Reviewerly/exHarmony huggingface dataset:
Developing Music Harmony Awareness in Young Students through an Augmented Reality Approach

This paper presents AREmbody, an augmented-reality mobile application for the development of tonal harmony awareness.Continuing from previous prototypes based on full body and desktop interaction, AREmbody benefits from a very simple portable setup which allows physical interaction and supports the activity of one or more users.The application combines in a single mobile device a video processor, a media player and a movement tracker, opening the way to the design of harmonic games with challenges and recordable scores.Thus the application not only fosters music education activities in the classroom, but also extends them outside the school times and places, promoting educational practices in informal and private contexts.
OUTDOOR AR-APPLICATION FOR THE DIGITAL MAP TABLE

Abstract. In many fields today, it is necessary that a team has to do operational planning for a precise geographical location. Examples for this are staff work, the preparation of surveillance tasks at major events or state visits and sensor deployment planning for military and civil reconnaissance. For these purposes, Fraunhofer IOSB is developing the Digital Map Table (DigLT). When making important decisions, it is often helpful or even necessary to assess a situation on site. An augmented reality (AR) solution could be useful for this assessment. For the visualization of markers at specific geographical coordinates in augmented reality, a smartphone has to be aware of its position relative to the world. It is using the sensor data of the camera and inertial measurement unit (IMU) for AR while determining its absolute location and direction with the Global Navigation Satellite System (GNSS) and its magnetic compass. To validate the positional accuracy of AR markers, we investigated the current state of the art and existing solutions. A prototype application has been developed and connected to the DigLT. With this application, it is possible to place markers at geographical coordinates that will show up at the correct location in augmented reality at anyplace in the world. Additionally, a function was implemented that lets the user select a point from the environment in augmented reality, whose geographical coordinates are sent to the DigLT. The accuracy and practicality of the placement of markers were examined using geodetic reference points. As a result, we can conclude that it is possible to mark larger objects like a car or a house, but the accuracy mainly depends on the internal compass, which causes a rotational error that increases with the distance to the target.
Robust illumination invariant depth recovery by combining local phase and intensity information of images

Both intensity and phase information of images have been the most important similarity measures in solving the general stereo matching problem. Intensity contains most of the imaging information of the scene/object, yet the phase information could reflect the local structure of images, which is more robust than the grayscale value. Plenty of work has been done in intensity-based or phase-based stereo matching methods. However, neither of them could work well enough when process images were taken under varied illuminations. A robust depth recovery method by making use of both intensity and phase information of stereo images properly is proposed. Firstly, 2D signal analysis is conducted by using the multiscale monogenic wavelet transform, from which local phase and intensity amplitude information are extracted into different scales. Secondly, disparity maps are estimated in different scales based on the intensity information. Thirdly, the optimal disparity is obtained by weighted-combining the disparity maps in different scales. The weighted coefficients are computed by making use of the phase information. Extensive experimental evaluation demonstrates the benefits of the proposed method.
ACLSTM：A Novel Method for CQA Answer Quality Prediction Based on Question-answer Joint Learning

Given the limitations of the community question answering (CQA) answer quality prediction method in measuring the semantic information of the answer text, this paper proposes an answer quality prediction model based on the question-answer joint learning (ACLSTM).The attention mechanism is used to obtain the dependency relationship between the Question-and-Answer (Q&A) pairs.Convolutional Neural Network (CNN) and Long Short-term Memory Network (LSTM) are used to extract semantic features of Q&A pairs and calculate their matching degree.Besides, answer semantic representation is combined with other effective extended features as the input representation of the fully connected layer.Compared with other quality prediction models, the ACLSTM model can effectively improve the prediction effect of answer quality.In particular, the mediumquality answer prediction, and its prediction effect is improved after adding effective extended features.Experiments prove that after the ACLSTM model learning, the Q&A pairs can better measure the semantic match between each other, fully reflecting the model's superior performance in the semantic information processing of the answer text.
Applications of the Quantum Algorithm for st-Connectivity

