Reviewerly/exHarmony huggingface dataset:
Face Restoration Based on GANs and NST

Image restoration technology is a research hotspot in the field of deep learning computer vision. However, due to the complexity of facial textures, the existing face restoration algorithms lack visual coherence. We propose a face restoration algorithm (GNST) based on Generative Adversarial Networks (GANs) and Neural Style Transfer (NST), which uses the generative network to repair the damaged facial contents and then uses the style transfer to adjust the overall style. At the stage of repairing content, we design four losses from different aspects. We add two total variation losses (tv loss) besides the traditional content loss and adversarial loss. The first tv loss can make the generated image smoother and cleaner. The second tv loss can effectively prevent the generator's gradient collapse when generator "cheat" the discriminator. In addition, it is used to highlight facial important features. Experiments show that G-NST achieves better results than existing methods.
Completion Reasoning Emulation for the Description Logic EL

We present a new approach to integrating deep learning with knowledge-based systems that we believe shows promise. Our approach seeks to emulate reasoning structure, which can be inspected part-way through, rather than simply learning reasoner answers, which is typical in many of the black-box systems currently in use. We demonstrate that this idea is feasible by training a long short-term memory (LSTM) artificial neural network to learn EL+ reasoning patterns with two different data sets. We also show that this trained system is resistant to noise by corrupting a percentage of the test data and comparing the reasoner's and LSTM's predictions on corrupt data with correct answers.
Object Intelligent Detection and Implementation Based on Neural Network and Deep Learning

Aiming at the disadvantage of high false detection rate in target classification detection using existing feature training classifiers, this paper proposes a moving target detection algorithm based on convolutional neural network on the basis of deep learning. On the image to be checked, sliding windows of different scales are used to determine whether there is a object window. In object detection, a convolutional neural network is trained with a large number of positive and negative samples. In order to better adapt to object detection, the topology of the convolutional neural network is improved. The window of suspected object is input into the improved convolutional neural network for object detection, and the false detection rate is reduced on the basis of maintaining the original detection rate.
THE EFFECT OF USING POWERPOINT MEDIA ON STUDENT LEARNING OUTCOMES IN THE MATERIAL ADAPTATION OF LIVING THINGS

The purpose of this study was to determine the effect of using PowerPoint media on student learning outcomes in the material adaptation of living creatures. The research method uses a quasi-experimental design with a nonequivalent control group design. The study was conducted in classes 6 (six) A and B, each of which numbered 25 students with the same level of homogeneity. Class 6A was chosen as the experimental class and Class 6B as the control class. The sampling technique uses purposive sampling which is to determine the sample based on special characteristics by the expected research objectives. The study was conducted at Bakti Mulya 400 Pondok Indah South Jakarta Elementary School in grade 6. The learning achievement test instrument used multiple-choice questions that had been validated with content and construction. Hypothesis test results through the Independent Samples Test showed a significance value of 2 (two) directions (t-tailed) of 0.002 <0.05, so it was concluded that there were significant scores between the control group and the experimental group. These results indicate the influence of the use of PowerPoint media on student learning outcomes in the material adaptation of living things. Although not the only variable that can affect learning outcomes, the use of PowerPoint media by teachers can be an alternative in efforts to increase students' interest and motivation to learn. so that their learning outcomes can improve
A Moving Target Defense Technology Based on SCIT

Moving target defense technology is one of the revolutionary techniques that is “changing the rules of the game” in the field of network technology, according to recent propositions from the US Science and Technology Commission. Building upon a recently-developed approach called Self Cleansing Intrusion Tolerance (SCIT), this paper proposes a moving target defense system that is based on server switching and cleaning. A protected object is maneuvered to improve its safety by exploiting software diversity and thereby introducing randomness and unpredictability into the system. Experimental results show that the improved system increases the difficulty of attack and significantly reduces the likelihood of a system being invaded, thus serving to enhance system security.
SeqCheck

In this paper we describe SeqCheck, a model checking tool which allows us to investigate if certain properties hold for an interactive system. These properties allow us to determine if the interaction sequence model of an interactive system's overlap component behaves as expected. We describe the properties we have defined for this overlap component and then demonstrate the use of SeqCheck to identify when these do not hold.
RETRACTED ARTICLE: Parameter estimation of network signal normal distribution applied to carbonization depth in wireless networks

