Reviewerly/exHarmony huggingface dataset:
An enlarged map-reduce using 2logmean-PSO optimization for unstructured data

Text clustering system is a proper technique which mainly segments large measure of textual documents into clusters. The size of the material influences the clustering of text by reducing its performance. In this manner, the textual document comprises sparse and uninformative features, and thus raises the computational time and decreases the execution of primary clustering process. Feature selection is a crucial system to choose another subset of instructive text feature to enhance text clustering execution and diminish computational time. The implemented model proposes a 2logmean-particle swarm optimization algorithm for the unstructured text clustering. In this newly proposed technique, all the texts are initially converted into ASCII value, and then by using the particle swarm optimization, the document text is clustered. The outcomes display that clustering accuracy of the implemented method is high compared to the existing K-means algorithm. Furthermore, performances of newly implemented techniques are evaluated concerning scalability, less computation speed with colossal dimensionality reduction.
A Novel Method of Clone Detection by Neural Networks

Code clone is that type of engine that helps to find duplicate code patterns find within the whole code. Programmers usually adopt code reusability task from previous few years, so that time consumption can be reduces. Code reusability can be done via replication or by just copy-paste. Code reusability leads to not writing code from scratch, just copy paste the useful part of the code. In finding of duplicated code fragment or text, plagiarism detection also work pretty well but it is not applicable to the large system in finding functional clone and also it is more time consuming even at small scale which make the detection method inappropriate. In this paper, we proposed a pattern similarity conditions on the basis of textual similarity for finding the code or text clones in the large content on the basis of SVM, Neural Network using Java coding, Neural Network and Sim Cad. This approach detects code or text clones from original one. The resultant simulation is taken place in the MATLAB environment, and it has shown that it is providing better results. The proposed algorithm performance is measured using parameters i.e. FRR, FAR and Accuracy.
Research on Network Security Risk Assessment Model Based on Grey Language Variables

Abstract In order to realize effective network security risk assessment, in the process of quantifying qualitative assessment, in order to eliminate the fuzziness of qualitative assessment and the uncertainty caused by lack of information, this paper studies multi-person multi-attribute assessment method based on Grey linguistic variables, establishes a network security risk assessment model based on Grey linguistic variables, and establishes a network security risk assessment model based on Grey linguistic variables. And use Python to implement the network security risk assessment model.
Universities’ Policy of Supporting Students in Academically Diverse Environment

The aim of this work is to review and analyse the support and development practices for students based on their educational background and academic performance. The increasingly widespread concept of a responsible university involves an opportunity to close or make smaller the gap between academically diversifi ed students. Thus, the purpose is to systematise the practice of working with both low and high-achieving students and assess the possibilities for improving the quality and effectiveness of this work. The analysis regarding existing practices of Russian and international universities is based on the literature review of more than 1600 sources. The review provides identifi cation, comparison and systematisation of common practices used by departments and universities rather than individual teachers. A classifi cation of practices at the stages of identifying, developing and evaluating low and high-achieving students is proposed. Recommendations are formulated for expanding the support and development of students under the conditions of academic diversity and for improving the quality and effectiveness of the educational process.
Particle behaviour modelling in a diffuser type apparatus

The paper considers issues related to the movement of particles in an up stream of fluid in diffuser-type apparatuses. Equations of particle motion in an up stream are proposed. Solutions are obtained using a mathematical model in the Maple Computer Mathematics (CMS) system. The results of the studies show that controlling the regime of particle motion in an upstream allows to choosing the conditions for creation the best option for using this technique in wastewater treatment technology.
Equal-scale structure from motion method based on deep learning

Two problems exist in traditional multi-view geometry method to obtain the three-dimensional structure of the scene. First, the mismatching of the feature points caused by the blurred image and low texture, which reduces the accuracy of reconstruction; second, as the information obtained by monocular camera is lack of scale, the reconstruction results can only determine the unknown scale factor, and cannot get accurate scene structure. This paper proposes a method of equal-scale motion restoration structure based on deep learning. First, the convolutional neural network is used to obtain the depth information of the image; then, to restore the scale information of the monocular camera, an inertial measurement unit (IMU) is introduced, and the acceleration and angular velocity acquired by the IMU and the camera position acquired by the ORB-SLAM2 are demonstrated. The pose is coordinated in both time domain and frequency domain, and the scale information from the monocular camera is acquired in the frequency domain; finally, the depth information of the image and the camera pose with the scale factor are merged to reconstruct the three-dimensional structure of the scene. Experiments show that the monocular image depth map obtained by the Depth CNN network solves the problem that the output image of the multi-level convolution pooling operation has low resolution and lacks important feature information, and the absolute value error reaches 0.192, and the accuracy rate is up to 0.959. The multi-sensor fusion method can achieve a scale error of 0.24 m in the frequency domain, which is more accurate than that of the VIORB method in the frequency domain. The error between the reconstructed 3D model and the real size is about 0.2 m, which verifies the effectiveness of the proposed method.
BRIDGING ADVERSARIAL SAMPLES AND ADVERSARIAL NETWORKS

