Reviewerly/exHarmony huggingface dataset:
Testing Stability of Digital Filters Using Multimodal Particle Swarm Optimization with Phase Analysis

In this paper, a novel meta-heuristic method for evaluation of digital filter stability is presented. The proposed method is very general because it allows one to evaluate stability of systems whose characteristic equations are not based on polynomials. The method combines an efficient evolutionary algorithm represented by the particle swarm optimization and the phase analysis of a complex function in the characteristic equation. The method generates randomly distributed particles (i.e., a swarm) within the unit circle on the complex plane and extracts the phase quadrant of function value in position of each particle. By determining the function phase quadrants, regions of immediate vicinity of unstable zeros, called candidate regions, are detected. In these regions, both real and imaginary parts of the complex function change signs. Then, the candidate regions are explored by subsequently generated swarms. When sizes of the candidate regions are reduced to a value of assumed accuracy, then the occurrence of unstable zero is verified with the use of discrete Cauchy's argument principle. The algorithm is evaluated in four benchmarks for integer- and fractional-order digital filters and systems. The numerical results show that the algorithm is able to evaluate the stability of digital filters very fast even with a small number of particles in subsequent swarms. However, the multimodal particle swarm optimization with phase analysis may not be computationally efficient in stability tests of systems with complicated phase portraits.
IMPLEMENTATION OF REVERSIBLE CIRCUITBASED KOGGE STONE PREFIX MODULO ADDER

For high speed computing, the need for an efficient adder is essential.In addition module, carry propagationbecomes the bottleneck problem which increases the delay. To avoid thisproblem, arithmetic functions can be implementedusing Residue Number System (RNS) which provides carry free and parallel operation. Though RNS performs severalmoduli in parallel form, it consumes more time and has high power dissipation.Reversible logic is a very promisingalternative to conventional logic system forreducing the power dissipation.Reversible logic is the memory less logic which isused for ultra-low power reliable circuits.In this paper the Kogge stone prefix adder architecture is used which is consideredas fast adder. The adder architecture is used to perform modulo 2n-1 operation and is implemented using reversible circuits.This method offers high speed computing and also minimum power dissipation. The proposed method is simulated usingXilinx 12.4 ISE and the functions are evaluated using MODELSIM 6.3c.
The Effect Of Learning Styles And Games On Seventh Grade Students’ Vocabulary Mastery At MTsS. Pp. Amiruddiniyah Academic Year 2018/2019

The objectives of the study are (1) to investigate whether thestudents’ achievement in mastering vocabulary with high learning stylessignificantly is higher than low learning styles and (2) to determine whetherthere is any significant interaction between learning styles and games onstudents’ achievement in mastering vocabulary. The research method for thisstudy was a quantitative method including an experimental group and a controlgroup. Both groups were given pre-test and post-test. A vocabulary test wasused as the research instrument. The determined subject of the research was 56students in the seventh grade of MTs. PP. Amiruddiniyah, with 28 studentsdecided as the experimental group and 28 others as a controlled group. Theresearch data were taken by distributing a vocabulary test to the students asthe research subjects. Then, the data analysis was conducted by comparing thepre-test result and post-test results. Based on the findings of the experimentalclass in pre-test and post-test, 38 was the lowest score of pre-test and 76 wasthe highest score. Other than that, 65 was the lowest score of the post-test and90 was the highest score on that test. The average gained score that the teachergot was 19.57. This could be said that students’ average score who gottreatment using high learning styles were higher than their average pre-test.
Mobile-Based Signature Database (MOBSIGN DB): A New Signature Database

We have presented here an online signature database. The main issue in signature verification is to acquire the reliable signature database for checking the performance of the developed system. In this work, we have collected a signature database, mobile-based signature database (MOBSIGN DB). We have followed the same protocols as followed in SVC2004 signature database. We have collected dynamic as well as offline images of the signatures. The dynamic properties include xy coordinates and actual time taken for doing the signature (which was arbitrarily taken in SVC2004) for each data point of the captured signatures.
The Determination of Optimal Structure of the Fund of Evaluative Means in Organizing an Independent Work

