Reviewerly/exHarmony huggingface dataset:
READINESS OF FUTURE MUSIC TEACHERS FOR CREATIVE SELF-ACTUALIZATION

Purpose of the study: The article deals with the problem of the readiness of future general education music teachers for creative self-actualization. The issue is regarded as an integral personality formation ensuring the achievement of creative self-actualization and including two interrelated components – professional readiness and personal readiness.&#x0D; Methodology: The research was carried out at the Institute of Philology and Intercultural Communication, Kazan Federal University, and based on the methods of observation, interviewing students and teachers, studying academic and methodical literature.&#x0D; Main Findings: The research showed the variability of the formation of the readiness for creative self-actualization, owing to a complex of subjective psychological and objective pedagogical factors, in particular, various types of pre-university training of students.
Automatic Examinee Validation System using Eigenfaces

In most of the developing countries the seats of educational institutions are limited and barley 30% of the total eligible candidates get the opportunity to get admitted after facing competitive admission test. The scenario is worse in the job market. The growing graduation to unemployment ratio has become a major concern in developing and underdeveloped countries. As a result, job recruitment tests have become more competitive than ever. In order to survive in these types of competitive examinations, many candidates try to take unethical approaches. Because of large number of candidates, exam administrators cannot validate each and every candidate one by one. Moreover, modified image in registration paper makes it difficult to identify valid examinee. This is the weakest point of examinee validation system of competitive examination and significant number of candidates try to pass the examination by exploiting this weakness. The easiest way to exploit this weakness is to hire and send proxy examinee who has higher possibility of passing the exam instead of actual examinee. In this paper, we are proposing an automatic, fast, efficient and accurate yet simple to implement examinee validation system using eigenfaces. Our system will make it almost impossible for a proxy examinee to enter the exam-center.
Statistical bootstrap-based principal mode component analysis for dynamic background subtraction

Background subtraction is needed to extract foreground information from a video sequence for further processing in many applications, such as surveillance tracking. However, due to the presence of a dynamic background and noise, extracting foreground accurately from a video sequence remains challenging. A novel projection method, namely Principal Mode Component Analysis (PMCA), is proposed to capture the most repetitive patterns of a video sequence, which is one of the key characteristics of the video background. The patterns are captured by applying the bootstrapping method together with the statistic mode measure. The bootstrapping method can model the distribution of almost any statistic of the dynamic background and complicated noise. This is different from current methods, which restrict the distribution to a closed-form function. We introduce a mathematical relaxation that can formulate the statistical mode measure for a continuous video data. A fast exhaustive search method is proposed to find the global optimal solution for the PMCA. This fast method adopts a simplification procedure that makes the optimization procedure independent of the video size. The proposed method is computationally much more traceable than existing ones. We compare the proposed method with 10 different methods, including several state-of-the-art techniques, for 19 different real-world video sequences from two popular datasets. Experiment results show that the proposed method performs the best in 16 cases and second best in 2 cases.
IT Roadmap to Improve Business Strategy using TOGAF ADM: A Case Study of Government-Owned Electricity Company

Abstract Service industry in Indonesua has applied IT to conduct and establish their business process through automation and integration in order to achieve effectiveness and efficiency, especially in the industry of electricity. However, there is still no alignment between business strategy and IT assets. Thus, it is necessary to implement Enterprise Architecture to have appropriateness between the organizational’s goal and the strategy’s execution in the field. This research identifies various components in the respected industry by implement TOGAF ADM as the framework through observation and interview, with begin with preliminary phase until migration planning. The purpose is to create IT Roadmap in the form of recommendation for the electricity company in order to achieve their short and long term objectives.
Toward Improved Artificial Intelligence in Requirements Engineering: Metadata for Tracing Datasets

Data is the driver of artificial intelligence in requirements engineering. While some applications may lend themselves to training sets that are easily accessible (such as sentiment detection, feature request classification, requirements prioritization), other tasks face data challenges. Tracing and domain model building are examples of applications where data is not easily found or in the proper format or with the necessary metadata to support deep learning, machine learning, or other artificial intelligence techniques. This paper surveys datasets available from sources such as the Center of Excellence for Software and Systems Traceability and provides valuable metadata that can be used by re-searchers or practitioners when deciding what datasets to use, what aspects of datasets to use, what features to use in deep learning, and more.
UnLearnerMC: Unsupervised learning of dense depth and camera pose using mask and cooperative loss

