Reviewerly/exHarmony huggingface dataset:
Effects of Explicit Feature Traceability on Program Comprehension.

Developers spend a substantial amount of their time with program comprehension. To improve their comprehension and refresh their memory, developers need to communicate with other developers, read the documentation, and analyze the source code. Many studies show that developers focus primarily on the source code and that small improvements can have a strong impact. As such, it is crucial to bring the code itself into a more comprehensible form. A particular technique for this purpose are explicit feature traces to easily identify a program’s functionalities. To improve our empirical understanding about the effects of feature traces, we report an online experiment with 49 professional software developers. We studied the impact of explicit feature traces, namely annotations and decomposition, on program comprehension and compared them to the same code without traces. Besides this experiment, we also asked our participants about their opinions in order to combine quantitative and qualitative data. Our results indicate that, as opposed to purely object-oriented code: (1) annotations can have positive effects on program comprehension; (2) decomposition can have a negative impact on bug localization; and (3) our participants perceive both techniques as beneficial. Moreover, none of the three code versions yields significant improvements on task completion time. Overall, our results indicate that lightweight traceability, such as using annotations, provides immediate benefits to developers during software development and maintenance without extensive training or tooling; and can improve current industrial practices that rely on heavyweight traceability tools (e.g., DOORS) and retroactive fulfillment of standards (e.g., ISO-26262, DO-178B).
DEVELOPMENT OF UNIVERSITY VIRTUAL LABORATORY PRACTICES

A. Macías, A. Al-Kassir, J.M. Rodríguez-Rego, R. Sánchez-Sánchez, F.J. MoralUniversity of Extremadura (SPAIN)
Neural network dealing with Arabic language

Artificial Neural Networks have proved their efficiency in a large number of research domains. In this paper, we have applied Artificial Neural Networks on Arabic text to prove correct language modeling, text generation, and missing text prediction. In one hand, we have adapted Recurrent Neural Networks architectures to model Arabic language in order to generate correct Arabic sequences. In the other hand, Convolutional Neural Networks have been parameterized, basing on some specific features of Arabic, to predict missing text in Arabic documents. We have demonstrated the power of our adapted models in generating and predicting correct Arabic text comparing to the standard model. The model had been trained and tested on known free Arabic datasets. Results have been promising with sufficient accuracy.
A Critical Assessment of Macro-Economic, Management and Policy Reforms in Africa

The paper assesses the macro-economic, management, and policy reforms implemented in Africa with the assistance of the Bretton Woods institutions.There is no doubt that they have, to some extent, improved the socioeconomic development of African countries.However, they did not take into account the social dimension of their economies (health, education, poverty, employment, etc.).Thus, they were regarded not to have a human face.Therefore, redressing and adjusting African economies with the help of the Bretton Woods institutions are inadequate to propel the continent to higher and sustainable economic growth rates.Hence, African countries should elaborate and implement themselves policies, reforms, and programs adapted to their economies and development needs.
A Comparison of Some t-Norms and t-Conorms over the Steady State of a Fuzzy Markov Chain

This chapter shows a comparison of several t-norms and t-conorms used to compute the steady state of a fuzzy Markov chain. The effect of every of the selected norms over the mean and variance of a fuzzy Markov chain is evaluated using some simulations. Some recommendations and concluding remarks are given.
Voice Conversion Attacks on Speaker De-Identification Schemes

Speaker de-identification can be used to protect the privacy of a speaker. One class of de-identification schemes uses voice conversion techniques to transform utterances of a source speaker into utterances of a target speaker. This paper presents two new generic attacks on such schemes. The attacks have been implemented for Gaussian mixture model-based schemes. Experimental results show that the first new attack achieves success rates that are considerably higher than the rates of existing attacks. The second new attack shows that these rates can be increased further if the attacker has access to an utterance of the target speaker.
Identifikasi Komptensi Pustakawan Digital Berdasarkan Analisis Pekerjaan Pustakawan dari Tahun 2016-2018 di Indonesia

New information environment requires modern skills and librarians must adapt to the new environment (Chinien and Boutin, 2011). In the digital information environment, librarians need to acquire digital competencies (Graham, 2003), because they are responsible for building digital libraries for their readers (Seshaiah and Veeraanjaneyulu, 2009). However, the results of the study indicate that university library in Indonesia haven’t moved quickly to digital libraries, so that the competence of digital librarians is only a small part of needed. Librarianship in Indonesia is still moving from the traditional library towards the hybrid library. On the other hand, these developments must be addressed by the organizers of library science courses to be able to prepare librarian candidates capable of managing digital libraries.
LibGuides: Learning Online during COVID: Bienvenidos y Welcome

Online learning and educational activities during school closures and COVID-19 social distancing measures. We are here to help you find online materials and ideas for teaching and learning from home. Welcome and let us know how we can serve you!
DOES HIGH SCHOOL EDUCATION ENHANCE THE FINANCIAL LITERACY?

