Reviewerly/exHarmony huggingface dataset:
Component Based Systems: A Novel Methodology in Software Engineering

Background/Objectives: The component based systems make the possibility to develop a better quality system in a faster way in order to satisfy the customer needs. Component based software development is the new technology in software engineering. Component based methods phases the importance of reusing the existing components in software engineering thereby, ensuring lower cost which leads to a versatile development approach. Method/Statistical Analysis: In this study, a novel methodology of Component Based Software Development, its various issues, advantages and limitations in developing software is proposed. This study also focuses on component-based software engineering and to reveal how it can be utilised efficiently and effectively to develop high-quality software products. Findings: Under ideal situations, with minimal testing efforts, integration of reliable software components should produce high-quality software. It additionally, it provides a short summary of the various factors; its models and additionally highlights a number of points regarding the performance and quality factors that are necessary for a software development. Application/Improvements: Feature enhancements in this can be a new technology that could overcome the existing drawbacks Keywords: Component Deployment, Reliable Software, Object Oriented Software, Software Engineering
Development of Teaching Materials for Teachers about Small Astronomical Telescopes

Textbooks used in elementary and junior high schools must be written based on the Courses of Study in Japan. Science textbooks show observation of the moon and the sun using a small astronomical telescope. However, most in-service teachers have never used an astronomical telescope. Teachers have to use it safely to secure the safety of pupils, students, and teachers. I examined wrong and unsafe acts while in-service teachers and students assembled astronomical telescopes. Then I developed digital teaching materials for teachers including risks caused by wrong acts.
A Context-Aware Method for View-Point Invariant Long-Term Re-identification

In this work, we propose a novel context-aware framework towards long-term person re-identification. In contrast to the classical context-unaware architecture, in this method we exploit contextual features that can be identified reliably and guide the re-identification process in a much faster and accurate manner. The system is designed for the long-term Re-ID in walking scenarios, so persons are characterized by soft-biometric features (i.e., anthropometric and gait) acquired using a Kinect $$^\mathrm {TM}$$ v.2 sensor. Context is associated to the posture of the person with respect to the camera, since the quality of the data acquired from the used sensor significantly depends on this variable. Within each context, only the most relevant features are selected with the help of feature selection techniques, and custom individual classifiers are trained. Afterwards, a context-aware ensemble fusion strategy which we term as ‘Context specific score-level fusion’, merges the results of individual classifiers. In typical ‘in-the-wild’ scenarios the samples of a person may not appear in all contexts of interest. To tackle this problem we propose a cross-context analysis where features are mapped between contexts and allow the transfer of the identification characteristics of a person between different contexts. We demonstrate in this work the experimental verification of the performance of the proposed context-aware system against the classical context-unaware system. We include in the results the analysis of switching context conditions within a video sequence through a pilot study of circular path movement. All the analysis accentuate the impact of contexts in simplifying the searching process by bestowing promising results.
Domain-aware Neural Model for Sequence Labeling using Joint Learning

Recently, scholars have demonstrated empirical successes of deep learning in sequence labeling, and most of the prior works focused on the word representation inside the target sentence. Unfortunately, the global information, e.g., domain information of the target document, were ignored in the previous studies. In this paper, we propose an innovative joint learning neural network which can encapsulate the global domain knowledge and the local sentence/token information to enhance the sequence labeling model. Unlike existing studies, the proposed method employs domain labeling output as a latent evidence to facilitate tagging model and such joint embedding information is generated by an enhanced highway network. Meanwhile, a redesigned CRF layer is deployed to bridge the 'local output labels' and 'global domain information'. Various kinds of information can iteratively contribute to each other, and moreover, domain knowledge can be learnt in either supervised or unsupervised environment via the new model. Experiment with multiple data sets shows that the proposed algorithm outperforms classical and most recent state-of-the-art labeling methods.
An Improved Algorithm for Iterative Matrix-Vector Multiplications over Finite Fields

Cryptographic computations such as factoring integers and computing discrete logarithms over finite fields require solving a large system of linear equations. When dealing with such systems iterative approaches such as Wiedemann or Lanczos are used. Both methods are based on the computation of a Krylov subspace in which the computational cost is often dominated by successive matrix-vector products. We introduce a new algorithm for computing iterative matrix-vector multiplications over finite fields. The proposed algorithm consists of two stages. The first stage (preprocessing) sorts the elements of the matrix row by row in ascending order and produces permutation tables. After preprocessing, many consecutive multiplications can be performed by the second stage of the algorithm using sequential additions on vector elements by the guidance of the permutation tables. We show that the preprocessing cost of the proposed algorithm can easily be amortized after several matrix-vector multiplications are performed. We implemented the algorithm using the C++ programming language and compared the performance with a classical method. The proposed algorithm exhibits significant improvement between $$35\%$$ and $$67\%$$ .
‘Learning to Rank’ Text Search Engine Platform for Internal Wikis

