Reviewerly/exHarmony huggingface dataset:
On the Formation and Development of Meaning Interaction of Students with a Musical Work in General Education

Thearticle is devoted to the meaning aspects of music and musical work. On the basis of the analysis of theoretical and practical experience the ways of orientation of musical training and education in General education on formation and development of the meaning relation to music are outlined. It is stated that in the sense of music as an art form includes all its meanings, properties and functions. It has an emotional content that expresses the meaning of actions, relationships, experiences of people, and interaction with music always includes social content: values, opinions, generally accepted values of the components of musical “language”, which allows you to make music different meanings. Suggested ways of formation and development of meaning relationship to music are (1) organization for music lessons situations meaningful meetings with the piece of music; (2) discussion of pieces, composers, performers; (3) discussions about the artistic value of genres, performers, musical instruments; discussion of musical interests, tastes, experiences of students, etc. Author emphasizes that the meaning side of music is revealed through the mutual transitions “sensuality – text – meaning” in human interaction with the musical work was included in the process of development of his spiritual world, and appeal to her sense is only possible when “building” their own sense of the creators, performers and listeners.
Predicting Social Emotions based on Textual Relevance for News Documents

Due to the rapid rise in internet population, the content over web is increasing and a large number of documents assigned by reader's emotions have been generated through new portals. Earlier works have focused only author's perspective, our work focuses on reader's emotions generated by news articles. Social emotions of news articles from reader's perspective are predicted with the help of user ratings. More specifically, we form Communities based on the ratings that are present in the news articles. Further, a Textual Relevance is computed based on the word frequency for a particular document. Experiments are conducted on the news articles and as a result, it is observed that the proposed method results in predicting reader's emotions are much better when compared with the existing method Opinion Network Community (ONC) [1].
Tight Focusing of a Second-order Cylindrical Vector Beam

In this paper we have investigated the tight focusing of a second-order cylindrical vector beam. It was shown numerically by Richarsd-Wolf formulae and FDTD-simulation that in the center of the focal spot there is a region with negative values of the projection of the Poynting vector to the optical axis (Poynting vector has direction opposite to the direction of propagation of the beam). The negative values are comparable in absolute value with positive values.
An Efficient Privacy Preserving Computation of Multiset Intersection Cardinality

The multi-set intersection cardinality operation is used for calculation of similarity between two sets which has various applications such as cluster analysis, image segmentation, social network analysis, etc. The need of Privacy Preserving Computation of Multi-set Intersection Cardinality (PPCMIC) operation is raised when two parties want to compute similarities between their datasets without disclosing their data to each other. Existing methods for PPCMIC are either insecure or inefficient. In our work, to address this gap, PPCMIC protocol based on lightweight randomization protocol is proposed which is secure and efficient in terms of computation cost. The experimental work has been done on simulated and real datasets to show that proposed protocols are more efficient then the existing techniques.
Application Development wtih Service Oriented Architecture

Today, it is one of the most invested areas of organizations to ensure the interoperability of existing corporate applications and newly developed or purchased applications. Developed systems are designed to be shaped according to the needs of the businesses that use it. Classical systems are automation systems that operate from point to point and from other platforms in the enterprise without interaction. Unlike these, the service-oriented architecture (SOA) contains a structure that is ready to interact. In this study, a system has been designed in order to reduce the integration costs between the applications in the long term by using SOA approach, to shorten the business processes in the institution and to reduce the economic costs. In the project, work pieces were published as a method of service over the service layer. Thus, the platform independent main application is accessible to each work piece. At the same time, these work pieces can come together and be reconfigured according to the request of the enterprise. Some of the advantages achieved by using SOA are: Providing improved work processes for services and actors, ensuring collaboration, utilizing from existing and reuse with savings, providing business flexibility by responding to changing business needs.
E-Reading System Based on Android

The system mainly aims at smartphone users, realizing the purpose of real-time reading and browsing books. Eclipse is chosen as the development tool. Under the framework of MVC mode, Java language and SQLLite lightweight database are used. It mainly realizes the functions of local bookshelf, collection bookshelf, reading theme change, e-book reading main interface font increase and decrease, bookmark management, etc. It has the advantages of simple interface, cute design style, simple operation, strong basic functions, and provides users with a good reading experience.
Quantum Behavior in Mesoscale Lasers

