Reviewerly/exHarmony huggingface dataset:
Deep Attentive End-to-End Continuous Breath Sensing from Speech

Modelling of the breath signal is of high interest to both healthcare professionals and computer scientists, as a source of diagnosis-related information, or a means for curating higher quality datasets in speech analysis research.The formation of a breath signal gold standard is, however, not a straightforward task, as it requires specialised equipment, human annotation budget, and even then, it corresponds to lab recording settings, that are not reproducible in-the-wild.Herein, we explore deep learning based methodologies, as an automatic way to predict a continuous-time breath signal by solely analysing spontaneous speech.We address two task formulations, those of continuousvalued signal prediction, as well as inhalation event prediction, that are of great use in various healthcare and Automatic Speech Recognition applications, and showcase results that outperform current baselines.Most importantly, we also perform an initial exploration into explaining which parts of the input audio signal are important with respect to the prediction.
Mobile Robot Path Planning in an Obstacle-free Static Environment using Multiple Optimization Algorithms

This article presents the implementation and comparison of fruit fly optimization (FOA), ant colony optimization (ACO) and particle swarm optimization (PSO) algorithms in solving the mobile robot path planning problem. FOA is one of the newest nature-inspired algorithms while PSO and ACO has been in existence for a long time. PSO has been shown by other studies to have long search time while ACO have fast convergence speed. Therefore there is need to benchmark FOA performance with these older nature-inspired algorithms. The objective is to find an optimal path in an obstacle free static environment from a start point to the goal point using the aforementioned techniques. The performance of these algorithms was measured using three criteria: average path length, average computational time and average convergence speed. The results show that the fruit fly algorithm produced shorter path length (19.5128 m) with faster convergence speed (3149.217 m/secs) than the older swarm intelligence algorithms. The computational time of the algorithms were in close range, with ant colony optimization having the minimum (0.000576 secs).&#x0D; Keywords: Swarm intelligence, Fruit Fly algorithm, Ant Colony Optimization, Particle Swarm Optimization, optimal path, mobile robot.
A super-network equilibrium optimization method for operation architecture with fuzzy demands

From the view of information flow, a super-network equilibrium optimization model is proposed to compute the solution of the operation architecture which is made up of a perceptive level, a command level and a firepower level. Firstly, the optimized conditions of the perceptive level, command level and firepower level are analyzed respectively based on the demand of information relation, and then the information supply-and-demand equilibrium model of the operation architecture super-network is established. Secondly, a variational inequality transformation (VIT) model for equilibrium optimization of the operation architecture is given. Thirdly, the contraction projection algorithm for solving the operation architecture super-network equilibrium optimization model with fuzzy demands is designed. Finally, numerical examples are given to prove the validity and rationality of the proposed method, and the influence of fuzzy demands on the super-network equilibrium solution of operation architecture is discussed.
Projective Latent Interventions for Understanding and Fine-Tuning Classifiers

High-dimensional latent representations learned by neural network classifiers are notoriously hard to interpret. Especially in medical applications, model developers and domain experts desire a better understanding of how these latent representations relate to the resulting classification performance. We present Projective Latent Interventions (PLIs), a technique for retraining classifiers by back-propagating manual changes made to low-dimensional embeddings of the latent space. The back-propagation is based on parametric approximations of \(t\)-distributed stochastic neighbourhood embeddings. PLIs allow domain experts to control the latent decision space in an intuitive way in order to better match their expectations. For instance, the performance for specific pairs of classes can be enhanced by manually separating the class clusters in the embedding. We evaluate our technique on a real-world scenario in fetal ultrasound imaging.
Secure Communication across the Internet by Encrypting the Data using Cryptography and Image Steganography

Sharing the information has become a facile task nowadays just like one-tap which can take the information to any component of the world.This whole thing transpired over the evolution of the cyber world, which avails to stay connected with the entire world.Due to the wide-spread utilization of the cyber world, it leads a peril of data breaching by some incognito or unauthorized people while it is being sent from one utilizer to another.Unauthorized people can get access to the data and extract utilizable information from it.The confidential data being sent through the web which may get tampered while reaching the other end-utilizer.So, to dispense this data breaching, we can encrypt the data being sent and the receiver can only decrypt the message so that we can conceal the data.It routes a tremendous way to do this, the most popular one is cryptography, and another is steganography.Anteriorly there subsist many ways in these techniques like Image Steganography, Secret key Cryptography, LSB method, and so on which are being used to encrypt data and secure communication.One of the algorithms of cryptography is utilized along with Image Steganography to encrypt the data to ascertain more security which resembles the two-step verification process.In proposed paper we utilized new Huffman coding algorithm in step of the Image Steganography to ascertain that even an astronomically immense data can fit into a minute image.The ciphertext is compressed utilizing Huffman Coding and then it gets embedded into an image utilizing LSB method of Image Steganography in which the least paramount bits of the image are superseded with the data from the antecedent step.We implemented the analytical using python and it shows better compression results with large volumes of data to transfer easily through network.
Study of Effectiveness of Prior Knowledge for Smart Home Kit Installation

