Reviewerly/exHarmony huggingface dataset:
Forward Engineering Completeness for Software by Using Requirements Validation Framework (S)

In software development environment, software companies usually ignore the user requirements validation process in requirement gathering phase, which results in large number of modifications being required in the software maintenance phase to fulfill the customer requirements.Identification of accurate requirements from user stories and determining the effectiveness of work deliverable of software industry has always been a challenging task.In this paper, a new measurement approach for forward engineering completeness for software was introduced by using requirements validation framework.The forward engineering completeness for software was measured in two steps.In the first step, software component structure was developed in order to find the functional and nonfunctional requirements rejected by the customers in the requirement validation framework.In the second step, completeness of software from component-based development was determined in which the following parameters, such as functional, non-functional completeness attributes, were considered in the measurement process, and the unadopted attributes of the reuse code were also considered.Quality level for the attributes were assigned based upon the valuation of interior quality of the source code.Therefore, it resulted in the reduction of development time required for the software and the cost required for the software development was also reduced.A case study was incorporated in this research to explain the measurement process of forward engineering completeness.If the forward engineering code is satisfying the quality standards, then the code is in the completeness form.The attributes of code that negates to be used were considered as unadopted attributes.
On the Effects of Developers' Intuition on Measuring Similarity Between UML Models

Software design models play a key role in many activities of information systems engineering, such as documenting software artefacts, communicating project decisions, and code generation. In this scenario, the techniques for comparison of software design models are used for several purposes, such as, for detecting clones, and model evolution. In the last decades, academia proposed different techniques for comparing software models. Even using these different techniques for model comparison, this process is still an activity of a subjective nature, because during this process, different developers can interpret the similarity differently. Thus, the problem is that it is still unknown if developers has the same intuition in order to resolve comparison of software design models. For this, the main objective of this work is to explore the effects of their experience level, i.e., experienced and inexperienced developers, relative to their effort and correctness for resolving activities of comparing software design models. Therefore, a controlled experiment was conducted to evaluate the developer's experience level regarding on similarities of UML Models. The results show that the developer's experience does not affect the understanding of similarities activities.
(En)COUNTERing SUSHI: How Fishy Are Our Stats

In this presentation, I look at the way in COUNTER Statistics help us understand our collections better, and how they may be deficient. I will discuss how to set up SUSHI retrieval in Alma and how to create more meaningful COUNTER reports in Analytics. I will talk about the difficulty of applying some of the statistics we collect to evaluating our collections. I am interested in feedback from the audience in terms of their own experiences. I will also offer suggestions as to how we can use stats from our OpenURL logs to guide our interpretations.
Estimating Parameters for Deblurring in Two-Dimensional Linear Motion

The technology in the field of multimedia image processing improves every day, but there are still some problems that deserve to be further explored and improved. People like to take and preserve the impressive scenery as an unforgettable memory. However, if the object is moving or the photographer is shaking, the captured image is easily blurred, and this blur is called motion blur. However, deblurring an image without the information of speed and direction of moving objects is still a well-known ill-posed problem. In this paper, we proposed a system to deblur image that can estimate important parameter advance to reduce the complexity of deblurring process. The data of sensor of moving object is collected. The BPN neural network is used to train to classify the speed and direction of the object from the sensor data. After that, we can estimate the speed and direction of objects without other algorithms. With such important parameters, deblurring processing will more efficient.
Implementation of Vehicle Traffic Statistics Algorithm Based on ViBe Image Processing

Abstract This project uses all kinds of main roads and intersections of UAV aerial photography cities to obtain information on traffic flow and real-time traffic conditions of various main roads, and feeds back information to unmanned airborne image processing devices through big data and clouds. The extracted image is processed and analyzed, and then a traffic flow safety prediction and evaluation model database based on road vehicle conflict is established.Through the analysis of the data, relevant departments can better plan the layout of traffic facilities on the road, so that the traffic facilities can be fully utilized, relieve traffic pressure, and reduce the incidence of traffic accidents.
LibGuides: DIY Success: Library 101: Technology Checkout

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Fall Detection Using Kinematic Features from a Wrist-Worn Inertial Sensor

