Reviewerly/exHarmony huggingface dataset:
AUTILE Framework: An AUTOSAR Driven Agile Development Methodology to Reduce Automotive Software Defects

This article introduces AUTOSAR Driven Agile Development (AUTILE) Framework as a new methodology to develop automotive software with an aim to reduce the number of defects and their severity. Automotive software, developed in a traditional/legacy way, currently must be rewritten from scratch to support new features or modifications in associated hardware. Disruptive automotive megatrends: connectivity, electrification, and autonomous driving continue to demand new features in a complex software-base. As a result, greater quality issues are faced when more functionality is added to traditional, nonstandardized automotive software. Several researchers have proposed standardization of automotive software to improve the software quality. However, as yet, the industry lacks a de facto standard. At the heart of the AUTILE Framework, we recommend following automotive open system architecture standard as the basis to design modular and open software architecture. To further realize the benefits of modular software architecture designed in the first phase, AUTILE chooses and integrates Agile methodologies as its software development approach. Widespread adoption of Agile methodologies for automotive software development has not occurred and this research highlights the enablers and barriers in this domain. The proposed framework was applied practically to develop automotive software projects, demonstrating that defect reduction can be accomplished using the steps outlined in this new methodology.
Fanoos: Multi-resolution, Multi-strength, Interactive Explanations for Learned Systems

Machine learning is becoming increasingly important to control the behavior of safety and financially critical components in sophisticated environments, where the inability to understand learned components in general, and neural nets in particular, poses serious obstacles to their adoption. Explainability and interpretability methods for learned systems have gained considerable academic attention, but the focus of current approaches on only one aspect of explanation, at a fixed level of abstraction, and limited if any formal guarantees, prevents those explanations from being digestible by the relevant stakeholders (e.g., end users, certification authorities, engineers) with their diverse backgrounds and situation-specific needs. We introduce Fanoos, a framework for combining formal verification techniques, heuristic search, and user interaction to explore explanations at the desired level of granularity and fidelity. We demonstrate the ability of Fanoos to produce and adjust the abstractness of explanations in response to user requests on a learned controller for an inverted double pendulum and on a learned CPU usage model.
Design methodology and material evaluation criteria for membranophone shells

Materials play a controversial role in the manufacture membranophone shells. Luthiers and manufacturers propose a lot of different materials with the aim of offering sound variability and users assess the capabilities of the different materials used in the manufacture shells. Previous research recognizes the difference in timbral undertones generated using different materials although attempts to establish a direct relationship between shell resonances and membrane have concluded with a weak influence of shell on the total spectrum of the instrument. Therefore, considering its influence despicable. This paper demonstrates shell influence on the final spectrum of a snare drum by a combination of simulations by numerical analysis and experimental validation. Considering how the shell conditions and the tension generated by the tuning of the membrane has the effect of significant modal variations from existing numerical models. A coupling rule is detected, proposing a new nomenclature to define interactions between membrane and shell and evaluating mechanisms of these couplings. Finally, this article proposes a new methodology for shell design and evaluation based on the material properties and the tuning range of the upper membrane.
Assertoric Content and General Compositionality

It is a common view in formal semantic theories that the compositional semantic values of sentences in contexts are identical to the assertoric contents expressed by these sentences. Recently, however,there have been challenges to this simple view on the basis of difficulties in theorizing various ‘shifty’ phenomena. In this paper, I argue that these difficulties can be ‘explained away’ by introducing a revised notion of compositionality which allows an expression to have different semantic values in different environments. I then claim that the proposed framework can be not only compatible with the identity but also plausible as a natural language semantics.
Identification of Inundation Using Low-Resolution Images from Traffic-Monitoring Cameras: Bayes Shrink and Bayesian Segmentation

This study presents a comparative assessment of image enhancement and segmentation techniques to automatically identify the flash flooding from the low-resolution images taken by traffic-monitoring cameras. Due to inaccurate equipment in severe weather conditions (e.g., raindrops or light refraction on camera lenses), low-resolution images are subject to noises that degrade the quality of information. De-noising procedures are carried out for the enhancement of images by removing different types of noises. For the comparative assessment of de-noising techniques, the Bayes shrink and three conventional methods are compared. After the de-noising, image segmentation is implemented to detect the inundation from the images automatically. For the comparative assessment of image segmentation techniques, k-means segmentation, Otsu segmentation, and Bayesian segmentation are compared. In addition, the detection of the inundation using the image segmentation with and without de-noising techniques are compared. The results indicate that among de-noising methods, the Bayes shrink with the thresholding discrete wavelet transform shows the most reliable result. For the image segmentation, the Bayesian segmentation is superior to the others. The results demonstrate that the proposed image enhancement and segmentation methods can be effectively used to identify the inundation from low-resolution images taken in severe weather conditions. By using the principle of the image processing presented in this paper, we can estimate the inundation from images and assess flooding risks in the vicinity of local flooding locations. Such information will allow traffic engineers to take preventive or proactive actions to improve the safety of drivers and protect and preserve the transportation infrastructure. This new observation with improved accuracy will enhance our understanding of dynamic urban flooding by filling an information gap in the locations where conventional observations have limitations.
The Influence of Music Appreciation Courses on The Formation of College Students’ ideological Quality under The Environment of Big Data