A particular place in the Federal State Educational Standard of basic general education takes a system of achievement’s evaluation of planned results of education. It requires the so-called evaluative means and methods of evaluation that let determine a level of achievements of planned results of the main educational program of general education.The seeing a concrete level of knowledge, skills and abilities let a teacher make corrections in the further process of teaching. It allows to make required remarks and recommendations and to show his or her attitude to a student’s diligence too [2].It is students’ independent work that influences on development of cognitive, regulative, personal and communicative universal educational operations immensely. A correctly formed FEM used when organizing the independent work takes a particular meaning. The absence of methodic recommendations or approaches to form such a fund is a problem nowadays. When creating a fund a teacher must be able to evaluate a lot of factors that influence on effectiveness of the usage of evaluative means correctly and objectively. The main component of a student’s independent work is homework. The fulfillment of homework happens without a teacher’s help. The organization of homework has always been a topical issue. It demands a serious attention of a teacher to the possible educational and personal results of its fulfillment by a student.In such a way an issue of forming an optimal structure of a set of evaluative means when organizing homework becomes topical.An approach to form an optimal structure of a set of evaluative means when organizing homework based on building an optimization model is offered in the paper. With the help of the expert marks’ method a matrix of connections of evaluative means and criteria of complication is built; an optimal set of evaluative means on the discipline “Information Sciences and ICT” is formed for students of the 7th grade.The developed approach to form an optimal fund of evaluative means can be used by teachers who know ICT competences in the framework of any disciplines taught in comprehensive schools.The built model of an optimal forming of the FEM can be realized not only for organization of students’ independent work. This method is a universal for building any FEM of any discipline.
GANChat : A Generative Adversarial Network approach for chat bot learning

Recently a new method for training generative neural networks called Generative Adversarial Networks (GAN) has shown great results in the computer vision domain and shown potential in other generat ...
팬데믹 발생 시 개인 및 기업 데이터 보호 - 온라인 피해 연구가 주는 교훈 -

The world has been undergoing an unprecedented time of crisis since the outbreak of the Corona Virus 19 (COVID-19) pandemic. Among the notable changes brought by the pandemic are social distancing measures and stay-at-home directives, which have led to temporary closure of non-essential businesses as well as people working remotely in their homes. Moreover, increased numbers of the pandemic-themed phishing and fraud cases have been reported in different parts of the world. With the current work-at-home trend expected to continue, it is important to mitigate the threats posed by motivated offenders in cyberspace. Based on existing research evidence, this paper discusses strategies to prevent online victimization and protect business data during a pandemic.
Hiding critical transactions using a modified un-realisation approach

Huge amount of data generated from the hospitals, social media sites and online departmental stores contains sensitive data. Nowadays finding association rules among these data may lead to privacy issues, in turn to leakage of sensitive information that may reveal the facts about an individual. In this paper an algorithm is proposed for hiding the sensitive association rules by minimising the ghost rules and lost rules. This maximum association rule (MAR) hiding performs better than GA and PSO algorithms. These traditional algorithms may lack in hiding the association rules due to its computational complexity, hiding failure, lost rules and ghost rules. The performance of this approach has been improved by using a modified un-realisation algorithm for hiding the sensitive association rules.
SPAM REVIEW PREDICTIONS THROUGH MULTIPLE FEATURES USINGSENTIMENT ANALYSIS AND NEURAL NETWORKS

Todays sophisticated technology in each and every sector have one way or the other paved way for people to strictly adoptthe modern technologies to make their lifestyle way more easier. The online shopping is one of its major outcomes of thismodern world. People tend to rely on others opinions who they really dont know as a person. This scenario wouldencourage the haters or non ethical owner to create bad impression on their opponents product by adding fake review ontheir products site. There are a number of detection methods to detect fake reviews. This methodology helps in predictingthe true reviews by its polarity based as well as reviewer-based features and producing a trust percentage level to knowwhether the review is legitimate or not. Yelps Challenge dataset is been used to work on for predicting the legitimatereviews. This process is achieved using Neural Networks classification algorithm, which is one of the best classifiers in theresearch field. The proposed methodology improves the accuracy of the classifier.
False-Positive-Free and Geometric Robust Digital Image Watermarking Method Based on IWT-DCT-SVD

This paper presents a new hybrid image watermarking method based on IWT, DCT, and SVD domains, to solve the problem of false-positive detection and scale down the impact of geometric attacks. Properties of IWT, DCT, and SVD enable in achieving higher imperceptibility and robustness. However, SVD-based watermarking method suffers from a major flaw of false-positive detection. Principal component of watermark is embedding in the cover image to overcome this problem. Attacker cannot extract watermark without the key (eigenvector) of the embedded watermark. To recover geometrical attacks, we use a synchronization technique based on corner detection of the image. Computer simulations show that the novel method has improved performance. A comparison with well-known schemes has been performed to show the leverage of the proposed method.
Analysis Completeness of Outpatient Medical Record Documents Completion Based on Motivation and Compliance with Basic Tasks and The Function of Officers