Generative adversarial networks have achieved remarkable performance on various tasks but suffer from training instability. Despite many training strategies proposed to improve training stability, this issue remains as a challenge. In this paper, we investigate the training instability from the perspective of adversarial samples and reveal that adversarial training on fake samples is implemented in vanilla GANs, but adversarial training on real samples has long been overlooked. Consequently, the discriminator is extremely vulnerable to adversarial perturbation and the gradient given by the discriminator contains non-informative adversarial noises, which hinders the generator from catching the pattern of real samples. Here, we develop adversarial symmetric GANs (AS-GANs) that incorporate adversarial training of the discriminator on real samples into vanilla GANs, making adversarial training symmetrical. The discriminator is therefore more robust and provides more informative gradient with less adversarial noise, thereby stabilizing training and accelerating convergence. The effectiveness of the AS-GANs is verified on image generation on CIFAR-10 , CelebA, and LSUN with varied network architectures. Not only the training is more stabilized, but the FID scores of generated samples are consistently improved by a large margin compared to the baseline. The bridging of adversarial samples and adversarial networks provides a new approach to further develop adversarial networks.
Monocular vision based pose measurement of 3D object from circle and line features

Abstract An approach for estimating object pose from establishing equations between the features and their projection is proposed. In the case that the focal length of camera is unknown, this paper establishes the mathematical model between the circular feature of 3D Object and its projection through a series of basic changes, and adds common linear feature to provide spatial geometric constraint for pose measurement, then provides the nonlinear equations with seven unknown parameters. The equations are transformed into objective function, then the algorithm of the One-Dimensional search is applied to obtain the optimal value of the focal length. The other parameters can be calculated by the focal length. Experimental results verify the validity and accuracy of the method.
Evaluation of Interaction Techniques for Early Phase Satellite Design in Immersive Augmented Reality

The development of a satellite is a complex endeavor, requiring expertisein numerous different domains, such as mathematics, physics and engineering. Scientists and engineers are facing the challenge of explaining their special knowledge to experts of other subject areas. In order to communicate the specific constraints of their respective subsystem appropriately, creative communication methods, such as 3D visualizations are necessary. Due to communicative misinterpretation of the requirements a time-consuming process to the construction of a satellite arises. Recent display technologies developed for immersive augmented reality (AR) offer new ways of presenting and interacting with computer-generated content. The aim is to ensure a fast and intuitive interaction with the virtual objects in order to enable a quick visualization of the placed objects. The integrated hand gesture technique is compared with anexternal controller-based interaction technique by using an optical see-through head-mounted display. For this purpose a virtual satellite with corresponding manipulable components is being designed and the interaction controls are being implemented. Finally, a user study is conducted to evaluate the usability and difference of the developed interaction techniques. Advantages and disadvantages of manipulation methods concerning the satellite assembly task in AR, as well as potentials for the enhancement of the proposed user’s interface, are revealed.
Book Review: A.S. Klujev. The Summa of Music. Saint Petersburg: Aletheia, 2017

The professional pianist and philosopher Alexandr Klujev’s book The Summa of Music is a fundamental work that sets a goal to comprehend the essence of music, to reveal the implicit that is “behind the notes,” that is little discussed but that defines the meaning of this art. The book presents Klujev’s concept of the philosophy of music and consists of his three works: “Music and life: on the place of Musical Art in the Developing World,” “The Ontology of Music” and “Music: A Path to the Absolute.” There is an Appendix to the book, which contains fragments from works on music by prominent authors of the 20th and 21st centuries: K.R. Eiges, N.O. Lossky, A.F. Loseva, T. Adorno, N. Hartmann, R. Ingarden, Schonberg and others. The opinions of these authors, on the one hand, confirm and, on the other hand, complement the ideas of A. Klujev. Therefore, the book’s author creates a kind of polylogue on music.
Brazilian Coins Recognition Using Histogram of Oriented Gradients Features

