Reviewerly/exHarmony huggingface dataset:
Dissertationen: geotechnik 1/2020

geotechnikVolume 43, Issue 1 p. 50-56 Dissertationen Dissertationen: geotechnik 1/2020 First published: 05 March 2020 https://doi.org/10.1002/gete.202070109Citations: 2Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Citing Literature Volume43, Issue1March 2020Pages 50-56 RelatedInformation
Experimental Evaluation of Open Source Data Mining Tools: R, Rapid Miner and KNIME

In the current scenario of Big Data, open source Data Mining tools are very popular in business data analytics. The paper presents a comprehensive study of three most popular open source data mining tools – R, RapidMiner and KNIME. The tools are compared by implementing them on two real datasets. Performance is evaluated by creating a decision tree of the datasets taken. Our objective is to find the best tool for classification. The study can help researchers, developers and users in selecting a tool for accuracy in their data analysis and prediction. Experiments depict that accuracy level of the tool changes with the quantity and quality of the dataset. The results show that RapidMiner is the best tool followed by KNIME and R.
Lock vendor rapped over ‘false claims'

Tapplock, which supplies fingerprint-based smartlocks, has been censured by the US Federal Trade Commission (FTC) over allegations that it falsely claimed its products were “unbreakable”, and also failed to protect its customers' data.
University Students’ Preparation towards Building Knowledge Economy in Africa: A Case of Universities in Ghana

For a country to develop, universities need to prepare students to be confident in facing the demands of the contemporary world of work.This paper focuses on examining how well universities are preparing students in Ghana in building a knowledge economy.The paper further explored the relationships among university preparation, students' preparation, and confidence of students.Using a cross-sectional survey design, data was gathered from 690 students from three public universities in Ghana, through a multi-stage sampling technique.A closed-ended questionnaire was used as the main data collection instrument.It was found that students prepared themselves towards a knowledge economy.Although the universities made efforts in preparing students, there were still lapses in preparing students towards a knowledge economy.Further analysis revealed that students were not confident enough to face the demands of a knowledge economy.Analysis from the mediation analysis using bootstrapping approach (5000 bootstrap samples), revealed that university preparation of students significantly mediated the relationship between students' personal preparation and confidence level of students, b=.0798, CI [.0427-.1189].The authors, by their findings, called for innovative and practical teaching and learning in various universities in Ghana by lecturers and students.
Exploration of Face Detection methods in Digital Images

Face detection is a challenging computer vision task that identifies and localizes the faces of human beings from digital images or video streams. It is predominantly the first phase in the process of developing a wide range of face applications such as face recognition, emotion recognition, authentication, surveillance systems etc. The process of face detection is easy from the human perspective but, a complex task for computers that involves searching of the face in variable circumstances of pose, colour, size, occlusion, illumination etc. If the outcome of face detection is intended to be input for another algorithm, an accurate, well informed selection of an appropriate face detection technique is essential because the overall performance of face application is dependent on face detection algorithm’s precision. The survey paper presents a review of three commonly used face detection algorithms available in literature namely Viola Jones, Neural networks (NN) and Local Binary Pattern (LBP) for the purpose of ascertaining the most suitable face detection algorithm to implement for our future work in developing an ‘Online student concentration level recognition system’.
Medical Image Compression by Optimal Filter Coefficients Aided Transforms using Modified Rider Optimization Algorithm

Owing to a large amount of images, image compression is requisite for minimizing the redundancies in image, and it offers efficient transmission and archiving of images. This paper presents a novel medical image compression model using intelligent techniques. The adopted medical image compression comprises of three major steps such as, Segmentation, Image compression, and Image decompression. Initially, the Region of Interest (ROI) and Non-ROI regions of the image are split by means of a Segmentation procedure using Modified Region Growing (MRG) algorithm. Moreover, the image compression process begins which is varied for both ROI and Non-ROI regions. On considering the ROI regions, the compression is carried out by Discrete Cosine Transform (DCT) model and SPIHT encoding method, whereas the compression of Non-ROI region is carried out by Discrete Wavelet Transform (DWT) and Merge-based Huffman encoding (MHE) methods. As a main contribution, this paper intends to deploy the optimized filter coefficients in both DCT and DWT techniques. Here, the optimization of both filter coefficients is performed using Modified Rider Optimization Algorithm (ROA) called Improvised Steering angle and Gear-based ROA (ISG-ROA). In the final step, decompression is done by implementing the reverse concept of compression process with similar optimized coefficients. The filter coefficients are tuned in such a way that the Compression Ratio (CR) should be minimum. In addition, the comparative analysis over the state-of-the-art models proves the superior performance of the proposed model.
Implementation of Scrum Work Framework in the Development of Quality Assurance Information System

