Reviewerly/exHarmony huggingface dataset:
3D Data Processing with Factor Models Application

Summary The main attention in the report focuses on the features of factor models application in the processing of 3D data. As an example, models of 3D residual statics correction are used, which are the basis for developing one of the key procedures of modern processing complexes. Their creation was completely based on heuristic considerations, which are built on the kinematic principles of wave propagation. The factor nature of seismic models and, in particular, static correction models, brings them closer to the analysis of variance models, allowing the use of statistical approaches developed by R. Fisher in genetic investigation.
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Game Edukasi Berhitung Angka 1-20 untuk Anak Kelas 1 SDLB B Yayasan Rehabilitasi Tuna Rungu dan Wicara Surakarta

Children with special needs or extraordinary children are children who deviate from normal children in terms of mental characteristics, sensory abilities, physical look, neuromuscular, social and emotional behavior, communication ability or a combination of two or more of the things that has been mentioned. There is one example of special need children in SDLB B Yayasan Rehabilitasi Tuna Rungu Wicara Surakarta. The child is a deaf and mute, he is on the first grade. Deaf and mute children are often easy to feel bored join manual learning method. They are more interest in learning using media such as games. Using game as learning media, children will be interested to repeating learning not only in the school. The objective of this research is to make an interesting learning process with an educational game to learn to count numbers 1-20 so the material can be easily understood by the deaf and mute children in the first grade of SDLB Yayasan Rehabilitasi Tuna Rungu Wicara Surakarta. In the making of this game, researcher use construct 2 application and cocoon.io to convert it into android version. Besides to design the object, the researcher use coreldraw X7. Method of this research is direct observation. This game is tested directly on the first grade students of SDLB B Yayasan Rehabilitasi Tuna Rungu Wicara Surakarta. The result of the questioner can be concluded that this game attracts students’ interest in learning to recognize and counting numbers 1-20. The test results after trying game students fill out questionnaires with the direction of the author and the results of the questionnaire can be concluded that 87% of respondents said this application was easy to play, 60% of respondents said it was interesting, 80% of respondents said the language used was easy to understand and 47% of respondents said the menu was easy to use
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Verifying Multi-Partite Entanglement with a Few Detection Events

We introduce a new entanglement-verification method and use it to experimentally verify the entanglement in a photonic six-qubit cluster state, created at telecommunication wavelengths, by detecting only 20 copies of the quantum state. © 2019 The Author(s)
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Tailored generation, manipulation, and application of photonic temporal modes

Pulsed temporal modes form a high-dimensional basis for quantum information applications. By controlling these modes, we can tailor quantum states and measurements for applications such as quantum state tomography or quantum metrology.
Learning Diverse Gaussian Graphical Models and Interpreting Edges

Gaussian graphical models are used to discover patterns of variable dependencies in many scientific applications, yet there is no guarantee that the assumption of identically distributed (IID) samples is correct. In many cases, the non-IID nature of the data is due to the fact that some observations are produced by a different underlying process than the other samples. Therefore, it is informative to the analyst who is trying to understand patterns in their data to explore different graphical models produced by various subsets of the data. Learning a graphical model for every one of a combinatoric number of data subsets is intractable. We solve the problem with an interactive machine learning approach, by first learning a Gaussian graphical model from data, then finding a different subset of the data that would produce the most different Gaussian graphical model, allowing the user to explore the most diverse Gaussian graphical models that fit various subsets of their data. To find the most different Gaussian graphical model, we define an optimization problem that can be solved by gradient-based algorithms. Furthermore, to gain insight into the learned Gaussian graphical model, we explain an edge in the graph by finding the subset of observations in the dataset that are critical for defining the correlation that the edge represents. That is, we interpret edges by finding subsets of data such that their removal from the dataset will lead to eliminating an edge in the graph. This relational information enables analysts to interpret each edge in terms of its robustness and relationship to observations in the data. Our method finds patterns in data from the Mars rover and online recipes. By bringing transparency and interpretability, we enable the practitioners to create and use models that they are confident and insightful about.
장르 문법의 위상과 의미 - 교육 내용과 연구 방법으로서의 장르 문법 -

