Reviewerly/exHarmony huggingface dataset:
TubeMap: A Projection for Omnidirectional View Interpolation

We propose a projection for omnidirectional images called TubeMap which is efficient for omnidirectional view interpolation. This new projection keeps linearity on the captured scenes and covers almost every direction in a single region with no discontinuities by extending the projection planes of a cube map along the direction from one camera towards another. TubeMap makes it easy to construct correspondence between two omnidirectional images while eliminating distortion on interpolated views.
Managing software evolution in embedded automotive systems

Customer requirements evolve frequently in the automotive domain. A the same time, rapid integration of innovative solutions is important to maintain and extend the customer base (Vitale et al. 2020). Thus, automotive software components are subject to constant change and variations, even when the vehicle is already on the road (Guissouma et al. 2018).
Model Weighting for One-Dependence Estimators by Measuring the Independence Assumptions

The superparent one-dependence estimators (SPODEs) is a popular family of semi-naive Bayesian network classifiers, and the averaged one-dependence estimators (AODE) provides efficient single pass learning with competitive classification accuracy. All the SPODEs in AODE are treated equally and have the same weight. Researchers have proposed to apply information-theoretic metrics, such as mutual information or conditional log likelihood, for assigning discriminative weights. However, while dealing with different instances the independence assumptions for different SPODEs may hold to different extents. The quest for highly scalable learning algorithms is urgent to approximate the ground-truth attribute dependencies that are implicit in training data. In this study we set each instance as the target and investigate extensions to AODE by measuring the independence assumption of SPODEs and assigning weights. The proposed approach, called independence weighted AODE (IWAODE), is validated on 40 benchmark datasets from the UCI machine learning repository. Experimental results reveal that, the resulting weighted SPODEs delivers computationally efficient low-bias learning, proving to be a competitive alternative to state-of-the-art single and ensemble Bayesian network classifiers (such as tree-augmented naive Bayes, k-dependence Bayesian classifier, WAODE-MI and etc).
BabelEnconding at SemEval-2020 Task 3: Contextual Similarity as a Combination of Multilingualism and Language Models.

This paper describes the system submitted by our team (BabelEnconding) to SemEval-2020 Task 3: Predicting the Graded Effect of Context in Word Similarity. We propose an approach that relies on translation and multilingual language models in order to compute the contextual similarity between pairs of words. Our hypothesis is that evidence from additional languages can leverage the correlation with the human generated scores. BabelEnconding was applied to both subtasks and ranked among the top-3 in six out of eight task/language combinations and was the highest scoring system three times.
Comparative Analysis of Various Image Splicing Algorithms

Daily millions of images are uploaded and download to the web, as a result the data is available in the paperless form in the computer system for organization. Nowadays, with the help of powerful computer software such as Photoshop and Corel Draw, it is very easy to alter the contents of the authenticated image without leaving any clues. This led to a big problem due to the negative impact of image splicing. It is highly recommended to develop image tampering detection technique to recognize the authentic and temper images. In this paper, we propose an enhanced technique for blind images splicing by combing Discrete Cosine Transform Domain (DTC) and Markov feature in the spatial domain. Moreover, Principal Component Analysis (PCA) is used to select the most significant features. Finally, Support Vector Machine (SVM) is applied to classify the image as being tempered or genuine on the publicly available dataset using ten-fold cross-validation. By applying different statistical techniques, the results showed that the proposed technique performs better than other available detection techniques in the literature.
Smart Systems Identification Problem

In the first Chapter, the problem of identification of a smart system is considered from the standpoint of compliance of the functioning of the system with its purpose of functioning and evaluation of this compliance. The main issues here are: In the first Chapter, the problem of identification of a smart system is considered from the standpoint of compliance of the functioning of the system with its purpose of functioning and evaluation of this compliance. The problem of identification of smart systems is to answer the question: does the system satisfy the objective of its operation at each moment in time, and if so, how much? Thus, the problem of identification is divided into the following two tasks: 1. Does the system S satisfy the objective of its operation P(S)? 2. To assess how the system S satisfies the goals of its functioning P(S)? Also, we construct a structural (by the structure of system attributes) analogue of the econometric method of principal components, developed by the outstanding Russian algebraist D. K. Faddeev.
Research of Application of Artificial Intelligence in Preschool Education

Abstract At present, preschool education has been included in China’s compulsory education and has become an important part of China’s basic education. With the continuous development of information technology, kindergartens in some coastal areas have begun to apply artificial intelligence technology to the daily teaching. The use of artificial intelligence technology for teaching can not only improve children’s enthusiasm for learning but also help the reform of preschool education institutions. In view of this, the article mainly analyzes the application of artificial intelligence technology in preschool education, and proposes corresponding solutions to the problems in artificial intelligence education and teaching.
Visualization transforms of non-spatial data for convolutional neural networks