Abstract For the average state of the normal distribution parameter estimation, regular normal distribution parameter gives an estimation, but the carbonation depth of influence factors is more of a parameter estimation, shooting low deficiencies; therefore, putting forward application in the carbonation depth of the normal distribution parameter is estimated. A normal distribution parameter estimation model is constructed, and a normal distribution parameter estimation model framework is constructed by using the least squares method to determine the expression of normal distribution parameters. Based on the linear deviation calculation of normal distribution parameters and the determination of the maximum similar value of parameters, the parameter estimation is realized by using the Bayesian function of carbonization depth. The parameter estimation of network signal based on carbonization depth is proposed. Parameter estimation can play an important role in the intelligent analysis of big data, and it is also an important basic guarantee for machine learning algorithms. Using the integrity test results and error rate test result, variable parameters calculated from measured parameters, substitution shooting parameters calculation formula of parameter estimation is put forward by the conventional parameter estimation methods, which shot up to 22.12%, is suitable for the carbonation depth of the normal distribution parameter estimation.
e-LAHOK: An e-Participatory Platform for Disaster Risk Reduction and Management

Abstract The study aimed to design, develop and determine the extent of user acceptability of e-LAHOK (Legazpi Alisto at Handang magbigay ng Opinyon para sa Kaligtasan), an electronic and participatory Disaster Risk Reduction and Management Platform for the City of Legazpi that fosters civic engagement and open participatory governance by soliciting Disaster Risk Reduction (DRR) strategies and incident reports from the local populations. An integrated Design Thinking (DT) and Extreme Programming (XP) framework (DT@XP) was utilized in the design and development of e-LAHOK whereas, a user’s evaluation based on ISO 9126 software quality metrics was conducted to determine the extent of user acceptability. Significant findings showed that e-LAHOK facilitated orderly solicitation and submission of DRR strategies and incident reports from the local populations by allowing the creation, management, consolidation, analysis and visualization of survey responses as well as submission, verification and acceptance of submitted incident reports for analysis. These results could serve as basis to concerned agencies in the development and implementation of DRR plans and policies to ensure preparedness and effective response by the barangays and its citizens in the event of a disaster. Further, evaluation results show that the system was rated highly acceptable in terms of functionality and usability. This concluded that the features of e-LAHOK provided an organized way of gathering inputs from the local communities of Legazpi City, fostering civic engagement and open, participatory governance. Thus, utilization of the proposed system is recommended.
Math Trails through Digital Technology: An Experience with Pre-Service Teachers

This paper describes part of an on-going study that aims to understand the potential of digital technology in outdoors mathematics from the perspective of pre-service teachers.We followed a qualitative approach and data was collected through observation, two questionnaires and photographic records.The study involved forty-eight participants that used Math City Map to do a math trail in the city centre of Viana do Castelo.Results show that they valued the experience, highlighting the possibility of solving realistic problems, developing cooperative work, critical thinking and the establishment of mathematical connections.They found the app to be user friendly and motivating, mentioning its contribution for students' engagement through active learning, spatial orientation, autonomy and being more interactive then the paper version.
Discrete Fourier Transform-Based Steganography

In this chapter, discrete Fourier transform (DFT)-based steganographic process has been discussed in details. The generalized DFT and IDFT equation pair is given from where sub-image-based (small nonoverlapping window) reversible computations using DFT and IDFT is done. Numerical examples are given for both 1D and 2D DFT and IDFT transform computation for confirmation of reversibility. LSB encoding on 8 x 4 sub-image is also given completely with flow diagram. A complete algorithm of DFT-based steganography is given with a capacity of embedding payload of 0.75 bpB along with its implementation results and analysis.
Environmental data set for the design and analysis of the Photovoltaic system in the Jordan Valley

Renewable energy penetration in the national electrical grid in Jordan has been rapidly increasing in the last few years, touching nearly 30%. Limited grid capacity has been a driver to slow down large-scale projects and has motivated increased attention towards off-grid photovoltaic (PV) systems. Planning properly-sized on-grid and off-grid systems requires accurate knowledge of the environmental and irradiance conditions at the installation site. As off-grid systems are equally of interest to non-critical types of loads, investments are directed towards agricultural applications like water pumping and desalination. To assess the potential of expanding the renewable energy penetration in agricultural areas in the Jordan valley and surrounding areas, this article presents annually measured environmental data including irradiance, temperature and wind speed, in addition to data related to soling on an existing off-grid PV system installed in the Jordan Valley. These data are used in the research article entitled “Performance Analysis of Off-grid PV systems in the Jordan Valley”, Al-Addous et al., 2017, and in the research article entitled “Modelling and Quantifying of Dust Accumulation Impact on PV Module Performance” Al-Addous et al., 2019. Data were collected and gathered using calibrated, high accuracy sensors installed at different parts of the installed plant.
Performance and Interpretability in Fuzzy Logic Systems – Can We Have Both?

