Reviewerly/exHarmony huggingface dataset:
Technologies of Organization of Students' Independent Work in Studying Pedagogical Disciplines at Classic Universities

The chapter deals with the problem of preparing a graduate of a classical university, possessing both professional and special competencies, which include expertise, knowledge, and skills obtained in the study of pedagogical disciplines. The effectiveness of student preparation largely depends on the results of their independent work, which is given a significant amount of academic time. At the same time, the teacher's task is to accompany the student's independent work and to draw up a system of non-standard tasks that develop communicative and creative skills, skills of working with scientific texts and project activities. The aim of the chapter is to reveal the possibilities of technologies of independent work organization that motivate students to dynamic cognitive activity while studying pedagogical disciplines (on the example of studying the course “Pedagogy” at Saint Petersburg State University).
Applying patterns in embedded systems design for managing quality attributes and their trade-offs

Embedded systems comprise one of the most important types of software-intensive systems, as they are pervasive and used in daily life more than any other type, e.g., in cars or in electrical appliances. When these systems operate under hard constraints, the violation of which can lead to catastrophic events, the system is classified as a critical embedded system (CES). The quality attributes related to these hard constraints are named critical quality attributes (CQAs). For example, the performance of the software for cruise-control or self-driving in a car are critical as they can potentially relate to harming human lives. Despite the growing body of knowledge on engineering CESs, there is still a lack of approaches that can support its design, while managing CQAs and their trade-offs with noncritical ones (e.g., maintainability and reusability). To address this gap, the state-of-research and practice on designing CES and managing quality trade-offs were explored, approaches to improve its design identified, and the merit of these approaches empirically investigated. When designing software, one common approach is to organize its components according to well-known structures, named design patterns. However, these patterns may be avoided in some classes of systems such as CES, as they are sometimes associated with the detriment of CQAs. In short, the findings reported in the thesis suggest that, when applicable, design patterns can promote CQAs while supporting the management of trade-offs. The thesis also reports on a phenomena, namely pattern grime, and factors that can influence the extent of the observed benefits.
Applying Multiphase Sampling to Selecting Testlets With Constraints on Item Blocks

Abstract In assembling testlets (i.e., test forms) with a pool of new and used item blocks, test security is one of the main issues of concern. Strict constraints are often imposed on repeated usage of the same item blocks. Nevertheless, for an assessment administering multiple testlets, a goal is to select as large a sample of testlets as possible. In this study, the algorithm of multiphase sampling was applied to selecting and augmenting the sample of testlets to be administered. Several topics related to the algorithm are discussed, such as the termination of the algorithm, the dynamics of sample size, and the effectiveness of the algorithm. A real database of testlets was used in the study.
Secure Query Processing over Encrypted Data Using a Distributed Index Structure for Outsourcing Sensitive Data

As the outsourcing of sensitive data has been spotlighted, data encryption schemes are required to protect the data. Accordingly, it is necessary to develop not only a distributed index structure to efficiently manage the large amount of encrypted data, but also a query processing scheme over the encrypted data. Meanwhile, the existing query processing schemes over the encrypted data cannot support top-k query processing algorithm which aim to quickly retrieve k number of the highest ranking tuples. To solve the problems, in this paper, we propose a secure query processing scheme over the encrypted data using a distributed index structure. The proposed distributed index structure guarantees data privacy preservation and performance improvement for the various types of queries. Finally, we show from our performance analysis that our proposed index structure and secure query processing scheme are suitable for protecting the data privacy of the sensitive data.
Designing and Developing Multi-agent Systems for Management of Common Renewable Resources

In this paper, we present a new approach of design and developing multi agent systems. Our approach is based on Model Driven Architecture (MDA), which aims to establish the link between existing agent architectures and the models or meta-models of multi-agent systems that we build from AUML. We have designed a generic and scalable class diagram to develop complex multi-agent systems. The source code of the models is generated by an open source tool called AndroMDA [Elallaoui et al. in Automated model briven testing using AndroMDA and UML2 testing profile in scrum process. Procedia Comput. Sci. 83, 221–228, 2016, 13]. The model and source code will facilitate the design and development of applications to implement and simulate multi-agent models for Management of Common Renewable Resources. This approach allows reuse of the model and generated source code to develop new applications.
Assessing the Performance of a Biometric Mobile Application for Workdays Registration

