Reviewerly/exHarmony huggingface dataset:
Simulation of SAW-based CIPP Evaluation Model Calculation in Determining Improvement Priority for E-Learning Services

This research has the main objective to explain the steps of the SAW (Simple Additive Weighting) method calculation process used to determine one improvement priority aspect in CIPP (Context-Input-Process-Product) evaluation model so the e-learning services more effectiveness. This research used a quantitative approach to obtain the lowest preference value from the evaluation aspect of the CIPP model. The research subjects who were involved in this research were 64 people. The tool used in collecting simulation data was questionnaires. This research was conducted in several health colleges in Bali Province. The technique was used to analyze the data from the SAW method calculation was the technique of the lowest preference value searching. This research result showed the accuracy of the SAW-based CIPP model calculation in determining the priority aspect of improvement with a preference value was 0.37192.
Implementation of the Auxiliary Sampling Importance Resampling Particle Filter on Graphics Processing Unit

In this paper, we investigate the performances of sampling importance resampling (SIR) particle filter and auxiliary sampling importance resampling (ASIR) particle filter. We mention the reasons behind ASIR particle filter which is the improved version of SIR particle filter. We use target tracking with radar application whose measurements are highly nonlinear in the experiments. At first, we implement the serial versions of these particles filters on a single processor. Although their qualities improve as the number of particle increases, their execution times deteriorate rapidly. Then we implement them on a graphics processing unit (GPU). GPU offers promising solutions for the particle filters as it has many cores in its architecture. We implement SIR and ASIR particle filters by using CUDA programming language and achieve important speed ups along with same qualities on GPU.
Should Kernels Be Trained in CNN?- a Paradigm of AG-Net

The Convolutional Neural Network constantly updates the weights of kernels to learn the feature representation, which makes the computational cost unaffordable. This work first proposed a Randomized Convolution Kernel with a kernel group to extract the multidimensional feature of each pixel. An AG-Net is then constructed, which can generate a layer containing multiple Gaussian Mixture Models to replace the convolutional layer. There are several Randomized Convolution Kernels in AG-Net to generate several multidimensional feature sets according to different multidimensional features. And each multidimensional feature set gets a Gaussian Mixture Model with Adaptive Resonance Theory. In training, each input is mapped by the Gaussian Mixture Models and the kernel sets. Then a fully-connected layer is used for high-level reasoning. Experiments show that the weights of kernels can be random, and the feature maps based on the similarity of pixels in multidimensional features can be well used in image processing.
Preparing Software Quality Assurance Professionals: Metamorphic Exploration for Machine Learning

Computer science (CS) subjects have been rapidly growing in popularity, and demand for CS education and training has put increasing pressure on teaching resources in higher education (HE) and elsewhere. HE in the People's Republic of China (PRC) has also been developing, with one product of this evolution being Sino-foreign HE institutions (SfHEIs). Much of the popularity growth for CS can be linked to the growth of CS-based technology and innovation, especially in the form of Artificial Intelligence (AI) and Machine Learning (ML). AI/ML-based innovation has been forecast to offer increases in quality of life for consumers. However, AI/ML systems face a challenge for software quality assurance (SQA): They are so-called “untestable systems” - identifying the correctness of AI/ML system outputs or behaviour may not be feasible. Preparing SQA professionals to be able to ensure AI/ML SQA will require innovative and creative education and training. An SQA approach called metamorphic testing (MT) has a proven track record of alleviating the oracle problem, and has great potential as a testing methodology for AI/ML systems. Metamorphic exploration (ME) is a new addition to the MT literature, and involves developing the user's understanding of the system under study. This paper reports on experiences at an SfHEI of using ME and MT to test an AI/ML system.
Generation of Symmetric Key Using Randomness of Hash Function

In a highly secure and robust key generation process, a key role is played by randomness and random numbers when current real-world cryptosystems are observed. Most present-day cryptographic protocols depend upon the Random Number Generators (RNG), Pseudo-Random Number Generator (PRNG). These protocols often use noisy channels such as Disk seek time, CPU temperature, Mouse pointer movement, Fan noise to obtain actual random values. Despite being cost-effective, these noisy channels may need additional hardware devices to communicate with them continuously. On the other hand, Hash functions are Pseudo-Random (because of their requirements) in nature. So, they are a suitable replacement for these noisy channels and have low hardware requirements. This paper discusses some of the key generation methodologies and their drawbacks. Further, it explains how hash functions can be used in key generation, how to combine Key Derivation Function (KDF)s with hash functions.
Research on Power Grid Fault Diagnosis Based on Bayesian Information Fusion