The need to increase financial literacy is stressed especially in conditions of the transition economy where is a part of the overall transformation of the way of thinking of the whole and for each of its members. Based on the thirty years of experiences it can be stated that this ability depends more than a systematic preparation on the practical experience. This is the basis for our research. Our research aims to find out if and how the level of financial literacy increases during the high school studies. The research was conducted at an eight-year grammar school. The methodology was based on the questioning of the three student groups: the first group includes students of the second class, the second group includes students of the fifth class and the third group includes the students of the eighth class of the grammar school. The used questionnaire was based on the set of questions used in similar research (Belás et al., 2016) but the questions and tasks we have modified concerning the conditions of grammar school.The results confirmed the higher level of the financial literacy of upper-class students of the grammar school. This confirmed our assumptions that during the study of the high school the financial literacy is increasing as a result of courses oriented on these questions. But these results must not be overestimated in their informative ability – the increase of the elements of financial literacy cannot be unambiguously attributed to secondary school studies, and thus can be an incentive for the further research aimed to find out the role of the practical experiences in the financial literacy increasing or the role of other factors, for example family backgrounds, residence, age, etc.
Context Aware Intensive Care Unit Wireless System Analysis

Context Aware environments are key elements in order to implement Smart Health systems. Wireless communication systems are employed as the mechanisms in order to achieve interactivity, with high mobility and scalable properties. In this work, the analysis of wireless system operation in term of radio channel behavior is presented for the ICU premises within the Hospital Complex of Navarra, considering the operation of multiples wireless systems in the 2.4 GHz frequency band (i.e., WiFi, Wireless Sensor Networks and Bluetooth devices).
Parallel Generalized Suffix Tree Construction for Genomic Data

After a decade of digitization and technological advancements, we have an abundance of usable genomic data, which provide unique insights into our well-being. However, such datasets are large in volume, and retrieving meaningful information from them is often challenging. Hence, different indexing techniques and data structures have been proposed to handle such a massive scale of data. We utilize one such technique: Generalized Suffix Tree (GST). In this paper, we introduce an efficient parallel generalized suffix tree construction algorithm that is scalable for arbitrary genomic datasets. Our construction mechanism employs shared and distributed memory architecture collectively while not posing any fixed, prior memory requirement as it uses external memory (disks). Our experimental results show that our proposed architecture offers around 4-times speedup with respect to the sequential algorithm with only 16 parallel processors. The experiments on different datasets and parameters also exhibit the scalability of the execution time. In addition, we utilize different string queries and demonstrate their execution time on such tree structure, illustrating the efficacy and usability of GST for genomic data.
Methods of Blocking Vulnerabilities of XSS Type Based on the Service Oriented Architecture

Web-applications are developed in several languages and deployed in various operating systems. This is connected with the various functions that web-application provides to its users. E-commerce applications must take into account various interfaces required for interoperability, security, and availability of a web-applications. Therefore, applications are developed using various languages such as PHP, ASP, JSP, NET, Python, etc., based on web-application requirements. Applications are constantly checked for vulnerabilities, and when they are vulnerable, they can be attacked. Research data shows that about 70% of web-applications are vulnerable to attacks of XSS form. This is due to the fact that entering data by users is allowed in text fields in web-application forms. This increases the threat to a web-application, allowing hackers the embedding of malicious content into the web-application. This article presents a new solution for blocking Cross-Site Scripting (XSS) attacks, which does not depend on the languages, in which web-applications are developed and eliminates XSS vulnerabilities arising from other interfaces. The solution is directed on providing independent services with specific interfaces that can be invoked to perform their tasks in a standard way without prior knowledge of the calling application by the service and without the application knowing how the service actually performs its tasks. The solution is based on a service-oriented architecture (SOA) approach. A method has been developed for blocking vulnerabilities of the XSS type based on the ability to protect applications from XSS attacks using XML and XSD. This includes creating an XML-document based on all form controls submitted by the user.
A Framework for Generating Aviation Risk Information Mobile User Interfaces from Knowledge Graphs.

