Reviewerly/exHarmony huggingface dataset:
Evaluating the effectiveness of data quality framework in software engineering

&lt;span lang="EN-US"&gt;The quality of data is important in research working with data sets because poor data quality may lead to invalid results. Data sets contain measurements that are associated with metrics and entities; however, in some data sets, it is not always clear which entities have been measured and exactly which metrics have been used. This means that measurements could be misinterpreted. In this study, we develop a framework for data quality assessment that determines whether a data set has sufficient information to support the correct interpretation of data for analysis in empirical research. The framework incorporates a dataset metamodel and a quality assessment process to evaluate the data set quality. To evaluate the effectiveness of our framework, we conducted a user study. We used observations, a questionnaire and think aloud approach to provide insights into the framework through participant thought processes while applying the framework. The results of our study provide evidence that most participants successfully applied the definitions of dataset category elements and the formal definitions of data quality issues to the datasets. Further work is needed to reproduce our results with more participants, and to determine whether the data quality framework is generalizable to other types of data sets.&lt;/span&gt;
Firefly optimized robust, imperceptible, secure watermarking scheme

A multi-objective optimized hybrid image watermarking technique is being proposed considering robustness, imperceptibility and security aspects using two different scaling factors. In this technique, original image is subjected to third level lifting wavelet transform (LWT) followed by singular value decomposition (SVD). Watermark is split into two parts to embed each of them into a different subband. In the suggested scheme, firefly algorithm is employed to get optimum solutions for two scaling factors to balance trade-off amid invisibility and robustness. Security in digitized data is an important aspect of image processing. It is improved with a key, an input to Arnold transform for scrambling watermark, to watermark embedding and extraction procedures. All the performance parameters like peak signal to noise ratio (PSNR), structural similarity index measure (SSIM), normalized correlation coefficient (NCC) and bit error rate (BER) are used in formulating maximization objective function. Evaluation of the proposed algorithm indicates that it is characterized by fairly good robustness, invisibility and security.
Cross-Domain Authentication Technology of UAV Based on Alliance Chain

As the application scope of UAV expands, the demand for the cross-domain mission execution capability of UAV is increasing, which also puts forward higher requirements for the cross-domain certification capability of UAV. This paper proposes and verifies a cross-domain authentication scheme based on alliance chain technology. The experimental results show that compared with the original scheme, it greatly improves the efficiency of cross-domain authentication while solving the problem of trust between control stations.
End-to-end deep auto-encoder for segmenting a moving object with limited training data

&lt;span lang="EN-US"&gt;Deep learning-based approaches have been widely used in various applications, including segmentation and classification. However, a large amount of data is required to train such techniques. Indeed, in the surveillance video domain, there are few accessible data due to acquisition and experiment complexity. In this paper, we propose an end-to-end deep auto-encoder system for object segmenting from surveillance videos. Our main purpose is to enhance the process of distinguishing the foreground object when only limited data are available. To this end, we propose two approaches based on transfer learning and multi-depth auto-encoders to avoid over-fitting by combining classical data augmentation and principal component analysis (PCA) techniques to improve the quality of training data. Our approach achieves good results outperforming other popular models, which used the same principle of training with limited data. In addition, a detailed explanation of these techniques and some recommendations are provided. Our methodology constitutes a useful strategy for increasing samples in the deep learning domain and can be applied to improve segmentation accuracy. We believe that our strategy has a considerable interest in various applications such as medical and biological fields, especially in the early stages of experiments where there are few samples.&lt;/span&gt;
Recognition of crowd abnormal activities using fusion of handcrafted and deep features

Constant vigilance is extremely important at crowded public places where some unusual activities such as sudden dispersion or continuous gathering of people may lead to chaotic and disastrous situations. Automatic recognition of such collective activities of people is indeed an important task to ensure people safety. In this view, we propose a novel approach for automatic recognition of crowd merging and sudden dispersion events. The proposed method detects dispersion and merging using fusion of features extracted by deep networks with a novel set of optical flow based and density based handcrafted features. These proposed features are not affected by occlusion and illumination. These features complement the features extracted by deep network and their fusion improves the performance of event recognition by significant amount. The method is tested profoundly on benchmark public datasets as well as private datasets. Abnormal activity recognition data often suffers from high class imbalance. However, the proposed method could successfully recognize sudden dispersion and merging activities on such very small datasets having class imbalance. This proves the effectiveness and robustness of the proposed features. The proposed method also shows better performance than other state of the art methods based on deep networks.
Recommender systems: a novel approach based on singular value decomposition