In this paper, we propose an unsupervised learning framework, named UnLearnerMC, for jointly training monocular depth, camera pose, and segmentation from videos. Existing unsupervised methods typically exploit optical flow consistency to train the segmentation network and eliminate interference from moving objects. Our key point is to create SegMaskNet and corresponding training losses for distinguishing between moving objects and static scenes. Specifically, we eliminate misleading by exploiting boundary masks when moving objects are beyond the view boundary. The SegMaskNet constantly adjusts the allocation of static and moving object pixels via a cooperative loss to minimize the total loss during training. For the moving areas, we can use the re-estimated static depth and pose to eliminate interference from moving areas by using the proposed photometric loop consistency loss. Experiments on KITTI datasets show that UnLearnerMC achieves state-of-the-art results in single-view depth and camera ego-motion, which illustrate the benefits of our approach.
Blockchain for Cyber-Physical System in Manufacturing

This paper proposed a blockchain manufacturing framework that involves the utilization of secondary validation and collaborative distribution concepts. Conventional and blockchain manufacturing simulators were developed to investigate the effects of blockchain technologies on manufacturing systems in terms of time and product quality. The simulation results illustrated that the products manufactured in the blockchain-based system are of better quality than the conventional system but does not necessarily reduce the manufacturing times due to the secondary validation and collaborative distribution processes, which are also responsible for ensuring manufacturing quality. The results also emphasized the importance of utilizing collaborative distribution models to improve workcell utilization within blockchain-based systems. By implementing the collaborative distribution logic within the blockchain, the manufacturing times will be lessened. This paper highlights the potential of utilizing blockchain technologies to enhance Cyber-Physical Systems in the manufacturing industry.
Reengineering the lifecycle of Arrowhead applications: from skeletons to the client library

Traditionally, the development workflow of Arrow-head applications is based on the usage of source code skeletons. This makes it difficult to update applications when the skeletons are changed, for example to fix security vulnerabilities or add new functionality. In fact, to update an application the developer has to either recreate the application on the new skeleton version or recreate the skeleton changes on the previous version of the application. Instead, we propose a client library, which allows the developers to create Arrowhead applications by referring to a library. Not only does this allow the Arrowhead Consortium to update the library without requiring changes to applications, it also eases the creation of new Arrowhead applications, reduces code duplication and increases readability. This paper describes the design and the structure of this client library, provides insights on how to employ the library in applications, and surveys a few sample applications that use the library.
Toward an End-to-End Calibration for Mobile C-Arm in Combination with a Depth Sensor for Surgical Augmented Reality Applications

C-arm X-ray imaging is commonly applied in operating rooms for guiding orthopedic surgeries. Augmented Reality (AR) with C-arm X-ray images during surgery is an efficient way to facilitate procedures for surgeons. However, the accurate calibration process for surgical AR based on C-arm is essential and still challenging due to the limitations of C-arm imaging systems, such as instability of C-arm calibration parameters and the narrow field of view. We extend existing methods using a depth camera and propose a new calibration procedure consisting of calibration of the C-arm imaging system, and 3D/2D calibration of an RGB-D camera and C-arm system with a new method to achieve reliable data and promising accuracy and, at the same time, consistent with standard surgical protocols. For the calibration procedure, we apply bundle adjustment equations with a 3D designed Lego multi-modal phantom, in contrast to the previous methods in which planar calibration phantoms were applied. By using our method, the visualization of the X-ray image upon the 3D data was done, and the achieved mean overlay error was 1.03 mm. The evaluations showed that the proposed calibration procedure provided promising accuracy for AR surgeries and it improved the flexibility and robustness of existing C-arm calibration methods for surgical augmented reality (using C-arm and RGB-D sensor). Moreover, the results showed the efficiency of our method to compensate for the effects of the C-arm movement on calibration parameters. It was shown that the obtained overlay error was improved for the non-zero rotation movement of C-arm by using a virtual detector.
Maximal Information Leakage based Privacy Preserving Data Disclosure Mechanisms

It is often necessary to disclose training data to the public domain, while protecting privacy of certain sensitive labels. We use information theoretic measures to develop such privacy preserving data disclosure mechanisms. Our mechanism involves perturbing the data vectors to strike a balance in the privacy-utility trade-off. We use maximal information leakage between the output data vector and the confidential label as our privacy metric. We first study the theoretical Bernoulli-Gaussian model and study the privacy-utility trade-off when only the mean of the Gaussian distributions can be perturbed. We show that the optimal solution is the same as the case when the utility is measured using probability of error at the adversary. We then consider an application of this framework to a data driven setting and provide an empirical approximation to the Sibson mutual information. By performing experiments on the MNIST and FERG data sets, we show that our proposed framework achieves equivalent or better privacy than previous methods based on mutual information.
A comparative study of sentiment analysis approaches

