Reviewerly/exHarmony huggingface dataset:
Suggestive Potential of Music for the Formation of Personality

The article discusses the suggestive potential of music, coupled with three key factors that can be distinguished as part of an interdisciplinary approach. The first factor is the ability of a piece of music to impart otherness to an existing subjective experience and relive it in a new way. The second factor is the sociocultural context of the perception and understanding of any musical work, which has historical prerequisites, as music is born and develops as a group communication practice. Thus, during individual listening in the absence of direct communication, the effect of the presence of the Other, “reflecting” the experience of the subject, is still formed. The third factor is emotional coloring, interpreted as the energization of a “consonant” experience, which has a biological basis and can be described as a stimulation of a dominant, based on the concept of A.A. Ukhtomsky. Actualization of a subjective experience leads to a strengthening of the dominant and, as a result, to a significant decrease in the perception of signals coming from outside. The listed factors, according to the author, seem to be interdependent, and their philosophical understanding is of fundamental importance for understanding the role of music in self-regulation. Special attention is paid to the ability of music to actualize subjective experience without the loss of integrity. At the same time, the author does not understand integrity as plenitude or completeness but as dynamism, implying both openness and closeness in relation to something another and new. This approach to the consideration of the integrity of subjective experience allows us to interpret its transformation and enrichment, including what happens in an altered state of consciousness, and sheds light on the effect of increasing the personal resources achieved by music and used in therapy.
A study on accelerating convolutional neural networks

Recent deep-learning methods have been paid more attention than shallow-learning ones because they have deep and complex structures to approximate functions. The salient feature of deep neural networks is to use many layers where many of them are used to extract data features and few are for classification or regression. The most severe problem of a deep neural network is using too many parameters that cause too much memory usage and computing resources for both training and inference. Thus, deep learning approaches are not suitable for real-time industrial applications that have limited computing resources such as memory and CPU. For example, a famous convolutional neural network (CNN), AlexNet, uses up to 60 million parameters to train ImageNet dataset and many imaging projects apply AlexNet to their own applications as transfer learning. Thus, this work proposes a feasible solution to trim the CNN, speed it up, and keep the accuracy rate similar. Two main types of CNNs and AlexNet, were validated, respectively, in THUR15K, Caltech-101, Caltech-256, and GHIM10k datasets. The results show that the parameter amount greatly decreased (76%) but the recognition rate dropped slightly (1.34%).
Analyzing Transaction Fees with Probabilistic Logic Programming

Fees are used in Bitcoin to prioritize transactions. Transactions with high associated fee are usually included in a block faster than those with lower fees. Users would like to pay just the minimum amount to make the transaction confirmed in the desired time. Fees are collected as a reward when transactions are included in a block so, on the other perspective, miners usually process first the most profitable transactions, i.e. the one with higher fee rate. Bitcoin is a dynamic system influenced by several variables, such as transaction arrival time and block discovery time making the prediction of the confirmation time a hard task. In this paper we use probabilistic logic programming to model how fees influence the confirmation time and how much fees affect miner’s revenue.
On the Constructions of Bigraphical Categories

Bigraph was proposed as a formal theoretical model in attempt to provide a rigorous platform for designing, simulating and analyzing ubiquitous computing systems. As the mathematical basis of bigraphs, bigraphical categories become important aspects of their theory and they are related to precategoy, category, s-category, wide category and symmetric partial monoidal category. However, we detect trouble spots in the construction of bigraphical quotient category and illustrate that not all s-category can be converted to the symmetric partial monoidal category by the support quotient. Both support quotient and lean-support quotient, hence, are not always the symmetric partial monoidal category. And, likewise, the quotient wide s-category may not the wide symmetric partial monoidal category in general. Also, quotient wide re-active system may not be abstract, consequently for the same reason. There-fore, we must make clear what conditions on s-category allow us to obtain a symmetric partial monoidal category, and ensure that the behavioral congruence is preserved for the abstract bigraphical reactive system.
Feature Fusion Detection Network for Multi-scale Object Detection