In this paper, we introduce a new framework called Flight Planner for generating Aviation Risk Information mobile-compatible user interfaces from Knowledge Graphs. The framework is divided into three components, a templating engine, a Linked Data Knowledge Graph and a mobile app.Traditional Linked Data frameworks provide poor support for user interface generation and common web frameworks do not natively support Linked Data-based Knowledge Graphs. Moreover,Aviation Risk Information systems is specialised area mainly studied by organisational psychologists or experts in human factors and it has received little attention by computer scientists.Thus, a flexible, efficient, and usable framework for Aviation Risk Information is needed. A comparative evaluation was done with Linked Data Reactor framework as a benchmark.It is observed that Flight Planner produces equivalently flexible and efficient, but less usable results despite being an early prototype.
Cryptanalysis of A New Cryptosystem of Color Image Using a Dynamic-Chaos Hill Cipher Algorithm: A Chosen Ciphertext Attack

This paper carries out a chosen ciphertext attack on the image encryption proposed by Said Hraoui et al. [1]. The scheme proposed by Said Hraoui et al. uses PWLCM to generate key matrix and other confidential information. But, the whole encryption algorithm boils down to a simple Affine-Hill Cipher where only one component is generated dynamically for each pixel encryption. Since the relationship between the plaintext and the ciphertext is linear, the cipher suffers from the same weaknesses as that of the Hill Cipher. Particularly, this cipher exhibits its vulnerabilities in the face of an all zero plaintext. This paper exploits that weakness and breaks the cipher.
A Modular Software Library for Effective High Level Synthesis of Convolutional Neural Networks

Convolutional Neural Networks (CNNs) have applications in many valuable domains such as object detection for autonomous cars and security using facial recognition. This vast field of application usually places strict non-functional requirements such as resource-efficient implementations on the hardware devices, while at the same time requiring flexibility. In response, this work presents a C++-based software library of reusable modules to build arbitrary CNNs that support High-Level-Synthesis to be implemented as FPGA hardware accelerators for the inference process. Our work demonstrates how parametrization and modularization of basic building blocks of a CNN enable easier customization of the hardware to match the software model. This project also works with low-precision parameters throughout the CNN to provide a more resource-efficient implementation.
Toward Ameliorating K-Means Clustering Algorithm

Mining knowledge and predicting the behavior of the data have become major challenge with the advent of unprecedented escalation in the volume of the existing databases. Generally, clustering is adopted for voluminous and intricate data. In the present work, two techniques of K-means clustering, namely, K-means algorithm with random sampling (without realignment) and K-means algorithm with realignment sampling, are compared in terms of time taken and number of moves made for clustering the given data. The first one checks for any transfers between the clusters after inserting all the data. The second one is to check for any transfers between clusters for each new data inserted into cluster. The experimental results reveal that K-means clustering algorithm with realignment has performed reasonably well against K-means clustering algorithm without realignment.
Design of Sequential Logic Circuits

Finite State Machine (FSM) is a tool for designing sequential logic circuits by defining states. The general models of Moore and Mealy machines are presented. The design of Sequence detector using Moore and Mealy machines are explained and illustrated with an example. The preparation of algorithmic state machine chart and the application of state reduction techniques (Row elimination and Implication table methods) are also illustrated with examples.
Presenting research findings

This chapter discusses the different formats for disseminating research findings and the different sections of a research paper or report. It also describes the best ways to present statistical results and discusses guidelines for research articles. Examples are given throughout.
Session 5: How to Organize Everything

This chapter contains information on how to develop effective organizational systems. Information is presented on how to assess the organizational needs of schoolwork, a backpack, a locker, and materials needed for projects. The question of how to transport items to and from school is addressed. Also, the chapter includes a discussion of how to develop systems for other items that might be problematic, including papers, electronic files, clothing, sports equipment, and other items.
THE EFFECTS OF THE INTEGRATION OF CROWDSOURCING AND NETWORKING IN THE CURRICULA OF RUSSIAN UNIVERSITIES

I. Ainoutdinova1, A. Blagoveshchenskaya1, A. Nurutdinova2, E. Dmitrieva3 1Kazan Federal University (RUSSIAN FEDERATION) 2International Academic Centre for Language and Development (RUSSIAN FEDERATION) 3Kazan State Power Engineering University (RUSSIAN FEDERATION)
RETRACTED CHAPTER: Ethnic Characterization in Amalgamated People for Airport Security Using a Repository of Images and Pigeon-Inspired Optimization (PIO) Algorithm for the Improvement of Their Results