The threats and injuries caused by falling are significant and pose risks to life among the elderly population. Detecting falls and providing prompt care helps in reducing the risks associated with falling. Technology solutions are being employed for the detection of falls and to enable provisions to access immediate assistance. These solutions require the user to wear specialized equipment continuously that are inconvenient for prolonged usage, especially among the elderly population. To address this concern, numerous research works have focused on developing solutions that are minimally intrusive to the user. Insufficiency of clinical fall dataset limits the investigation of diverse approaches to improve the accuracy using devices that offer user comfort. Algorithms developed through research fall short of extensive datasets to offer reliable detection of falls through a wrist-worn device. However, such algorithms cannot generalize falls using data collected from a specific population. This research discusses an approach for the generalization of falls by the inclusion of kinematic parameters associated with falls. A hybrid detection model comprising of threshold parameters and a machine learning based classifier is proposed. The detection model ignored all the activities of daily living. An accuracy of 92% was observed in the detection of actual falls among non-falling actions with kinematics similar to those of falling action.
A Case Study of a Software Development Process Model for SIS-ASTROS

Context: technological innovation projects, developed in universities in partnership with companies and/or the government need processes that can handle the characteristics of the institutions involved.The academic environment is often used to dynamic methods, but contracts require plan-driven processes.Goal: the goal of this research is to understand the needs of the parties involved (university and government/enterprise) and provide an adapted software process to satisfy those necessities.Method: a case study considering a project between the Federal University of Santa Maria (UFSM) and the Brazilian Army (BA) for the development of an Integrated Simulation System was conducted.Initially, problems in the development were detected and a process was defined.It was then evaluated and improved over the iterations, through team meetings.Results: the experience acquired in the project was consolidated as lessons that could be used to assist the process definition of projects with similar characteristics.Conclusion: innovation projects involving the collaboration of universities, government and/or companies are successful if an adequate process is established to treat specificities of the academy, not only in relation to characteristics of the work but also the team.
Strategy Synthesis for Multi-Agent Systems with Imperfect Information and Imperfect Recall

This report aims to study and compare currently available tools for uniform strategy synthesis in multi-player versus environment games of imperfect information and imperfect recall. These games ar ...
Semi-automatic analysis of Navyanya¯ya compounds

At mid-december 2013, after learning of Arjuna’s diﬃculties, G´erard set to design an experimental version of the Heritage Reader, tuned for recognition of Navyany¯aya compounds. Here are the salient features of this extension: • Databanks are added for taddhita suﬃxes inﬂected forms • Suﬃxes involved are -taa (Fem), -tva (Neu), and -vat/-vat¯i (all 3 genders) • Segmenter transitions added to accommodate taddhita productivity • Word mode for single pada in order to curb overgeneration • Graph interface used to display the huge solution space • Technical vocabulary of Navyany¯aya acquired in lexicon Experiments unveiled a few weak spots to ﬁx, such as avagraha treatment, and required lexicon acquisition of a small number of vocabulary items.
Selection of Vanishing moments of Symlet and Coiflet Wavelets on CT Images

The main aim of the work is to develop image compression algorithms with high quality and compression ratio. The objective also includes finding out an best algorithm for medical image compression techniques. The objective is also alert towards the choice of the developed image compression algorithm, which do not modify the characterization behavior of the image. In this paper, image compression algorithm based on discrete symlet and coiflet wavelet transform is implemented for decomposing the image. The selection of different vanishing moments are discussed based on the values of peak signal to noise ratio (PSNR), compression ratio (CR), means square error (MSE) and bits per pixel (BPP). The optimum moments for compression are also chosen based on the results.
LEV VYGOTSKY’S SOCIOCULTURAL APPROACH AND AUTHENTIC MATERIALS IN EFL CLASSROOMS

There are barriers in teaching and learning foreign language but there are always ways in overcoming the problems. One of the most challenging tasks faced by (English as a Foreign Language)  EFL teachers is how to elicit the students’ interest so that they will be more motivated to learn and successfully develop their English proficiency. Educational psychology theories developed by Russian psychologist, Lev Vygotsky have been recognized as having great contribution to language education and teacher training. Education experts also insisted that in order to motivate the students, English which is presented in EFL classrooms should be authentic in order to immerse students into the real usage of English communication. This article discusses Vygotsky’s sociocultural approach and the authentic materials used in the classroom to develop students’ English proficiency.
Deaths Due to Electrocution-A Retrospective Study

Retrospective analysis of deaths because of electric shock from the medico-legal death records our college i.e. J.N.M.C., A.M.U., Aligarh, Uttar Pradesh, India. Most of the deaths were of men having the age in between 11–50 years. All deaths were inadvertent and most part of them were during the time of monsoon as compared to the deaths in western countries where, baths, warmers or hair dryers were the source of electric shocks. The death rate reported because of electric shock was 0.34 per lakhs (100000) of the population in the present study as against the figures of 0.94 and 0.14 from Bulgaria and Canada respectively. A large portion of the deaths were either prompt or quick. It implies that individuals living at home did not have basic knowledge of dangers of electric shock. In this way mindfulness about utilization of good quality electric machines is the need of great importance.
Applications of Linear Defeasible Logic: combining resource consumption and exceptions to energy management and business processes