We present quantum algorithms for various problems related to graph connectivity. We give simple and query-optimal algorithms for cycle detection and odd-length cycle detection (bipartiteness) using a reduction to st-connectivity. Furthermore, we show that our algorithm for cycle detection has improved performance under the promise of large circuit rank or a small number of edges. We also provide algorithms for detecting even-length cycles and for estimating the circuit rank of a graph. All of our algorithms have logarithmic space complexity.
İşbirlikçi Filtreleme için Pearson Korelasyonu Üzerine Statik ve Dinamik Önem Ağırlıklandırma Çarpanları Çalışması

Recommender systems as a field of data mining and knowledge discovery have a tremendous impact on movie recommendation platforms. Proper recommendation for the audience, considering profiles, is a measurable argument. By inferencing the linear combinations between some numerical data such as user rating actions, statistical analyses can be done. Thus, any item such as a movie can be recommended or not. The numerical calculation of correlations, namely the similarity weight, should be recomputed before prediction to increase the effect of user similarities for further constant multiplications. This method is named as the significance weighting that processes one more step to stress the impact of similarities. The affinity between users can simply be the total number of co-rated items, or any further inference using more complex computations. In this work, the significance weighting method related to Pearson Correlation is inspected using comparative approaches. The MovieLens dataset, both including ML100K and ML1M releases, are used in the experiments. k-fold cross-validation method is applied in a shifting fashion to increase the number of tests. After having Pearson Correlation Coefficients for user-user similarities, weights are signified using three different approaches. Then, neighbors are sorted to choose the top-N closest users for the user in the test. Concerning experimental results, over two other techniques, an explicit method that utilizes only the co-rated item count is preferred taking its simplicity and performance into account. In the plots of experimental results section, accuracy and error metrics are presented for three different significance weighting approaches. Especially for the ML100K dataset, the simple weighting method outperforms in terms of the error metrics.
Quasi-Multitask Learning: an Efficient Surrogate for Obtaining Model Ensembles

We propose the technique of quasi-multitask learning (Q-MTL), a simple and easy to implement modification of standard multitask learning, in which the tasks to be modeled are identical.With this easy modification of a standard neural classifier we can get benefits similar to an ensemble of classifiers with a fraction of the resources required.We illustrate it through a series of sequence labeling experiments over a diverse set of languages, that applying Q-MTL consistently increases the generalization ability of the applied models.The proposed architecture can be regarded as a new regularization technique that encourages the model to develop an internal representation of the problem at hand which is beneficial to multiple output units of the classifier at the same time.Our experiments corroborate that by relying on the proposed algorithm, we can approximate the quality of an ensemble of classifiers at a fraction of computational resources required.Additionally, our results suggest that Q-MTL handles the presence of noisy training labels better than ensembles.
A CONSTRUCTIVE MODEL OF TRAINING BACHELORS IN CYBERSECURITY IN AN EDUCATIONAL-DIGITAL ENVIRONMENT

The article presents a constructive model of training bachelors in cybersecurity in an educational-digital environment. The concepts of "model" and "modeling" are analyzed. The meaning of the definition of "construct" as a whole, distinguished from other entities of a certain area, which is inaccessible to direct observation, but hypothetically deduced and / or constructed logically on the basis of observed features, with a sufficient degree of experimentally and logically validated, is verified. the concept used to represent it. The scientific position in relation to the conceptual field of the model of preparation of bachelors in cybersecurity in the conditions of educa-?ional-digital environment is determined. Based on theoretical analysis, the constructive model is able to reflect the spatial pedagogical relationships between the elements of the object under study. It is determined that the conceptual model of training bachelors in cybersecurity in an educational-digital environment consists of five constructs. Each of the five model constructs is characterized, namely target, conceptual, content, activity-technological and result-corrective. The purpose of the model is defined to form the readiness of bachelors in cybersecurity in the educational-digital environment for professional activity. It has been found out that the result of constructive model of preparation of bachelors in cybersecurity in the conditions of digital educational environment is the formed readiness of bachelors in cybersecurity for professional activity. Keywords:
Why and when should you pool? Analyzing Pooling in Recurrent Architectures