This paper tends to the issue of coin recognition when dealing with shading and reflection variations under the same lighting conditions. In order to approach the problem, a database containing Brazilian coin images (both front and reverse side of the coin) consisting of five different denominations have been used which is provided by the kaggle-diverse and largest data community in the world. This work focuses on an automatic image classification process for Brazilian coins. The imagebased classification of coins primarily incorporates three stages where the initial step is Region of Interest (ROI) extraction; the subsequent advance is extraction of features and classification. The first step of ROI extraction is accomplished by segmenting the coin region using the proposed segmentation method. In the second step i.e., feature extraction; Histogram of Oriented Gradients (HOG) features are extracted from the image. The image is converted to a vector containing feature values. The third step is where the extracted features are mapped to the class and are known as classification. Three classification algorithms i.e., Support Vector Machine (SVM), Artificial Neural Network (ANN) and K-Nearest Neighbour are compared for classification of five coin denominations. With the proposed segmentation methodology, the best classification accuracy of 92% is achieved in the case of ANN classifier.
THE EDUCATIONAL AND FORMATIVE POTENTIAL OF MULTILINGUAL DIGITAL STORYTELLING: A STUDY IN A CULTURALLY DIVERSE PRIMARY SCHOOL IN ITALY

THE EDUCATIONAL AND FORMATIVE POTENTIAL OF MULTILINGUAL DIGITAL STORYTELLING: A STUDY IN A CULTURALLY DIVERSE PRIMARY SCHOOL IN ITALY
The Role of Management Practices of Higher Education Institutions in Knowledge Storage and Knowledge Accessibility

This study aims to explore the management practices of Higher Education Institutions (HEIs) with regard to knowledge storage and knowledge accessibility. Data were gathered from public sector HEIs in Peshawar using semi-structured interviews. Based on previous studies, themes and management practices were identified for knowledge storage and knowledge accessibility. Data were analyzed using content analysis, wherein matching themes and emergent themes were identified. The findings of the study revealed that emergent themes in technological perspective for knowledge storage were MOODLE platform, Enterprise Resource Planning (ERP), databases for storage of HEIs’ own journals, Campus Management Solution (CMS), faculty members’ and students’ email groups, and social media. While emergent themes in non-technological perspective for knowledge storage were libraries of HEIs at their central and departmental levels. Similarly, emergent themes in technological perspective of knowledge accessibility were MOODLE, HEC smart university project, Enterprise Resource Planning (ERP), databases for accessibility of HEIs’ own journals, firewall deployment for internet, Campus Management Solution (CMS), faculty members’ and students’ email groups, and social media. While emergent themes in non-technological perspective for knowledge accessibility were libraries of HEIs at their central and departmental levels. Finally, it was found that there are some management practices for knowledge storage and knowledge accessibility which need to be followed by the HEIs for effective knowledge storage and accessibility.
Sampling-Free Learning of Bayesian Quantized Neural Networks

Bayesian learning of model parameters in neural networks is important in scenarios where estimates with well-calibrated uncertainty are important. In this pa- per, we propose Bayesian quantized networks (BQNs), quantized neural networks (QNNs) for which we learn a posterior distribution over their discrete parameters. We provide a set of efficient algorithms for learning and prediction in BQNs without the need to sample from their parameters or activations, which not only allows for differentiable learning in quantized models but also reduces the variance in gradients estimation. We evaluate BQNs on MNIST, Fashion-MNIST and KMNIST classification datasets compared against bootstrap ensemble of QNNs (E-QNN). We demonstrate BQNs achieve both lower predictive errors and better-calibrated uncertainties than E-QNN (with less than 20% of the negative log-likelihood).
Internet of Things Device Capability Profiling Using Blockchain

Currently, Internet of Things (IoT) devices can integrate into an existing network where they may interact with a myriad of other devices that may host a range of capabilities. Such IoT devices may need to share data that is consumed by other devices or services. This data is generated by the capabilities built into devices within the ecosystem. A typical IoT ecosystem that is heterogeneous in nature should be able to have devices that offer a range of capabilities that could be explored in the event a device breakdown or malfunction. This is to ensure that the system is self-sustaining, and adequately perform during undesirable conditions. Hence, an IoT ecosystem should be able to collaborate, self-organize itself to explore these capabilities towards achieving an overall goal. As such, interoperability of these devices which will improve functionality, availability, and robustness of the IoT ecosystem must be achieved. Also, Several IoT representations today store their data centrally which gives rise to inherent issues such as single point of failure, and other possible vulnerabilities. Addressing these deficiencies alongside proper profiling of IoT device capability and other device details is viewed as the first stage in securing IoT ecosystems and this was explored in this research. This study presents the use of Distributed Ledger Technology which has the inherent property of being secure to profile the capability of IoT devices within self-organized IoT ecosystems. A system overview, data structures, and algorithms are presented.
MOBILE ASSISTED LANGUAGE LEARNING (MALL): WILL IT EVER BE AN ENGLISH LEARNING REALITY IN SRI LANKA?