Describing the operation of mesoscale lasers consisting of several atom or semiconductor quantum dot emitters in an optical cavity is a challenge that, if properly addressed, may contribute to a new generation of efficient, ultra-small, photonic components. In dramatic contrast to successful continuum mean-field models of conventional macroscopic laser diodes, a quantum mechanical description of mesoscale lasers predicts steady-state and transient behavior that is dominated by fluctuations in the photon field and, in the limit of few emitters, the existence of symmetry-protected long-lived emitter states. Current understanding of these mesoscale laser phenomena suggests a very promising and challenging field for future study.
Inverse Rectification for Efficient Procam Pattern Correspondence

A method called inverse rectification, is proposed which facilitates the establishment of correspondences across a projected pattern and an acquired image. A pattern of features comprising vertical dashes is warped by the inverse of the rectifying homography of the projector-camera pair, prior to projection. This warping imparts upon the system the property that projected features will fall on distinct conjugate epipolar lines of the rectified projector and acquired camera images. This reduces the correspondence search to a trivial constant-time table lookup once a feature is found in the camera image, and leads to robust, accurate, and extremely efficient disparity calculations. A projectorcamera range sensor is developed based on this method, and is shown experimentally to be effective, with bandwidth exceeding some existing consumer-level range sensors.
Multi-Channel Biometrics for eHealth Combining Acoustic and Machine Vision Analysis of Speech, Lip Movement and Face: a Case Study

The purpose of this work is to present a solution combining user-friendliness and cost-effectiveness use of audio (speech) & visual (video/image) biometrics, for eHealth, able to achieve better accuracy and increase the ability to avoid counterfeiting. This work shows the evaluation results for an eHealth pilot study that tested the security, privacy, usability and cost-effective features of a user authentication platform for the management of sensitive heterogeneous multi-scale medical data (i.e. medical imaging such as MRI/CT scans, physical reports, and laboratory results), through easy acquisition of biometric data via laptops, and tablets equipped with cameras and microphones. Regarding the user enrollment and verification, audio-visual biometric information from an individual is captured, processed and stored as a biometric template. In subsequent uses, biometric information is captured and compared with the biometric templates. If the comparison is successful the verified user could be allowed to sign in to a medical collaboration platform of the hospitals infrastructure. In this work we present the biometric platform developed, the testing methodology and the administrative framework and legal processes, related to GDPR, for the eHealth pilot study and the results from our quantitative and qualitative analysis that was performed.
Association Rule Mining of Anaphora Based on ParCorFull Corpus

Based on the Apriori algorithm, the association rules of the anaphora in ParCorFull corpus are mined, and the similarities and differences between the written and spoken language represented by speech and discussion are compared. The rules of the whole mining show that the most important anaphora types in news, speech and discussion styles are noun phrases and pronouns respectively. In news style, pronoun is easier to realize the function of antecedent, while in speech and discussion style, noun phrase is more commonly used as anaphora. The rules of local mining show that in journalism, verb phrases are generally not omitted, noun phrases are more directly appeared without modification, and pronouns often point to singular person entity objects with the subject. The rules of speech and discussion are more flexible. Subordinate clauses are all used as anaphoric objects in anaphoric without anaphoric functions.
Estimation of Subjective Probabilities through the Prism of Karni-Jaffray Theorem and Stochastic Approximation

Theoretical formulations deriving from a theorem of Karni-Jaffray under the light of the Savage and von Neumann theory are discussed in the paper. The main purpose is to develop an approximation based methodology for the assessment of subjective probabilities. The basic information is the decision-maker (DM) preferences explicitly expressed in a cardinal way (`yes', `no', `no preference'). The elicitation procedure uses the Lpτ pseudo-random Sobol' sequences and the stochastic approximation approach. The dialog DM-computer is modeled numerically and the results are presented.
Analyzing CS1 Student Code Using Code Embeddings

We present a machine learning model to obtain vector representations of student code submissions for a CS1 programming problem. These vectorembeddings can be used to compare code, cluster code submissions, and identify errors. We hope to use these embeddings to identify conceptual misunderstandings in student code.
The Audit4Cloud Platform for Auditing the Networking Performance of Public Clouds

Elastic resource outsourcing is a growing trend that simplifies and makes more efficient the management of resources, by embracing all the features of the execution of services over public clouds, such as high availability and automated scalability management of resources. Therefore, modern enterprise services are increasingly leveraging inter/intra-cloud deployments and the choice of the right cloud provider to support the execution of them becomes a fundamental operational choice. The paper presents our Audit4Cloud platform, an open-source tool for auditing the performance of virtual resources made available by various commercial cloud providers, with specific focus on cloud networking. In particular, we claim that Audit4Cloud is an enabling key in choosing the right cloud vendor as it not only offers the visibility of current values of some significant performance indicators about the offered cloud resources, but also provides users with a complete picture of those performance indicators over time. We have already performed a large experimental campaign by considering primary commercial cloud providers; the collected results show the feasibility of the approach and that Audit4Cloud can play the role of a solid third-party auditing tool to estimate real performance and costs of cloud resources.
Analysis and Implementation of Key Technologies for Teaching Quality Evaluation System in Universities Based on Data Mining