Smart-Home in a Box (SHiB) is a ubiquitous system that intends to improve older adults’ life quality. SHiB requires self-installation before use. Our previous study found that it is not easy for seniors to install SHiB correctly. SHiB CBLE is a computer-based learning environment that is designed to help individuals install a SHiB kit. This article presents an experiment examining how smart home sensor installation was affected by knowledge gained from two methods, SHiB CBLE, and a written document. Results show that participants who were trained by the CBLE took significantly (p&lt;0.05) less time in the installation session than those in the control group. The accuracy rate of SHiB kit installation is 78% for the group trained by the CBLE and 77% for the control group. Participants trained by the CBLE showed significantly (p&lt;0.01) higher confidence in the actual installation than those in the control group. These results suggest that having a training before the actual installation will help installers avoid unnecessary work, shorten the installation time, and increase installers’ confidence.
The Impact of Leadership on Employees’ Loyalty and on Organizational Success: Do Transformational and Transactional Leadership Ensure Organizational and Work Commitment?

effective leadership, it is difficult for the members of the organization to maintain profitability, productivity and competitive advantage (Lussier and Achua, 2007).Work commitment is important for understanding employee work behavior (Mowday et al.,
Structure of Fuzzy Dot d-subalgebras

The concepts of fuzzy dot d-subalgebras, and fuzzy dot d-ideals of a d-algebra have been investigated.The product of fuzzy dot d-ideals and strong fuzzy relation and the corresponding strong fuzzy dot d-ideals has been introduced.Different characterizations are discussed.
Generating Referring Expressions from RDF Knowledge Graphs for Data Linking

The generation of referring expressions is one of the most extensively explored tasks in natural language generation, where a description that uniquely identifies an instance is to be provided. Some recent approaches aim to discover referring expressions in knowledge graphs. To limit the search space, existing approaches define quality measures based on the intuitiveness and simplicity of the discovered expressions. In this paper, we focus on referring expressions of interest for data linking task and present RE-miner, an algorithm tailored to automatically discover minimal and diverse referring expressions for all instances of a class in a knowledge graph. We experimentally demonstrate on several benchmark datasets that, compared to existing data linking tools, referring expressions for data linking substantially improve the results, especially the recall without decreasing the precision. We also show that the RE-miner algorithm can scale to datasets containing millions of facts.
Irrational Components Of Value Rationality

from the standpoint of identifying irrational, unconscious and irrational components.The ideas and works of I.
CURRENT PROBLEMS OF CREATION OF RESEARCH INFRASTRUCTURE FOR SYNTHESIS OF NEW MATERIALS IN THE FRAMEWORK OF THE DIGITAL TRANSFORMATION OF SOCIETY

The article discusses the main aspects of the importance of developing a high-performance environment for scientific research in the context of digital transformation for solving the problems of synthesizing new materials with desired properties. An analysis of the experience of the FRC IU RAS in the creation of a modern high-performance platform and its use for scientific research is given.
High School Educational Processes Development: From Differential To Personalized Approach

The article reveals the essence of personalized learning, in general, and higher education institutions, in particular, aimed at developing student's personality on the basis of his/her personal goal-setting, psychological needs, self-discovery and independent reflexive control.The article analyses the theoretical aspects of differentiated and personality-oriented approaches, as well as individualization of learning, which forms the basis for personalization as a modern pedagogical technology.Based on the issues in the field of higher education and the requirements that a society and employers set for university graduates, the authors propose to consider personalized learning as a modern educational technology, including professional education that can solve the tasks.The article reveals the principles and components of personalized learning at a university as a modern pedagogical technology and examines teaching methods that implement the personalization principles.In this paper, the authors provide a definition of personalized learning, which involves not only the development of skills and competencies necessary to solve professional task, but also personal development, self-education and independence throughout life.The authors conclude that the personalization of education allows students to comprehend and interpret the most important concepts of modern cross-cultural life, as well as to understand themselves and their positions from the point of view of personal goal-setting and social values.Understanding oneself as a person in future professional life, as well as in the context of social and cultural processes enables students to understand the value of education taking into account the values of modern life and culture.
Programming Metamorphic Algorithms: An Experiment in Type-Driven Algorithm Design