The professors in the State Technical University of Quevedo - Ecuador (UTEQ) must register the workdays (workday entries and workday exits) in the attendance management software provided by the Human Resources department through static biometric devices. In some cases, the biometric devices are not close to their offices or classrooms, so they forget to register their workdays, wrong workdays registrations. With the aim of improving this registration process we have developed bioFACE, a novel mobile application for biometric authentication by face recognition, which allows to convert the user smartphones in biometric devices, connected to the attendance management software, avoiding large crowds in rush hours moments, especially. With the aim to assess its performance, we have carried out some experiments measuring the features accuracy and workdays registration time. Despite the limited CPU and memory capabilities of today’s mobile phones, the obtained results are very promising, shows a high accuracy facial identification and a faster and easy alternative to the workday registration.
Improved versions of the GLOBAL optimization algorithm and the globalJ modularized toolbox

Solving global optimization problems plays a key role in most branches of natural sciences whilst dealing with everyday problems. With the help of optimization algorithms and the exponentially fast growth of the capabilities of the underlying hardware, the scale of feasible optimization tasks is reaching new levels. We help this goal with revisiting GLOBAL, a stochastic optimization method aiming to solve non-linear constrained optimization problems by the penalty function approach. It is a versatile tool for a broad range of problems, proven to be competitive in multiple comparisons. With the similarly rapid evolution of programming habits and tools, time naturally passed by GLOBAL’s latest implementation so that it became a drawback. Now, we present GlobalJ, a fully modularized Java framework refurbishing and extending the potential of this algorithm.
Personas Go Agile

To manage software development, many companies have over the past 20 years been applying agile processes. The waterfall model contains an iterative process, but makes it difficult to change during development, as it defines the final product from the beginning.
Discriminative Feature Selection by Optimal Manifold Search for Neoplastic Image Recognition

An endocytoscope provides ultramagnified observation that enables physicians to achieve minimally invasive and real-time diagnosis in colonoscopy. However, great pathological knowledge and clinical experiences are required for this diagnosis. The computer-aided diagnosis (CAD) system is required that decreases the chances of overlooking neoplastic polyps in endocytoscopy. Towards the construction of a CAD system, we have developed texture-feature-based classification between neoplastic and non-neoplastic images of polyps. We propose a feature-selection method that selects discriminative features from texture features for such two-category classification by searching for an optimal manifold. With an optimal manifold, where selected features are distributed, the distance between two linear subspaces is maximised. We experimentally evaluated the proposed method by comparing the classification accuracy before and after the feature selection for texture features and deep-learning features. Furthermore, we clarified the characteristics of an optimal manifold by exploring the relation between the classification accuracy and the output probability of a support vector machine (SVM). The classification with our feature-selection method achieved 84.7% accuracy, which is 7.2% higher than the direct application of Haralick features and SVM.
Facilitation of the ongoing development of fingerprint subject matter expertise

Although considerable expertise exists in the fingerprint domain, there are opportunities for improvements to be made regarding the development and maintenance of expertise, and in the reliable application of scientific methods and principles. Here, we propose a framework for the facilitation of ongoing development of fingerprint subject matter expertise in order to improve reliability and minimize errors. We describe two essential elements to support ongoing development: scientific leadership and continued education. Furthermore, we recommend that examiners should be exposed to opportunities for practice and feedback on the full myriad of tasks that they engage in (e.g. comparisons, report writing, court presentations, and evaluation of the latest scientific developments). These elements and the rationale behind them are discussed here in detail.
A Methodology to Design Online Testable Reversible Circuits

The bottleneck of high power consumption in VLSI circuits has attracted research community to explore a new low power computing era. Reversible computing is a pioneering step towards that direction. This paper proposes a methodology to design online testable (OT) reversible circuits which can be applied to any parity preserving logic circuit. An ancilla input (L) having constant value zero is augmented with the parity preserving circuit in order to make it online testable. To demonstrate the proposed method, an online testable Fredkin gate (TFR) and an online testable Feynman double gate (TF2G) are implemented. Cascading TFR and TF2G, a master-slave (MS) D flip-flop and a dual edge triggered (DET) D flip-flop are implemented which signify the efficacy of the proposed methodology. Comparison results report that the proposed flip-flops outperform the previous designs in terms of quantum cost, number of gates used, number of constant inputs and number of garbage outputs.
Single-image super-resolution reconstruction via generative adversarial network