Quality Assurance Information System Development is required to accellerate accreditation achievement. This Information System application is an integrated model of quality assurance information systems based on the integration of BAN-PT accreditation and ISO 9001: 2008. The purpose of this research is to develop a quality assurance information system by implementing the Scrum Framework. Scrum is one of the popular frameworks in Agile Development Methodology. In this way, the development of productivity increases significantly. In this Applied Research the Action Research approach is used. This Multi-Year Applied Research is the final research of previous studies. The results of this study presented the quality assurance information system that was produced using the complete Scrum framework. This information system is expected to contribute significantly to ISO-certified higher education in increasing the BAN-PT Accreditation assessment for their study programs.
Convolutional Neural Networks

This textbook introduces fundamental concepts, major models, and popular applications of pattern recognition for a one-semester undergraduate course. To ensure student understanding, the text focuses on a relatively small number of core concepts with an abundance of illustrations and examples. Concepts are reinforced with hands-on exercises to nurture the student's skill in problem solving. New concepts and algorithms are framed by real-world context and established as part of the big picture introduced in an early chapter. A problem-solving strategy is employed in several chapters to equip students with an approach for new problems in pattern recognition. This text also points out common errors that a new player in pattern recognition may encounter, and fosters the ability for readers to find useful resources and independently solve a new pattern recognition task through various working examples. Students with an undergraduate understanding of mathematical analysis, linear algebra, and probability will be well prepared to master the concepts and mathematical analysis presented here.
Algorithmic Modeling of Arts and Other Hard-to-Formalize Subjects

This article is devoted to open questions related to modelling and using of modelling in various areas that hardly can be formalized, such as arts, creativity process, and some hard cases in natural science. We are discussing here the advantage of using modelling and the global question: why we need modelling in such areas and why. Several music and scientific centres across the globe are studying the ways of modelling the logical laws of creativity, by exploring generalized parameters of works of art including music. In recent years, Russian scientists have become more interested in modelling the process of musical creativity and music programming. Most frequently, these works are applied in the computer analysis of works of art, to determine the author or the period of creation, to attribute the work of art to a particular school or group. Fewer papers provide a deeper analysis, including psychological aspects of the perception of art (in particular, music). The authors have developed a method to construct models in subject areas difficult to formalize, applying it to create a model of musical creativity based on the structural analysis of musical texts, the cyclical structuring of statistical data, and the structural analysis of statistical information. This approach allows creating texts that satisfy the previously obtained or manually provided parameters.
Analysis of a Canonical Labeling Algorithm for the Alignment of Correlated Erdős-Rényi Graphs

Graph alignment in two correlated random graphs refers to the task of identifying the correspondence between vertex sets of the graphs. Recent results have characterized the exact informationtheoretic threshold for graph alignment in correlated Erdős-Rényi graphs. However, very little is known about the existence of efficient algorithms to achieve graph alignment without seeds. In this work we identify a region in which a straightforward O(n11/5 logn)-time canonical labeling algorithm, initially introduced in the context of graph isomorphism, succeeds in aligning correlated Erdős-Rényi graphs. The algorithm has two steps. In the first step, all vertices are labeled by their degrees and a trivial minimum distance alignment (i.e., sorting vertices according to their degrees) matches a fixed number of highest degree vertices in the two graphs. Having identified this subset of vertices, the remaining vertices are matched using a alignment algorithm for bipartite graphs.
Bit Plane Slicing based Digital Watermarking Technique in DWT Domain

Authenticity of image and its copyright protection are one of the essential application of watermarking. In this paper, a hybrid technique for watermarking in DWT domain is presented for its application in the field of providing authentication to images. In this work binary image is used as watermark and is embedded in the 'host image'. Before embedding the watermark in the host, the host image is splitted into 8 bit planes using bit plane slicing. Followed that DWT is applied to the least significant bit plane which partitions the respective plane into low frequency (LL subband) and high frequency (HH, HL and LH subbands). The SVD is applied to HH subband of least significant bit plane and watermark is embedded on the singular matrix part of SVD. To analyse the robustness of the scheme proposed in this paper, watermarked image is attacked by different image processing attacks. Original watermark and extracted watermark is compared on the scale of normalized correlation to measure the robustness of the scheme against various attacks.
Phonological markedness effects in sentence formation