Many datasets in important fields like healthcare and finance are often in a tabular format, where each observation is expressed as a vector of various feature values. While there exist several competitive algorithms such as random forests and gradient boosting, convolutional neural networks (CNNs) are making tremendous strides in terms of new research and applications. In order to exploit the power of convolution neural networks for these tabular datasets, we propose two vector-to-image transformations. One is a direct transformation, while the other is an indirect mechanism to first modulate the latent space of a trained generative adversarial network (GAN) with the observation vectors and then generate the images using the generator. On both simulated and real datasets, we show that CNNs trained on images based on our proposed transforms lead to better predictive performance compared to random forests and neural networks that are trained on the raw tabular datasets.
The impact of knowledge management on human capital in higher education: perception from a developing country

Knowledge management (KM) and human capital (HC) are prominent topics in the academic environment. Knowledge is considered as a critical factor of organisation competitiveness, particularly in higher education institutions. As such, this study aims to investigate the interaction between KM practices and HC in a developing country, Jordan. It also presents a conceptual framework that promotes KM practices for HC advancement in higher education (HE) institutions in Jordan. The objectives are to provide guidance on the successful transformation of Jordanian HE institutions to knowledge institutions, in addition to helping HE improve the management of their human resources, and build an efficient HC utilisation culture. This study uses content analysis approach to systematically analyse participants' feedback and opinions and address the research questions. Overall, the results extend existing findings by highlighting the importance of incorporating the factors that affect KM practice implementation with the objective of HC improvement. We believe that this study has important implications for policy makers who focus on enhancing the competitive position of HE institutions in Jordan.
Efficient cryptography techniques for image encryption in cloud storage

In recent years and with the great progress of the use of cloud computing and their uses that covered most aspects of modern life as well as provide a variety of services, such as the formation of computing resources, cost control, sustainability, mobility and service flexibility. However, there are challenges to cloud computing, the most important of which is data security and transmission. Cryptographic is the science of protecting data by converting data (plain text) into an incomprehensible format (cipher text) for unauthorized individuals through the use of mathematical techniques. This paper provides work for the most common encryption algorithms that are utilized to encryption of data in cloud computing and presented some of papers that based on the most common cryptographic techniques such as DES, 3DES, Blowfish, AES, RSA, D-H, ECC And others. This way paves the way for finding the suitable encryption algorithm to protect the data in the cloud environment. The AES outperforms the other algorithms in term of encryption time.
Triangle Enumeration on Massive Graphs Using AWS Lambda Functions

Triangle enumeration is a fundamental task in graph data analysis with many applications. Recently, Park et al. proposed a distributed algorithm, PTE (Pre-partitioned Triangle Enumeration), that, unlike previous works, scales well using multiple high end machines and can handle very large real-world networks.This work presents a serverless implementation of the PTE algorithm using the AWS Lambda platform. Our experiments take advantage of the high concurrency of the lambda instances to compete with the expensive server-based experiments of Park et al. Our analysis shows the trade-off between the time and cost of triangle enumeration and the numbers of tasks generated by the distributed algorithm. Our results reveal the importance of using a higher number of tasks in order to improve the efficiency of PTE. Such an analysis can only be performed using a large number of workers which is indeed possible using AWS Lambda but not easy to achieve using few servers as in the case of Park et al.
Research on MapReduce Parallel Optimization Method Based on Improved K-means Clustering Algorithm

The traditional K-means clustering algorithm occupies a large quantity of memory resources and computing costs when dealing with massive data. It is easy to be restricted by something such as the initial center point as well abnormal data, and usually can not achieve effective clustering of large-scale data. In order to effectively solve the limitations of the algorithm, we propose a MapReduce parallel optimization method based on improved K-means clustering algorithm. Firstly, differential evolution theory is introduced to determine the optimal initial clustering center, after that, on the basis of the influence of samples on clustering results, the corresponding weighted Euclidean distance is designed to achieve effective data differentiation, so as to effectively reduce the impact of samples on clustering analysis. The negative effect of abnormal data on clustering analysis can improve the accuracy of clustering. Finally, MapReduce programming model is used to realize parallel clustering. We use UCI datasets to verify the parallel optimization method. From the experimental results we can clearly know that the method we proposed has relatively stable parallel clustering results, faster operation speed, and effectively saves the operation time.
Moodle Based Worksheet on Scientific Article Writing: A Students Perceptions

