Reviewerly/exHarmony huggingface dataset:
Randomized multi-scale kernels learning with sparsity constraint regularization for regression

This paper presents a simple multiple kernel learning framework for complicated data modeling, where randomized multi-scale Gaussian kernels are employed as base kernels and a [Formula: see text]-norm regularizer is integrated as a sparsity constraint for the solution. The randomly pre-chosen scales provide random basis functions with diversity approximation ability and lead to extremely low computational complexity in finding the optimal solution. The random parameter appearing in the probability distribution and the regularizing factor are decided by the training data with cross validation techniques and the combination weights are solved by a well-posed linear system. Comparison experiments on one function approximation and three real-world regression problems of six learning algorithms are carried out. The way that multi-scale kernels fit the objective function is illustrated, the sparsity and the system robustness analysis with respect to the regularizing factor are given.
An Analysis of Fragmented Reading and Its Social Impact on College Students in the Era of New Media

Abstract: In the era of new media, people's lifestyle, education and the like are inseparable from the Internet. At the same time, due to the emergence of smart devices such as mobile phones and tablet computers, people's reading habits have changed. Fragmented reading has become an important form of reading for college students. College students themselves have the strong ability to accept change, so fragmented reading has gradually become an important form of student learning. This paper mainly analyses fragmented reading by college students and its impact on society in the era of new media.
A Face Aging Network Based on Conditional Cycle Loss and The Principle of Homology Continuity

Due to the broad application prospects in cross-age face recognition and various entertainment projects, face aging has attracted extensive attention from computer vision, graphics, psychology and other fields. In recent years, the method based on Generative Adversarial Networks(GANs) has achieved great success in generating high quality images. Among them, as a special case of GANs, Conditional GANs (CGANs) introduces prior information in the process of image generation to guide the generator to generate sample images with specific conditions. Inspired by CGANs, and in order to solve some problems in face aging research: (1) the accuracy of aging; (2) the aging effect is realistic and natural; (3) identity information is invariant, we propose a face aging simulation method based on conditional cycle loss and the principle of homology continuity. Different from the previous CGANs method for aging process simulation, we reconstruct the input face by using the synthesized aging face and the age label of the input face. By minimizing the reconstruction loss, the identity information during the process of face aging is maintained. At the same time, in order to ensure the accuracy of aging, we introduce an age classification network based on the Principle of Homology-Continuity, which is more consistent with the process of human “cognition”. The experimental results show that the proposed method generates pleasing face aging results, and significantly reduces the number of parameter.
Paradoxes of the higher education modernization: Results of the 20-year monitoring in the Sverdlovsk Region

The article considers modernization of the Russian higher education based on the data of the regional monitoring (1995-2016). The methodology of the research consists of the multidimensional approach (combination of systemic, institutional, resource and other approaches) and interpretations of modernization of the higher education as an integral part of modernization of the social-economic system of Russia. Modernization of the higher education is defined as a continuous process of transformational changes focused on improving the quality and competitiveness of all subsystems of the higher education. The article aims at explaining how modernization affects the resource potential of students, at showing to what extent students see its results and can use the opportunities provided by it. The authors focus on the question whether modernization is implemented for students, with or without students: how the quality of education changes due to modernization, how it affects the students’ satisfaction with the quality of education and professional training. The article reveals the paradoxes of modernization in terms of achieving its main goal - training of highly qualified professionals satisfying the needs of the economy in personnel; and points to the strengthening of material, technical and financial educational inequality of Russian regions and sectors of economy. The quality of the higher education is considered a way of social self-regulation. Its indicators include students’ satisfaction with different aspects of education important for the development of professional knowledge. The article presents the students’ estimates of the accessibility of the higher education in the Sverdlovsk Region by such basic indicators of modernization as academic mobility, informatization, relationship of education, science and production. Based on the results of the monitoring the authors prove the low impact of modernization changes on the students’ satisfaction with the quality of education and professional training.
Secured Audio Encryption using AES Algorithm

Speech to text conversion is the process of converting spoken words into written texts.In this paper, a voice encryption system is developed as a real-time software application.Basically, the speech is taken as an input and is encoded to be decoded by authenticated users only.The algorithm used to perform this cryptography is Advanced Encryption Standards (AES).This algorithm has its own particular structure to encrypt and decrypt sensitive data and is applied in hardware and software all over the world.We take the information in the form of audio as an input using a microphone which is to be transmitted over the channel to the intended receiver.The audio input is converted into text format which ensures speech-to-text conversion.Then, the text is encrypted using the AES algorithm to form cipher text.This cipher text is sent over a channel to the receiver.The receiver requests to perform decryption of the information only if he has the correct secret key otherwise the request is declined.If the key matches, the decryption is successful and the receiver get the message as text.For 128 bit, about 2128 attempts are needed to break.This makes it very difficult to hack it as a result it is very safe protocol.
Towards Massive RDF Storage in NoSQL Databases

