysis of the degraded base stations and their key performance indicators (KPIs). Idea: The main idea of this paper is to empirically evaluate the application of machine learning for the problem of anomaly detection, in the field of telecommunications, specifically to long term evolution (LTE) networks. Data: Data used in the analysis contains information about base transceiver stations (BTS) behaviour through the time. The data are gathered from a cellular network provider located in Serbia. The data are collected on an hourly basis, for a period of two weeks, resulting in almost 700 thousand rows. The behaviour is assessed by 96 transport KPIs coming from BTS, describing the package losses, delays, transmission success rates, etc. Tools: Two main algorithms, ensemble-based Isolation Forest and autoencoder neural network, are elaborated and applied in order to identify patterns of anomalous behaviour. Findings: The results show that machine learning can be successfully applied in the field of LTE networks for the problem of anomaly detection. Machine learning can significantly reduce the time needed for the domain experts to identify anomalies within the network. In addition to time efficiency, one of the algorithms tested is able to identify anomalous KPIs separately, which is crucial when performing root cause analysis, by using drill-down approach, in order to identify which component is degraded. Contribution: This paper enriches existing research related to anomaly detection in LTE networks and provides an innovative approach to automated root-cause analysis of network degradation.
The Effect of WebQuest on Students' Writing Skill

This research investigated WebQuest in Writing Skill Descriptive Text. Writing is an activity of the ability to think and express ideas and find information in describing object that is contained by using the features of WebQuest application and producing good and interesting work for the readers. This research aimed to find out the effectiveness of using WebQuest in Writing Descriptive Text on the significant difference score achieved by students before and after WebQuest applied.. The design of this research used quantitative with pre-experimental design that includes one group pre-test and post-test. The population of this study was the tenth grade students of MA Al-Ibrohimy Galis in the academic year of 2018/2019. The data was obtained by administering Writing test. The research was started by giving pre-test, treatments, and post-test to the experimental class. The data of the test were analyzed by using dependent T-test. The results indicate that the use of WebQuest seems to be more effective in teaching learning process. The score of students' post-test have higher than before have been taught by WebQuest. Based on the findings of this research, using WebQuest media appears to be a plausible alternative for teaching English Writing Skill in Descriptive Text to the tenth year students of MA Al-Ibrohimy Galis.
Correction to: Multiple criteria ranking method based on functional proximity index: un-weighted TOPSIS

This erratum is published due to proofing error as author corrections with equation 1 were overlooked.
The Effect of Using Padlet as a Vocabulary Knowledge Enhancement Application on Saudi EFL Female Learners, and their Attitudes toward its Value in Learning

This study aimed to investigate the effects of using the digital learning tool Padlet to teach English vocabulary to Saudi females learning English as a Foreign Language (EFL) within the context of Communicative Language Teaching (CLT).Specifically, it examined the attitudes and thoughts of these females toward the application and the effectiveness of using it as a learning platform.The study was set out to answer two questions; the first questions is "How effective is the application Padlet with regard to using a CLT approach to teach English vocabulary skills to Saudi female EFL learners?"while the second one is "What are users' attitudes toward the online learning platform Padlet?".The experiment was conducted for two weeks using Padlet.The population size was 50 Saudi females who were level-eight students at the English Language and Translation Department of Al-Imam Muhammad Ibn Saud Islamic University in Riyadh, Saudi Arabia.After inclusion criteria (i.e., the results of an English language placement test) were applied, 30 students were selected for the study; this was done to ensure that the group was homogenous with regard to the level of English skill.The remaining participants then took a pretest; subsequently, the researcher and subjects engaged in seven instruction sessions using Padlet, during which the researcher followed a Communicative Language Teaching approach to presenting English vocabulary.After the experiment was concluded, the participants were given a posttest identical to the pretest in order to assess their knowledge and compare their vocabularies before and after the instructions were provided.In addition, the participants responded to an online survey designed to measure their attitudes toward Padlet.The participants' scores and mean scores were calculated using the Statistical Package for Social Science (SPSS); specifically, a one-sample t-test and a paired sample t-test were conducted.The findings revealed that the vocabulary instructions given using Padlet were effective at improving the English lexical knowledge of learners.The survey also revealed that the learners' attitudes toward the tool were positive.Finally, the results indicated that the implementation of Communicative Language Teaching through Padlet was crucial to developing the participants' English vocabulary skills.
DIGITAL CULTURE – A HISTORICAL STAGE IN THE DEVELOPMENT OF THE INFORMATION CULTURE OF SOCIETY