This paper proposes a power grid fault diagnosis method based on cluster analysis. The method uses multi-source fault information to determine faulty components, and it can get accurately result in the absence of information. The multi-source fault information used in this paper includes wide-area system measurement information, fault recorder information and protection action information. The method firstly uses multi-level information analysis method to analyze the power grid fault information, extracts the fault features, and determines the suspicious fault component set through the clustering result and the switch action information. Then using the improved Bayesian probability model to fuse fault information and identify faulty components. Finally, this paper verifies the effectiveness and feasibility of the proposed method through concrete examples.
Sharpening of 3D Digital Objects using Triangulation

Image blurring is a very common phenomenon in both 2D and 3D images. Deblurring of such blurred images can be achieved by image sharpening. Two novel approaches for sharpening of 3D digital objects, using triangulation, have been proposed in this paper. As a preprocessing step, polygons are constructed from a given triangulated data set of a 3D digital object. These polygons are again triangulated by the two methods proposed in this paper. Both the methods enhance the quality of the object surface, thereby leading to 3D object sharpening. The computational complexity of the two proposed algorithms is 0 ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> ), where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> is the count of faces in the triangulated data set of the object taken as input. The experimental results of the proposed algorithm applied on different 3D digital objects shows its efficacy.
An Approach to Support Variant Management on Safety Analysis using CHESS Error Models

Software Product Lines (SPL) and component-based approaches have been largely adopted in the development of safety-critical systems due to benefits such as increased product quality, shorter time-to-market and large-scale reuse. Safety-critical system families built upon SPLs, have to address safety, reliability and availability properties across different system variants. Variations in design and usage context may impact on safety properties such as hazards, their causes, and safety requirements to mitigate hazard effects. Therefore, the adoption of SPLs in the development of critical systems requires the introduction of safety analysis into product line processes. The manual analysis of safety properties across different system variants may be time consuming and error-prone, resulting in project delays and in the increase of development costs. Model-based techniques automate system design and safety analysis and have been recognized by safety standards. AMASS is a model-based platform that supports system design, safety analysis and variability management. In this paper we propose a systematic approach, built upon the AMASS platform, to support the reuse of safety assets and generation certifiable evidence for variant-intensive systems. The approach was evaluated in a realistic variant-intensive automotive braking system and reduced the complexity of performing safety analysis on system variants through the reuse of safety information. It has also automated safety analysis and improved the traceability between development and functional safety assets.
Difference Between 2D and Stereoscopic Image Quality Assessment

With the rapid development of stereo and multi-view systems and a wide adoption of these systems, stereoscopic image quality assessment (SIQA) has become an important and challenging problem faced in numerous application such as 3D films, stereo visualization and 3D enhancement. Compared with 2D image quality assessment methods, SIQA needs considering complex binocular visual properties. The difference between SIQA and 2D IQA is introduced firstly in this chapter. Then stereoscopic image quality databases are listed and discussed detailedly. Finally, the general designed frameworks of SIQA methods are provided.
Encryption and Decryption: Unraveling the Intricacies of Data Reliability, Attributed by Incorporating the Usage of Color Code and Pixels

In this day-to-day modernizing era where data security is becoming a major concern, data security refers to securing information in potentially hostile environments. Several methods or ways have been proposed in the area of concern, i.e., securing data and assuring its safe and sound transmission from the sender to the receiver. The specialized method focused here is encryption and decryption. Decryption is the process of de-transforming encrypted information so that it gets intelligible. Encryption translates actual data into another form, or code so that only people with access to a secret key (formally called a decryption key) or password can decrypt it. Encryption is the process of transforming information, so it is unintelligible to anyone but the intended recipient. This method emphasizes the substitution of characters with RGB color format later in the form image pixel, because of which it becomes unable for the hacker to find out where the data is hidden, thus, before cracking the data they need to first find where the data is. It becomes useful in various fields such as organizational level, national level to maintain the integrity of data and can be used at a personal level too to avail the security benefits using the created platform.
Inheritance and Protection Strategies for Tibetan Folk Chess Under the Background of “Internet+”