Contraposing to the problems of professionalization of curriculum design, subjective fairness of teaching evaluation and diversity of importance among itemsets caused by selection factors of employers, an improved Apriori algorithm for mining association rules is proposed by introducing the concept of influence factors and combining with the fast pruning mechanism of address mapping. For the problems of indistinguishability of importance and large candidate itemsets in classical algorithms, a fast pruning of candidate itemsets is accomplished by address mapping, and the pseudo-code description of the algorithm is provided and explained in detail. Then, the performance of the improved algorithm is verified and analyzed, which shows the feasibility and efficiency of the algorithm, to make help in teaching evaluation system of universities.
UAV Attitude Estimation using Visual and Inertial Data Fusion based on Observer in SO(3)

In this paper, a visual and inertial data fusion using an observer in SO (3) is presented. Visual data are acquired using Optical flow technique where the Lucas and Kanade method is used to estimate the displacement of points (pixels) representing lines selected in a scene. The orientation vectors for each line are computed and combined with the gyrometer data for data fusion with an observer in SO (3) to estimate the attitude of an Unmanned Aerial Vehicle UAV in 3D space.
Developing QR Payment to Enhance the Technological Service in Sederhana Restaurant Network

This research is conducted in the Sederhana Restaurant network that use the system based on profit sharing among stakeholders with a ratio of the number 10: 7: 3 for employees, investors and brand holder respectively. Nowadays the restaurant network is managed manually with traditional cash payment. In order to meet the globalization and technology development, the restaurant planned to use QR for payment, so that the customers can make payment easily. This research uses SDLC model that consists of five stages such as planning, analysis, design, implementation, and maintenance. Subjects of the research are people who use the system such as customers and stakeholders. This research is still on going and the researchers would like to present the design of the system. Research findings show that QR payment can support restaurant and it is proper to be implemented.
Integration of BYOD Technology in Traditional Classroom: A Statistical Approach

Today we are moving from the world of cellular communication to the era of internet. Mobile technology has paved a way into our pockets. We use mobile phones as an extension of our body. With the advent of these pervasive devices MOOCs, m-learning, e-learning has found a way into our classroom. Smart learning technology is growing exponentially with smartphones as a major part. In this work, we try to model the traditional classroom with Bring Your Own Device (BYOD) concept. We statistically find if these mobile devices are suitable for classroom or not. The data collected by the survey was used to find the adaptability of mobile devices in the traditional classroom using JASP Tool. A descriptive statistics analysis, linear regression techniques, and Bayesian sample paired t-test are done on sample survey data to find the compliance of smart devices in the classroom. The vast expansion of mobile technology has not only enhanced student-teacher interaction but has also improved the quality of education.
SSL-STR: Semi-Supervised Learning for Sparse Trust Recommendation

Trust is widely applied in recommender systems to improve recommendation performance by alleviating well-known problems, such as cold start, data sparsity, and so on. However, trust data itself also faces sparse problems. To solve these problems, we propose a novel sparse trust recommendation model, SSL-STR. Specifically, we decompose the aspects influencing trust-building into finer-grained factors, and combine these factors to mine the implicit sparse trust relationships among users by employing the Transductive Support Vector Machine algorithm. Then we extend SVD++ model with social trust and sparse trust information for rating prediction in the recommendation system. Experiments show that our SSL-STR improves the recommendation accuracy by up to 4.3%.
Comparing SVM and KNN Algorithms for Myanmar News Sentiment Analysis System

Sentiment analysis is one of the natural language processing research fields that identify the polarity and subjectivity of documents. With the increasing of web technology, large volumes of data are available from many resources. Sentiment analysis applications are widely applied in many domains from news, financial, election, blogs, and post. News is very important and provides valuable information for society. News is tagged as positive and negative in this system. News is collected from many websites and ALT tree bank. N-gram and TF-IDF feature extraction and selection methods are used in this system to get more performance. Supervised machine learning algorithm is a classification algorithm that uses labeled data. K nearest neighbors is a simple algorithm that stores all available features and classifies new features based on a similarity measure. SVM is one of the classifier that has higher accuracy. This paper shows comparison of performance results for sentiment analysis system by using support vector machine (SVM) and K nearest neighbor (KNN) algorithms.
A Mixed Active Learning Model for Multilabel Classification