Earlier research has found that phonological markedness constraints (for example, against stress clash or sibilant sequences) statistically influence speakers’ choices between particular syntactic constructions and between synonymous words. In this study, we test phonological constraints not just in particular cases, but across the board. We employ a novel method that statistically models the distribution of word bigrams (consecutive two-word sequences) and how this distribution is influenced by phonological constraints. Our study of multiple corpora indicates that several phonological constraints do indeed play a statistically significant role in English sentence formation. We also show that by examining particular subsets of the corpora we can diagnose the mechanisms whereby phonologically marked sequences come to be underrepresented.We conclude by discussing modes of grammatical organization compatible with our findings.
Assamese Handwritten Character Recognition using Supervised Fuzzy Logic

This paper presents a state of the art supervised fuzzy pattern recognition system for recognition of Assamese handwritten characters. The fuzzy classifier is well suited for applications with ambiguities and handwritten character recognition is such a task. The dataset used in this experiment is taken from ISI Kolkata. After preprocessing images are normalized into uniform size 42x32 and then two features namely distance vector and density vector have been extracted. The experiment has two stages, training and testing. In first stage we extract distance vector and density features from uniform zones of the binary images for training classes and estimate the mean and variance for each class. In second stage we use this mean and variance to calculate the membership values for each unknown character of the testing set of data. An exponential fuzzy membership function is used for this purpose. Finally we recognize an unknown test character as that class for which it gives highest membership value. Finally result is stored in editable document. The highest recognition accuracy achieved in the experiment is 88.29%, 86.55% and 82.74% for numerals, vowels and consonants respectively.
VPN Encrypted Traffic classification using XGBoost

Classification network traffic are becoming ever more relevant in understanding and addressing security issues inInternet applications. Virtual Private Networks (VPNs) have become one famous communication forms on the Internet. In this study, a new model for traffic classification into VPN or non-VPN is proposed. XGBoost algorithm is used to rank features and to build the classification model. The proposed model overwhelmed other classification algorithms. The proposed model achieved 91.6% accuracy which is the highest registered accuracy for the selected dataset. To illustrate the merit of the proposed model, a comparison was made with sixteen different classification algorithms
Optimising the DTLS handshake design for TEE enabled sensor nodes

This paper focuses on highlighting the vulnerabilities of DTLS handshake process and then optimising the handshake process of the DTLS protocol to prevent the software-based attacks in the smart sensor client and sensors (resource servers). We discussed the scenarios where the handshake process is prone to software attacks and proposed the trusted execution environment-based design of the DTLS handshake to enhance the communication security by eliminating the risk of intermediate keying materials being exposed to a non-secure environment. Our design also considers the resource constrained nature of the sensor nodes and thus split the handshake process such that the memory footprint of the implementation does not overload the TEE. We implemented a DTLS client and a DTLS server on a TEE enabled hardware and compared the performance thereof. Our preliminary experimental results show significant gain for memory footprint, but with a minor penalty in handshake time consumption.
Predictive Analysis of Emotions for Improving Customer Services

Human emotions plays an important role in everyday communication. Emotions are formed by the combination of cues such as relative actions, facial expressions, and gestures and reactions. Emotions are also present in written texts like in social media, chats, customer reviews. By getting inspired by works done in the domain of sentiment analysis, this chapter explores advances to automatic detection of emotions in text which help in Improving Customer Services. This chapter presents a framework for automatic detection of emotions in customer reviews based on different emotions theories in the fields of psychology and linguistics. This framework uses advanced Machine Learning (ML) techniques with Natural Language Processing (NLP) methods for better understanding of emotion detection and recognition in customer reviews. The text under study comprises data collected from leading Indian e-commerce portals like Flipkart, Snapdeal and Amazon, which contains text rich in emotions. The advantages and application based emotion detection framework has been incorporated with suitable examples.
Smart Surveillance System using Deep Learning