Different scientific approaches to digital culture have been analysed in the article: digital culture is considered as a special form of being, a special integral design that includes audiovisual, semiotic, technological, logical, communication, network and other subsystems that exists at five levels: material, functional, symbolic, mental and spiritual; and as a set of values ofmodern society based on digital coding; and as a system of changes in practices, products of human activity associated with the culture of the digital age; and as a special level of digital literacy and competency. The anthropological aspect of digital culture has been emphasized. The main causes and stages of the development of digital culture as a modern stage in the development of the information culture of society also have been considered.
Traffic Image Dehazing Based on Wavelength Related Physical Imaging Model

Fog concentration is of crucial importance for traffic image dehazing. However, existing methods neglect it, resulting in inferior dehazing results. To tackle this problem, we propose a dehazing method based on the wavelength related physical imaging model. First, we present a wavelength related physical imaging model for traffic imaging. Then, according to the prior that colors of objects in an image are decided based the reflection of different wavelengths, we design a transmission estimation strategy based on the maximal fuzzy correlation and graph cut result. To be specific, the segmentation can be obtained by the maximal fuzzy correlation and the graph cut algorithm, and then it can be used as the guided image in the guided filter for getting a transmission map with continuous scene information. Such a segmentation design can take advantage of both threshold-based segmentation methods, e.g., maximal fuzzy correlation, and spatial-based segmentation methods, e.g., graph cut. Furthermore, the proposed iterative algorithm also improves the computational efficiency of image dehazing. Finally, the atmospheric light is predicted by the sky region in the segmentation result, and a haze-free image is obtained via the wavelength related physical imaging model. Experiments conducted on 500 synthetic images and real-world images have demonstrated that our algorithm can improve dehazing precision by at least 7% and shorten running time by roughly 15% compared to existing dehazing methods. Hence, the proposed method can be used for image dehazing in the traffic monitoring system.
USING THE INTERNET OF THINGS TO FIGHT VIRUS OUTBREAKS

Although much less fatal than the Ebola and previous SARS virus epidemics, the current coronavirus outbreak (COVID-19) has spread to more people (more than 125,000 in fewer than 50 days) in more countries (more than 120 countries) in much shorter time frame (50 days). On March 11, 2020, the WHO has formally declared COVID-19 a global pandemic.
Automated Configuration of NoSQL Performance and Scalability Tactics for Data-Intensive Applications

This paper presents the architecture, implementation and evaluation of a middleware support layer for NoSQL storage systems. Our middleware automatically selects performance and scalability tactics in terms of application specific workloads. Enterprises are turning to NoSQL storage technologies for their data-intensive computing and analytics applications. Comprehensive benchmarks of different Big Data platforms can help drive decisions which solutions to adopt. However, selecting the best performing technology, configuring the deployment for scalability and tuning parameters at runtime for an optimal service delivery remain challenging tasks, especially when application workloads evolve over time. Our middleware solves this problem at runtime by monitoring the data growth, changes in the read-write-query mix at run-time, as well as other system metrics that are indicative of sub-optimal performance. Our middleware employs supervised machine learning on historic and current monitoring information and corresponding configurations to select the best combinations of high-level tactics and adapt NoSQL systems to evolving workloads. This work has been driven by two real world case studies with different QoS requirements. The evaluation demonstrates that our middleware can adapt to unseen workloads of data-intensive applications, and automate the configuration of different families of NoSQL systems at runtime to optimize the performance and scalability of such applications.
A Multimodal Pairwise Discrimination Network for Cross-Domain Action Recognition

