Reviewerly/exHarmony huggingface dataset:
An adaptive algorithm for frequent pattern mining over data streams using diffset strategy

Frequent pattern mining using sliding window over data streams is commonly used due to its wide applicability. Determining suitable window size and detection of concept change are the major issues and can be addressed by having flexible window based on amount of changes in frequent patterns. For mining frequent patterns over data streams, vertical mining algorithms can be used. However, in these algorithms, size of transaction identifiers (tidsets) and the time for computation of intersection between tidsets is large. Moreover, presence of null transactions does not contribute any useful frequent patterns. A new algorithm called recent frequent pattern mining based on diffset with elimination of null transactions (RFP-DIFF-ENT) over data streams using variable size window is proposed. It stores difference of tidsets and eliminates null transactions which minimise memory and mining time. Experimental results show that proposed algorithm saves computation time, memory usage and minimises the number of frequent patterns.
An efficient crypto-compression scheme for medical images by selective encryption using DCT

Nowadays, modern communication inevitably uses computer networks. The images transmitted across these networks are special because of their large amount of information. Thus, the use of the information technology in the medical field generates many applications (especially telemedicine) where the exchange of medical information remains the foundation of their success. The transmission of these images raises a large number of unresolved problems. The efficiency of a transmission network depends, on the one hand, on the degree of security and, on the other hand, on the times of transmission and archiving. These requirements can be satisfied by encryption and compression. This work presents a method of a partial or selective encryption for medical images. It is based on the encryption of some quantified discrete cosine transform (DCT) coefficients in low and high frequencies. The results of several experiments show that the proposed scheme provides a significant reduction of the processing time during the encryption and decryption, without tampering the high compression rate of the compression algorithm.
A novel encryption compression scheme using Julia sets

The intent of the paper is to propose a novel fractal-based encryption compression scheme using logistic map and Julia sets. In our study of medical images, we obtain significant lossless compression and secure encryption of the image data. The proposed technique is expected to be useful for the transmission of various confidential image data relating to medical imaging, military and other multimedia applications.
End-to-End Security for Local and Remote Human Genetic Data Applications at the EGA

Sensitive genomic data should remain secure - whether on disk for storage, or analysis, or in transport. However, secure storage, delivery, and usage of genomic data is complicated by the size of files and diversity of workflows. This paper presents solutions developed by GA4GH and EGA to use customized encryption, encrypted file formats, toolchain integration, and intelligent APIs to help solve this problem.
Comparison of different RSA variants

RSA is the first public key algorithm used for encryption and decryption. Its simplicity and complexity lies in factoring a very large composite integer. It is still popular even after 39 years of its origin. In this long journey, RSA is studied many times and many security loopholes are found. To remove the loop holes researchers designed many variants of RSA. The work shows the study of different RSA variants which are popular in literature. This study includes the analysis in terms of performance and security of different RSA variants.
Testing the Power-law Distribution of Lifelong Education Institutes’ Characteristics