Multilabel classification algorithms attract more attention in text mining field. In general, there are few labeled instances while large number of unlabeled data. Random sampling from the dataset is also not conducive to training multilabel classifiers. Therefore, active learning algorithms are introduced to select uncertain samples to label dynamically to improve the performance of classification model. In this paper, we propose a mixed active learning model, called MAMix-RankSVM, that minimizes the expected error and the version space from a large number of unlabeled instances. We carry on experiments over various datasets. The experimental results show that our method is better than the baselines in both accuracy and efficiency.
Remote Usability Testing of Data Input Methods for Web Applications

The main purpose of the paper was to conduct a comparative analysis and examine the usability of selected methods and patterns of data entry in web systems and websites. A dedicated web application was developed as an experimental tool for conducting remote unmoderated usability tests. Implemented data entry design patterns and tested with real users included various alignment of labels on forms, entering of logical values, small numbers, dates and time. The metrics collected during the test comprised: time to complete a task, a number of mouse clicks, number of errors committed, the Single Ease Question survey, closed and open questions regarding the subjective assessment of tested patterns. Based on the collected results, recommendations for the best patterns and methods of data entry in the specific context of use were formulated.
Representing Objects in Video as Space-Time Volumes by Combining Top-Down and Bottom-Up Processes

As top-down based approaches of object recognition from video are getting more powerful, a structured way to combine them with bottom-up grouping processes becomes feasible. When done right, the resulting representation is able to describe objects and their decomposition into parts at appropriate spatio-temporal scales. We propose a method that uses a modern object detector to focus on salient structures in video, and a dense optical flow estimator to supplement feature extraction. From these structures we extract space-time volumes of interest (STVIs) by smoothing in spatio-temporal Gaussian Scale Space that guides bottom-up grouping. The resulting novel representation enables us to analyze and visualize the decomposition of an object into meaningful parts while preserving temporal object continuity. Our experimental validation is twofold. First, we achieve competitive results on a common video object segmentation benchmark. Second, we extend this benchmark with high quality object part annotations, DAVIS Parts <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , on which we establish a strong baseline by showing that our method yields spatio-temporally meaningful object parts. Our new representation will support applications that require high-level space-time reasoning at the parts level.
Self-Guided Novel View Synthesis via Elastic Displacement Network

Synthesizing a novel view from different viewpoints has been an essential problem in 3D vision. Among a variety of view synthesis tasks, single image based view synthesis is particularly challenging. Recent works address this problem by a fixed number of image planes of discrete disparities, which tend to generate structurally inconsistent results on wide-baseline, scene-complicated datasets such as KITTI. In this paper, we propose the Self-Guided Elastic Displacement Network (SG-EDN), which explicitly models the geometric transformation by a novel non-discrete scene representation called layered displacement maps (LDM). To generate realistic views, we exploit the positional characteristics of the displacement maps and design a multi-scale structural pyramid for self-guided filtering on the displacement maps. To optimize efficiency and scene-adaptivity, we allow the effective range of each displacement map to be `elastic', with fully learnable parameters. Experimental results confirm that our framework outperforms existing methods in both quantitative and qualitative tests.
A Secure Architecture for Modular Division over a Prime Field against Fault Injection Attacks

Fault injection attacks pose a serious threat to many cryptographic devices. The security of most cryptographic devices hinges on a key block called modular division (MD) over a prime field. Although a lot of research has been done to implement the MD over a prime field in hardware efficiently, studies on secure architecture against fault injection attack are very few. A few of the studies that focused on secure architecture against fault injection attack can only detect faults but not locate faults. In this regard, this paper designs a novel secure architecture for the MD over a prime field, which can not only detect faults, but also can locate the error processing element. In order to seek the best optimal performance, four word-oriented systolic structures of a main function module (MFM) were designed, and three error detection schemes were developed based on different linear arithmetic codes (LACs). The MFM structures were combined flexibly with the error detection schemes. The time and area overheads of our architecture were analyzed through the implementation in an application-specific integrated circuit (ASIC), while the error detection and location capabilities of our architecture were demonstrated by C++ simulation, in comparison to two existing methods. The results show that our architecture can detect single-bit error (SBE) with 100% accuracy and locate the erroneous processing element (PE), and correctly identify most of the single PE errors and almost all of the multi-PE errors (when there are more than three erroneous PEs). The only weakness of our architecture is the relatively high time and area overhead ratios.