&lt;span lang="EN-US"&gt;Due to modern information and communication technologies (ICT), it is increasingly easier to exchange data and have new services available through the internet. However, the amount of data and services available increases the difficulty of finding what one needs. In this context, recommender systems represent the most promising solutions to overcome the problem of the so-called information overload, analyzing users' needs and preferences. Recommender systems (RS) are applied in different sectors with the same goal: to help people make choices based on an analysis of their behavior or users' similar characteristics or interests. This work presents a different approach for predicting ratings within the model-based collaborative filtering, which exploits singular value factorization. In particular, rating forecasts were generated through the characteristics related to users and items without the support of available ratings. The proposed method is evaluated through the MovieLens100K dataset performing an accuracy of 0.766 and 0.951 in terms of mean absolute error and root-mean-square error.&lt;/span&gt;
Development and Evaluation of Learning Resource Package in Optics for Grade 10 Science

Learning complicated topics in Physics is one of the most challenging tasks students experi-ence in the classroom. Physics teachers use supplementary materials and laboratory exercises to help students visualize and imagine what they are studying in order to better understand the topics. These learning aids, however, are sometimes not sufficient. Students would still struggle to learn due to lack of effective teaching materials. Without effective teaching materials, it is impossible to master Physics concepts. The study focused on developing and evaluating a learning resource package in Optics for Grade 10 Science. The results showed that the resource package will provide an effective and alternative method for students to be exposed to the scientific method and to explore and observe what the beauty of physics does in our lives. These are very helpful for teachers when teaching Physics concepts. Furthermore, because the learning resource package is already complete, the teacher may feel more prepared to teach optics classes. It is recommended that teacher-writers create lessons that are similar to those already used in science classrooms which may directly address the needs of students.
An overview of number theory research unit variant development security

Number &lt;span&gt;theory research unit (NTRU) become the most important of securi-ty in recent, with its modification of their variant, this paper search of the literature and A number of studies have examined the in public key variant development and security. In general, prior work is limited to a subset of public key increasing complexity but the benefits of speed up encryption/ decryption have not been fully established. So this paper will be the basis for those who want to develop and find proposed solutions for new studies of the NTRU algorithm. This paper aims to develop a framework to investigate the NTRU development, had been discovered that despite its development over the years and even its acceptance in round three of post quantum cryptograph, then found that limit study in the new scope of quantum facility and the ability of hybrid of new study&lt;/span&gt;.
A new curve fitting based rating prediction algorithm for recommender systems

The most algorithms for Recommender Systems (RSs) are based on a Collaborative Filtering (CF) approach, in particular on the Probabilistic Matrix Factorization (PMF) method.It is known that the PMF method is quite successful for the rating prediction.In this study, we consider the problem of rating prediction in RSs.We propose a new algorithm which is also in the CF framework; however, it is completely different from the PMF-based algorithms.There are studies in the literature that can increase the accuracy of rating prediction by using additional information.However, we seek the answer to the question that if the input data does not contain additional information, how we can increase the accuracy of rating prediction.In the proposed algorithm, we construct a curve (a low-degree polynomial) for each user using the sparse input data and by this curve, we predict the unknown ratings of items.The proposed algorithm is easy to implement.The main advantage of the algorithm is that the running time is polynomial, namely it is θ(n 2 ), for sparse matrices.Moreover, in the experiments we get slightly more accurate results compared to the known rating prediction algorithms.
Innovative Research on Intelligent Classroom Teaching Mode in the “5G” Era

The integration of “5 G + education” has become a consensus and trend. The maturity and application of technologies represented by artificial intelligence, virtual reality, and big data in the 5 G era have provided a new and greater impetus for education and teaching reform. Education must adhere to the concept of people-oriented, high-quality talent training is the foundation and core of higher education, and colleges and universities need to quickly adapt to the development trend of education in the new era. Therefore, it is of significance to investigate some innovative strategies to address the existing problems in the intelligent classroom teaching mode in the “5 G” Era. This paper discusses the characteristics of high speed and low latency in the 5 G era, discusses the development of the intelligent classroom teaching promoted by 5 G technology, and creatively puts forward different teaching strategies in combination with the problems existing in an intelligent classroom in colleges and universities. Students interact with the constructed virtual or virtual real scenes in real time. With the help of artificial intelligence technology, can establish artificial intelligence teaching assistants and learning partners, support one-to-one skills training, will also provide teachers with an intelligent learning environment, integrate teaching resources, optimize the teaching path, improve teaching interaction, provide a reference for future teaching activities, explore the application of innovative teaching mode based on 5 G communication. Big data will record the learning behavior in the process and use it for the diagnosis and evaluation of dynamic learning results, providing a basis for educators to practice “people-oriented” and help students grow up.
Best S-box amongst differently sized S-boxes based on the avalanche effect in ‎the advance encryption standard algorithm