Compared to Go, Chess and other chess played by many people, Tibetan chess are mostly played in Tibetan areas. Tibetan chess, especially Tibetan Go, is becoming extinction.To protect it, more and more attention must be paid to its inheritance and protection. Under the background of “Internet+”, great changes have taken place in the ways of inheritance and protection of intangible cultural heritage. This paper proposed strategies on inheritance and protection for Tibetan chess. Firstly, the status quo of Tibetan folk chess development is analyzed and the existing problems are concluded. Then Some prospective and practical strategies on inheritance and protection for Tibetan chess are put forward aiming at solve these problems.
Formation Of Schoolchildren Eco-Oriented Life When Studying A Cultural Landscape

The article reveals a pedagogical problem related to the development of environmentally-oriented activities of students in cultural landscapes, the priority of which is due to the advanced function of modern education as the main factor in changes in the direction of sustainable development. The scientific integral and cultural-ecological potential of the cultural landscape as an educational environment is noted, which contains a wide range of opportunities for creating culturally-oriented integrated situations that encourage students to use eco-oriented activities. The aim of the article is a theoretical justification, development and experimental confirmation of the effectiveness of the formation of methods of eco-oriented life based on culturally-oriented integral situations. The leading research methods were system-structural analysis, synthesis, systematization, modeling, pedagogical experiment, where 70 members of school geographical clubs took part. The use of theoretical methods made it possible to determine a number of significant scientific and theoretical propositions, ideas, and approaches: cultural and ecological, axiological, landscape-environmental, personal-activity, integral-situational and to use them effectively in the process of designing technology for the formation of methods of environmentally-oriented life activity on the basis of integral culturally-oriented situations. Empirical methods made it possible to test the developed technology in educational practice and to prove its effectiveness. The results obtained in the study can be useful for teachers, students of pedagogical specialties and curators of children's associations.
Using the Probe Methodology to Investigate the User Experience in a CRM System

This paper presents a self-ethnographic experience for defining a new system to manage customer relationship in a department of internal sales. The paper focuses on the definition of strategies for deciding the future of new a CRM (Customer Relationship Manager) that will replace an older version used for many years by a tourism company. We followed an approach based on the Probe Methodology (PM), considering a context in which the user of the platform could participate, alongside the company, in the development of the CRM software. Our results showed that the PM could help to identify the main characteristics, modes of use and problems related to the specific environment of the software. Furthermore, it was possible to identify the ways in which users adapted to overcome issues that emerged while interacting with the system, and brought valuable insights into the development of the CRM. Based on techniques typically associated with the social sciences, the project collects information about intangible concepts to transfer them to a software development project. The PM proved efficient in identifying relationships that are not visible at first sight and to perceive requirements and needs not directly expressed.
From Musicology to the Cultural Direction of Music Education Pedagogy: Research Introspection

The article describes the role of the scientific school by E. B. Abdullin in the formation and gradual development of the author’s research positions in the field of pedagogy of higher school music education. The first stage is characterized by considering problem of forming the artistic culture of future music teachers in a pedagogical University.The research embodied in his doctoral thesis, developed under the influence of the vectors of musicological and pedagogical thought. In includes analyses of the interaction of music with other art forms as manifestations of the General laws of artistic activity of man, revealed in the phenomenon of artistic culture. Pedagogical interpretation of artistic culture allowed us to consider this category: a) as an integral component of the content of music and pedagogical education; b) as a professionally significant quality of the music teacher’s personality. The concept changed the status of historical and theoretical training of future teachers-musicians, which received the working title “pedagogical musicology”. The content of pedagogical musicology was determined by a culturological approach to the learning process, designed to solve practical problems of mastering music in synchronic and diachronic interaction with other types of arts.The second stage is to expand the cultural component of professional training of music teacher, becoming a cultural orientation that integrates musicology, cultural studies and the pedagogy of music education on the basis of higher spiritual principles of music as an art form. The cultural direction allows developing new scientific approaches in the content of master’s educational programs in line with the methodological concept by E. B. Abdullin.
A systematic review of automated feature engineering solutions in machine learning problems

During the last decades, machine learning has played an important role in building data-driven experiments. Based on information extracted from a variety of sources, new patterns can be identified, predictions can be made more easily and decisions made faster and more effective. The specialized application of machine learning solutions requires specific knowledge in areas such as math, computation, and statistics, as well as being extremely costly in time and having a high chance of incurring any kind of human error during the process. Automated Machine Learning Techniques (AutoML) seek to automate parts of the process of building machine learning applications, allowing non-experts to perform this process. An important part of this kind of problem is the feature engineering part which creates a transformation in the data, making it more representative for the final model. This paper presents a systematic review of automated feature engineering solutions in machine learning problems. With the main objective of identifying and analyzing the existing methods and techniques for performing the automated feature engineering step within the framework of machine learning problems.
Live Cloud Ingest Using Open Alternatives RIST &amp; SRT