평생교육기관 간의 편차 증가는 지역 간 평생교육 기회 격차 증가로 이어질 수 있으며 이는 정책 방향의 변화와도 관련된다. 그러나 국내외 관련 연구들은 평생교육기관의 편차에 초점을 두지 않거나 양적 분석을 위한 방법론에서의 한계점을 보이고 있다. 이에 본 연구에서는 양극화 현상을 분석하기 위한 복잡계 연구의 멱함수 분포 검정을 시도하였다. 연구 목적은 평생교육기 관의 특징(소재지역 인구, 직원, 평생교육사, 강사, 현재수강인원, 프로그램)이 양극화의 특성을 검정하는 멱함수 분포를 보이는지를 실증적으로 검정하는 것으로 국내 평생교육기관 전수 조사 자료인 2016년 평생교육통계조사 자료를 활용하였다. 분석결과는 첫째, 평생교육기관 특성의 기술통계와 중앙값-평균 비율 산출 결과, 현재수강인원, 프로그램, 강사가 정적 편포를 보였다. 둘째, 시각적 분석결과, 소재 지역 인구를 제외한 직원, 평생교육사, 강사, 현재수강인원, 프로그램의 멱함수 분포가 나타났다. 셋째, 수리적 분석결과, 직원, 강사, 현재 수강인원, 프로그램의 멱함수 분포가 나타났다. 이상의 분석결과를 활용하여 논의 및 결론을 제시하였다.The number of lifelong education institutes has been increased quickly in Korea. With the increase of lifelong education institutes, the deviation of characteristics among institutes also has been increased. The characteristics was the population in local government and the number of staff, lecturer, learner, program. Sometimes the deviation means the unequality and polarization of lifelong education institutes and conditions. The purpose of this study is to teat whether the characteristics(population in local government, the number of staff, lecturer, learner, and program) of lifelong education institutes is the power-law distribution or not. For the purpose, we used the data of 2016 National Lifelong Learning Survey by Korea Education Development Institute. And for the purpose the data was analyzed according to the procedures for testing power-law distribution. The results are like following: First, descriptive analyses showed that staff, learner, and program were skewed positively. Second, based on visual testing, population in local government was not power-law distribution. But the number of staff, lecturer, learner, and program showed the ‘L’ curve which meant power-law distribution. Third, based on statistical testing, staff, lecturer, learner, and program were power-law distribution. Based on these results, implications and limits of this study were discussed.
An analysis method for eye motion and eye blink detection from colour images around ocular region

This paper examines an analysis and measurement method that to aim to involve incorporating eye motion and eye blink as functions of an eye-based human-computer interface. Proposed method has been achieved by analysing the visible-light images captured without using special equipment, in order to make the eye-controlled input interface independent of gaze-position measurement. Specifically, we propose two algorithms, one for detecting eye motion using optical flow and the other for classifying voluntary eye blinks using changes of eye aperture areas. In addition, we tried evaluating experiment both detection algorithms simultaneously assuming application to input interface. The two algorithms were examined for applicability in an eye-based input interface. As a result of experiments, real-time detection of each eye motion and eye blink could be performed with an accuracy of about 80% on average.
Cyber-squatting: a cyber crime more than an unethical act

Cyber-crimes are increasing day by day. They equally affect a person or a society as compared to other crimes. There are so many cyber-crimes such as hacking, phishing and cyber terrorism, etc. However there are some unethical activities that also affect a person or society such as cyber-squatting. This paper gives a glance about cyber-squatting which is consider as unethical practice, but as per the rate it is increasing day by day it is becoming necessary that it should be included in constitution. This paper illustrates cyber-squatting with the help of examples and some case studies.
Towards Design Space Exploration and Optimization of Fast Algorithms for Convolutional Neural Networks (CNNs) on FPGAs

Convolutional Neural Networks (CNNs) have gained widespread popularity in the field of computer vision and image processing. Due to huge computational requirements of CNNs, dedicated hardware-based implementations are being explored to improve their performance. Hardware platforms such as Field Programmable Gate Arrays (FPGAs) are widely being used to design parallel architectures for this purpose. In this paper, we analyze Winograd minimal filtering or fast convolution algorithms to reduce the arithmetic complexity of convolutional layers of CNNs. We explore a complex design space to find the sets of parameters that result in improved throughput and power-efficiency. We also design a pipelined and parallel Winograd convolution engine that improves the throughput and power-efficiency while reducing the computational complexity of the overall system. Our proposed designs show up to 4.75$\times$ and 1.44$\times$ improvements in throughput and power-efficiency, respectively, in comparison to the state-of-the-art design while using approximately 2.67$\times$ more multipliers. Furthermore, we obtain savings of up to 53.6\% in logic resources compared with the state-of-the-art implementation.
Approximation and Non-parametric Estimation of ResNet-type Convolutional Neural Networks