Abstract At present, with the deepening of China’s education system reform, China’s music education is facing a rare opportunity to develop, music education developing the students’ quality comprehensively, promoting the construction of socialist spiritual civilization will play a greater role. This article will music combined with large data environment in the school, to explore big data, and the practicality of music education in education, some problems and try to combine big data with innovative ideas to solve these problems, in order to enrich the content of music of music on college students’ thought positive impact of sustainability. Finally, after expounding the relevant concepts and theories, this paper demonstrates the feasibility of bringing positive influence of music appreciation course on the ideological quality of college students behind the use of big data technology from the aspects of music education methods, approaches, evaluation, contents and objects.
High Speed Color Image Encryption Using Pixel Shuffling With 1-D MLCA

Due to the development of the Internet and the communication network environment, image transmissions occur very frequently. In such an environment, the color image is likely to be distorted by noise. Image shuffling is a technique which resists distortion and deletion attacks. In this paper, we propose a speed-up method for shuffling pixels in the color image encryption system. In that step of shuffling, we use 1-D MLCAs to shuffle the pixel position of an image. In fact 1-D MLCA is higher in randomness than the conventional chaotic map and faster than the 3-D generalized chaotic cat map. The comparison of the execution time between the proposed method and the existing method is made.
Do not blame it on the algorithm

In the debate about filter bubbles caused by algorithmic news recommendation, the conceptualization of the two core concepts in this debate, diversity and algorithms, has received little attention in social scientific research. This paper examines the effect of multiple recommender systems on different diversity dimensions. To this end, it maps different values that diversity can serve, and a respective set of criteria that characterizes a diverse information offer in this particular conception of diversity. We make use of a data set of simulated article recommendations based on actual content of one of the major Dutch broadsheet newspapers and its users (N=21,973 articles, N=500 users). We find that all of the recommendation logics under study proved to lead to a rather diverse set of recommendations that are on par with human editors and that basing recommendations on user histories can substantially increase topic diversity within a recommendation set.
The Power of Leibniz-Like Functions as Oracles

A Leibniz-like function $$\chi $$ is an arithmetic function (i.e., $$\chi : \mathbb {N}\rightarrow \mathbb {N}$$) satisfying the product rule (which is also known as “Leibniz’s rule”): $$\chi (MN) = \chi (M) \cdot N + M \cdot \chi (N)$$. In this paper we study the computational power of efficient algorithms that are given oracle access to such functions. Among the results, we show that certain families of Leibniz-like functions can be use to factor integers, while many other families can used to compute the radicals of integers and other number-theoretic functions which are believed to be as hard as integer factorization [1, 2].
The Model of Fuzzy Logic in IoT System as Decision Support System for Determining Flood Disaster Status

Abstract Flood is the most frequent disaster in the world. The impact of floods is very large, like human and animal death, the villages vanished and other environments damaged. In disaster preparedness, it is very necessary to divide the stages of the disaster status level. Therefore the public can prepare themselves according to these stages. The stage of disaster preparedness status is also beneficial for the government and related institutions to act according to the status stage of the disaster. This paper will propose the early warning system for flood disaster based on fuzzy logic in IoT system. This algorithm was built 8 rules. The inputs are the rainfall and height of water. The output is the status of flood disaster. The types of disaster status are normal, moderate 1, moderate 2, emergency 1, and emergency 2. From the simulation result, it is concluded that this fuzzy logic model can to determine the status of flood disaster very well.
Methods of Organization of Spoc Course on Planimetry Training for Future Teachers of Mathematics

The article presents using the van Hiele theory of geometric thinking for the organization of mixed training of students of the pedagogical Department of the N.I. Lobachevsky Institute of mathematics and mechanics KFU on the basis of SPOC course «Elementary mathematics: Plane geometry». Training includes the use of various IT-techniques and tools that can improve the quality of learning and form the necessary professional competence of the future teacher of mathematics. The course implements the technology of project-modular and inverted learning.
Application Analysis of Data Mining Technology in Oil Painting Course Resource Management