In recent years, action recognition has become a hot research topic in the computer vision and machine learning domain. Despite many well-designed action recognition approaches have been proposed, we point out that some limitations still exist including the separated fusion of different Spatio-temporal features and the reconstruction classification model, and the requirement of similar environmental conditions when capturing the training and testing data. Thus, research interest has shifted from traditional action recognition towards cross-domain action recognition. To solve these limitations, in this work, we propose a novel multimodal pairwise discrimination network (short for MPD) for cross-domain action recognition that is an end-to-end network architecture. In MPD, it can jointly fuse different Spatio-temporal features from the video, learn domain invariant features for different action domains (source and target domains), and build the classification model. To characterize the shift between these domains, subnetwork parameters in corresponding layers of MPD are required to be relevant, but not identical. Besides, the domain invariant feature discrimination needs to be improved. Extensive experimental results on two different public benchmarks including indoor environment and outdoor environment demonstrate that our MPD solution can significantly outperform state-of-the-art methods with a 4% to 20% improvement in average accuracy.
Salient Object Detection With Importance Degree

In this article, we introduce salient object detection with importance degree (SOD-ID), which is a generalized technique for salient object detection (SOD), and propose an SOD-ID method.We define SOD-ID as a technique that detects salient objects and estimates their importance degree values.Hence, it is more effective for some image applications than SOD, which is shown via examples.The definition, evaluation procedure, and data collection for SOD-ID are introduced and discussed, and we propose its evaluation metric and data preparation, whose validity is discussed with the simulation results.Moreover, we propose an SOD-ID method, which consists of three technical blocks: instance segmentation, saliency detection, and importance degree estimation.The saliency detection block is proposed based on a convolutional neural network using the results of the instance segmentation block.The importance degree estimation block is achieved using the results of the other blocks.The proposed method accurately suppresses inaccurate saliencies and estimates the importance degree for multi-object images.In the simulations, the proposed method outperformed state-of-the-art methods with respect to the F-measure for SOD; and Spearman's and Kendall rank correlation coefficients, and the proposed metric for SOD-ID.
Creative Potential of Media Education as a New Paradigm of the Information Age

The article aims to study the creative potential of modern media education as a new paradigm of the information age.The article is based on the many years of experience of the author, a well-known cultural scientist, media theorist, and specialist of the university system of media education, who has been developing her personal methodology for more than twenty years.Considering the practice of different media schools that have developed in the countries of Europe and the USA, as well as in post-Soviet Russia, the author proves that media education is a comprehensive approach to personality development, combining various humanities (cultural studies, pedagogy, psychology, art history, semiotics, etc.).The experience of implementing media education in the curriculum of humanitarian disciplines shows the effectiveness of the formation of a comprehensively developed personality, which provides competitive advantages to future professionals and confirms the effectiveness of methods of media pedagogy.Analyzing the experience of educators from different countries, as well as personal experience, the author proceeds from the fact that UNESCO identifies media education as the highest priority sphere of cultural and pedagogical development of the XXI century, and therefore media pedagogy has great prospects.
Live Video Analytics with Microsoft Rocket for reducing edge compute costs

Microsoft Rocket, an open-source project from Microsoft Research, provides cascaded video pipelines that combined with Live Video Analytics from Azure Media Services, makes it easy and affordable for developers to build video analytics applications in their IoT solutions. Unprecedented advances in computer vision and machine learning have opened opportunities for video analytics applications that are of wide-spread interest to society, science, and business. While computer vision models have become more accurate and capable, they are also becoming resource-hungry and expensive for 24/7 analysis of video. As a result, live video analytics across multiple cameras also means a large computational footprint on premises built with a good amount of expensive edge compute hardware (CPU, GPU etc.). Total cost of ownership (TCO) for video analytics is an important consideration and pain point for our customers. With that in mind, we integrated Live Video Analytics from Azure Media Services and Microsoft Rocket (from Microsoft Research) to enable an order-of-magnitude improvement in throughput per edge core (frame per second analyzed per CPU/GPU core), while maintaining the accuracy of the video analytics insights.
Target Classification Aided Variable-Structure Multiple-Model Algorithm