Convolutional neural networks (CNNs) have been shown to achieve optimal approximation and estimation error rates (in minimax sense) in several function classes. However, previous analyzed optimal CNNs are unrealistically wide and difficult to obtain via optimization due to sparse constraints in important function classes, including the Holder class. We show a ResNet-type CNN can attain the minimax optimal error rates in these classes in more plausible situations -- it can be dense, and its width, channel size, and filter size are constant with respect to sample size. The key idea is that we can replicate the learning ability of Fully-connected neural networks (FNNs) by tailored CNNs, as long as the FNNs have \textit{block-sparse} structures. Our theory is general in a sense that we can automatically translate any approximation rate achieved by block-sparse FNNs into that by CNNs. As an application, we derive approximation and estimation error rates of the aformentioned type of CNNs for the Barron and Holder classes with the same strategy.
A Study of Disagreement Response in the College Students' Discussion Discourse

이 연구에서는 대화가 진행되는 상황에서, 이전 발화에 대한 비협력적 태도가 포함된 반응을 ‘비동의 응답 표현’이라고 규정하고 토론 담화의 비동의 표현의 실현 유형과 빈도를 살피고 그 기능적 측면에 대한 질적 연구의 기초를 다지고자 하였다. 토론 담화에서 비동의 표현은 적극적 비동의 (72.5%) > 소극적 비동의(24.8%) > 약화된 비동의(2.7%)로 나타났다. 적극적인 비동의 표현은 상반된 의견 제시, 문제점 지적, 거부 표현 등으로 나타나고, 소극적 비동의 표현은 반문, 부분 인정, 추측으로 나타나며, 약화된 비동의 표현은 확인 질문, 보류 등의 방법으로 나타남을 확인하였다. 토론 담화의 경우 승부의 원리가 작용하는 논쟁이 주된 내용이기 때문에 적극적 비동의 표현을 전략적으로 활용하여 설득력을 높이고자 하며, 특히 상반된 의견을 제시하는 것으로 주장의 논리성을 확보하고자 한다.In this study, the reaction involving the non-cooperative attitude toward the previous utterance is defined as the disagreement response and its scope is first specified. In the discussion discourses, the strong disagreement(72.5) is followed by the passive disagreement(24.8) and the weakened disagreement(2.7). It is identified that the strong disagreement is realized through counter-arguing, pointing out of a problem, or rejecting, the passive disagreement is performed through a counter question, partial acceptance, or supposition, and the weakened disagreement is conveyed through a confirmation question or hesitation. In the case of the discussion discourses, the high frequency of the disagreement response, specially the strong disagreement responses can be attributed to the nature of the discourses that the discussion operation on the principle of competition.
SCALABLE INFORMATION RETRIEVAL SYSTEM IN SEMANTIC WEB BY QUERY EXPANSION AND ONTOLOGICAL BASED LSA RANKING SIMILARITY MEASUREMENT

In recent days, semantic web presents a key role in intelligent retrieval of information system that resolves vocabulary mismatch problem by query expansion process. However, achieving the scalable information retrieval (IR) in semantic web is a challenging issue in a large dataset. The semantic IR problem is addressed by an ontological-based semantic similarity measurement using natural language processing. The two novel algorithms namely syntactic correlation coefficient (SCC) and mapping-based K-nearest neighbour (M-KNN) for semantic similarity measurement is proposed which improves the accuracy of relevant result. The ontological constructs with word sense disambiguation (WSD) algorithm for document repository improves the conceptual relationships, reduces the ambiguities in ontology and improves scalability by intensely analysing the semantic relationship as well as dynamically reconstructing the ontology when numbers of documents are updated. Ranking is done with latent semantic analysis (LSA) after semantic similarity analysis, which improves the retrieved result and reduces the complexity in relevancy. The performance of the system is analysed with respect to different metrics such as processing time, F-measure (0.97), time complexity, precision (0.95), recall (0.98) and space complexity.
NEURO FUZZY COGNITIVE CONNECTION FUNCTIONAL POINT FOR ENTERPRISE NETWORK MANAGEMENT