&lt;span lang="EN-US"&gt;Substitution boxes are essential nonlinear modules that are popular in block ‎cipher algorithms. They ‎also play a significant role in the security area because of ‎their robustness to different linear ‎cryptanalysis. Each element of the state in a S-‎box is nonlinearly replaced using a lookup table. This ‎research presents the S-‎box, one of the fundamental parts of the advanced encryption standard ‎‎(AES) ‎algorithm. The S-box represents the confusion part in the AES. However, when ‎information ‎is shared between different devices in an authorized manner, the ‎algorithm should be able to ‎combine a sufficient number of confusion layers to ‎guarantee the avalanche effect (AE). ‎Subsequently, this research selects the best ‎S-box by comparing different sizes (4×4, 8×8, and ‎‎16×16) and measuring them ‎on the basis of the million-bit encryption. The AE is the main criterion ‎used in ‎choosing the best S-box. A robust and strong cryptography algorithm should be ‎able to ‎confirm the AEs. Results indicate that the 16×16 S-box with a 52% AE ‎ratio is the superior S-box&lt;/span&gt;
Morphology for Hexagonal Image Processing

Abstract Morphological operators for binary and grayscale images are commonly used to eliminate noise, recognize contours or specific structures, and arrange shapes in image processing. Even though morphology has been substantially developed in square-pixel-based-image-processing (SIP), no effort has been made to construct morphological operators in hexagonal-pixel-based-image-processing (HIP) yet. In this paper, we transform basic SIP-domain-morphological operators such as dilation, erosion, closing, and opening into HIP-domain and compare their performance with their SIP counterparts, taking into account 65 similarity- dissimilarity metrics for binary and four metrics for grey-scale proposed so far in the literature. It is the first time to give the fundamental morphological operators in the HIP domain. The operators developed in this paper initiate the research about morphology in the HIP domain by successfully filling a significant gap by eliminating HIP's lack of basic operators.
Effectiveness of Low-Numerical Rank Approximation to Image Compression in Wavelet Domain

In this paper, we report a modeling of approximation for images by finding numerical rank in the wavelet domain through singular value decomposition of approximation coefficients. Firstly, the digital image is transformed into the frequency domain. Then high-frequency sub-bands are quantized to zero. This is quite obvious in wavelet-based image compression. Simultaneously, the low-frequency sub-bands are compressing by using truncated singular value decomposition (TSVD) through a numerical rank. Finally, reconstruct the approximation matrix via inverse discrete wavelet transform with low computational intricacy. This mathematical model is more adequate for solving engineering problems arises in digital image processing such as the transmission of image (reducing the bandwidth size of a communication channel) and storage capacity (space saving). The simulation results on gray and color images show that there is a gain in: (i) the compression ratio with acceptable visual quality as per human vision system; (ii) balancing of performance measures over conventional SVD methods.
Penerapan Metode Building Information Modeling (BIM) Pada Pembangunan Gedung Integrated Laboratory for Natural Science and Food Technology Universitas Jember

Universitas Jember built high rise building and had a high complexity of work, it requires a construction project management to simplify and minimize errors in its process. Project management performance can be described through the concept of Building Information Modeling (BIM). The application of BIM method in this study used Revit Architecture for software support to obtain volume at each job in the monitoring process to determine MC0 and termin setting processes. The data used namely shop drwaning / forcon which is used as a reference in model. Building modeling in this study is divided into 3 modeling phases, namely lower structure modeling, upper structure and architecture. Based on the model, a tie beam volume was 26.74 m3 with reinforcement was 4,361 kg, 1st floor column concrete modeling was 52.87 m3 with reinforcement was 12.176,39 kg and perimeter wall volume on the 1st floor was 320.71 m2. The error that appeared in the calculation of the concrete volume in the BIM modeling with the conventional method is 0% while the error that appears in the reinforcement calculation is 0-0.042%.
Features of Constructing a Solution Heterogeneous Equation and Clausen-Type Systems