Moodle based Worksheet on Scientific Article Writing: A Students Perceptions. Objectives: This research aims to find out necessity analysis of student worksheet development based on Moodle for scientific article writing reviewed by student perceptions. Method: The data were collected by using questionnaire and interview activity. The questionnaire was used to find out the students’ perceptions. The interview was used to adjust and match the questionnaire data result with the actual condition. The technique of analyzing data is descriptive qualitative analysis. Findings: The findings showed that:1) students perceived the scientific article writing material important to learn, 2) the learning process had not facilitated the students, 3) the learning material was only centered on textbook, 4) the technology utilization in composing learning material should be improved, and 5) writing material presentation should be revised based on the students’ characteristics. Conclusion: It is necessary to develop Moodle-based student worksheet on scientific articles writing. Keywords: Moodle-based worksheet, scientific article, students perceptions. Abstrak: Lembar kerja siswa berbasis Moodle p ada penulisan artikel ilmiah ditinjau dari persepsi siswa. Tujuan: Penelitian ini bertujuan untuk mengetahui analisis kebutuhan terhadap pengembangan lembar kerja siswa (LKS) berbasis Moodle pada penulisan artikel ilmiah ditinjau dari persepsi siswa. Metode: Data dikumpulkan dengan menggunakan angket dan wawancara. Angket digunakan untuk mengetahui persepsi siswa. Wawancara digunakan untuk menyesuaikan dan mencocokkan antara hasil data angket dan kondisi di lapangan. Teknik analisis data yang digunakan yaitu teknik analisis deskriptif kualitatif. Temuan: Hasil penelitian menunjukkan bahwa 1) siswa menganggap materi penulisan artikel ilmiah penting untuk dipelajari, 2) proses pembelajaran belum membantu siswa, 3) bahan ajar yang digunakan hanya terpusat pada buku, 4) pemanfaatan teknologi dalam menyusun bahan ajar perlu ditingkatkan, dan 5) penyajian materi perlu direvisi sesuai dengan karakteristik siswa. Kesimpulan: Perlu dilakukan pengembangan LKS berbasis Moodle pada penulisan artikel ilmiah. Kata kunci: lembar kerja siswa berbasis Moodle, artikel ilmiah, persepsi siswa. DOI: http://dx.doi.org/10.23960/jpp.v10.i1.202013
Patching as translation

Machine Learning models from other fields, like Computational Linguistics, have been transplanted to Software Engineering tasks, often quite successfully. Yet a transplanted model's initial success at a given task does not necessarily mean it is well-suited for the task. In this work, we examine a common example of this phenomenon: the conceit that patching is like language translation. We demonstrate empirically that there are subtle, but critical distinctions between sequence-to-sequence models and translation model: while program repair benefits greatly from the former, general modeling architecture, it actually suffers from design decisions built into the latter, both in terms of translation accuracy and diversity. Given these findings, we demonstrate how a more principled approach to model design, based on our empirical findings and general knowledge of software development, can lead to better solutions. Our findings also lend strong support to the recent trend towards synthesizing edits of code conditional on the buggy context, to repair bugs. We implement such models ourselves as proof-of-concept tools and empirically confirm that they behave in a fundamentally different, more effective way than the studied translation-based architectures. Overall, our results demonstrate the merit of studying the intricacies of machine learned models in software engineering: not only can this help elucidate potential issues that may be overshadowed by increases in accuracy; it can also help innovate on these models to raise the state-of-the-art further. We will publicly release our replication data and materials at this https URL.
Using Quasi-Experimentation to Gather Design Information for Intelligent Tutoring Systems

The development of any Intelligent Tutoring System (ITS) requires the acquisition of diverse and extensive design information. This chapter describes what specific design information is needed to build an ITS. It outlines several issues with regard to using empirical studies to gather design information of which the ITS designer should be aware. A traditional ITS is divided into four distinct modules: expert model, student model, instructional model (which incorporates diagnostic and pedagogical processes), and the instructional environment, or user interface. The chapter briefly describes each of these components, including the types of information ITS designers should try to acquire for the specific modules. It presents two case studies of field research conducted for different ITSs, one for teaching spreadsheet programming, the other for teaching object-oriented modeling. Finally, the chapter presents some suggestions for designing and conducting quasi-experimental field research for the purpose of designing an ITS.
Absolute flatness measurements by Fizeau interferometer and 3D scanning deflectometric profiler