Software effort estimation (SEE) is one of the vital roles in enterprise management. This created SEE process used to minimise the incomplete data involvements. It may require large amount of data, which increases the efficiency of the effort estimation system. So, in this paper introduces the enterprise data analytics process by using the size and judgmental software effort estimation process. This method analyses the effort in terms of using expert's opinions, use case, functional points and software size unit information. This method evaluates the neuro fuzzy cognitive connection-based functional points are used to estimates the effort with effective manner. This method examines the connectivity between one requirement to another requirements and it constructs the relationship graph that eliminates the incomplete requirements and details successfully. It reduces the time for examining the software effort. Then the performance of the system is calculated with the support of experimental results such as MRE and VAF.
Peningkatan Hasil Belajar Bahasa Indonesia melalui Pendekatan Metode Inkuiri Model Kooperatif Tipe Make A Match Siswa Kelas V SD Negeri 008 Pulau Lancang

The purpose of class action research carried out to improve the activities and learning outcomes of Indonesian language subject by using the Inquiry Method cooperative learning model type make a match of the fifth grade at SDN 008 Pulau Lancang, Benai District, Kuantan Singingi District. To obtain the data, researcher used observation sheet to collect student activities that were fulfilled by the observer and test the learning outcomes given at the end of the cycle. Student learning activity data was analyzed by determining the number and percentage of students involved at each meeting. The improvement of learning outcomes can be seen from the number of students who completed at the end of the cycle. The results showed that the study of learning and student learning outcomes used improvement where in the initial data from 14 students only 2 people who achieved SKM after being implemented in the first cycle increased to 8 people (57.1%) and increased in the second cycle to 85.7 students who achieve completeness, so it increased by 28.6 % but there were still some students who had not yet achieved the SKM score. Overall, it can be concluded that inquiry into the cooperative learning model type make a match can improve learning outcomes Indonesian Language of the fifth grade at SDN 008 Pulau Lancang, Benai District, Kuantan Singingi District.
Mining Implicit Treatment Concepts for Neural Precision Medicine

Precision medicine (PM) is regarded as an information retrieval (IR) task in which biomedical articles containing treatment information about specific diseases or genetic variants are retrieved in response to patient record for the purpose of providing medical evidence to the point-of-care. In existing PM approaches, manual keywords, such as “treatment” and “therapy,” are considered direct indicators of treatment information and are thereby introduced to expand the original query. However, the common medical concepts that are implicitly related to treatment (such as “oncogene” and “tumor”), and differ the relevant documents from the non-relevant ones, are yet to be utilized. To bridge the gap, in this paper, we propose an extension of the state-of-the-art neural IR (NIR) models, including K-NRM and DRMM, to encapsulate the PM solutions within a neural network framework, referred to as NIR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PM</sup> . Specifically, the proposed approach mines a global list of common medical concepts from documents that are judged pertinent to different queries. Thereafter, the mined implicit concepts are incorporated within an NIR framework to enhance the effectiveness of PM. The experimental results on the standard Text REtrieval Conference (TREC) PM track benchmark confirm the superior performance of the proposed NIR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PM</sup> model.
A Study of Resource Allocation Techniques In Cloud Computing

The process of allocation and reallocation of resources is the key to accommodating unpredictable demands and improving the return on investment from the infrastructure supporting the cloud. A cloud environment consists of multiple customers requesting for resources in a dynamic environment with possible constraints. In cloud computing allocation of resources for every client with high gain and the profit is the major task for the cloud administrator. Cloud administrator has the problem for allocating the same resources with many users. So while allocating the resources prioritisation of tasks is very important. The literature concerns seven scheduling algorithms and six allocation techniques as well as the parameter measures for effectiveness of scheduling. Methodical analysis of this research helps the researchers to find the importance of a scheduling algorithm for particular allocation technique for increasing profit and return on investment in cloud.
Improvement of image compression approach using dynamic quantisation based on HVS