In dependently typed programming, proofs of basic, structural properties can be embedded implicitly into programs and do not need to be written explicitly.Besides saving the effort of writing separate proofs, a most distinguishing and fascinating aspect of dependently typed programming is that it makes the idea of interactive type-driven development much more powerful, where expressive type information becomes useful hints that help the programmer to complete a program.There have not been many attempts at exploiting the full potential of the idea, though.As a departure from the usual properties dealt with in dependently typed programming, and as a demonstration that the idea of interactive type-driven development has more potential to be discovered, we conduct an experiment in 'type-driven algorithm design': we develop algorithms from their specifications encoded in sophisticated types, to see how useful the hints provided by a type-aware interactive development environment can be.The algorithmic problem we choose is metamorphisms, whose definitional behaviour is consuming a data structure to compute an intermediate value and then producing a codata structure from that value, but there are other ways to compute metamorphisms.We develop Gibbons's streaming algorithm and Nakano's jigsaw model in the interactive development environment provided by the dependently typed language Agda, turning intuitive ideas about these algorithms into formal conditions and programs that are correct by construction.
Detection of Breast Cancer on Magnetic Resonance Imaging Using Hybrid Feature Extraction and Deep Neural Network Techniques

Breast cancer is one of the most occurring cancers in women due to the uncontrolled growth of abnormal cells in the lobules or milk ducts. The treatment for the breast cancer at an early stage is important using Magnetic Resonance Imaging (MRI) which effectively measures the size of the cancer and also checks tumors in the opposite breast. The deposition of calcium components on the breast tissue is known as micro-calcifications. The calcium salts deposited in the breast are involved with the cancer and were not diagnosed accurately due to the low effectiveness of existing imaging technique namely Haralick feature extraction technique. The MRI breast cancer diagnosis creates problems during classification of breast image and leads to misclassifications, such as unidentified calcium deposits in the existing K-Nearest Neighbour (KNN) classifier. The misclassification issues are overcome by an accurate classification and identification of calcium salts and checks whether deposited salt on breast tissue is involved with cancer or not. Initially, Contrast-Limited Adaptive Histogram Equalization (CLAHE) is used to remove the unwanted noise in the MRI and Morphological, Multilevel Otsu’s Thresholding and region growing techniques perform segmentation to mask unwanted breast tissues. The proposed Hybrid LOOP Haralick feature extraction technique is developed by combining the both Local Optimal Oriented Pattern (LOOP) and Haralick texture feature and the hybrid parameters are applied to the Stacked Auto Encoder based (SAE) to classify the breast MRI image as a Malignant or Benign. The performance of the proposed hybrid LOOP Haralick feature extraction shows significant accuracy improvement of 3.83% when compared to the Haralick feature extraction technique.
Adopting Text Similarity Methods and Cloud Computing to Build a College Chatbot Model

A chatbot is a computer program which is designed to interact with users and answer questions.Nowadays, chatbots are one of the most common systems that are used in many fields and by different companies to achieve different tasks.Cloud computing is gaining increasing interest.A myriad of fields and applications have been developed based on cloud computing.In this paper, a college chatbot was developed and implemented to assist students to interact with their college and ask questions related to faculty, activities, exams, admission, amongst other tasks.Text similarity algorithms were adopted to achieve the proposed system.More specifically, cosine similarity and jaccard similarity algorithms were used to find the closest question in the dataset.Firebase real-time database, which is one of the Google cloud services, was used as a connector channel between users and the chatbot server.Experiments were conducted to evaluate the performance of cosine similarity and jaccard similarity methods, and to compare the results of both.In addition, real-time database was also evaluated as a chatbot connecter channel.
A Probabilistic Prediction Approach for Memory Resource of Complex System Simulation in Cloud Computing Environment