In industry and research area big data applications are consuming most of the spaces. Among some examples of big data, the video streams from CCTV cameras as equal importance with other sources like medical data, social media data. Based on the security purpose CCTV cameras are implemented in all places where security having much importance. Security can be defined in different ways like theft identification, violence detection etc. In most of the highly secured areas security plays a major role in a real time environment. This paper discusses the detecting and recognising the facial features of the persons using deep learning concepts. This paper includes deep learning concepts starts from object detection, action detection and identification. The issues recognized in existing methods are identified and summarized.
An Analysis of Derivative-Based Optimizers on Deep Neural Network Models

In machine learning, deep neural networks find its applications in many areas like aerospace design, mechanical engineering, and chemical engineering to solve different problems like object detection, pose recognition, object tracking, etc. By selecting the appropriate objective function deep neural networks, the most commonly used machine learning algorithm allows us to encode more and more complex features. To enhance the effect of the model, an increase in the number of hidden layers in the deep neural network imposes significant challenges in their training. Optimizers are employed to train the deep neural network by changing the attributes such as weights, learning rates of the neural model to minimize the losses and improve the model. Optimizers help to minimize the effort required or to maximize the expected outcome by finding the condition that offers the maximum or minimum value of a function. Most of the optimization algorithms automatically compute the learning rate. A decent optimizer not only trains the model quickly but also helps to succeed in the prediction without being stuck in local minima. Selection of appropriate optimizers with the appropriate parameters helps to improve the accuracy of the neural network. In this work, derivative-based optimization approaches such as Stochastic Gradient Descent (SGD), Adaptive Gradient (AdaGrad), Root Mean Square Propagation (RMSProp), Adaptive Moment Estimation (Adam), Adaptive Delta (AdaDelta), Adaptive Moment Estimation Extension Based on Infinity Norm (Adamax), Nesterov-Accelerated Adaptive Moment Estimation (Nadam), and Stochastic Gradient Descent (SGD) with momentum are studied from the theoretical as well as practical perspective to understand their relevance in context of training the neural network to improve the accuracy. These algorithms empirically have been tested on artificial neural models ResNet50, VGG19, DenseNet201, and EfficientNetB5 to highlight their strengths and weaknesses. The models ResNet50, VGG19, DenseNet201, and EfficientNetB5 trained using the COVID-19 dataset that is publicly available on Kaggle. The overall experiment results have been evaluated and analyzed using different performance metrics like accuracy and loss function to obtain the better performance of the models. It has been observed that DenseNet201 gives the best performance compared to other architectures like VGG16, ResNet50, and EfficientNetB5, with the highest validation accuracy of 98% using the Adam optimizer.
Recommendation of items using a social-based collaborative filtering approach and classification techniques

With the large amount of data generated every day in social networks, the use of classification techniques becomes a necessity. The clustering-based approaches reduce the search space by clustering similar users or items together. We focus in this paper on the personalised item recommendation in social context. Our approach combines in different ways the social filtering algorithm and the traditional user-based collaborative filtering algorithm. The social information is formalised by some social-behaviour metrics such as friendship, commitment and trust degrees of users. Moreover, two classification techniques are used: an unsupervised technique applied initially to all users and a supervised technique applied to newly added users. Finally, the proposed approach has been experimented using different existing datasets. The obtained results show the contribution of integrating social information on the collaborative filtering and the added value of using the classification techniques on the different algorithms in terms of the recommendation accuracy.
MP-K-Means: Modified Partition Based Cluster Initialization Method for K-Means Algorithm

In k-means algorithm, initial cluster centroids are selected arbitrarily which leads to diverse formation of clusters in each run. Consequently, accuracy and performance of k-means is majorly depends on the selection of initial centroids. Thus, the initial cluster centroids shall be chosen carefully to obtain better accuracy and performance of k-means algorithm. In view of this, a new Modified Partition based Cluster Initialization method for k-means called as MP-k-means is proposed in this paper. MP-k-means is an amended version of P-k-means [1] in which the range of values of each dimension is divided into ‘k’ equi-sized partition based on arithmetic average. This division of range into ‘k’ equi-sized partition is affected by outliers present in the data. In order to remove the effect of outliers in P-k-means, the partitioning of each dimension is made based on positional average instead of arithmetic average in MP-k-means. Six popular datasets are used for empirical evaluation of the algorithms. The empirical results are compared and validated based on various external and internal clustering validation measures. The comparative results show that MP-k-means is significantly superior to the basic k-means and P-k-means. The proposed method may also be applied to other clustering algorithms which are based on the concept of selection of initial cluster centroids.