Single-image super-resolution (SISR) reconstruction is important for image processing, and lots of algorithms based on deep convolutional neural network (CNN) have been proposed in recent years. Although these algorithms have better accuracy and recovery results than traditional methods without CNN, they ignore finer texture details when super-resolving at a large upscaling factor. To solve this problem, in this paper we propose an algorithm based on generative adversarial network for single-image super-resolution restoration at 4x upscaling factors. We decode a restored high-resolution image by the generative network and make the generator output results finer, more realistic texture details by the adversarial network. We performed experiments on the DIV2K dataset and proved that our method has better performance in single image super-resolution reconstruction. The image quality of this reconstruction method is improved at the peak signal-tonoise ratio and structural similarity index and the results have a good visual effect.
Vein Detection System Using Quad-Core ARM Processor and Near-Infrared Light

Venipuncture is the process of puncturing the vein to withdraw blood or to carry out an intravenous injection. It requires high level of expertise to achieve high rates of accuracy. In traditional ways, success depends heavily on the experience of the practitioner. Consequently, venipuncture has been reported as one of the leading causes of injury to patients. The estimation of failure ranges from 20 to 33% overall. Specifically in populations, which include children, obese and old people it ranges from 47 to 70%. To improve first stick accuracy, we propose a system which will help identify the suitable subcutaneous veins. With the help of near-infrared radiation (NIR), images are been captured. The captured images are processed to identify the veins. These identified veins can be further used by practitioner to carry out further analysis. These processed images of veins can be further projected directly on the limb.
An Optimized Regularization Method to Enhance Low-Resource MT

Overfitting caused by scarce parallel corpus is a serious problem in low-resource machine translation task, resulting in the weak generalization ability of translation models. Dropout and Dropconnect can address this issue by reducing training neurons or weights randomly with increasing the generalization ability. In this paper, we optimize Dropconnect by adopting Gaussian approximation in the Bernoulli distribution in low-resource machine translation tasks, and make an integration to alleviate the uneven sampling effect in Dropout and Dropconnect, especially the inadequate training problem. It is an effective approach to approximate mask calculations to linear operations while being fully trained. An interesting finding is that the adhesive language is more sensitive to our regular methods. Our approach outperforms the Dropout and Dropconnect for low-resource translation tasks.
A sufficient condition for <i>r</i> - graphic sequences to be potentially

An r-graph is a multigraph in which two vertices are joined by at most r edges. An r-complete graph on n vertices, denoted by , is an r-graph on n vertices in which each pair of vertices is joined by exactly r-edges. The r-split graph on l+m vertices is denoted by . A non increasing sequence p = (d, d, … , d) of non negative integers is said to be r-graphic if it is realizable by an r-graph on n vertices. An r-graphic sequence π is said to be potentially -graphic if it has a realization containing as a subgraph. In this paper, we obtain conditions for r-graphic sequences to be potentially and give a Rao-type characterizations for π to be potentially -graphic.
FinSec 3.0: Theory and Practices in Financial Enterprise

The rapid growth of information and communications technology (ICT) and implementation of financial technology together make it more convenient for enterprises to do business. Meanwhile, the innovations change the ecosystem of financial industry but also present potential threats. Cloud computing, mobile working application, and smart city system are being deployed to enhance life and working conditions for citizens. Nevertheless, cyber-attacks are increasing while security awareness and protection measures need to be improved. In this paper, security theory and practices of the use of cloud, mobile apps, and smart city system are to discuss. Connected with practice of practitioners in the industry, ideas of security architectures are presented and featured as key points of the financial security 3.0 framework.
Convexity and Connectivity Principles Applied for Left Ventricle Segmentation and Quantification

We propose an unsupervised method for MRI image segmentation, and global and regional shape quantification, based on pixel labeling using image analysis, connectivity constraints and near convex region requirements for the LV cavity and the epicardium. The proposed method is developed in the framework of the MICCAI Left Ventricle Full Quantification Challenge. At first the LV cavity is approximately localized based on the strong intensity contrast in the myocardium region between the two ventricles (left and right). The requirement of a near convex connected component is then applied. The image intensity statistical parameters are extracted for three classes: LV cavity, myocardium and chest space. Even if the whole background is completely inhomogeneous, the application of topological, connectivity and shape constraints permits to extract in two steps the LV cavity and the myocardium. For the later two approaches are proposed: regularization using B-spline smoothing and adaptive region growing with boundary smoothing using Fourier coefficients. On the segmented images are measured the significant clinical global and regional shape LV indices. We consider that we have obtained good results on indices related to the endocardium for both Training and Test datasets. There is place for improvements concerning the myocardium global and regional shape indices.
Reference analytics as an unexpected collection development tool