A large number of companies maintain an internal workplace wiki to document specific goals or processes pertaining to different projects. These wikis can grow exponentially in terms of content size and hence must be supported with an efficient searching platform to facilitate fast lookup of the desired content. However, since these wikis contain highly sensitive matter, relying on external proprietary search engines such as Google, Bing is not possible. Companies, thus, rely heavily on existing open-sourced search engine platforms such as Lucene, Sphinx. Since the nature of the internal wikis can vary greatly, current user experience shows that the result produced by such search engine platform is often inaccurate. In this paper, we aim to present a search engine powered by ‘Learning to Rank’ system, having the capability to model its ranking algorithm according to the needs of the company.
Local Outlier Detection Algorithm Based on Gaussian Kernel Density Function

With the rapid development of information technology, the structure of data resources is becoming more and more complex, and outlier mining is attracting more and more attention. Based on Gaussian kernel function, this paper considers three neighbors: k nearest neighbors, reverse k neighbors and shared nearest neighbors. A local outlier detection algorithm based on Gaussian kernel function is proposed. Firstly, the algorithm stores the nearest neighbors of each data object through kNN maps, including k-nearest neighbors, reverse k-nearest neighbors, and shared nearest neighbors, forming a kernel neighbor set S. Secondly, Estimating density of data objects through kernel density estimation KDE method. Finally, the relative density outlier factor RDOF is used to estimate the degree of data objects deviating from the neighborhood, and then determines whether the data objects are outliers, and the validity of the algorithm is proved on the real and synthetic data sets.
Descriptive Topological Spaces for Performing Visual Search

This article presents an approach to performing the task of visual search in the context of descriptive topological spaces. The presented algorithm forms the basis of a descriptive visual search system (DVSS) that is based on the guided search model (GSM) that is motivated by human visual search. This model, in turn, consists of the bottom-up and top-down attention models and is implemented within the DVSS in three distinct stages. First, the bottom-up activation process is used to generate saliency maps and to identify salient objects. Second, perceptual objects, defined in the context of descriptive topological spaces, are identified and associated with feature vectors obtained from a VGG deep learning convolutional neural network. Lastly, the top-down activation process makes decisions on whether the object of interest is present in a given image through the use of descriptive patterns within the context of a descriptive topological space. The presented approach is tested with images from the ImageNet ILSVRC2012 and SIMPLIcity datasets. The contribution of this article is a descriptive pattern-based visual search algorithm.
The 1st International Workshop on Context-Aware Recommendation Systems with Big Data Analytics (CARS-BDA)

With the explosive growth of online service platforms, increasing number of people and enterprises are doing everything online. In order for organizations, governments, and individuals to understand their users, and promote their products or services, it is necessary for them to analyse big data and recommend the media or online services in real time. Effective recommendation of items of interest to consumers has become critical for enterprises in domains such as retail, e-commerce, and online media. Driven by the business successes, academic research in this field has also been active for many years. Through many scientific breakthroughs have been achieved, there are still tremendous challenges in developing effective and scalable recommendation systems for real-world industrial applications. Existing solutions focus on recommending items based on pre-set contexts, such as time, location, weather etc. The big data sizes and complex contextual information add further challenges to the deployment of advanced recommender systems. This workshop aims to bring together researchers with wide-ranging backgrounds to identify important research questions, to exchange ideas from different research disciplines, and, more generally, to facilitate discussion and innovation in the area of context-aware recommender systems and big data analytics.
Basic Framework of Vocoders for Speech Processing

The main objective of this paper is to develop a basic framework of a vocoder for speech processing. The basic framework of a vocoder consists of voice preprocessing, encoder, decoder, and post-processing blocks and performing multithreading among these blocks. A GUI can be prepared which will take the input from the user and set the environment as required. The voice subsystem consists of transmission path (TX chain) and receiver path (RX chain) having various signal processing blocks. These blocks are executed in sequential order to process the voice signal. This basic framework allows user to completely analyze the effect of preprocessing blocks, post-processing blocks, and vocoders on the speech signal at different points. A thread is a process which takes samples as input, calls an appropriate block for processing, and gives processed samples as output.
Prediction of Susceptibility to Motion Sickness