Small-scale-object usually occupies a small area in an image. The existing object detection methods are performing well for small-scale object detection in real scenes. This paper proposes, a Feature Fusion Detection Network (FFDN), for multi-scale objects detections. Firstly, the FFDN applies three feature maps in the improved VGG16 (Visual Geometry Group Network 16) with a proposed resolution expansion module to achieve the same resolution of the three feature maps. Then the FFDN fuses these three feature maps by a lightweight feature fusion method. Finally, it generates the feature pyramid by the fused feature map to achieve multi-scale object detection. In addition, we design a default box matching concession method which enables to train the real targets, and increases the number of positive samples. The experiments show that FFDN has better performance compared with the existing neural networks. It improves the recall rate for small-scale objects detection and the accuracy for large-scale object detection.
Building Face Recognition System with Triplet-based Stacked Variational Denoising Autoencoder

Face recognition is a fundamental and critical topic in computer vision. In this work, a face recognition system based on stacked variational denoising autoencoders with triplet loss is proposed to overcome some existing challenges regard to face variations including poses, illumination, expression and low resolution with less training data. In our proposed system, a stacked variational denoising autoencoder is used to build a deep architecture for extracting salient and latent features from data. Together with that, by using a triplet loss function, we can preserve categorical similarity between faces, then improve the performance of the autoencoders in the clustering task. The proposed system is evaluated in some benchmark face datasets including ORL, Yale, Youtube Faces. Preliminary results demonstrate that the proposed system yields comparable results to other deep convolutional neural networks (CNN) and none-deep CNN based methods.
Traffic signs recognition using dynamic-scale CNN

This paper presents an improved CNN-based structure to recognize the traffic signs. In the conventional CNN structure, only features extracted by the last convolutional layer are fed into the first fully connected layer. A recently proposed method for using more features, the MS-CNN uses features extracted by all convolutional layers, but this method may ignore the differences among layers. In our improved structure, the previous convolutional feature maps are combined to the first fully connected layer by a dynamic priority algorithm, which is inspired by the dynamic priority scheduling in the operating system. Our improved structure assigns different priority weights for certain convolutional layers, and priority is dynamically changing. This structure emphases differences among various convolutional layers and trains the network from different scales of features with assigned priorities. The results on the GTSRB dataset show that our method achieves recognition accuracy up to 97.61%, which is a considerable improvement compared to the conventional CNN and the MS-CNN.
An Implementation and Optimization of Artificial Neural Network Based on Intra-chip Heterogeneous

In recent years, deep learning has been widely used in various fields, and powerful computing power is the basis for supporting the development of deep learning. Heterogeneous computing has become a mainstream direction for improving computing power due to its excellent performance and flexible structure. However, it also requires a lot of optimization from the algorithm to the hardware structure to perform large scale operations under low power conditions. In order to solve the above problems, this paper proposes a flexible optimization and implementation based on intra-chip heterogeneous. First, we divide tasks and assign code segments of the same operation to the same subtask. Secondly, according to the requirements of subtasks, a reusable and configurable acceleration kernel is developed in the FPGA, which has high multiplexing rate and flexibility. In addition, the performance and power consumption of the core is improved by optimizing the memory access and interface communication. The experiment result on a convolutional neural network with proposed method demonstrates that it only takes 1.1ms to identify a picture and power is 2.5W. Compared with the efficiency of running on ARM, the speed of convolution operation based on heterogeneous platform is 46.8 times faster.
A Novel Tuning Approach of the H<sub>∞</sub> Filter for Longitudinal Tracking of Automated Vehicles

This paper contributes to the challenging field of reliable vehicle tracking as a pivotal part of automated driving. The Kalman filter is an optimal state estimator based on the assumption of an accurate dynamical model and known noise statistic. In order to achieve a fast, robust and efficient vehicle state estimation in the omnipresence of model imperfections and measurement noise, a synergetic combination of the Kalman filter and H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filter is proposed, making optimal usage of their advantages. The performance of the filters and their combination is analyzed throughout a sensor data fusion example. Based on the determination of the position and velocity of a vehicle, the improvements, but also the limits of the different approaches are discussed. Additionally, the possibility to detect sensor faults such as time-varying offsets by augmenting the state-space model is explained.
Designing Virtual Reading Room (VRR Digi_Litikon) for learning batik in Vocational High Schools: Curriculum material improvement