Fuzzy Logic Systems can provide a good level of interpretability and may provide a key building block as part of a growing interest in explainable AI. In practice, the level of interpretability of a given fuzzy logic system is dependent on how well its key components, namely, its rule base and its antecedent and consequent fuzzy sets are understood. The latter poses an interesting problem from an optimisation point of view – if we apply optimisation techniques to optimise the parameters of the fuzzy logic system, we may achieve better performance (e.g. prediction), however at the cost of poorer interpretability. In this paper, we build on recent work in non-singleton fuzzification which is designed to model noise and uncertainty ‘where it arises’, limiting any optimisation impact to the fuzzification stage. We explore the potential of such systems to deliver good performance in varying-noise environments by contrasting one example framework - ADONiS, with ANFIS, a traditional optimisation approach designed to tune all fuzzy sets. Within the context of time series prediction, we contrast the behaviour and performance of both approaches with a view to inform future research aimed at developing fuzzy logic systems designed to deliver both – high performance and high interpretability.
A Combined Method for Usage of NLP Libraries Towards Analyzing Software Documents

Natural Language Processing (NLP) library is widely used while analyzing software documents. The numerous toolkits result in a problem on NLP library selection. The selection of NLP library in current work commonly misses some objective reasons, which may pose threats to validity. And it is also not clear that whether the existing guideline on selection still works for the latest versions. In this work, we propose a solution for NLP library selection when the effectiveness is unknown. We use the NLP libraries together in a combined method. Our combined method can utilize the strengths of different NLP libraries to obtain accurate results. The combination is conducted through two steps, i.e., document-level selection of NLP library and sentence-level overwriting. In document-level selection of primary library, the results are obtained from the library that has the highest overall accuracy. Through sentence-level overwriting, the possible fine-gained improvements from other libraries are extracted to overwrite the outputs of primary library. We evaluate the combined method with 4 widely used NLP libraries and 200 documents from 3 different sources. The results show that the combined method can generally outperform all the studied NLP libraries in terms of accuracy. The finding means that our combined method can be used instead of individual NLP library for more effective results.
Performance Analyses of Different Text Feature Extraction Algorithms in Restaurant Fake Review Detection

Blog reviews about restaurants may have a great influence on the customers’ choice as the use of social network services (SNS) increases. Thus, few restaurant owners bribe to post fake positive reviews on the social networking sites, which can fool many customers. Since the number of the fake reviews increases sharply, detecting the fake reviews become important to protect customers. In this paper, we detected the fake reviews by using the similarity between other fake reviews. To analyze the similarity of the review contents, we use feature extraction algorithms from the documents. Specifically, we compared performance of three different feature extraction algorithms: Term Frequency (TF), Term Frequency–Inverse Document Frequency (TF-IDF), and doc2vec. doc2vectakes accounts for the meaning of keywords in the documents similarly that famous word2vec algorithm does, while TF and TF-IDF only consider the occurrence of the keywords. Accordingly, doc2vec demonstrated the best performance in detecting the fake reviews.
Multi-Factor One-Way Authentication and Key Retrieval Based on Ideal Threshold Secret Sharing

It is not a secret that one-factor authentication protocols suffer from many attacks nowadays. So there exist numerous solutions that use two or more authentication factors. The most popular is two-factor authentication via mobile network where the Password and SMS are used. More critical applications require even biometric authentication procedure. All these protocols use distinct algorithm for processing different factors. Therefore, developing a universal, secure and low-cost authentication scheme is an important task. We propose a new universal multi-factor one-way authentication and key retrieval protocol based on ideal threshold secret sharing. Our protocol allows to retrieve a key that encrypts user data stored on the server. We also provide the ways of combining our protocol with traditional authentication factors like Password, Personal Data, Biometrics, Geometrical Lines and Random Strings. In addition, our protocol does not use costly mathematical operations and can be implemented on various low-energy devices.
State of the Art in Transform Encoding for Reversible Steganography and Authentication

In this chapter, the state of the art information on reversible steganography is given. The information on steganographic techniques provides here from 1996 to 2019 selectively. More precisely, the state of the art on last five years is emphasized. To incorporate recent trends, eleven articles of 2019 have been studied.
P1846HISTORY OF ARTIFICIAL INTELLIGENCE AND ITS APPLICATION IN NEPHROLOGY