Pooling-based recurrent neural architectures consistently outperform their counterparts without pooling on sequence classification tasks.However, the reasons for their enhanced performance are largely unexamined.In this work, we explore three commonly used pooling techniques (mean-pooling, max-pooling, and attention 1 ), and propose max-attention, a novel variant that captures interactions among predictive tokens in a sentence.Using novel experiments, we demonstrate that pooling architectures substantially differ from their nonpooling equivalents in their learning ability and positional biases: (i) pooling facilitates better gradient flow than BiLSTMs in initial training epochs, and (ii) BiLSTMs are biased towards tokens at the beginning and end of the input, whereas pooling alleviates this bias.Consequently, we find that pooling yields large gains in low resource scenarios, and instances when salient words lie towards the middle of the input.Across several text classification tasks, we find max-attention to frequently outperform other pooling techniques. 2
Personal Files and Personal Provenance Fonds of Philologists Studying Foreign Literature in the Archives of the Perm Krai: Continuity and Innovation

The relevance of the study is due to the interest of the humanities in the study of the processes of formation and development of foreign philology as a field of scientific and educational activities in the Perm University, since its foundation in 1916. In the Perm archives we study personal files and personal provenance fonds of philologists and historians affiliated with the Perm State National Research University (PGNIU), whose field of interests is foreign literature and language, as well as other archival documents. Research methods are source study and archival methods, methods of philological analysis. Personal files of the Perm philologists of the first third of the last century A. A. Smirnov, B. A. Krzhevsky, B. L. Bogaevsky, N. P. Obnorsky, A. F. Shamray, E. O. Preobrazhenskaya, who taught language and literature or even history and language (V.V. Weidle, N.P. Ottokar, V.E. Krusman) contain documents related not only to their professional pedagogical activity, but also documents on their scientific interests, everyday and family life, places of residence, reflecting the realities of time and representing the era in Perm. Personal files of the staff of the Department of Foreign Literature (since 1999 – the Department of World Literature and Culture) A. A. Belsky, R. F. Yashenkina, N. S. Leites, E. P. Khanzhina, A. F. Lyubimova, G. S. Rutskoi, who worked there in the 1950s-2010s, have not yet undergone evaluation and remain in the departmental storage. The personal provenance fonds of philologists B. M. Proskurnin and N. S. Bochkareva, containing a large amount of hand- and typewritten documents, can be considered not only as archival sources, but also within the framework of philological discourse as a post-modern text created as a game of different texts that refer to different cultural layers, languages, realities. Documents of the personal fond, reflecting the methods of scientist’s work with literature, play a pedagogical role in the formation of the competencies of a university graduate. Today, archives need to create personal provenance fonds and collections of philologists who worked in the 1950s and later, via interaction with the scientists themselves or their relatives.
Askeri Otonom Sistemlerde Blokzincir Tabanlı Veri Güvenliği

Advances in technology have enabled the increased use of autonomous systems such as unmanned aerial vehicles (UAVs) in military operations and other critical military communications. While the use of autonomous systems has greatly facilitated military operations, provided a global view of the operational environment and eased sensitive data collection, making possible reduced casualties, it has also created a greater cyber attack surface due to its high level of automation. The existence of adversaries targeting this attack surface can seriously damage military operations by tampering with critical message content used in autonomous systems decision making. In order to ensure the successful operation of autonomous military systems, mechanisms must be developed to strictly protect the integrity of the collected / exchanged data and messages, and an immutable record of each message must be provided. These mechanisms should also be used to control autonomous systems under critical failures or attacks occurring during or after military operations. Blockchain has recently emerged as a technology that provides a decentralized architecture to achieve an unchangeable history of interactions between parties that are part of a distributed network. While blockchain is currently used in various fields such as cryptocurrencies, supply chain management and e-voting systems, it also has the potential to provide secure communication in autonomous systems. In this study, a blockchain-based communication architecture is proposed that guarantees integrity assurance and permanent recording of messages exchanged between all parties, including UAVs and ground control stations, in a military autonomous system network. The proposed secure communication architecture has been theoretically evaluated in terms of its resistance to the types of cyber attacks frequently encountered in distributed systems, and it has been shown to provide protection against attacks that compromise data integrity as well as spoofed authentication attempts. The proposed blockchain-based architecture is promising to increase the resilience of military autonomous systems against cyberattacks that aim to hurt the success of military operations through data content manipulation.
A General Method for Robust Learning from Batches