It is very appealing to try to predict susceptibility to motion sickness. Very many before me and no doubt many after, will pursue this goal. As you will see, my experiences have been very disappointing. I spent many years evaluating motion sickness history questionnaires and the seemingly predictive test of cupulometry—all to no avail. The more time I spend with people who suffer from chronic motion sickness, the more intrigued I am by their stories. These stories frequently seem illogical in terms of apparently widely different responses to stimuli that seem to be very similar. Suffice to say that in our laboratory we are frequently surprised by the responses of individuals. Those with a seemingly “bad” history often do better on our motion devices than others whose history seems less severe. We still have a lot to learn.
Research and design of industrial monitoring RTU middleware

Aiming at the problems of traditional industrial site monitoring system, multiple bus protocols coexist and incompatible, and the data platform is difficult to interact with each other, this paper uses ARM hardware platform and Linux operating system to design an embedded middleware system. The middleware system can realize the receiving, converting, and storing of different protocol data returned by the field collecting device, and the data interaction between different buses, and the middleware system provides a unified data format for the upper computer. It is convenient for the unified analysis and management of industrial field data by the host computer.
CRITERIA FOR LECTURERS ASSESSMENT BY STUDENTS OF LAW FACULTIES IN RUSSIAN UNIVERSITIES IN THE XIX - EARLY XX CENTURY

The article aims to consider the factors forming the criteria for assessing lecturers by law students in the period of the XIX - early XX century. As a result, depending on the students’ perception of the political situation in the country, the author identifies political and non-political criteria. Basing on the analysis of the Russian university graduates’ memories, the degree of influence of personal perception, as well as cognitive distortions on the students’ opinions is determined. The author pays special attention to the study of the law graduates’ memoirs.
Analysis of Medical Documents with Text Mining and Association Rule Mining

Text mining techniques extracts meaningful information from large amounts of semi-structured and unstructured texts. In this work, the MetaMap tool was used to extract medical entities like diseases and syndromes from discharge summaries. Also, association rule mining algorithms such as Apriori and FP-Growth were applied to the extracted entities in order to find associations between them. The dataset used consists of 1237 discharge summaries obtained from the 2008 i2b2 Obesity Challenge. The rules that have a principal diagnosis as antecedent showed that the cardiac disease frequently occurred with other diseases like hypertension and diabetes. Most of the rules describe associations between diabetes and other diseases like hypertension, dyslipidemia, nephropathy, heart disease, lung diseases, and arthritis. These rules have a confidence parameter of above 0.5.
Context-Aware Visualization of Entity-Entity Relationships in a Document Corpus

The paper presents an interactive graphical environment, which enables the detection and graphical visualization of concepts in a document or a document collection. Concepts are expressed by (multiple) entities extracted from the document and by the relationships between them. The tool offers an entity-centered view, which graphically shows the most important relationships of a central entity or entity-group, consisting of multiple co-occurring entities. By specifying prefixes and an additional available type system, complex filters can be created that allow the disclosure of various relationships between entities. Entities and their relationships are determined at the time of indexing and stored in appropriate data structures, so that an interactive search and exploration of relationships between entities is easily possible at runtime. The tool is available for online demonstration at https://www.smiffy.de/CoOcViz (credentials: adc2019, password: demo) .
Practice and Evaluation of a Class Improvement Support System Using a Hierarchical Item Bank in Every Lesson

Class evaluation questionnaire is conducted as one of Faculty Development in higher education institutions. However, it has not been fully utilized. We have developed a hierarchical item bank that can feedback class improvement information even with a small number of item. In addition, we developed a system that can feed back time series data using a hierarchical item bank. In this paper, we report practice research in order to verify the effectiveness of the system. As a result, the system and its feedback based on time series data is effective for class improvement.
Virtual Information in the Light of Kant’s Practical Reason