Abstract Background and Aims The application of artificial intelligence and neural networks in medicine is used to help solve problems that cannot be handled by the classical approach. The common name “cybernetics” encompassed the fields of management, information technology and biomedicine, but these disciplines continued to evolve independently due to the explosion of new knowledge. Over time, the development of neural networks has been turbulent and is now widely used in various fields of medicine and even in nephrology. The aim of the paper is to analyze the history of the development of artificial intelligence and its application in nephrology. Method Data were collected from books, magazines, encyclopedias and databases. Results Basic research on cybernetics and medicine was done by Golgi and Kelley doctors after Isaak Newton and Hermann von Helmholtz. The first theoretical mathematical models were derived in 1943 by Warren Mc Culloch and Walter Pitts. A few years later, a more contemporary contribution to the development of neural networks was given by Norbert Wiener and John von Neumann because they thought that research into biomedicine based on human brain function would be very interesting. In addition, in 1948 Norbert Wiener was the first to publish a work explaining the term cybernetics. At that time, the first experiments were made and new theories in the field of artificial intelligence were put forward by Marvin Misnki. The first training of neurons and the basis of all methods for training neurons was described by the Canadian Donald O Hebb. After the first successful neurocomputer in 1957, on which Rosenblatt worked, scientists have perfected various models of neural networks to this day. So far, mostly retrospective studies have been done in clinical nephrology, transplantation and dialysis with the help of algorithms used in neural networks. Particularly complex nephrologic patient relationships as well as assistance with timely implementation of new good clinical practice guidelines, patient prediction in at least the next month, and patient selection for palliative care are just some segments in nephrology that require the introduction of such tools into daily clinical practice with the aim of sensitive patient populations have better treatment outcomes, with physicians having more comprehensive insight and control over the mass of data. Conclusion Today‘s application of artificial intelligence in nephrology is based on retrospective research. The dizzying rise in technological development so far will allow the use of cybernetics and available tools based on neural network algorithms to enable and improve the nephrologists’ dedication and effectiveness.
Transformation of the foreign language teacher’s role in regional university: comparative analysis of teachers’ subjective well-being

И. Е. Абрамова, Е. Н
A modified Efficient Manifold Ranking Algorithm for Large Database Image Retrieval

The efficient manifold ranking (EMR) algorithm is used quite effectively in content-based image retrieval (CBIR) for large image databases where images are represented by multiple low-level features to describe about the color, texture and shape. The EMR ranking algorithm requires steps to determine anchor points of the image database by using the k-means hard clustering and the accuracy of the ranking depends strongly on the selected anchor points. This paper describes a new result based on a modified Fuzzy C-Means (FCM) clustering algorithm to select anchor points in the large database in order to increase the efficiency of manifold ranking specially for the large database cases. Experiments have demonstrated the effectiveness of the proposed algorithm for the issue of building an anchor graph, the set of anchor points determined by this novel lvdc-FCM algorithm has actually increased the effective of manifold ranking and the quality of images query results which retrieved of the CBIR.
Pengaruh Self Efficacy Terhadap Hasil Belajar Matematika Siswa Kelas III SD Kristen Kondo Sapata, Makassar, Indonesia

This reseacrh to knowing of students of class III SD Kristen Kondo Sapata. This research was an “ex-post facto” research which was co relational. The population in this study was all class III at SD Kristen Kondo Sapata which also became a research sample. The result of descriptive statistical analysis showed that the average score of mathematics learning outcomes of students in 2018/2019 academic year was 67,88285 and was in the high category with a standard deviation of 19,33323. The average score of self-efficacy was 74,73 and included in the high category with a standard deviation of 13,492. The result of inferential statistics analysis showed that self-efficacy had a positive effect on the learning outcomes of class III SD Kristen Kondo Sapata students with a contribution of 61.8%.
Boundary Extension Features for Width-Based Planning with Simulators on Continuous-State Domains

Width-based planning algorithms have been demonstrated to be competitive with state-of-the-art heuristic search and SAT-based approaches, without requiring access to a model of action effects and preconditions, just access to a black-box simulator. Width-based planners search is guided by a measure of the novelty of states, that requires observations on simulator states to be given as a set of features. This paper proposes agnostic feature mapping mechanisms that define the features online, as exploration progresses and the domain of continuous state variables is revealed. We demonstrate the effectiveness of these features on the OpenAI gym "classical control" suite of benchmarks. We compare our online planners with state-of-the-art deep reinforcement learning algorithms, and show that width-based planners using our features can find policies of the same quality with significantly less computational resources.