In many applications, data is collected in batches, some of which are corrupt or even adversarial. Recent work derived optimal robust algorithms for estimating discrete distributions in this setting. We consider a general framework of robust learning from batches, and determine the limits of both classification and distribution estimation over arbitrary, including continuous, domains. Building on these results, we derive the first robust agnostic computationally-efficient learning algorithms for piecewise-interval classification, and for piecewise-polynomial, monotone, log-concave, and gaussian-mixture distribution estimation.
Distributionally Robust Parametric Maximum Likelihood Estimation

We consider the parameter estimation problem of a probabilistic generative model prescribed using a natural exponential family of distributions. For this problem, the typical maximum likelihood estimator usually overfits under limited training sample size, is sensitive to noise and may perform poorly on downstream predictive tasks. To mitigate these issues, we propose a distributionally robust maximum likelihood estimator that minimizes the worst-case expected log-loss uniformly over a parametric Kullback-Leibler ball around a parametric nominal distribution. Leveraging the analytical expression of the Kullback-Leibler divergence between two distributions in the same natural exponential family, we show that the min-max estimation problem is tractable in a broad setting, including the robust training of generalized linear models. Our novel robust estimator also enjoys statistical consistency and delivers promising empirical results in both regression and classification tasks.
A Distributed Item Based Similarity Approach for Collaborative Filtering on Hadoop Framework

Now a day’s multiple website provides millions of product to their on-line users. Due to the interaction of millions customer with the e-commerce websites creates massive volume of data. Recommendation system is a dynamic data capturing system filters massive volume of information generated through the interaction of users to web-portals &amp; generate suggestion that fits the user expectations. Recommendation framework is data filtering tools that make use of algorithms and user rating data to recommend the most relevant items to a particular user. Collaborative filtering is one of the successful techniques in the recommendation system to recommend the top N-item. In the majority of the Recommendation framework information sparsity, high dimensionality, adaptability which are normal issue in the RS domain have adversely influence the exhibition of CF. The proposed system is developed to resolve the mentioned problems in most of the recommendation system using item based similarities approach in CF with the help of user sub space clustering approach on hadoop framework. In the proposed system user subspaces formed by considering the interest of the users in the items like Interested, Neither Interested nor Uninterested (NIU), and Uninterested. After the user subspace clustering the neighbor item tree is constructed. To find out the similarities between the items the similarities measures is developed from the neighbor item tree. It observed that in traditional item-based collaborative filtering method requires more computational cost but the computation of item similarities is performed off line &amp; computational cost required for on line prediction is less. In proposed work to improve the computation speed of off line item-item similarity the computation is performed on various nodes in cluster in the hadoop distributed system. The similarity between the two items is used to predict the rating provided by user on the target items. The proposed method tested on the Movie lens 100K, Movie lens 1M in order to make comparisons with the existing techniques. The proposed method improves the performance of the recommendation systems by resolving the issues like scalability, high dimensionality, data sparsity etc.
Open Metadata Registry–Supporting Metadata Interoperability

"Open Metadata Registry–Supporting Metadata Interoperability." Technical Services Quarterly, 37(4), pp. 465–466
Recursive Inference for Variational Autoencoders

Inference networks of traditional Variational Autoencoders (VAEs) are typically amortized, resulting in relatively inaccurate posterior approximation compared to instance-wise variational optimization. Recent semi-amortized approaches were proposed to address this drawback; however, their iterative gradient update procedures can be computationally demanding. To address these issues, in this paper we introduce an accurate amortized inference algorithm. We propose a novel recursive mixture estimation algorithm for VAEs that iteratively augments the current mixture with new components so as to maximally reduce the divergence between the variational and the true posteriors. Using the functional gradient approach, we devise an intuitive learning criteria for selecting a new mixture component: the new component has to improve the data likelihood (lower bound) and, at the same time, be as divergent from the current mixture distribution as possible, thus increasing representational diversity. Compared to recently proposed boosted variational inference (BVI), our method relies on amortized inference in contrast to BVI's non-amortized single optimization instance. A crucial benefit of our approach is that the inference at test time requires a single feed-forward pass through the mixture inference network, making it significantly faster than the semi-amortized approaches. We show that our approach yields higher test data likelihood than the state-of-the-art on several benchmark datasets.