RefAnalytics, part of Springshare's LibAnswers, is a tool intended for tracking various statistical datasets, but Chicago State University’s Library decided to use it in part as a tool for collection development, in order to reorder missing items, update older books on various topics, and order new books on issues/topics that the library did not yet purchase. The article presents the various types of data collected and the way the data is used for collection development purposes.
Impact Assessment of Policies and Practices for Agile Software Process Improvement

This chapter proposes the application of computer simulation models for the evaluation of software quality improvement through the implementation of policies and practices of software development processes and performing the measurement of the evolution of the quality through the Six Sigma methodology. In order to provide decision makers of process improvement, a model of dynamic simulation of systems was proposed. Model validation was performed using data collected from various projects developed in an information technology company headquartered in Belo Horizonte-MG. The models mentioned in this chapter are able to provide the decision makers a tooling of process improvement able to perform predictions, predictions, analysis of “what if” scenarios in the model. As a result of this work, a dynamic simulation model of systems capable of performing the evaluation of software quality in Sigma level is found. Also, there are comparisons and analyses of actual data of software development projects with the simulation data of the models presented. The earnings with the new version of the case exceed by more than 50% the Sigma level, the quality of software developed, and reduction of more than 55% of the time of development of the project.
Mathematical Analysis about Signal Propagation Characteristics of Neuronal Networks

It is important to understand the relationship between spatiotemporal patterns of neuronal populations and information processing of the brain for medical and engineering purposes. Though, there are some methods to record neuronal population, only the information about firing rate can be obtained from those methods basically. Therefore, some methods are suggested to estimate the internal state of the neuronal population from the information about firing. In this research, we investigated the relationship between firing rates and responses to external electrical stimuli by using neuronal population models. We developed a method to manipulate neuronal parameters to make neuronal populations exhibit different dynamical properties while keeping firing rates intact. As a result, we found that there are some cases even though they share the same firing rate, the internal states are different. This result suggests that there is some information that cannot be obtained by using only the information about firing rates.
“Is This an Example Image?” – Predicting the Relative Abstractness Level of Image and Text

Successful multimodal search and retrieval requires the automatic understanding of semantic cross-modal relations, which, however, is still an open research problem. Previous work has suggested the metrics cross-modal mutual information and semantic correlation to model and predict cross-modal semantic relations of image and text. In this paper, we present an approach to predict the (cross-modal) relative abstractness level of a given image-text pair, that is whether the image is an abstraction of the text or vice versa. For this purpose, we introduce a new metric that captures this specific relationship between image and text at the Abstractness Level (ABS). We present a deep learning approach to predict this metric, which relies on an autoencoder architecture that allows us to significantly reduce the required amount of labeled training data. A comprehensive set of publicly available scientific documents has been gathered. Experimental results on a challenging test set demonstrate the feasibility of the approach.
Back to the Future: User Research as a Critical Tool for Novel Design

Innovation is about creating new products, and new solutions, sometimes in ways people can’t even anticipate. Why is it then important of to use user-centered design processes? What is the value and ROI of User Experience (UX)-based practices for entrepreneurs? And how can those be achieved?This chapter will introduce User Experience principles, their value add for innovation, and the best practices for achieving them. Children-centric design examples will be incorporated.
A Case Study in Collaborative Learning via Participatory Music Interactive Systems: Interactive Tango Milonga

This chapter investigates design strategies for developing digital musical instruments (DMIs) for participatory music. In particular, we present strategies to enhance collaborative musical skills such as rhythmic entrainment and listening/responding to other participants: building skills of this kind has the capacity to motivate long-term usage and adoption of the DMI by a broad range of communities. The design strategies described here address the problems of developing DMIs for long-term use, both in collaborative, mixed skill level contexts, and in established musical and dance traditions. Interactive Tango Milonga—presented here as a case study—is an interactive dance system allowing social tango dancers to drive musical outcomes in real-time via their dance movement. Motion sensors are attached to dancers, and the signals from these sensors are sent to a computer, where an algorithm transforms them into tango music. The impact of the interactive tango system on the musical listening and response of tango dancer participants is analyzed and discussed.