The resource description framework (RDF) is a model for representing information resources on the web. With the widespread acceptance of RDF as the de-facto standard recommended by W3C (World Wide Web Consortium) for the representation and exchange of information on the web, a huge amount of RDF data is being proliferated and becoming available. So, RDF data management is of increasing importance and has attracted attention in the database community as well as the Semantic Web community. Currently, much work has been devoted to propose different solutions to store large-scale RDF data efficiently. In order to manage massive RDF data, NoSQL (not only SQL) databases have been used for scalable RDF data store. This chapter focuses on using various NoSQL databases to store massive RDF data. An up-to-date overview of the current state of the art in RDF data storage in NoSQL databases is provided. The chapter aims at suggestions for future research.
Assessment of Global Solar Radiation at Selected Points in Nigeria Using Artificial Neural Network Model (ANNM)

In this study, spatial distribution, temporal variations, annual distribution, estimation and prediction of solar radiation in Nigeria was carried out using ANNs. Levenberg-Marquardt backpropagation algorithms was used for the training of the network using solar radiation data along the years (1979-2014). The data records were divided into three portions (training, testing and validation). The network processed the available data by dividing it into three portions randomly: 70% for the training, 15% for validation and the remaining 15% for testing. Input parameters were chosen as latitude, longitude, day of the year, year while observed solar radiation was chosen as targeted data (from a processed file). The output parameter was the estimated solar radiation. The network designs were tested with root mean square error and then the most successful network (taken to be best network) which is network with less error was used to carry out the study. The hyperbolic tangent sigmoid transfer function was also used between the input and the hidden layers as activation function, while the linear transfer function was used from hidden layers to the output layer as the activation function. The performance of ANNs was validated by; estimating the difference between the annual measured and estimated values were determined using coefficient of determination (R2). Results revealed that the R2 result was 0.82 (82%). The result of spatial variations indicated that both wet and dry seasons have their highest concentration in North-East of Nigeria. It is pertinent to also note that the lowest concentration occurred in North-West during wet season, while the lowest occurred at the South-South and South-West of Nigeria in dry season. In addition, the lowest in dry season is about 25W/m2, while that of wet season is about 15W/m2. The agreement between the temporal and annual variation of observed and estimated solar radiation reveals that the model exhibits good performance in studying solar radiation. The model was further used to predict two years ahead of the years of study.
Multiple Bayesian Filtering as Message Passing

In this manuscript, a general method for deriving filtering algorithms that involve a network of interconnected Bayesian filters is proposed. This method is based on the idea that the processing accomplished inside each of the Bayesian filters and the interactions between them can be represented as message passing algorithms over a proper graphical model. The usefulness of our method is exemplified by developing new filtering techniques, based on the interconnection of a particle filter and an extended Kalman filter, for conditionally linear Gaussian systems. Numerical results for two specific dynamic systems evidence that the devised algorithms can achieve a better complexity-accuracy tradeoff than marginalized particle filtering and multiple particle filtering.
Effect of various dimension convolutional layer filters on traffic sign classification accuracy

The paper presents the study of an effective classification method for traffic signs on the basis of a convolutional neural network with various dimension filters.Every model of convolutional neural network has the same architecture but different dimension of filters for convolutional layer.The studied dimensions of the convolution layer filters are: 3 × 3, 5 × 5, 9 × 9, 13 × 13, 15 × 15, 19 × 19, 23 × 23, 25 × 25 and 31 ×31.In each experiment, the input image is convolved with the filters of certain dimension and with certain processing depth of image borders, which depends directly on the dimension of the filters and varies from 1 to 15 pixels.Performances of the proposed methods are evaluated with German Traffic Sign Benchmarks (GTSRB).Images from this dataset were reduced to 32 × 32 pixels in dimension.The whole dataset was divided into three subsets: training, validation and testing.The effect of the dimension of the convolutional layer filters on the extracted feature maps is analyzed in accordance with the classification accuracy and the average processing time.The testing dataset contains 12000 images that do not participate in convolutional neural network training.The experiment results have demonstrated that every model shows high testing accuracy of more than 82%.The models with filter dimensions of 9 × 9, 15 × 15 and 19 × 19 achieve top three with the best results on classification accuracy equal to 86.4 %, 86 % and 86.8 %, respectively.The models with filter dimensions of 5 × 5, 3 × 3 and 13 × 13 achieve top three with the best results on the average processing time equal to 0.001879, 0.002046 and 0.002364 seconds, respectively.The usage of convolutional layer filter with middle dimension has shown not only the high classification accuracy of more than 86 %, but also the fast classification rate, that enables these models to be used in real-time applications.
Security of the Suffix Keyed Sponge