Abstract This study aimed at designing Virtual Reading Room (VRR Digi_Litikon) for learning batik in Vocational High Schools in Indonesia. The design was also a part of curriculum material improvement with the touch of information technology. As a web-based application, VRR Digi_Litikon saved all the contents and database on its server. It was also proven that it had frontend as its user interface. The user interface was also PHP-based with Code Igniter framework. The application was designed using Software Development Life Cycle (SDLC) method consisting of such phases as system engineering, analysis, design, coding/implementation, testing, and maintenance. The results of the application design were digital books equipped with scientific journal articles in relation to batik. The digital platforms designed for VRR Digi_Litikon enabled students to have information not only in a form of texts but also in audio/voices of the contents of a book they were learning. The application was tested in various devices and had been proven to be eligible to use as a learning media application particularly for learning batik in vocational high schools.
On the Problem of Reducing the Prestige of a Profession “Teacher of Higher School”

The article discusses the problem of the prestige loss of the university teacher profession. The main reasons that adversely affect the prestige of the profession are identified. There is a need to develop measures aimed at enhancing the prestige of the profession.
Hybrid Model Architectures for Enhancing Data Classification Performance in E-commerce Applications

Data mining algorithms are used for analyzing and extracting large volume of data from different sources. Applying data mining in e-commerce applications can help making better decisions for new integrated technologies. Data mining create a way for decision-makers to make their decision more effective for improving businesses. In this paper, a framework is proposed to enhance the performance of classification techniques that are applied to an online shopping agency dataset by applying the best hybrid algorithm called EMLMT algorithm. Another proposed model framework is built to enhance the performance and decrease the execution time of the classification techniques that have been applied on a real dataset for easy cash company by applying the best hybrid algorithm which called DBKNN algorithm on this dataset. The conducted experimental results of the two proposed model architectures achieved high rates in accuracy, precision, recall, F-measure, and ROC when compared to classification only algorithms.
Recent Advances and Challenges in Design of Non-goal-Oriented Dialogue Systems

Human being is pervasively surrounded by smart devices that provide numerous services to them. These devices are equipped with interfaces that are natural and intuitive with the aim of providing user an effortless and organic interaction with the devices. A dialogue agent is one such interface that interacts with the user in natural language. Recent paradigm classifies them as goal-oriented and non-goal-oriented dialogue systems. The aim of goal-oriented dialogue systems is to assist the user in completing a task. Evidently the design of goal-oriented-dialogue agents has made a lot of progress. But the interactions with non-goal-oriented dialogue systems are reasonable, open- domain and more applicable to real-world applications. This paper reviews the state-of-the-art in Non-goal-oriented dialogue systems. The design of such systems has advanced due to Big Data hence most of the recent models are data-driven. This paper is a comprehensive study of data driven systems - advances in learning models and recent frontiers. Also provide an insight on datasets and evaluation methods and the limitations that the data-driven methods, datasets and evaluation methods present.
Øyvind Torvund - Øyvind Torvund: The Exotica Album. BIT20 Ensemble, Møster, Træen. Hubro, HUBROCD2580

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Sparse Least Squares Low Rank Kernel Machines

A general framework of low rank least squares support vector machine (LR-LSSVM) is introduced in this paper. The special structure of controlled model size of the low rank kernel machine brings in remarkable sparsity and hence gigantic breakthrough in computational efficiency. In the meantime, a two-step optimization algorithm with three regimes for gradient descent is proposed. For demonstration purpose, experiments are carried out using a novel robust radial basis function (RRBF), the performances of which mostly dominate.
Exploiting wavelet transform and support vector machine algorithm to perform side channel attacks on advanced encryption standard (AES)

As the scale of the Internet of Things continues to expand, cryptographic chips have become one of the key elements to protect sensitive data. Although the cryptographic algorithms used in the chips are mathematically safe, they can still be cracked through side channel attacks (SCA) based on the leaked power information. However, a successful attack is time consuming and large data is needed to be processed. In this paper, a novel SCA method based on wavelet transform and machine learning algorithms was proposed to improve the attack efficiency. In this method, the collected power information of the Advanced Encryption Standard (AES) algorithm was preprocessed by wavelet transform, data normalization and principal component analysis to extract the data feature and remove the unrelated data. Then, the preprocessed data were adopted to train the support vector machine, and the unknown key could be recovered by the trained model finally. Compared with the existing support vector machine (SVM) method, this proposed technique could improve the key recovery capability by 20%.
Appraisal expression extraction based on semantic and dependency parsing