Because the varieties of general aviation aircraft and their performance differences become increasingly large, the traditional multiple model tracking algorithm needs to employ more motion models to describe the actual maneuver model of a moving target. This fact is easy to degrade the tracking accuracy and brings a large computational load. Thus, a target classification aided variable-structure multiple-model algorithm (TCA-VSMM) is proposed to solve the above problem. In the proposed TCA-VSMM algorithm, the target classification aided is introduced to improve the accuracy of state estimation. Then, the target classification aided in the screening of the motion model set is analyzed. Concretely, the target classification information and velocity information in automatic dependent surveillance-broadcast (ADS-B) measurements are incorporated into the variable-structure multiple-model filter. Consequently, the screening model set is more approximated to the real motion model of a moving target. Experiments show that the TCA-VSMM algorithm can obtain better performance with small computation load and high estimation accuracy than those of the model-group switching variable-structure multiple-model algorithm.
Comparative Analysis of the Methods of Wavelet-and Spline-extrapolation in Problems of Predicting Self-similar Traffic

The task of predicting self-similar traffic of IoT network objects with a significant number of pulsations and the property of long-term dependence is considered, which makes it difficult to predict in practice. Using the method of wavelet extrapolation based on Haar-wavelet, the results of prediction of self-similar traffic are obtained. We compared the results of traffic prediction based on the Haar-wavelet and the cubic spline function using wavelet and spline extrapolation. This will allow you to choose one or another extrapolation method to improve the accuracy of the prediction, while ensuring scalability and the ability to use it for various IoT applications to prevent network overloads.
Analysis of quantum coherence for localized fermionic systems in an accelerated motion

Although quantum coherence is a well known phenomenon in quantum information theory and quantum optics, it has been investigated from the resource theory perspective only recently. Furthermore, quantum coherence has important implications in relativistic quantum information where the degradation of entanglement can be attributed to decoherence. In this paper, we investigate the quantum coherence of 1+1 Dirac field modes localized in a cavity as observed by two relatively accelerated observers. The acceleration is assigned very small values and its effects are investigated in a perturbative regime. For this purpose, we use α parameterized two-qubit pure entangled state and a Werner state. We find that coherence shows a periodic degradation due to accelerated motion. However, this degradation can be balanced by adjusting the durations of uniform and accelerated motion. Moreover, it is found that dynamics of quantum coherence closely resembles that of entanglement under the same settings. This similarity confirms the recent attempts to relate the resource theories of coherence and entanglement in a relativistic regime.
Why AI Will Never Surpass Human Intelligence

A paper entitled “The unreasonable effectiveness of deep learning in artificial intelligence” argues that the way forward towards achieving general AI, that is to say a human level intelligence, is to copy how an organic brain does if for humans. The paper argues that AI has to move from a very limited 2D-space which is referred to as Flatland to a tera-dimensional space that represents the million billion synapses between the neurons in the cortex of the human brain. It is pointed out that the move from 2-D AI to tera-dimensional AI is actually a move in the wrong direction if they ever hope to achieve general AI. The fact is that although there are a million billion synapses between the neurons in the cortex, human consciousness is one dimensional or holistic. In order to achieve general AI the machine will have to do everything a human can do where there are no gaps or seams in the output. A model of the human brain is offered where different sections of the cortex are specialized for different functions and these disparate regions communicate with each other electronically at the speed of light via brainwaves and this is how the brain generates a global holistic 1-dimensional consciousness in us. Also as numbers don’t exist in Nature an organic brain, unlike deep learning, generates intelligent output without the aid of numerical programs or statistics.