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">As cloud production becomes an integral part of broadcasters’ live workflows, the corresponding cloud infrastructure becomes an integral part of the media transport network. Cloud infrastructure providers, often public in nature, are accessible either through the public internet or through leased lines. Regardless of public or private, experience shows that these connections are not up to the quality standards required for low latency live streaming media, making quality-enhancing technologies, such as retransmission or Automatic Repeat reQuest (ARQ), an imperative part of the network infrastructure to deliver on the requirements for broadcast quality transmission. Two solutions have emerged as front runners on the quest for the next de facto standard for broadcast retransmission—Reliable Internet Stream Transport (RIST) and Secure Reliable Transport (SRT). This report compares these two alternatives from a theoretical and performance perspective and compares them with a commercial reference alternative</i> .
Modification of Permutation Method in Multi-Criteria Decision Making Using PSO-SA

Permutation is one of the widely used methods in Multi-Criteria Decision Making (MCDM) for prioritizing alternatives. Making the best decision will become more challenging by growing the number of criteria. High computational cost is the main disadvantage of this method. In this paper, an innovative algorithm is applied for obtaining an optimized solution for the permutation method. In this research, a combined algorithm of particle swarm optimization (PSO) and simulated annealing (SA) is proposed and implemented for reducing the high calculation costs. This combined algorithm possesses the higher convergence properties of PSO in finding the best answer as well as holds the SA feature of avoiding from facing local minimum. This algorithm investigated two sets of numeric data. The results verify its highest efficiency in detecting an optimized sequence of prior alternatives choices at a shorter time. This work proved that the suggested methodology could be employed with high confidence for common permutation problems in case of the presence of many alternatives.
Radar Signal Sorting Algorithm Based on Pulse Repetition Interval

With the continuous enhancement of military strength in various countries, electronic warfare in modern battlefields has become increasingly fierce. More and more signal transmitting devices appear in the battlefield environment, causing the number of signals intercepted by the signal receiver to increase exponentially, which brings severe challenges to the sorting of radar signals. At the same time, when the number of signals is greatly increased, the modulation form of the signal is also ever-changing, which also brings challenges to the sorting work of the radar signal. In order to improve the application of radar in the military battlefield, it is urgent to study the radar signal sorting algorithm with higher recognition performance.
M-Estimation-Based Robust Kalman Filter Algorithm for Three-Dimensional AoA Target Tracking

An attractive issue in numerous applications is to track targets in three-dimensional (3-D) space with angle of arrival (AoA) measurements through nonlinear filters. The tracking performance degradation caused by outlier prompts a variety of robust filters. In this paper, an M-estimation-based robust bias compensation Kalman filter algorithm (MR-BCKF) is proposed. This algorithm recasts the AoA measurement equation to a linear form by pseudo-linearization, and then incorporates the M-estimation criterion into pseudo linear Kalman filter to enhance robustness, followed by the bias compensation to improve tracking accuracy. An improved three-segment weight function based on Mahalanobis distance is established to handle outliers for each element, which does not require the noise characteristics. Simulation demonstrates that MR-BCKF has enhanced robustness against outliers at different levels and achieves more accurate tracking compared with other robust filters.
Enhancement of Unsupervised Object Detection using Supervised Method

Reasoning in terms of objects is a critical skill for any intelligent agent. To reason in terms of objects, an agent needs a method for discovering and detecting objects in the visual world. This is achieved through unsupervised object detection. Unsupervised object detection involves the identification of a large number of objects within images, without any supervision or labels. This area has received significantly less attention as compared to its supervised counterpart, which has achieved human-level accuracy. In a lot of cases, labeling each object in the image is impractical and requires a lot of labor. While unsupervised techniques are suitable for the task of object localization, the learner cannot identify the classes of the localized objects unless it is explicitly specified. Combining supervised learning techniques with unsupervised learning techniques can improve the performance of existing algorithms for both detection and classification. In this paper, we have studied a state-of-the-art unsupervised object detection approach and extended it for object classification, by combining it with supervised techniques.