Accurate memory resource prediction can achieve optimal performance for complex system simulation (CSS) using optimistic parallel execution in the cloud computing environment. However, because of the varying memory resource demands of CSS applications caused by the simulation entity scale and frequent optimistic synchronization, the existing approaches are unable to predict the memory resource required by a CSS application accurately, which cannot take full advantage of the elasticity and symmetry of cloud computing. In this paper, a probabilistic prediction approach based on ensemble learning, which regards the entity scale and frequent optimistic synchronization as the important features, is proposed. The approach using stacking strategy consists of a two-layer architecture. The first-layer architecture includes two kinds of base models, namely, back-propagation neural network (BPNN) and random forest (RF). The root mean squared error-based pruning algorithm is designed to choose the optimal subset of the base models. The second-layer is the Gaussian process regression (GPR) model, which is applied to quantify the uncertainty information in the probabilistic prediction for memory resources. A series of experiments are presented to prove that the proposed approach can achieve higher accuracy and performance compared to RF, BPNN, GPR, Bagging ensemble approach, and Regressive Ensemble Approach for Prediction.
Songverse: a music-loop authoring tool based on Virtual Reality

We can define Digital Musical Instruments (DMIs) as hardware-software solutions that are interactive and crafted to output sound according to users' input. DMIs are well known to unleash users' creativity but also to allow different and innovative experiences for the creation process, for example, smoothing the learning curve towards musical concepts such as rhythm and composition. On the other hand, Virtual Reality (VR) allows users to explore spatial interfaces in a natural and limitless way, which shows potential synergy towards the rise of new DMIs. In this paper, we introduce Songverse, an immersive DMI placed in a Virtual Reality scenario that allows users to create music by interacting with an environment designed to resemble the outer space. By adding systems, planets, and satellites to the virtual environment, the user can shape the produced sound through interactions that were extensively tested during the development phase. We then evaluated the instrument with musicians and non-musicians by interviewing and applying the System Usability Scale (SUS) to assess the easiness for people to create music using Songverse. As a result, users reported the use of the DMI as intuitive and easy to use, also highlighting the produced song as enjoyable.
Word Embedding Based on Large-Scale Web Corpora as a Powerful Lexicographic Tool

The Aranea Project offers a set of comparable corpora for two dozens of (mostly European) languages providing a convenient dataset for nLP applications that require training on large amounts of data. The article presents word embedding models trained on the Aranea corpora and an online interface to query the models and visualize the results. The implementation is aimed towards lexicographic use but can be also useful in other fields of linguistic study since the vector space is a plausible model of semantic space of word meanings. Three different models are available – one for a combination of part of speech and lemma, one for raw word forms, and one based on fastText algorithm uses subword vectors and is not limited to whole or known words in finding their semantic relations. The article is describing the interface and major modes of its functionality; it does not try to perform detailed linguistic analysis of presented examples.
Image to Text Conversion Technique for Anti-Plagiarism System

Background/Objectives: The IMAGE TO TEXT CONVERSION TECHNIQUE FOR ANTI-PLAGIARISM SYSTEM is a design project on how the Optical Character Recognition will be utilized in order to extract text from images that can be used to increase the accuracy rate of an anti-plagiarism checker.It also highlights the integration of Convolutional Neural Network and its effect in the result of the conversion.Methods/Statistical analysis: Optical Character Recognition is a technology that recognizes text within an image.It is commonly used to recognize text in scanned documents, but it serves many other purposes as well.While Convolutional Neural network is a category of neural networks that have been proven very effective in performing image recognition and classification.The main objective of the study is to design a software that will convert images of text into plain editable text.The study aims to use a specific algorithm to extract useful information from the images.Findings: It will integrate the two algorithm, convolutional neural network and optical character recognition technology in order to develop a software.The input of the software is a document in .docxformat and will generate an output in the same format.Improvements/Applications: This software will be an aid to the existing anti-plagiarism checkers to generate a more thorough and better plagiarism check.
ARGUMENT CONSTELLATION IN JOURNAL ARTICLES: TOULMIN STEPHEN PERSPECTIVE (KONSTELASI ARGUMEN DALAM ARTIKEL JURNAL: PERSPEKTIF STEPHEN TOULMIN)

A constellation of arguments is present in each component of a journal article.Understanding the constellation of arguments in such journal articles is important because they will directly affect the writing of journal articles.This study aims to describe the constellation of arguments in the structure of the intended journal article.Data were collected by the observation method.The technique used in the framework of applying the method of referencing was a competent free observation technique.The next technique is note-taking.Data validation is done theoretically by confirming the available data on the theories.Also, validation is carried out to experts who master the matters of argument.The data validation was done after the data had been classified and verified.The data analysis method applied in this research is the method of distribution or distribution.The technique applied is a technique for direct elements.The data analysis method applied was the content analysis method.The results of the analysis were presented with informal presentation techniques.This research had produced the findings of the constellation of arguments in sections of journal articles.