The goal of this study is to specify the language of ‘genre grammar’ and present educational value in terms of content and method. Genre grammaticality is a point of view that the subject of language focuses on the process of verbalization its meaning in socio-cultural context. This is important as a method and procedure for analyzing and interpreting texts in various fields of text handling, as well as for content creation and development of grammar education. In terms of contents of education, first of all, ‘grammaing’ is set as the educational goal, and the contents of education can be constituted centering on it. In addition, from the viewpoint of genre grammar, it is possible to add contents or to develop new contents based on genre characteristics. In the aspect of research methodology, genre grammar can be used extensively as a perspective by proceeding the process of ‘discovery, analysis and interpretation’. In the future, detailed research on genre characteristic and refining of research procedures should be made more actively.
Predicting Elections Over A Twitter: A Campaign Strategies Of Political Parties Using Machine Learning Algorithms

Twitter is a social networking web application built to find out what is happening around us and all over the world. For this study, we analysed one million tweets for applying sentiment analysis is helpful to analyse the information where opinions are either good or bad/negative, or neutral and highly structured, heterogeneous in some cases. In this article we used a few machine learning algorithms like naïve Bayes, support vector machine, decision tree and neural network for analysis of elections results. Twitter data is used to predict the outcome of election by collecting data and analysing sentiments about the candidates. The experiential results show that the winning party's electoral success is expressively associated with social media/Twitter. It is a main promotional tool for new and upcoming parties in the 2019 general elections. This article mainly focused on Karnataka state elections of 2018 month of May for examining the analytical power of Twitter.
Periodisch zeitabhängige Größen

No AccessGrundgebiete der ElektrotechnikMay 2019Periodisch zeitabhängige GrößenArnold Führer, Klaus Heidemann, Wolfgang NerreterArnold FührerSearch for more papers by this author, Klaus HeidemannSearch for more papers by this author, Wolfgang NerreterSearch for more papers by this authorhttps://doi.org/10.3139/9783446460935.003SectionsAboutPDF ToolsAdd to FavoritesDownload CitationTrack CitationsCopy LTI LinkPDF key 'share (en)' returned an object instead of string.FacebookTwitterEmailLinkedIn previous chapternext chapter FiguresReferencesRelatedDetails 2019Pages: 73-93Print ISBN: 978-3-446-45954-0eISBN: 978-3-446-46093-5 Copyright & Permissions© 2019 Carl Hanser Verlag GmbH & Co. KGPDF downloadLoading ...
Vehicles detection and tracking using Convolutional Neural Networks on edge-computing platforms

Image classification and detection tasks have been approached in numerous ways throughout the years. Traditional approaches used conventional machine learning techniques dealing with hand-crafted vectors of features which had to be carefully extracted from the images with considerable domain-expertise. Deep learning algorithms have overcome this limitation by providing architectures able to work on raw data while outperforming traditional techniques. A deep learning architecture which proved to be very successful in the context of computer vision is the Convolutional Neural Network, an architecture able to achieve previously unreachable performances, making it, nowadays, a de-facto standard in classification and detection tasks. However, a drawback of deep learning architectures is their high computational footprint which has limited, until recently, their adoption only to high-performance servers and workstations. The availability of fast and efficient CNN models together with the release of AI accelerating low-powered platforms has enabled the use of CNNs on the edge, empowering a shift from a server-centric scenario to a fog scenario, lowering data bandwidth requirements for connected nodes and enabling smart features on any connected device. This work addresses the design and implementation of a CNN based framework for performing road traffic analysis through vehicles detection, classification and tracking, able to efficiently run on a low-powered edge-computing platform. The resulting platform can be positioned in close proximity to the node, enabling these smart-features on any traditional IP camera. The first efforts of this work went into selecting the most promising CNN models and fine-tune them on a vehicles specific dataset, evaluating the results both in terms of speed and detection performance. With a strong and efficient model, the later efforts went into developing the framework components: a detection pipeline, a REST server and a React web app. The design embraced a flow-based paradigm, keeping a constant eye on modularity and extensibility, making it easily tailorable to completely different scenarios.
Constrainted Loss Function for Classification Problems