Fine-grained sentiment analysis of online product reviews is important to both potential consumers and sellers. Automatically extracting appraisal expressions from online reviews is one of the key issues in fine-grained sentiment analysis. The existing method of using conditional random fields (CRF) extraction has achieved good results, but the mining of semantic features is not deep enough. On the basis of in-depth analysis of the semantic information of online reviews, we propose four semantic features, which are related word, distance from the beginning of sentence, forward-pointed turning word and backward-pointed adverb. CRF model with our features and traditional features is used to extract the appraisal expressions, then dependency parsing rules are used to supplement opinion phrases corresponding to isolated opinion targets. Experimental results on the mobile phone reviews corpus show that the accuracy of the proposed method is greatly improved compared with the baseline method 1. Compared with the baseline method 2, the accuracy of the method is not improved obviously, but the cost of manually marking the corpus is saved.
Application of KF improved by Hermite interpolation in target trajectory correction

In the application of the standard Kalman filter in the GPS data, the state equation cannot nearly reflect the actual motion trajectory of the target, leading to the poor filtering effect. In order to solve this problem, a piecewise cubic Hermite interpolation function is constructed by using the position information of the real points on the target motion trajectory and the direction information obtained by the position coordinate difference. Hence, the weighted correction for the variables in the state equation based on the Hermite function improves the standard Kalman filter. In the experimental analysis, the position coordinates of the target in the constant velocity curvilinear motion are corrected. The mean square error analysis of the position coordinates indicates that the model is significantly improved in accuracy. At the same time, the sensitivity analysis about the number of real points is carried out, showing that a small number of key points on the target trajectory can be used as real points to achieve the expected effect. The Kalman filter algorithm based on real value is simple and low-cost. Therefore, it has practical prospect.
Generating Initial Population of Farmland Fertility Algorithm with Chaotic Maps

In this study, Farmland Fertility Algorithm, which is one of the meta-heuristic optimization algorithms, has been investigated in detail. Three different approaches and different chaotic maps were used to generate the initial population of the algorithm. The chaotic mapped algorithms were tested with quality test functions and the performance of the algorithm was presented and interpreted through comparative tables and graphs. As a result of the tests, it was observed that the results of the approach of Generating the Whole Population and all dimensions with chaotic maps and the third approach in which the population was produced with one dimensional chaotic maps were generally similar or better with the Farmland Fertility Algorithm. In the second approach, it has been observed that the Farmland Fertility Algorithm, in which the tent (Tent) chaotic map is applied, produces more successful results for almost all dimensions and iterations. In the third approach, successful results were obtained in most of the chaotic map algorithms.
Klasifikasi Tingkat Maturitas Katarak Berbasis Citra Digital Berdasarkan Jangkauan (Range) Nilai Histogram

Cataract is an eye disorder that is at risk of causing blindness. The risk of blindness can be prevented by detecting cataracts as well as the right action in the form of surgery. Examination of cataracts in the eye poly usually uses equipment in the form of a slit lamp. This equipment is only available in hospitals that have eye poly. The lack of hospitals that have slit lamps will cause the number of cataract sufferers to increase. To be able to help overcome this we need a cataract detection system that is easy to implement. In this research, a classification system based on digital image based on the range of histogram values ​​was made. The equipment used to obtain digital images is the Nikon D90 12.3 Megapixel camera with Nikon 50 mm F1.8 AFD lens. The results obtained indicate that the range values ​​for normal eyes are 29–46, immature eyes 54 - 67 and mature eyes 91 - 121. It appears that mature eyes have the highest range. It is hoped that this method can help detect and classify cataracts based on digital image processing.
The main aspects of the methodology of scientific work (research)

The questions of the formulation of the main aspects of scientific research are considered.
Deep JEDi: Deep Joint Entity Disambiguation to Wikipedia for Russian

Over the past few years there has been a leap forward in both Entity Disambiguation and Entity Linking tasks. Meanwhile, Entity Disambiguation for Russian still lags behind advanced neural approaches developed for other languages. This paper introduces Deep JEDi—purely neural architecture, intended to identify the correct meaning for each mention in text. Combining sequence translation and sequence labeling approaches, our model achieves promising results on the Russian Wikipedia dataset. Significant improvement of its performance is attained by specific decoder that incorporates information about target mention position into attention mechanism. Additionally, we compare different approaches for learning distributed representations for tokens and entities and prove the importance of enriching joint embeddings with information about knowledge base structure.