We formalize and analyze the general suffix keyed sponge construction, a pseudorandom function built on top of a cryptographic permutation. The construction hashes its data using the (keyless) sponge construction, transforms part of the state using the secret key, and generates the tag from the output of a final permutation call. In its simplest form, if the key and tag size are at most the rate of the sponge, one can see the suffix keyed sponge as a simple sponge function evaluation whose input is the plaintext appended with the key. The suffix keyed sponge is, however, much more general: the key and tag size may exceed the rate without any need to make extra permutation calls. We prove that the suffix keyed sponge construction achieves birthday-bound PRF security in the capacity, even if key and tag size exceed the rate. Furthermore, we prove that if the absorption of the key into the state happens in a leakage resilient manner, the suffix keyed sponge itself is leakage resilient as well. Our findings show that the suffix keyed sponge compares favorably with the hash-then-MAC construction. For instance, to reach a security level of k bits, the side-channel protected component in the suffix keyed sponge just needs to process k bits of input besides the key, whereas schemes following the hash-then-MAC construction need a side-channel protected MAC function that processes 2k bits of input besides the key. Moreover, even if we just consider black-box attacks, the MAC function in a hash-then-MAC scheme needs to be cryptographically strong whereas in the suffix keyed sponge the key may be absorbed by a simple XOR. The security proofs are performed using the H-coefficient technique, and make effective use of the multicollision limit function results of Daemen et al. (ASIACRYPT 2017), both for arguing that state manipulation larger than the rate is tolerated after key processing and for upper bounding the amount of leakage an attacker may gain about the secret key.
Semantics and Homothetic Clustering of Hafez Poetry

We have created two sets of labels for Hafez 1 (1315-1390) poems, using unsupervised learning.Our labels are the only semantic clustering alternative to the previously existing, handlabeled, gold-standard classification of Hafez poems, to be used for literary research.We have cross-referenced, measured and analyzed the agreements of our clustering labels with Houman's chronological classes.Our features are based on topic modeling and word embeddings.We also introduced a similarity of similarities' features, we called homothetic clustering approach that proved effective, in case of Hafez's small corpus of ghazals 2 .Although all our experiments showed different clusters when compared with Houman's classes, we think they were valid in their own right to have provided further insights, and have proved useful as a contrasting alternative to Houman's classes.Our homothetic clusterer and its feature design and engineering framework can be used for further semantic analysis of Hafez's poetry and other similar literary research.
EMOMINER at SemEval-2019 Task 3: A Stacked BiLSTM Architecture for Contextual Emotion Detection in Text

This paper describes our participation in the SemEval 2019 Task 3 -Contextual Emotion Detection in Text.This task aims to identify emotions, viz.happiness, anger, sadness in the context of a text conversation.Our system is a stacked Bidirectional LSTM, equipped with attention on top of word embeddings pretrained on a large collection of Twitter data.In this paper, apart from describing our official submission, we elucidate how different deep learning models respond to this task.
Single-image Mesh Reconstruction and Pose Estimation via Generative Normal Map

We present a unified learning framework for recovering both 3D mesh and camera pose of the object from a single image. Our approach learns to recover outer shape and surface geometric details of the mesh without relying on 3D supervision. We adopt multi-view normal maps as the 2D supervision so that the silhouette and geometric details information can be transferred to neural network. A normal mismatch based objective function is introduced to train the network, and the camera pose is parameterized into the objective, it integrates pose estimation with the mesh reconstruction in a same optimization procedure. We demonstrate the abilities of the proposed approach in generating 3D mesh and estimating camera pose with qualitative and quantitative experiments.
PhAITV: A Phrase Author Interaction Topic Viewpoint Model for the Summarization of Reasons Expressed by Polarized Stances

This work tackles the problem of unsupervised modeling and extraction of the main contrastive sentential reasons conveyed by divergent viewpoints in text documents. It proposes a pipeline framework that is centered around the detection and clustering of phrases, assimilated to argument facets using a novel Phrase Author Interaction Topic-Viewpoint (PhAITV) model. The evaluation is conducted on all the components of the framework. It is mainly based on the informativeness, the relevance and the clustering accuracy of extracted reasons. The framework shows a significant improvement over several configurations and state-of-the-art methods in contrastive summarization on online debate datasets.
Research and Improvement of TF-IDF Algorithm Based on Information Theory