Producing good representative features is the key to perform high accuracy in conventional methods of computer vision in many tasks. Our work is to show that whether good features are still critical in deep learning models. We bring two loss functions so that one works well in the classification problems and the other achieve good performance in verification problems, together to see whether they will improve the performance on classification problems. In literature, loss functions for classification problems and verification problems are working independently within the deep learning domain, losses of classification emphasis the discrimininative power in distinguishing data of different classes while the verification losses focus on establishing invariant mapping within an embedding via metric learning. In this paper, the major work is to look for a better balancing for loss functions of different kinds. The verification loss together with the classification loss requires a novel scheme in achieving better tradeoff while there are conflicts exited between losses to be involved. Experiments are designed based on the well-known models of ResNet and VGG-16 evaluating on CIFAR-10 and CIFAR-100.
Teaching Empirical Social-Science Research to Cybersecurity Students: The Case of "Thinking Like a Thief"

We report on an educational experiment in which computer science master students performed empirical research regarding the human factor in cybersecurity.
Deep Integer-Position Samples Refinement for Motion Compensation of Video Coding

Motion compensation is a critical technology in video coding for temporal redundancy removal. In the motion compensation process, previously coded frames are utilized as the reference for generating the prediction of the blocks which are to be coded. However, despite being filtered after reconstruction, there are still some artifacts in the reference, which may affect the motion compensation performance. In this paper, we propose a deep convolutional neural network (CNN) based integer-position samples refinement method to additionally enhance the references before the motion compensation process. A lightweight convolutional neural network is designed to learn to remove the artifacts of the integer-position samples and facilitate motion compensation. 8 convolutional layers with 10 channels are designed to obtain a wider receptive field with fewer parameters for feature extraction. The residual learning technique is also utilized in the feature extraction process for accelerating the convergency of the network. Experimental results have demonstrated the efficiency of our CNN-based integer-position samples refinement method.
Reversible Non-Distorting Compression of the Dynamic Range of the Audio Signal

The problems of sound signal transmission and storage are considered. It is shown that the existing systems do not provide signal transmission with natural dynamic range. The algorithms used to compress and restore the audio signal lead to distortions and increase the signal-to-noise ratio only relative to the noise in the pause. An algorithm that ensures the preservation of the signal shape and increases the signal-to-noise ratio in the pause and on the signal background is considered. The results of objective measurements confirming the efficiency of the algorithm are presented.
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Bigraphic pairs with a realization containing a split bipartite-graph

Let Ks,t be the complete bipartite graph with partite sets {x1, …, xs} and {y1, …, yt}. A split bipartite-graph on (s + s′) + (t + t′) vertices, denoted by SBs + s′, t + t′, is the graph obtained from Ks,t by adding s′ + t′ new vertices xs + 1, …, xs + s′}, yt + 1, …, yt + t′ such that each of xs + 1, …, xs + s′; is adjacent to each of y1, …, yt and each of yt + 1, …, yt + t′ is adjacent to each of x1, …, xs. Let A and B be nonincreasing lists of nonnegative integers, having lengths m and n, respectively. The pair (A; B) is potentially SBs + s′, t + t′-bigraphic if there is a simple bipartite graph containing SBs + s′, t + t′ (with s + s′ vertices x1, …, xs + s′ in the part of size m and t + t′ vertices y1, …, yt + t′ in the part of size n) such that the lists of vertex degrees in the two partite sets are A and B. In this paper, we give a characterization for (A; B) to be potentially SBs + s′, t + t′-bigraphic. A simplification of this characterization is also presented.
Review of Different approaches for Face Recognition with Partial Face Recognition and Convolutional Neural Network

Face is the major part of the human which provides the identi_cation of the person. With the help of characteristics of the face, the face recognition system can be implemented. In traditional attendance system, the teachers call student and mark according to present and absent. These traditional techniques are time-consuming. In thispaper, the smart machine learning based face recognition approach has been proposed. The database is created by capturing the faces of the authorized students. The face is detected using deep learning based approach. The cropped images then stored as a database with respective labels. The features are extracted using PCA algorithm and classi_ed using SVM and KNN. The proposed approach achieves the recognition rate of 98% for SVM and 92% for KNN algorithm. The proposed system is implemented in real time using Raspberry pi 3 board.
ICSSP 2018—Special issue introduction