Nowadays, the latent danger that there is a terrorist attack in an airport anywhere in the world is a matter of first importance, that is why biometrics plays a vital role in our daily life—In our case it can determine the Facial characteristics of an individual, including their ethnicity. Given that this type of intelligent applications that detect and determine the facial attributes of people is highly safe and convenient, our society makes use of this technology almost everywhere, from the surveillance in the airport, as has been mentioned, to smart homes. in general in any aspect related to a smart city. In comparison with other biometric solutions, facial recognition produces greater advantages, since it does not require interaction or the permission of the subject. From this point of view, it represents a fast and effective way to increase our level of security, especially in open and crowded places. Automated facial recognition is a modern concept, and novel research related to image analysis. It was born in the 1960s and is still in permanent development. In 2006, the project “Facial Recognition Grand Challenge” (FRGC) evaluated the facial recognition algorithms available at that time. Tests with 3D scanners, high-quality images, and iris photographs. The FRGC showed that the algorithms available at that time were 10 times more accurate than those of 2002 and 100 better than those of 1995. Some recognition methods were able to overcome humans in face recognition and could even distinguish between twins identical In our case and using a novel algorithm called Pigeon-Inspired Optimization (PIO) Algorithm.
A Single Image Motion Deblurring Model of a Dynamic Scene

In recent years, convolutional neural network has achieved great success in the application of image deblurring, which has attracted more and more researchers' attention. At present, most of the image deblurring methods do not make full use of the hierarchical features of the original blurring image, so the performance is relatively low. This paper make full use of all level characteristics of convolution layer, introducing residual dense block, through intensive connection layer convolution extract rich in local features, puts forward a dynamic scenes based on residual dense blocks to motion blur model of multi-scale network, hereinafter referred to as residual multiple scales dense network (MSRDN) model, in the full extraction of partial feature of image at the same time, as far as possible to restore the higher quality of image information. The experimental results show that the proposed model performs better than similar algorithms in terms of peak signal to noise ratio (PSNR) and structural similarity (SSIM).
DISCRITIZATION OF WAVELET FUNCTION FOR COMPUTATION OF DERIVATIVE

M aurice Hasson and Rajendra Pandey have constructed wavelet function using Gaussian function and Maxican Hat function. Richardson extrapolation technique has been used in their constructions. There wavelet functions are applicable for computation of derivatives. In this paper, the authors have derived the discrete analogue of the wavelet function which can be applied to determine the first derivative of function. The discrete version is use full for better approximation of derivatives and the estimate of error can be analysed properly.
Reasoning About Degrees of Confirmation

We present a probabilistic logic for reasoning about degrees of confirmation. We provide a sound and strongly complete axiomatization for the logic. We show that the problem of deciding satisfiability is in PSPACE.
Founding The Domain of AI Forensics.

With the widespread integration of AI in everyday and critical technologies, it seems inevitable to witness increasing instances of failure in AI systems. In such cases, there arises a need for technical investigations that produce legally acceptable and scientifically indisputable findings and conclusions on the causes of such failures. Inspired by the domain of cyber forensics, this paper introduces the need for the establishment of AI Forensics as a new discipline under AI safety. Furthermore, we propose a taxonomy of the subfields under this discipline, and present a discussion on the foundational challenges that lay ahead of this new research area.
Person re-identification using kNN classifier-based fusion approach

Re-identification is the process of identifying the same person from images or videos taken from different cameras. Although many methods have been proposed for re-identification, it is still challenging because of unsolved issues like variation in occlusions, viewpoint, pose and illumination changes. The objective of this paper is, to propose a fusion-based re-identification method to improve the identification accuracy. To meet the objective, texture and colour features are considered. In addition the proposed method employs Mahalanobis metric-based kNN classifier for classification. The performance of proposed method is compared with the existing feature-based re-identification methods. CAVIAR, VIPeR, 3DPes, PRID datasets is used for experiment analysis. Results show that the proposed method outperforms the existing methods. Further it is observed that Mahalanobis metric-based kNN classifier improves the recognition accuracy in re-identification process.
NEW ADVANCED TEACHING TOOLS FOR SMART CREATIVE DESIGN

F. Pfeifer1, A. Anghel1, C. Milincu1, H. Filipescu1, F. Frigura-Iliasa2, I. Mohora1 1Universitatea POLITEHNICA Timisoara (ROMANIA) 2National Institute for Research and Development in Electrochemistry and Condensed Matter, Timisoara (ROMANIA)
Accurate Object Detection Based on Faster R-CNN in Remote Sensing Imagery

Object detection in remote sensing imagery is a fundamental and challenging problem for accurate object detection. Faster-regions convolutional neural network (Faster R-CNN) in remote sensing imagery has reduced the running time of the detection networks. But in the traditional method of convolutional neural network (CNN), the process is very slow it takes too much time. Nowadays, anchor boxes are widely adopted in the stage of model detection. This paper aims to provide a method to improve the detection accuracy in remote sensing imagery by using Faster R-CNN and its compatible Residual Network (ResNet). We compared the mean Average Precision (mAP) with the two backbone network ResNet-50 (Reference Backbone) and ResNet-101 (Proposed Backbone) with the same set of anchor ratio, we found that mAP achieves more than 1.376% significant improvement.