With the development of information technology and the increasing richness of network information, people can more and more easily search for and obtain the required information from the network. However, how to quickly obtain the required information in the massive network information is very important. Therefore, information retrieval technology emerges, One of the important supporting technologies is keyword extraction technology. Currently, the most widely used keyword extraction technique is the TF-IDFs algorithm (Term Frequency-Inverse Document Frequency). The basic principle of the TF-IDF algorithm is to calculate the number of occurrences of words and the frequency of words. It ranks and selects the top few words as keywords. The TF-IDF algorithm has features such as simplicity and high reliability, but there are also deficiencies. This paper analyzes its shortcomings for an improved TFIDF algorithm, and optimizes it from the information theory point of view. It uses the information entropy and relative entropy in information theory as the calculation factor, adds to the above improved TFIDF algorithm, optimizes its performance, and passes Simulation experiments verify its performance.
Layanan Dan Informasi Akademik Berbasis Bot Telegram Di Program Studi Teknik Informatika Universitas Mataram

Chatbot is considered as one of the hottest technology in recent years. It is used by various sector to serve its customer automatically. It gives benefits to business, primarily in customer care. Chatbot can be divided into 2 (two) types. One operates based on set of rules. It can be used with set of spesific command. The other types uses machine learning and artificial intelligence to provide its service. Chatbot can be utilised as well in education sector. Campus gives service to its students or faculty by providing information and academic service. Commonly, academic information and service has supported by information technology, usually in particular website. But, not all of the services are available and newest information does not always accessed timely. Hence, this research built a chatbot based on Telegram to provide information and academic services in informatic engineering department of mataram university. Telegram provides API that can be used to develop bot. The bot is built using Python, SQLite as the database and React. Prototyping model is used as a development method. The bot prototype is able to broadcast newest information to its register user and provides academic service such as theses program and internship program.
Study on Achievement Goals and Directionality in the Context of Learning Motivation

The article gives a brief historical overview of the origins and development of the theory of the achievement goals. It presents the analysis of the basic constructs of this theory: avoidance and achievement, valency, needs, motive, mastery and demonstration of results. It also introduces major authoritative models of goal achievement theories: diсhotomic, trichotomic, 2 x 2 and 3 x 2 models. The article examines systematized by Elliot and McGregor types of directionality of learning activities: focus on achieving mastery, focus on demonstration of results, focus on the avoidance of mastery, focus on avoidance of demonstrating results. The article includes the data of pilot research describing the link between personality directionality and directionality of learning activity.
A Tale of Two Set Theories

We describe the relationship between two versions of Tarski-Grothendieck set theory: the first-order set theory of Mizar and the higher-order set theory of Egal. We show how certain higher-order terms and propositions in Egal have equivalent first-order presentations. We then prove Tarski's Axiom A (an axiom in Mizar) in Egal and construct a Grothendieck Universe operator (a primitive with axioms in Egal) in Mizar.
GCN-Sem at SemEval-2019 Task 1: Semantic Parsing using Graph Convolutional and Recurrent Neural Networks

This paper describes the system submitted to the SemEval 2019 shared task 1 'Cross-lingual Semantic Parsing with UCCA'.We rely on the semantic dependency parse trees provided in the shared task which are converted from the original UCCA files and model the task as tagging.The aim is to predict the graph structure of the output along with the types of relations among the nodes.Our proposed neural architecture is composed of Graph Convolution and BiLSTM components.The layers of the system share their weights while predicting dependency links and semantic labels.The system is applied to the CONLLU format of the input data and is best suited for semantic dependency parsing.
Research on E-learning Teaching Assistant System Based on Improved Particle Swarm Optimization Algorithm

With the development of information technology, e-learning has become an important way of learning. Compared to the traditional way of learning, E-learning is not limited by time and space and can meet student’ learning needs at any time. In order to improve students’ autonomous learning efficiency of E-learning and teachers’ efficiency in managing students’ study, this paper puts forward that we shall apply the particle swarm optimization algorithm to the digital learning platform. Aiming at the problem of slow recommendation speed in recommendation methods of E-learning resources, should use particle swarm optimization (PSO) to seek the optimal teaching objective. Therefore, this paper designs the E-learning teaching assistants system based on the improved PSO. Experiments show that the improved PSO is more effective in solving optimization problems of group learning features in colleges and universities and the optimization results are ideal, which can improve the learning effect of college students.