In the modern era, the demands of the community health, especially health services, are increasingly high, one of which is the recording of medical records. One of the problems in the world of medical records is the incompleteness of medical record documents. The purpose of research is influence of motivation and compliance of the main tasks and functions of the officer on the completeness of filling outpatient medical record documents at the Tugu Trenggalek Health Center.The research method uses types correlational and cross sectional data collection with a sampling technique that is simple random sampling in order to obtain a total sample of 40 respondents. The data processing technique used is dummy regression analysis. The results showed that out of 40 outpatient medical record documents, 18 documents (45%) are in the low category. Some respondents in Puskesmas Tugu Trenggalek had a moderate motivation level of 22 respondents (55%) and the level of non-compliant respondents was 17 respondents (42.5%). Based on statistical test results obtained a significant value of motivation variable of (0,000 α 0.05) and a significance value simultaneous testing (0,000 <α 0,005) with an effect of 95.9%. From these results it is known that there is an influence of motivation and compliance of officers on the completeness of outpatient medical record documents at Tugu Trenggalek Health Center.It is necessary to improve performance in order to carry out an analysis of the completeness of outpatient medical record documents. Providing information about the importance of filling out and completing patient medical record data disciplines staff to provide feedback in the form of rewards and punishment so that the outpatient medical record documents are completely filled.
Adversarial Concurrent Training: Optimizing Robustness and Accuracy Trade-off of Deep Neural Networks.

Adversarial training has been proven to be an effective technique for improving the adversarial robustness of models. However, there seems to be an inherent trade-off between optimizing the model for accuracy and robustness. To this end, we propose Adversarial Concurrent Training (ACT), which employs adversarial training in a collaborative learning framework whereby we train a robust model in conjunction with a natural model in a minimax game. ACT encourages the two models to align their feature space by using the task-specific decision boundaries and explore the input space more broadly. Furthermore, the natural model acts as a regularizer, enforcing priors on features that the robust model should learn. Our analyses on the behavior of the models show that ACT leads to a robust model with lower model complexity, higher information compression in the learned representations, and high posterior entropy solutions indicative of convergence to a flatter minima. We demonstrate the effectiveness of the proposed approach across different datasets and network architectures. On ImageNet, ACT achieves 68.20% standard accuracy and 44.29% robustness accuracy under a 100-iteration untargeted attack, improving upon the standard adversarial training method's 65.70% standard accuracy and 42.36% robustness.
LibGuides: WCU Library Webinars: Home

This research guide provides links to all the webinars held for CAPS 401 for the Fall II 2019 term.
U-FIN: Unsupervised Feature Integration Approach for Salient Object Detection

Salient object detection is a challenging research field in computer vision. The existing saliency detection methods generally focus on finding feature maps for saliency computation. However, the combination of these feature maps significantly improves salient region(s) detection. In this paper, we propose a novel feature integration approach called U-FIN in which final saliency map is obtained by a weighted combination of individual feature maps. The proposed approach works in three phases viz. (i) artifact reference (AR) map generation (ii) weight learning and (iii) final saliency map computation. Firstly, AR map is produced using majority voting on the individual feature maps extracted from the input image. Secondly, linear regression is employed for weight learning which is used in the next phase. Finally, the individual feature maps are linearly combined using weights learned in the second phase to generate the final saliency map. Extensive experiments are conducted on two benchmark datasets, i.e., ASD and ECSSD to validate the proposed feature integration approach. The performance is measured in terms of precision, recall, receiver operating characteristic (ROC) curve, F-measure and area under the curve (AUC). Extensive experiments demonstrate the superiority of the proposed U-FIN approach against nine state-of-the-art saliency methods on ASD dataset and comparable on ECSSD dataset with the best performing methods.
An introduction to artificial neural networks

In this chapter, an introduction to neural networks (NNs) with an emphasis on classification and regression applications is presented. In this chapter, some preliminaries about natural and artificial neural networks (ANNs) are introduced first. Then, by giving initial concepts about classification and regression problems, appropriate overall structures of ANNs for such applications are explained. The simple structures of NNs and their limitations as well as some more powerful multilayer and deep learning models are introduced in the next part of this chapter. Finally, convolutional NNs and some of their well-known developments are briefly explained.
Fast and Accurate Group Outlier Detection for Trajectory Data

Previous approaches to solve the trajectory outlier detection problem exclusively examine single outliers. However, anomalies in trajectory data may often occur in groups. This paper introduces a new problem, group trajectory outlier detection (GTOD) and proposes a novel algorithm, named, CD $$k\text {NN}$$ -GTOD (Closed DBSCAN kNearest Neighbors for Group Trajectory Outlier Detection). The process starts by determining micro clusters using the DBSCAN algorithm. Next, a pruning strategy using $$k\text {NN}$$ is performed for each micro cluster. Finally, an efficient pattern mining algorithm is applied to the resulting subsets of group of trajectory candidates to determine the group of trajectory outliers. We performed a comparative study using real trajectory databases to evaluate the proposed approach. The results have shown the efficiency and effectiveness of CD $$k\text {NN}$$ -GTOD.
BLOCK CHAIN TECHNOLOGY IN SUPPLY CHAIN MANAGEMENT GLOBAL CHEMICALDISTRIBUTION