Abstract This paper firstly puts forward some obvious disadvantages of traditional oil painting curriculum management in the course of selecting oil painting for several decades. After detailing and comparing the types of data mining technology, this paper puts forward two proven advantages of applying data mining technology in oil painting course management, one is to customize the course according to the students’ own characteristics, the other is to promote the overall development of students’ artistic literacy.
Matrix graphs and MRD codes over finite principal ideal rings

Let R be a finite principal ideal ring and m,n,d positive integers. In this paper, we study the matrix graph over R which is the graph whose vertices are m×n matrices over R and two matrices A and B are adjacent if and only if 0<rank(A−B)<d. We show that this graph is a connected vertex transitive graph. The distance, diameter, independence number, clique number and chromatic number of this graph are also determined. This graph can be applied to study MRD codes over R. We obtain that a maximal independent set of the matrix graph is a maximum rank distance (MRD) code and vice versa. Moreover, we show the existence of linear MRD codes over R.
A Design of Smart IoT-Based College Room Using Arduino

Abstract Electrical appliances and local switches can be controlled distantly by utilizing the Internet of Things (IoTs) communication protocols. Those devices can be accessed and operated through mobile phones. A smart college room is one of the applications in smart campus which worked by means of Wi-Fi existence for distant observing of electrical devices or switches in the particular room. Hence, this paper describes the project’s design and development based on IoTs by implementation of Arduino. The project applies Arduino MEGA 2560 board in conjunction with ATmega2560 chip. The main controller (ATmega2560) is used to connect the board with Wi-Fi module as well as to control DC motor, servo motor and LED in the project. The inputs need to be controlled in the room are lights, fan, sliding door and curtain. Since internet is the sub-core to this project, it helps the project system to acknowledge what user has decided in their mobile phones. For instance, when user wants to turn light in the room, he can simply do it by touching the respective button in the mobile phones. The project is purposely designed for preventing electricity wastage among students which usually happens in colleges. Besides, it promotes accident-free environment while operating the electrical appliances or switches in the room.
Research on Virtual Piano Based on Computer Binocular Stereo Vision

Abstract The binocular stereo vision technology emerges along with the development of digital technology, which has been widely employed in virtual piano to effectively remedy the user-unfriendliness and unnaturalness of the keyboard and mouse used as the key. This paper discusses how binocular stereo vision technology can be made good use to realize the function of the piano so that a comfortable real-time human-computer interaction can be completed.
Multi-Channel FLANN Adaptive Filter for Speckle &amp; Impulse Noise Elimination from Color Doppler Ultrasound Images

The conventional fixed filters cannot be employed for removing the mixed noise of Color Doppler Ultrasound (CDUS) images because it affects the features of the image awkwardly. Consequently, identifying an internal blockage or hemorrhage of the patient become arduous in such conditions. Hence, the evolutionary multi-channel Functional Link Artificial Neural Network (M-FLANN) has been proposed to get rid of Speckle noise from the CDUS images. In this paper, the performance of the M-FLANN and other five competitive filters is evaluated in terms of qualitative and quantitative measures.
Classification of Live/Lifeless Assets with Laser Beams in Different Humidity Environments

Detecting the vitality of a certain distance person is important in natural disasters, search and rescue activities, urban warfare environments and in the fight against terrorism. In this study, a system is proposed that targets located at a certain distance are marked with a low-power laser beam, allowing them to be detected as live/lifeless. In addition, the performance of the proposed system in different relative humidity environments is analyzed. In this study, in a laboratory environment with 32%, 60%, 70% and 80% relative humidity, laser marks are obtained from the arms of 10 volunteer male subjects and 17 different objects, for each humidity rate. The signals obtained are trained in the developed deep learning network. In the literature, it is known that the performance of electronic devices at high relative humidity rates has decreased. As a result of experimental studies, it can be seen that the proposed system has a high classification performance even in the low humidity and very high humidity range. The fact that assets at a certain distance can be classified as live/lifeless with a high-performance rate, regardless of the humidity rate, demonstrates the applicability of the proposed system.
Recommendation Feature of Scientific Articles on Open Journal System Using Content-based Filtering