Nowadays, several platforms on the web and social networks like Facebook, Twitter, IMDB (Internet Movie Database) propose to share feelings and opinions on a variety of topics. This information is very important in several fields like policy, digital marketing, social or individual, and their analysis allow as to extract opinions and to determine the subjective information contained in the texts. However, the sentiment analysis area is confronted to several problems that distinguish it from traditional thematic research, since the sentiment is expressed in a very varied and very subtle ways.
An Improved Differential Privacy-Preserving Truth Discovery approach In Healthcare

Nowadays, the occurrence of breast cancer amongst women has been increasing tremendously. To predict the breast cancer occurrence, physicians are in need of computer assisted way of feature extraction and classification approaches. In this scenario, there is even a greater need to maintain the privacy of patients personalized details. Many algorithms exist in literature that are used for data anonymization. The fundamental disadvantage of all these algorithms lies in limiting the ability to derive better value and insights from the data. In this proposed approach a differential privacy preserving algorithm is employed. This algorithm alters particulars of patients vital information in the data set. Thus, it not only improves the privacy of patient details compared to other existing approaches but also helps in getting accurate results. The advantage is that it will have the least impact on the accuracy of the truth discovery approach as the appropriate features for cancer prediction data set are not altered. For predicting the truth discovery, this proposed system employs Expectation- Maximization Algorithm. This proposed approach is first of its kind in combining the differential privacy preservation technique with truth discovery strategy. Finally, the performance evaluation was estimated in terms of accuracy, precision, recall, and F1-score and the outcomes are compared to that of the existing approaches to prove the effectiveness of the proposed mechanism.
An Improved Model of Estimation Global Solar Irradiation From in Situ Data: Case of Algerian Oranie’s Region

Abstract The total monthly average of daily radiation on a horizontal surface at the site of Oran (35.38 deg N, 0.37 deg W) is achieved by applying two models. We present a comparison between the first one which is a regression equation of the Angstrom type and the second model, developed by the present authors: Some modifications were recommended using relative humidity as the input meteorological parameter) and longitude, latitude, and altitude as the astronomical parameters. The process of examining similarities is made using root mean square error (RMSE), the mean bias error (MBE), mean absolute error (MAE), and mean absolute percentage error (MAPE). This comparison shows that the second model is closer to the experimental values of the Angstrom model.
Exploring Dynamic Routing As A Pooling Layer

Dynamic routing is a routing-by-agreement mechanism which is important for achieving the equivariance and invariance properties for capsule network (CapsNet). It is valuable to explore the nature of dynamic routing for better understanding of the capsule idea and further improving the performance of neural networks. This paper explores the dynamic routing from the pooling perspective. We modify the original dynamic routing algorithm for better applying it in traditional Convolutional Neural Networks (CNNs) as a pooling layer. We also use a parameter λ in softmax to smoothly adjust the sparsity in the routing, which leads to lower cost compared to the original dynamic routing. We experimentally show that the dynamic routing can be applied to beyond the capsule network to improve the performance of CNNs, and the coupling coefficients generated by the routing can be used to generate heatmaps which provide visual explanations to some extent. Further, the proposed dynamic routing method, combining a CNN backbone, achieves better results with much fewer parameters than the baselines on aff-NIST and multi-MNIST tasks.
Incorporating medical code descriptions for diagnosis prediction in healthcare