In Supply Chain Management today new technologies involve Internet of Things (IoT), huge Dataand BlockchainTechnology to offer improved service tocustomers.Theseadvanced technologies allow severalapplication-developers to implement and arrive at complex supply chain services and products relying onimproved speed. With these technologies, a Team of Supply Chain Management can build several complex modelsand a strong methodology of supply chains using a constructed data-driven approach. While several suppliersoperate across the globe, SCM team is in need of approachinga huge amount of data. In this research paper, anindustrial scenario of SCM for chemical distribution is used to demonstrate the need of implementingadecentralized system based on a distributed data-driven application methodology such as blockchain, not only toassist in supply chainto maintain an inventory of the raw materials,hazard codes, chemicals, distributors, licensingauthorities, regulations, restrictions, applications and customers but also to monitor the industry, growth,challenges, quality, demand and supply, usage, etc
Performance Evaluation of Incremental PCA and Its Variants in Face Recognition

Face recognition is a challenging research problem with several critical applications. Incremental Learning is an approach which does not require all the training samples beforehand for model development. In this direction, a popular approach called incremental principal component analysis (IPCA) was suggested by Chandrashekara et al. Later, several variants of IPCA were suggested by various researchers from time to time. However, it is not clear which of these methods are better for face recognition. To determine this, in this paper, we have performed extensive experiments on two publicly available face datasets, i.e. AR and ORL. The performance of the methods is determined in terms of average classification accuracy and average training time. IPCA and its 11 variants as suggested in the literature are considered for comparison. It has been found that there is no clear winner among the compared methods. However, the performance of MCT-based incremental two-dimensional two-directional PCA (MI(2D)\(^2\)PCA), incremental indefinite kernel PCA (IIKPCA), incremental two-dimensional kernel PCA (I2DKPCA), and incremental two-dimensional PCA (I2DPCA) methods is comparable and better than rest of the methods.
Neural Network Ensemble-Based Prediction System for Chemotherapy Pathological Response: A Case Study

During the diagnosis of breast cancer, neoadjuvant chemotherapy is supplied intravenously. Physicians recommend chemotherapy before surgery to reduce the invasive tumor’s large size. This research work suggests a model for Neural Network Ensemble Machine Learning, Implementation of a series of machine learning algorithms to create an enhanced and efficient predictable solution patients ‘maximum pathological response after Neoadjuvant Chemotherapy. The quality of the neural network ensemble framework for machine learning is measured using multicriteria technique of decision making known as simple weighted additive (WSAW). The performance score for WSAW is calculated by taking into account ten measurements, namely accuracy, mean absolute error, root mean square error, TP rate, FP rate, accuracy, recall, F-measure, MCC, and ROC. The results are verified using the technique of cross-validation K-fold to achieve 97.20% accuracy. The findings are quite positive when the execution of the proposed system is coupled with the output of state-of-the-art classificators, for example, Bayes Net, Naïve Bayes, logistic, multilayer perceptron, SMO, voted perceptron, etc. With the increasing trend of artificial intelligence applications in cancer research, machine learning has a great future in forecasting and decision making.
LibGuides: Off Campus/Remote Library Access: Off Campus Access

This guide is intended to be a quick, helpful place to answer basic questions about Library Resources -- and how you can access them off campus. We will walk through how to gain access to library collections, regardless of your location.
Punctuation model (2020-08-04)

A python package that punctuates Icelandic text. The input data is unpunctuated text and punctuated text is returned. The user can choose between three different punctuation models, a BERT-based Transformer, a seq2seq Transformer and a bidirectional RNN ([Punctuator 2](www.github.com/ottokart/punctuator2)) in Tensorflow 2. We have written a paper on this topic and sent to Interspeech, the paper is pending publication.
LibGuides: Streaming Video Access & Support: Getting Help Contact List

This guide highlights frequently used streaming video sources and available support for instructional use.
New Ways to Assess the Quality of Education in Professional Higher Education Institutions

In this article, the problem of knowledge quality has become a subject of public attention in recent years. The creation of organizational models for quality control of education and the regulatory framework for the national system for evaluating the quality of education is one of the priorities of the educational policy of our Republic. The creation of such a system, on the one hand, forces the need to ensure state guarantees of education, and on the other-stimulates the legitimate interest of clients in quality education. The quality assessment means the evaluation of educational achievements of students, quality of educational programs, implementation of educational process in a particular educational institution, the educational process in the whole country and its territorial systems. The theoretical basis of these sets of core competencies are the provisions formulated in psychology. It is indicated that the subject of human communication, knowledge, work is manifested in the system of human relations to society, to other people, to themselves, to work; human competence has an axiological vector of development; professional competence includes competence.