Mobile Assisted Language Learning (MALL) is becoming a popular term along the line of CALL (Computer Assisted Language Learning) in the Western arena. Yet in Sri Lanka CALL and MALL have not sustained a place among the ESL (English as a Second Language) Learners. Unavailability of CALL software in Sri Lanka and high cost of software available internationally, along with the difficulties in maintaining computer laboratories make CALL unpopular. On the other hand, mobile phones, especially smart phones, have become popular and commonly available among the Sri Lankan University students. In the present context, though mobile phones are used for learning English their use for language learning is not studied among university students in Sri Lanka. The objective of this study is to find how the fourth year students at the Faculty of Arts of a Sri Lankan university use their mobile phones in English classes for learning English. In addition, this study explores the possible uses of mobile phones for learning English. Observation, followed by a written report and in-depth interviews with students are the main sources of data collection. In this study, it was revealed that students use mobile devices  mainly for acquiring vocabulary skills. Sometimes they access internet service for translating sentences and also for browsing model essays, letters, etc. In addition, they use mobiles for sharing information via WhatsApp and Viber. Nevertheless, these uses seem to be not productive enough for language development like the way students use CALL for learning a language.
Methods of mathematical modeling of non-Gaussian random variables quantities and processes

Mathematical methods allowing to model non-Gaussian random variables and processes are considered. The models and description of non-Gaussian correlated processes in the form of generated Gaussian noise are analyzed, as well as the methods of formation of stationary random processes defined by the one-dimensional density distribution of Vero -abilities and the autocorrelation function. Examples of formation of non-Gaussian random variables and processes are given.
Library Guides: Trinity LibTech Research Showcase 2019: LibTech 2019 Programme

An archive of the research presented at the Trinity LibTech Research Showcase, held as part of the 2019 LIBER Conference
How Much Does IVR Cost? | AVOXI Cloud Communications

If you have questions about what exactly an IVR is and how it can save you money, you are not alone. Before making any decision, it is important ...
A Novel Detection Method of Unmodulated Radar Pulses in Scenarios with Interference for Digital Wideband ESM Receivers

In this paper, we present a novel detection method of unmodulated radar pulses oriented to digital wideband Electronic Support Measures (ESM) applications for Electronic Warfare (EW). This novel method reduces the false alarm probability in the presence of interference signals as spurious or jammers. Since ESM receivers have no a priori knowledge about hostile’s electromagnetic emissions, the Generalized Likelihood Ratio Test (GLRT) for sinusoidal pulses with unknown phase, frequency, amplitude and time of arrival (TOA) becomes a powerful detection method for digital ESM applications. But, unfortunately, this method is not robust to spurious signals, which increase the probability of false alarm to non-operational values and cause degradation of detection performance. We propose a combination of the GLRT with a validation process, which is based on the L-out-of-N logic applied to the Short-Time Fourier Transform (STFT), which is the GLRT test statistic. Simulation results show significant reduction of the false alarm probability in the presence of interference while keeping operational detection performance.
Design and Build Information System for Waste Bank Management Case Study in Serious Waste Bank (BSPB)

Waste is the result of the dynamics of life that can cause problems if it is not properly managed. Many methods had been used to help overcome waste management. The Waste Bank is one of the solutions to help solve waste management. Bank Sampah Panggung Berseri is one of the communities that actively carries out waste management around the Pangung village. BSPB has problems related to solid waste management. The management in question is the management of waste data that is still conventional, archiving is not optimal, has not managed customer data savings properly. In this research also added a marketplace feature where people can make transactions from balances obtained from waste sold by buying basic necessities. The method in this research is to collect data at BSPB, analyze data and develop applications using the prototype method. The results of this study are all the features or functions of the system run well by testing the functionality used the Black Box testing method. In testing using the usability testing method has shown the level of satisfaction for the parameters of usability, ease of learning, ease of use and satisfaction gives an average value of 4.38 from the range of values ​​1-5. This shows that the system made can be said to satisfy BSPB users and customers. &#x0D;
Acquisition of Inflectional Morphology in Artificial Neural Networks With Prior Knowledge

How does knowledge of one language's morphology influence learning of inflection rules in a second one? In order to investigate this question in artificial neural network models, we perform experiments with a sequence-to-sequence architecture, which we train on different combinations of eight source and three target languages. A detailed analysis of the model outputs suggests the following conclusions: (i) if source and target language are closely related, acquisition of the target language's inflectional morphology constitutes an easier task for the model; (ii) knowledge of a prefixing (resp. suffixing) language makes acquisition of a suffixing (resp. prefixing) language's morphology more challenging; and (iii) surprisingly, a source language which exhibits an agglutinative morphology simplifies learning of a second language's inflectional morphology, independent of their relatedness.