The possibilities of constructing a solution to an inhomogeneous the third-order equation have been studied, in particular the Clausen equation in the vicinity of special points x=0 and x=∞. The construction of the method for indeterminate coefficients to the construction of partial solutions of the Clausen equation is shown. These ideas are extended to constructing private solutions of the simple Clausen system near a regular feature with solutions in the form of a product of two hypergeometric Clausen functions, each of which depends on one variable. A number of properties of the product of Clausen functions constructed near these features have been proved. The construction of a common solution of the main heterogeneous system of Clausen is investigated. Four new functions have been created, representing the private solutions of the heterogeneous equation and systems like Clausen.
Information and Digital Literacy of First-Year Students of Hispanic Studies

In the theoretical part of the paper, I present the definitions of information and digital literacy, where its role is depicted within the education systems in certain countries. In the research part of the paper, I raise the issue of information and digital literacy of first-year students of bachelor studies of the 2020/2021 generation of the Department of Iberian Studies of the Faculty of Philology at the University of Belgrade, Serbia. The aim of the research is to determine the development of information and digital skills based on the analysis of student papers. The research methodology is related to the analysis of papers written for the subject Spanish1, where it was required for students to list the sources used in the paper, with definitions of the given terms. Also, the analysis raises the question of whether students rely on books and textbooks or on internet websites. The results of the analysis indicate that 72.25per cent of the students did the task according to the given instructions, citing the relevant sources in the paper. In conclusion, I point out that the percentage of works that met the requirements of the work justified the expectations of the research taking into account the fact that there is no mandatory subject of information and digital literacy in bachelor studies. In this regard, the high percentage of successful papers is significant. Further research could focus on information and digital literacy in other years of bachelor studies of this department.
Natural Language Processing Application on Commit Messages: A Case Study on HEP Software

Version Control and Source Code Management Systems, such as GitHub, contain large amount ofunstructured historical information of software projects. Recent studies have introduced Natural Language Processing (NLP) to help software engineers retrieve information from very large collection of unstructured data. In this study, we have extended our previous study by increasing our datasets and ML and clustering techniques. Method: We have followed a complex methodology made up of various steps. Starting from the raw commit messages we have employed NLP techniques to build a structured database. We have extracted their main features and used as input of different clustering algorithms. Once labelled each entry, we have applied supervised machine learning techniques to build a prediction and classification model. Results: We have developed a machine learning-based model to automatically classify commit messages of a software project. Our model exploits a ground-truth dataset which includes commit messages obtained from various GitHub projects belonging to the HEP context. Conclusions: The contribution of this paper is two-fold: it proposes a ground-truth database; it provides a machine learning prediction model. They automatically identify the more change-proneness areas of code. Our model has obtained a very high average precision, recall and F1-score.
COMMUNICATION COMPETENCY FOR TEACHERS OF YAKUT SPECIAL SCHOOL PURWOKERTO: POTENTIAL AND CHALLENGES

The language skills of teachers in special schools (Sekolah Luar Biasa/SLB) possess specific characteristics and must be continuously improved. Language habituation for special students is a reflection of their daily communication skills. The teacher's need for classroom management for special students becomes a new challenge because teachers are expected to optimize the use of language in the classroom to enhance students' communication skills directly. Based on the background of the problem, it is essential to conduct a clinical pedagogical analysis for teachers in dealing with problems that occur in the classroom, especially in language learning and communication for special students at SLB B Yakut Purwokerto. In order to answer the formulation of the problem, this study employed a qualitative approach to answer the clinical-pedagogical analysis conducted. The analytical instruments include (1) analysis of vocal abilities such as tone, articulation, pronunciation, application of intonation in learning; (2) analysis of self-expression abilities such as mimics and gestures in learning; and (3) the attitude and mentality of communication in learning. Based on the results of the learning analysis, it shows that the teacher's language skills still need to be improved. On a scale of 100, the results of the analysis of four language skills in SLB teacher learning comprise of communication skills which are 8.2, and class management 7.8. The development of communication and interaction-based learning media acquired 7.0 by implementing learning media technology to initiate creative ideas from teachers of SLB B Yakut Purwokerto, which later can be a good atmosphere for other schools. Teachers improve pedagogic knowledge and competence in optimizing learning situations through quality and fun interactive learning. All teachers realize the urgency of mastering speaking skills: emphasizing essential points, intonation, clear articulation; hence, special students can understand the material quickly in enhancing students' abilities.