Digital-image compression can reduce the overall volume of the image by keeping the original image with the minimum degradation in the level of the reconstructed image quality; in other words, here, we speak about compression with loss. This work comes up with an improvement in an image compression method using the discrete wavelet transform (DWT) and neural networks. To improve this technique, we have added a new phase based on the Human Visual System (HVS) and the Weber-Fechner law to dynamically quantify the image signal. Such a new phase can improve the quality of compression by dynamically quantifying each pixel value of the original image compared to the values of the neighbour pixels according to a luminance detection threshold. This threshold is known as Weber constant.
Improving the Search Efficiency of Differential Evolution Algorithm by Population Diversity Analysis and Adaptation of Mutation Step Sizes

The aim of this research work is to improve the efficiency of differential evolution (DE) algorithm, at the cases of its unsuccessful searches. Initially, this work discusses and compares different methods to measure the population diversity of DE algorithm implemented for DE/rand/1/bin variant for a set of benchmarking functions. A method which well demonstrates difference in population diversity evolution at successful and unsuccessful cases of DE search is identified based on comparison. This work is then extended to detect unsuccessful searches in advance using the evolution of population diversity measured by the identified method. On detecting a search as unsuccessful, a parameter adaptation strategy to adapt the mutation step size (F) is added to DE algorithm to recover from it. The improved DE algorithm, which comprises of the logic of adapting F value based on the population diversity, is compared with its classical version and found outperforming. The comparison results are reported in this paper.
Optimized Replication Management with Reputation for Detecting Collusion in Large Scale Cloud Systems

Abstract In this work, an objective comparison between some common global appearance face recognition based methods (PCA, FLD, SVD, DCT, DWT and WPD) has been carried out when considering some natural effects that may decrease the performances. In particular, effects such as blur, motion, noise and their combination are taken into account. To evaluate the performances, FEI database containing images corresponding to 200 individuals are used. For each individual, 14 positions have been considered. The quality of face reconnaissance is measured using the well-known Equal Error Rate (EER) criteria. Interesting results are obtained highlighting the superiority, in some specific contexts, of some of the evaluated methods.
New Hybrid Framework to detect phishing web pages, based on rules and variant selection of features

Phishing phenomena are increasing day after day due to its simplicity to use; it is enough for hackers to clone legitimate website and send it by e-mail to victims to access it within the use of social engineering techniques to lure them and gain their confidence. Hackers use the lack of knowledge of regular users when surfing on the internet and understanding the role of uniform resource locator (URL) of a web page. This fact let hackers create malicious forms of URLs, like very long ones or containing some suspicious characters. Despite cloning web pages, hackers can inject malicious codes into this web page for nefarious uses, so detecting or preventing this kind of web pages is the objective of this paper. We present a new hybrid framework to identify phishing web pages based on different ways and methodologies for features extraction techniques using only the URL as the main entry and without having any visual experience before, also we use hybrid analysis to be complete and accurate.
Computer simulation of the partitioning by mutually orthogonal lines

The partitioning problem of areas into regions along mutually orthogonal lines is considered. A mathematical model of the problem has been proposed, taking into account economic, agrotechnical and fire prevention restrictions. A method has been developed for obtaining a local-optimal partition of a given region based on the construction of two sequences of decision trees. Computer simulation of practical problem of partitioning sowing areas has been carried out and the numerical results of its solution were presented.
Heat transfer dynamics modelling by means of clustering and swarm methods

This paper deals with the modelling problem of heat transfer dynamics in thermal exchanger process by using fuzzy prediction approaches. Clustering and swarm-based optimisation methods are used to derive heat transfer dynamical models to predict temperature variations of hot and cold fluids in the exchanger. The clustering method relies on a one-shot potential calculating strategy to extract the fuzzy sets distribution from the data space. However, the swarm optimisation method employs a subject function to optimise the premise and conclusion parameters of the fuzzy structure. Experimental data extracted from a pilot exchanger process is used to learn the fuzzy models, and their performances are compared on both training and testing measurement data.