In (D’Agostino M, Floridi L, Synthese 167:271–315, 2009) the authors face the so-called “scandal of deduction” (Hintikka J, Logic, language games and information. Kantian themes in the philosophy of logic. Clarendon Press, Oxford, 1973). This lies in the fact that the Bar-Hillel and Carnap theory of semantic information implies that tautologies carry no information. Given that any mathematical demonstration and more in general every logical inference in a first-order language can be reduced to a tautology; this would imply, that demonstrations bring no fresh information at all. Addressing this question (D’Agostino M, Floridi L, Synthese 167:271–315, 2009) offers both: (i) a logical model for a strictly analytical reasoning, where the conclusions depend just on the information explicitly present in the premises; and (ii) a proposal for the ranking of the informativeness of deductions according to their increasing recourse to so called “virtual information”, namely information that is temporarily assumed but not contained in the premises. In this paper I will focus on the status of virtual information in its connection with the Kantian philosophical spirit. Exploiting the standard Kantian difference between theoretical and practical reason, my aim is to show that the access to virtual information is due to what Kant calls practical reason rather then to the theoretical one, even though the effects of its deployment are purely theoretical, i.e. don’t lead an agent to any moral action but just to acquiring new information.
Segmentation Free Word Spotting for Handwritten Documents Using Bag of Visual Words Based on Co-HOG Descriptor

In this article, the authors propose a segmentation-free word spotting in handwritten document images using a Bag of Visual Words (BoVW) framework based on the co-occurrence histogram of oriented gradient (Co-HOG) descriptor. Initially, the handwritten document is represented using visual word vectors which are obtained based on the frequency of occurrence of Co-HOG descriptor within local patches of the document. The visual word representation vector does not consider their spatial location and spatial information helps to determine a location exclusively with visual information when the different location can be perceived as the same. Hence, to add spatial distribution information of visual words into the unstructured BoVW framework, the authors adopted spatial pyramid matching (SPM) technique. The performance of the proposed method evaluated using popular datasets and it is confirmed that the authors' method outperforms existing segmentation free word spotting techniques.
Support Vector Regression for Digital Video Processing

In this work we present a new offline application of regression analysis. The idea is to increase video quality with the use of support vector machines. This allows us to reduce noise and increase the resolution and the number of frames per second.
City Bus Monitoring Supported by Computer Vision and Machine Learning Algorithms

In this paper there are proposed methods and algorithms supporting city traffic controllers in effective perception and analysis of the visual information from the public transport monitoring system implemented in the City of Warsaw. To achieve this goal, public transport vehicles must be recognised and tracked in camera view. In this work, we describe a structure and give preliminary results for the detection and tracking system proposed. The algorithms discussed in this paper uses background subtraction to extract moving vehicles from the scene and the classification system to reject objects that are not city buses. Furthermore, a custom tracking module is utilized to enable labeling of city buses instances. During the test performed in the City of Warsaw the system was able to successfully detect 89% bus instances giving less than 15% erroneous detections.
SPECIFIC ROLE OF PROFESSIONAL CONTESTS IN THE VOCATIONAL EDUCATION SYSTEM

This article reveals the role of professional skills competitions in the system of secondary vocational education. The importance of professional competitions is shown as a tool for raising the competence and competitiveness of graduates.
Combining Transformation and Classification for Recognizing Textual Entailment

This paper introduces an approach combining transformation and classification methods for recognizing textual entailment. In transformation model, directional and undirected inference relations are recognized, and text fragments having such relations in text are replaced by the counterparts in hypothesis. In classification model, a hybrid kernel-based approach is introduced, and three kinds of features are employed for classifying entailment. Experimental results show that the combination approach achieves a better performance in comparison with the single classification system.
How Music Technology Will Impact Personal Creative Expression

This chapter is a position paper that explores issues relating to the design of new digital technologies that could enable even nonmusicians to create music they find meaningful and through which they can express their unique musical personality. We first propose criteria for evaluating the musical effectiveness of new tools, and then present a simple cognitive model to help explain key issues related to how humans are able to create and experience music. On this basis, we derive a set of guiding principles towards designing a new generation of tools that support creation. A major theoretical and practical challenge we raise is the need to bridge between mind and tool – that is, between the creator’s intents, which represent subjective musical experiences, and the computational formalisms required by digital tools in order to realize them. We also discuss the need for deep personalization on the levels of concepts, tools, and workflow. We finally propose components for a common framework that will enable the design of a new generation of tools that can explore many different approaches for expanding the bounds of personal creative expression
Making Sense of Field Research: A Practical Guide for Information Designers by Sheila Pontis

Information design seems to be growing in popularity with publishers. Making Sense of Field Research: A Practical Guide for Information Designers is the latest publication to enter the information ...