Abstract The International Conference on Software and System Processes (ICSSP) provides a leading forum for the exchange of research outcomes and industrial best practices in process development from software and systems disciplines. ICSSP 2018 was held in Gothenburg, Sweden, May 26 to 27, 2018, colocated with the 40th International Conference on Software Engineering (ICSE). The theme of ICSSP 2018 was studying “Demands on Processes, Processes on Demand” by recognizing the demands on processes that include the need for both well‐developed plans and incremental deliveries (agile and hybrid processes), utilization of increased automation (model‐based engineering and DevOps), higher degrees of customer collaboration, comprehensive analysis of existing products for reuse (open source and COTS), and performance requirements of enterprise‐level architectures. This special issue includes the revised and extended versions of the five highest ranked full research papers and industry experience papers of ICSSP 2018, including the two award‐winning papers.
Using Social and Physiological Signals for User Adaptation in Conversational Agents

In face-to-face communication, humans subconsciously emit social signals which are picked up and used by their interlocutors as feedback for how well previously communicated messages have been received. The feedback is then used in order to adapt way coming messages are being produced and sent to interlocutor, leading to communication to become as efficient and enjoyable as possible. Currently however, it is rare to find conversational agents utilizing this feedback channel for altering how multimodal output is produced during interactions with users, largely due to complex nature of problem. In most regards, humans have a significant advantage over conversational agents in interpreting and acting on social signals. Humans are however restricted to a limited set of sensors, the five senses, which conversational agents are not. This makes it possible for conversational agents to use specialized sensors to pick up physiological signals, such as skin temperature, respiratory rate or pupil dilation, which carry valuable information about user with respect to conversation. This thesis work aims at developing methods for utilizing both social and physiological signals emitted by humans in order to adapt output of conversational agent, allowing for an increase in conversation quality. These methods will primarily be based on automatically learning adaptive behavior from examples of real human interactions using machine learning methods.
Uniform attribute‐content model

There have been growing needs for text processing, such as classifying, retrieving and clustering. The foundation of such a process is to extract features, which can best describe the text. Great progress has been made in text modelling. However, most of the text modelling methods are based only on the content, nor only on the attributes. Although there have been some combined models proposed in recent years, the lack of universality limits such models. In this study, the authors propose a uniform attribute-content model, which uses the attributes to influence the content feature extraction process. They design the attributes as a special filter to each feature extracted from the content. Thus the mixed features contain both content information and attribute information, which can describe the text more precise. They also propose a Monte Carlo method to solve this model. Experimental results on the Enron email dataset demonstrate the effectiveness of the authors’ proposed models.
Tool support for managing repetitive program changes in evolving software

Software modification often requires consistent program changes, a group of similar, related changes, at multiple locations in a program. Developers are typically uneasy to (i) detect potential change anomalies such as omission errors or incorrect edits and (ii) determine related locations to apply consistent changes, which is a tedious and error-prone process. To address this problem, this study presents a technique for managing consistent program changes, checking and applying repetitive program transformation (CARP). Given program differencing results between original and edited program versions, CARP (i) infers change patterns to detect change anomalies, (ii) identifies required edit locations, and (iii) automatically applies adequate edits. It has been implemented in the context of the integrated development environment as an Eclipse plug-in. The authors evaluated CARP on three open-source projects and found that CARP detects seeded anomalies with 99.1% accuracy on average. Furthermore, it identifies change locations and transforms them with 98% accuracy. Their results show that CARP should help developers detect potential change anomalies in repetitive program changes and perform consistent changes automatically.
Decorators and Context Managers

Decorators and context managers are an advanced topic in Python, but they are useful in many real-world scenarios. Many popular libraries use decorators and context managers extensively to make their APIs and code cleaner. Initially, it might be a little tricky to understand decorators and context managers, but once you master them, they can make your code cleaner.
Use of Library and Information Science Books in Distance Education Library, Punjabi University, Patiala

The Library is the nerve centre of any educational institution. Library services are essential support services to Distance Education Students. The Library needs of Distance Learners are not unique;they have the same Library and information needs as on campus students. The use of books in Distance Educations plays a very interesting role. Distance Education or Distance Learning is a mode of delivering education and instruction, often on an individual basis, two students who are not physically present in a traditional setting such as a classroom. The present study is related to the distance education department Punjabi university Patiala. For the purpose of the study the sample was selected for the 147 books which were being repeatedly issued 1858 times in the last 5 years (2012–17). The objective of the study is to know the usage of books by the studentsof library science. The collected data was analysed using ’Simple Percentage Techniques.