Diagnosis aims to predict the future health status of patients according to their historical electronic health records (EHR), which is an important yet challenging task in healthcare informatics. Existing diagnosis prediction approaches mainly employ recurrent neural networks (RNN) with attention mechanisms to make predictions. However, these approaches ignore the importance of code descriptions, i.e., the medical definitions of diagnosis codes. We believe that taking diagnosis code descriptions into account can help the state-of-the-art models not only to learn meaning code representations, but also to improve the predictive performance, especially when the EHR data are insufficient.We propose a simple, but general diagnosis prediction framework, which includes two basic components: diagnosis code embedding and predictive model. To learn the interpretable code embeddings, we apply convolutional neural networks (CNN) to model medical descriptions of diagnosis codes extracted from online medical websites. The learned medical embedding matrix is used to embed the input visits into vector representations, which are fed into the predictive models. Any existing diagnosis prediction approach (referred to as the base model) can be cast into the proposed framework as the predictive model (called the enhanced model).We conduct experiments on two real medical datasets: the MIMIC-III dataset and the Heart Failure claim dataset. Experimental results show that the enhanced diagnosis prediction approaches significantly improve the prediction performance. Moreover, we validate the effectiveness of the proposed framework with insufficient EHR data. Finally, we visualize the learned medical code embeddings to show the interpretability of the proposed framework.Given the historical visit records of a patient, the proposed framework is able to predict the next visit information by incorporating medical code descriptions.
Object Detection for Similar Appearance Objects Based on Entropy

In order to detect objects with similar appearance more accurately, we propose an object detection algorithm with entropy loss. Applying entropy loss makes detector predicts the class of detected bounding boxes more robust with high score probability. It also leads to decrease of confidence loss. Therefore, the detection performance for similar objects is improved. We reconstructed the dataset from previous two datasets to evaluate our method, implemented experiments, and obtained high performance gain. In addition, we conducted an analysis of the score distribution for detected objects and the other loss terms, in order to observe the effects of applying entropy loss.
Anomalous Crowd Behavior Detection in Time Varying Motion Sequences

Automated crowd behavior detection has become a prime research area in recent years. Due to inherent complexities in video sequences and foreground motion patterns, crowd motion analysis faces many challenges. This work uses a statistical model for representation and extraction of local motion patterns in order to generate the feature set. It then utilizes a Genetic Programming (GP) based classifier to classify normal and abnormal behavior patterns through a supervised learning mechanism. The developed classifier is generic in nature and can be easily implemented in hardware. Experimental results on public datasets validate that the proposed scheme outperforms contemporary techniques in terms of classification accuracy and effectiveness.
Jaro–Winkler Distance Improvement For Approximate String Search Using Indexing Data For Multiuser Application

Abstract Word searching method has been developed in many ways and named as: Hamming Distance, Jaccard Distance, Jaro Distance, Jaro-Winkler Distance, Levenshtein Distance, etc. Those methods are used for lexicographic comparison to find words according to the similarity of the words which searched. The time needed for searching by using these words distance method can cause overhead as some difference user might try to search the same words all over. If these method is used in a multi user application where the user generally searching for some keywords repeatedly, then the user might have a longer searching time compared to exact search. In spite of this problem, we try to propose a method where the first search result of the previous user, will be recorded to the database for future usage by indexing the search keywords. In order to try this method, we use Jaro-winkler Distance method to search words. From the test result show that combining indexing and similarity word searching by using Jaro-Winkler Distance method can decrease the searching time to 90-92% compared to just using the Jaro-Winkler Distance method only. As the searched data increased, the processing time can be shorten.
General Mixed-State Quantum Data Compression With and Without Entanglement Assistance

We consider the most general finite-dimensional quantum mechanical information source, which is given by a quantum system <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$A$ </tex-math></inline-formula> that is correlated with a reference system <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula> . The task is to compress <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$A$ </tex-math></inline-formula> in such a way as to reproduce the joint source state <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho ^{AR}$ </tex-math></inline-formula> at the decoder with asymptotically high fidelity. This includes Schumacher’s original quantum source coding problem of a pure state ensemble and that of a single pure entangled state, as well as general mixed state ensembles. Here, we determine the optimal compression rate (in qubits per source system) in terms of the Koashi-Imoto decomposition of the source into a classical, a quantum, and a redundant part. The same decomposition yields the optimal rate in the presence of unlimited entanglement between compressor and decoder, and indeed the full region of feasible qubit-ebit rate pairs.
A systematic mapping of test case generation techniques using UML interaction diagrams

Abstract Model‐based test case generation techniques provide a mechanism to derive tests systematically. This study provides a systematic mapping of test case generation techniques based on UML interaction diagrams. The study compares the test case generation techniques regarding their capabilities and limitations, and it also assesses the reporting quality of the primary studies. We can conclude that the studies presenting test case generation techniques using UML interaction diagrams were not following the guidelines for research methods (eg, case studies or experiments). Solutions were not empirically evaluated in industrial contexts. Our study revealed that better tool support is needed to introduce the UML interaction diagram–based test case generation techniques in the industry.