The National Metrology Institute of Technology provides a flatness calibration service for large flat substrates using a Fizeau interferometer with 10 nm (k = 2) uncertainty over a measurement range of 300 mm. In the Fizeau interferometer, the surface profile of the reference flat is a measurement error factor because the measurand is the gap distance between the reference flat and the specimen. We have reduced uncertainty by constructing a surface profile map of the reference flat based on the three-flat test; however, it is difficult to manufacture a highly accurate reference flat and create a correction surface profile map in order to measure even larger flat substrates. Thus, we developed a three-dimensional scanning deflectometric profiler (3-D SDP) that does not require a reference flat and can directly measure a surface profile. Measuring devices based on deflectometry have been developed by many laboratories for highly accurate straightness profile measurement, but measurement by such systems is limited to a line (two-dimensional) profile. To solve this problem, we developed a novel method of measuring the surface topography by calculating the topography from radial lines obtained by rotating the specimen. In this study, we compared measurements between the Fizeau interferometer and 3-D SDP. We also report the results of measuring an optical flat with a diameter of 300 mm.
Reconfigurable rectifiers in active photonics

The important nonlinear effect of optical rectification in active metasurfaces,converts high frequency vis/infrared light to direct current. Recently, researchers discovered a reconfigurable optically rectifying junction. We focus on 3 junctions: 1) optical rectification in a simple, lithographically-defined Metal-Insulator-Metal (MIM) diode consisting of a planar Al electrode, a thin Al<sub>2</sub>O<sub>3</sub> barrier layer, and a planar Ag counter-electrode. Applying a voltage grows nm-scale filament from the Al side. 2) nanoplatelet and substrate that demonstrates single-electron tunneling (SET) predict and model low-energy (&lt; fJ) and high speed (&gt;MHz) synaptic operations 3) Experiments conducted to determine whether a ferromagnetic layer in MIM reoriented can change the direct current and the rectified current, when exposed to an incident laser beam. This large tunability enables adaptive ultrafast photon detectors, wireless power transmission, energy harvesting, advanced antennas, computing.
The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation

Recently there has been a growing interest in fairness-aware recommender systems including fairness in providing consistent performance across different users or groups of users. A recommender system could be considered unfair if the recommendations do not fairly represent the tastes of a certain group of users while other groups receive recommendations that are consistent with their preferences. In this paper, we use a metric called miscalibration for measuring how a recommendation algorithm is responsive to users' true preferences and we consider how various algorithms may result in different degrees of miscalibration for different users. In particular, we conjecture that popularity bias which is a well-known phenomenon in recommendation is one important factor leading to miscalibration in recommendation. Our experimental results using two real-world datasets show that there is a connection between how different user groups are affected by algorithmic popularity bias and their level of interest in popular items. Moreover, we show that the more a group is affected by the algorithmic popularity bias, the more their recommendations are miscalibrated.
Short Text Classification and Clustering based Mobile Application Traffic Identification Method

Abstract With the rapid development of mobile network, mobile traffic accounts for a large proportion of network traffic nowadays, and mobile application traffic identification is becoming increasingly important in network security. For mobile application traffic identification, recent works have focused on proposing supervised classifiers that have shown promising performance. However, it is difficult to obtain labeled traffic in practice and most of the traffic is unlabeled. In this paper, we propose a semi-supervised mobile application traffic identification method based on short text classification and clustering, which requires only a small number of labeled samples to classify traffic. First, plain text in mobile traffic is regarded as short text and its features are extracted using a short text classification algorithm. Then K-Means++ is used to cluster the samples and give the predictions. Experimental results show that the proposed method is more effective than the semi-supervised traffic identification methods that use statistical features.
Optimization of fuzzy classifier parameters with a combination of gravitational search algorithm and shuffled frog leaping algorithm

Abstract In the present article, we analyse the effectiveness of combining two metaheuristic algorithms for tuning parameters of a fuzzy classifier. To work with imbalanced data, a fitness function is used based on a compromise between the overall accuracy and the geometric mean of accuracy of each class. The experiment was performed on data sets from the “Knowledge Extraction based on Evolutionary Learning” repository with different imbalance coefficients.
Estimating capacity-oriented availability in cloud systems

Over the years, many companies have employed cloud computing to support their services and optimise their infrastructure utilisation. The provisioning of high availability and high processing capacity is a significant challenge when planning a cloud computing infrastructure. Even when the system is available, a part of the resources may not be offered due to partial failures in just a few of the many components in an IaaS cloud. The dynamic behaviour of virtualised resources requires special attention to the effective amount of capacity that is available to users, so the system can be correctly sized. Therefore, the estimation of capacity-oriented availability (COA) is an important activity for cloud infrastructure providers to analyse the cost-benefit tradeoff among distinct architectures and deployment sizes. This paper presents a strategy to evaluate the capacity-oriented availability of virtual machines combined to servers availability on a private cloud infrastructure. The proposed strategy aims to provide an efficient and accurate computation of COA and availability by means of closed-form equations. We compare our approach to the use of models such as continuous time Markov chains and SPN simulation model, considering execution time and values of metrics obtained with both approaches.