OJS (Open Journal System) is an open-source platform for online journals, used by many people because it is easy to implement. OJS has various features, such as editor management, online submission, comprehensive indexing, etc. However, OJS does not have any recommendation feature to suggest suitable articles for users. This research aims to develop and embed a recommendation feature into the existing OJS to suggest relevant articles for users using Content-based Filtering approach, which focuses on the similarity of the data contents. Some parts of the article, such as the title, the keyword, and the journal scope are used as the reference data for the recommendation. K-Means clustering and Cosine Similarity are used as matching method, and the results of the recommendations are compared to find the most appropriate method to use. The result shows that the average precision score for the recommendations given by K-Means clustering method is better than that of Cosine Similarity method. The average precision score for K-Means clustering method is 68%, and the percentage of the recall is 64%. As for Cosine Similarity method, the average precision score is 44.15%, and the recall is 64%.
Towards Complete SAT-based Cryptanalysis of RC5 Cipher

There are many concepts and methods of ciphers cryptanalysis. It depends on the type of the cipher investigated or sometimes modes of operations. Usually, cryptanalysts would like to check some important properties of well known and used ciphers, where so far there is no possibility to break them. Investigators try sometimes break a weaker version of the cipher for understanding which and how proper cipher's parts are cryptographically strong. SAT-based cryptanalysis is one of the used and efficient ways to investigate important properties of some symmetric ciphers. In this paper, we show our investigations and new experimental results in the case of SAT-based, direct cryptanalysis of the RC5 cipher. For this solution, having a given cipher, we firstly built a propositional logical formula that encodes the RC5 algorithm. Next, we randomly generate plaintext and a key. Then we compute the ciphertext. At the end of our computations, we use SAT-solvers, specially designed tools for checking about satisfiability of the Boolean formulas. For our experiments, we explore RC5 properties in the case of cryptanalysis with plaintext and ciphertext. In our investigations, we use and compare several SAT solvers: a few new ones and a few rather old but still e ffi cient. We present our results in the case of a little bit weaker version of the RC5 cipher.
CONCEPT OF FORMATION OF EMOTIONAL INTELLIGENCE OF A MUSICAL ART TEACHER IN THE PROCESS OF PROFESSIONAL TRAINING

The article grounds the conceptual bases of formation of emotional intelligence of a teacher of musical art in the process of professional training. The emotional intelligence of a teacher of music is regarded as an integral, individual-personal and professionally significant quality, acting as a system-forming factor of professional activity. It is emphasized that the process of formation of this integral quality should be carried out in accordance with modern methodological guidelines, defined, in particular, by provisions of the Concept of development of pedagogical education and taking into account the complex influence of external (integration) and internal (innovative) tendencies of development of the higher education system. This article shows tendencies of development of art professional education and its component – music-pedagogical education, awareness of which contributes to the entry of this field of higher education into the world cultural and educational space. The innovative context of the artistic educational space is considered as a reserve for improving the professional training of future teachers of music, in particular, the formation of their emotional intelligence. It is concluded that the dynamic and unstable development of modern society requires from the teacher not only a high level of education, competence, innovation, but also possession of socially oriented skills, in particular, emotional intelligence, which will provide him with rapid adaptation to society, demand, success in professional activity. The substantiation of the basic provisions of the author’s Concept outlines the purpose, tasks, guiding principles and organizational and methodological conditions that highlight the innovative approach to the selection of content, methods, forms and means of formation of the emotional intelligence of future teachers of music in the process of professional training.
Design and Construction of Homodyne Detectors for the Study of Quantum Optical States

Abstract Nowadays the fields of Quantum Optics and Quantum Information are experiencing a mayor development worldwide, in particular, in the experimental arena. The reason relies in the high perspectives of application of these fields in areas as computing, security, telecommunications and metrology, among others. A determinant fact for the experimental and technological development is the ability to design and construct electronic devices suitable for the study of light with quantum features, as well as the software which is necessary for the analysis and processing of experimental data. In this work we present a general overview of the theory, practical design and experimental tests of one of such devices called Homodyne Detector addressed to the study and engineering of quantum states of light.
Prioritization of Different Types of Software Vulnerabilities Using Structural Equation Modelling

Due to digitization and cloud computing, people nowadays prefer to save their important data online. But flaws in the software systems pose high risk to data’s security. Researchers are working on risk reduction and reducing time in fixation of such flaws. High risk is presented by these software vulnerabilities to the software systems security. If a particular type of flaw or vulnerability prevails or occurs repeatedly and poses highest risk, then our data can be saved by prioritizing such fixes. The work presented in this paper prioritizes those types of vulnerabilities which are most severe so that the testers and fault fixers can work on the most severe ones first and decrease the risk posed due to these vulnerabilities. Using SEM technique, we will first find out if the different types of vulnerabilities are dependent on each other and quantify their interdependence on each other. Later, we will develop a structural equation model to recognize the severity of these vulnerabilities which seems to be a more practical approach to find the priority of fixation of these flaws.