Linear Logic and Defeasible Logic have been adopted to formalise different features of knowledge representation: consumption of resources, and non monotonic reasoning in particular to represent exceptions.Recently, a framework to combine sub-structural features, corresponding to the consumption of resources, with defeasibility aspects to handle potentially conflicting information, has been discussed in literature, by some of the authors.Two applications emerged that are very relevant: energy management and business process management.We illustrate a set of guide lines to determine how to apply linear defeasible logic to those contexts.
MODELHealth: An Innovative Software Platform for Machine Learning in Healthcare Leveraging Indoor Localization Services

MODELHealth is an innovative software platform aiming at developing an end-to-end solution for the process of pumping, enriching and anonymizing heterogeneous health data, and implementing Machine Learning (ML) methods for healthcare and research purposes that also leverage indoor localization services. The ultimate goal of the platform is to provide any authorized Information System with powerful algorithms and tools through Application Program Interfaces in order to facilitate and enhance clinical decision-making techniques at a broad range of healthcare services.
Neural Architecture Search by Estimation of Network Structure Distributions

The influence of deep learning is continuously expanding across different domains, and its new applications are ubiquitous. The question of neural network design thus increases in importance, as traditional empirical approaches are reaching their limits. Manual design of network architectures from scratch relies heavily on trial and error, while using existing pretrained models can introduce redundancies or vulnerabilities. Automated neural architecture design is able to overcome these problems, but the most successful algorithms operate on significantly constrained design spaces, assuming the target network to consist of identical repeating blocks. While such approach allows for faster search, it does so at the cost of expressivity. We instead propose an alternative probabilistic representation of a whole neural network structure under the assumption of independence between layer types. Our matrix of probabilities is equivalent to the population of models, but allows for discovery of structural irregularities, while being simple to interpret and analyze. We construct an architecture search algorithm, inspired by the estimation of distribution algorithms, to take advantage of this representation. The probability matrix is tuned towards generating high-performance models by repeatedly sampling the architectures and evaluating the corresponding networks, while gradually increasing the model depth. Our algorithm is shown to discover non-regular models which cannot be expressed via blocks, but are competitive both in accuracy and computational cost, while not utilizing complex dataflows or advanced training techniques, as well as remaining conceptually simple and highly extensible.
If Blockchain is the answer what is the question

The purpose of the paper is to identify the issues and challenges to employing blockchain technology for different operations context. The paper analyses five cases to identify drivers and barriers to adopting the technology. Our findings suggest that interoperability and platform dependency can limit the wide-scale deployment of the technology. Additionally, trust in rule-based software automation and consensus among supply chain actors can inhibit effective adoption. In summary, the paper finds that care should be taken when applying blockchain, since there are many situations where its implementation is not feasible or not beneficial.
Modelling Dynamics of Indoor Environments with 3D Scene Graphs

Recent research on visual scene understanding attempts to build a scene graph. A scene graph is a rich semantic representation of the objects and their pair-wise relationships in an environment. Most of the existing work has studied how to generate a 2D scene graph that represents the objects and their relationships detected in a single image. A 2D scene graph is usually a partial description of only a single state of the environment. Therefore, most of the existing approaches do not address the dynamics and uncertainty of 3D real-world environments. This paper supposes both partially observable and dynamic 3D indoor environments that can be changed by an agent’s actions and tries to model the environmental changes with 3D scene graphs. The proposed system attempts to estimate the correct state of a dynamic environment by combining state recognition based on image analysis with state prediction based on action models. As a result of conducting lots of experiments with a large dataset collected from a virtual 3D indoor environment, AI2-THOR, we demonstrated the high performance of the proposed system.
The Cognitive Mechanism of the Number Systems

Number is everything and the number systems exist above the number itself. The paper studies the cognitive mechanism of number system from the aspects of embodiment, interactive feature and image schema models. As numbers, number system is also a cognitive tool with unique connotation and abundant extension.
Quadratic Segmentation Algorithm Based on Image Enhancement

Due to the indefinite position of the characters in the invoice and the difference of the color shades, which greatly increases the difficulty of intelligent identification and thus it is difficult to meet practical applications. In order to solve this problem, this paper proposes a quadratic segmentation algorithm based on image enhancement. Specifically, we firstly enhance the color of the image based on gamma transformation and then separate the machine-printing character from the blank invoice based on the color analysis of the machine-printing character. Then according to the open operation in the image processing field and the boundingRect algorithm, the pixel information of the machine-playing character is obtained, which is convenient for getting the character information. The algorithm can achieve effective extraction of machine-playing characters and also reduce the difficulty of invoice identification and improving the accuracy of invoice identification. Simulation results are given to confirm the proposed algorithm.
The Use of Artificial Neural Networks Optimized with Fire Fly Algorithm in Cancer Diagnosis

Today, the amount of biological data types obtained are increasing every day. Among these data types are micro arrays that play an important role in cancer diagnosis. The data analysis that are carried out through traditional approaches have proven unsuccessful in delivering efficient results on data types where data complexity is high and where sampling is low. For this reason, using a hybrid algorithm by merging the effective features of two distinct algorithms will yield effective results. In this study, a classification process was performed firstly by dimension reduction on micro array data that were obtained from the tissues from patients with a tumor in their central nervous system and then by using an artificial neural network algorithm that was optimized through Fire Fly Algorithm (FF), a hybrid approach. The data obtained were compared to K Nearest Neighbors (KNN), Support Vector Machine (SVM) and Artificial Neural Networks (ANN) classification algorithms, which are frequently used in the literature. Also, the results were compared to the findings that were obtained from artificial neural networks, which are reinforced by Genetic Algorithm (GA), another hybrid approach. Then the results were shared. The performance results obtained show that hybrid approaches present a highly precise and more efficient classification process but they show a slower performance than basic classification algorithms.
A Longitudinal Study on Vocational Maturity Development using Growth Mixture Model: From Late Adolescence to Early Adulthood

본 연구에서는 고등학교 2학년부터 고등학교 졸업 후 4년이 지난 시점까지 진로성숙도의 변화를 성장혼합모형을 적용한 잠재계층분석을 통해 알아보고자 하였다.「한국교육종단연구2005」의 진로성숙도 자료를 분석한 결과, 후기 청소년기부터 초기 성인기까지의 진로성숙도 변화에 있어서 초기값이 가장 높고 작은 변화율로 성장하는 집단, 초기값은 중간이고 시간에 따라 변화하지 않는 집단, 초기값이 가장 낮고 큰 변화율로 성장하는 집단으로 세 개의 잠재계층이 존재하는 것을 확인할 수 있었다. 또한 잠재계층의 특성을 알아보기 위하여 가정배경, 개인 특성 및 경험, 진로계획, 고교 특성 및 경험, 진로활동 영역의 다양한 배경변수의 계층 간 차이를 분석하였다. 개인특성 및 경험 변수 중 성별과 자아개념, 교사관계 변수가, 진로계획 영역에서는 교육포부, 고교특성 및 경험에서는 동아리 활동, 진로활동과 관련해서는 고등학교 재학 중 진로와 직업 교과를 이수했는지 여부가 응답자의 잠재계층 분류에 영향을 미치는 것으로 나타났다.The purpose of this study was to investigate vocational maturity development from second grade of high school to four years after high school graduation through latent class analysis by applying growth mixture model. Results from analyzing 5th-, 7th-, 9th-wave data collected by Korean Educational Longitudinal Study(2005) suggested that three latent classes were found: class with high initial status and small positive slope, class with medium initial status and insignificant slope, and class with low initial status and large positive slope. Additionally, in order to explore characteristics of latent classes, differences in variables related to family backgrounds, personal characteristics and experiences, career plan, high school characteristics and experiences, and career-related activities were investigated. Variables including gender, self-concept, and relation with teachers, aspiration for education, club involvement while attending middle and high schools, whether the subject took career and vocation class while attending high school were found to affect classification of subjects into latent classes.
Simulation of outcomes of educational policies and programmes

Based on a simulation model, this chapter analyses the effect of limiting structures within the educational system, if action is taken through educational policies aimed at improving the performance of the educational system. The focus is on the scope and possibilities of educational policies. First, the concepts of model and simulation are briefly introduced. Then the simulation model is developed from theoretical considerations and from the inequality of educational opportunity (IEO) model by Boudon, which constitutes a hierarchical system of graduation levels, possibly organized in vertical columns. The actions taken by educational policies are differentiated into those which are concerned with the social origin of the students (i.e. promotion of a specific social class), and those which treat the (however developed) talents of the students (i.e. promotion of gifted individuals). The performance of the educational system is evaluated in three ways: according to its ability to increase the general number of qualifications attained, its ability to decrease the inequality between students of different social origin, and its ability to promote excellence, that is, the proportion of talented students who graduate. The simulation model can thus answer questions such as whether the promotion of social classes rather than the promotion of talents increases excellence, or whether an early branching in the structure of the educational system decreases the output of qualifications, among others. Some results are unique, but others indicate a range of actions available to educational policy.
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