Reviewerly/exHarmony huggingface dataset:
Impact of the financial and economic activities of university on its development

In recent years, Russia and its society have been undergoing reforms in all spheres of life, aimed at modernizing the economy, establishing a strong and stable state, and building a sustainable and prosperous society. Achievement of this goal is determined primarily by the education system, as a sector shaping the intellectual potential of the nation and ensuring the deepening of the democratization of society. Education is also undergoing a transformation that affects its structure and its qual
Generalization of Model-Agnostic Meta-Learning Algorithms: Recurring and Unseen Tasks

In this paper, we study the generalization properties of Model-Agnostic Meta-Learning (MAML) algorithms for supervised learning problems. We focus on the setting in which we train the MAML model over $m$ tasks, each with $n$ data points, and characterize its generalization error from two points of view: First, we assume the new task at test time is one of the training tasks, and we show that, for strongly convex objective functions, the expected excess population loss is bounded by $\mathcal{O}(1/mn)$. Second, we consider the MAML algorithm's generalization to an unseen task and show that the resulting generalization error depends on the total variation distance between the underlying distributions of the new task and the tasks observed during the training process. Our proof techniques rely on the connections between algorithmic stability and generalization bounds of algorithms. In particular, we propose a new definition of stability for meta-learning algorithms, which allows us to capture the role of both the number of tasks $m$ and number of samples per task $n$ on the generalization error of MAML.
Practical Provably Secure Encryption Scheme Based on Hashed Bilinear Pairing

In this paper, we propose a new encryption scheme that is said to be IND-CPA secure and makes use of a keyed hash bilinear pairing to XOR the message under the standard model, where the key is encrypted by the RSA algorithm, which greatly improves the safety and practicality of the scheme. To give the proof, the paper designs a series of indistinguishable thinking games through the adversary’s challenge to the system established by the challenger, which reduces the security of the scheme to the hardness of the Decisional Bilinear Diffie-Hellman (DBDH) assumption and the Entropy Smoothing (ES) assumption.
Key Technology and Application of Computer Artificial Intelligence Recognition Based on Machine Vision

Abstract Computer artificial intelligence technology simulates human thinking and consciousness vividly by using the existing functions of computers, and displays various human thinking through computer programs. Artificial intelligence recognition technology belongs to the key technology in the field of computer artificial intelligence. With the rising level of science and technology, this technology has developed rapidly and has been applied to many fields of society. Intelligent recognition technology is computer system, scanning equipment, camera technology, etc., which can intelligently recognize target instructions and data information, so as to meet the needs of intelligent recognition in the current era. In this paper, the problems faced in the application of computer artificial intelligence recognition technology are studied, and the key technologies of computer artificial intelligence recognition based on machine vision are analyzed, trying to find ways to make the application of computer artificial intelligence recognition technology get out of the bottleneck and remove obstacles to further promote the development of computer artificial intelligence technology.
A sorted weighting lookahead sampling scheme for accurate and fast particle filtering

The particle filter is known to be a powerful tool for estimating hidden Markov processes in nonlinear and nonGaussian state space models. The filter involves generating new particles from old ones, from regions of high importance in the state space using a proposal distribution and then weighing them using the incoming observation. However a poor choice of the proposal distribution may migrate the new particles into regions that do not contribute to the posterior and hence lead to one particle accumulating all the weight (termed particle degeneracy). This degeneracy is overcome using the resampling step that eliminates those particles with low weights and replaces them by those with large weights. However this resampling step is a computationally demanding operation. In the literature, the methods that speed up the particle filter, like the Gaussian particle filter, trade tracking accuracy with speed while methods that sample particles from high importance regions, like the auxiliary particle filters and lookahead particle filters, trade speed with accuracy. In this paper we propose a simple lookahead sampling scheme. Here the particles that fall into high importance regions are predetermined (seen ahead) and then propagated in copies to make up for those that should be discarded. This strategy avoids the resampling step and consequently leads to high speed and accuracy. Using two nonlinear models, we show the tracking efficiency of the proposed method.
A Comparison of ML and DL Approaches for Crowd Analysis on the Hajj Pilgrimage

In proportion to the growth in human population, there has been a substantial rise in the number of crowds in public places. The more crowded a place, the more risk of stampedes. Therefore, crowd management is very critical to ensure the safety of the crowds. Crowd monitoring is an effective approach to monitor, control and understand the behavior of the density of the crowd. One of the efficient automated video monitoring techniques to ensure public safety is crowd density estimation. Crowd density analysis is used primarily in public areas that are usually crowded with people such as stadiums, parks, shopping malls and railway stations. In this research, crowd density analysis by machine learning is presented. The main purpose of this model is to determine the best machine learning algorithm with the highest performance for crowd density classification. This model is focusing on machine learning algorithms such as traditional machine learning algorithms and deep learning algorithms. For traditional machine learning algorithms, Histogram Oriented Gradients (HOG) and Local Binary Pattern (LBP) have been used to extract important features from the input crowd images before being fed into Support Vector Machine (SVM) for classification. For deep learning algorithms, custom Convolutional Neural Network (CNN) together with two famous CNN architectures named Residual Network (ResNet) and Visual Geometry Group Network (VGGNet) were implemented as other methods in this paper for comparison. Other than that, the performance evaluation of the algorithm was measured based on the accuracy of the models. The performance of all different models was recorded and compared.
An Improved Variational Bayesian Information Filter with Measurement Loss for Underwater Navigation

The marine environment is complex and changeable. The measurement information is inevitably lost owing to the complex and time-varying underwater environment. An improved variational Bayesian information filter (IVBIF) algorithm is proposed to handle with the problems of measurement loss and complex measurement noises. The proposed IVBIF, which takes advantages of variational Bayesian approach and information filter, is proved in theory and is verified by simulation experiments. Because of the characteristics of the Variational Bayesian approach, the bad influence of measurement noise can be eliminated by the IVBIF compared with traditional Kalman filter.
Recognition of OBIC's Variants by Using Deep Neural Networks and Spectral Clustering

Oracle bone inscriptions (OBIs) are the origin of Chinese characters and play a pivotal role in the study of Chinese civilization and the world civilization. The automatic recognition of OBI character (OBIC) images is very import to the research and promotion of OBI culture. However, a large amount of these ancient characters have variants with totally different appearance, which brings very serious negative impact on the OBI studies. In this paper, we proposed to recognize variants of OBICs by combining deep convolutional neural networks (DCNNs) with spectral clustering (SC). The former is employed to provide accurate descriptions for OBIC images, and the latter is used to find variants of each OBIC class. More specifically, the pretrained ResNet50 is exploited to obtain image features, and the normalized graph cuts is employed to find variants. Besides, a label propagation algorithm is used to find the label of test OBICs based on the clustering results. The proposed method is tested on an OBIC image set, in which all images are cropped from OBI rubbing images. Experimental results have shown that our method has the ability to recognize OBIC's variants.
Merchant Libraries in the Cultural Heritage of Irkutsk

The article considers the role of merchant libraries in the history of the city. The reviewed books open a new publishing series of the scientific library of Irkutsk University, dedicated to merchant libraries of Irkutsk. Its publications provide an opportunity to get familiar with the old book collections of Irkutsk residents, know more about the interest in books and reading in our city as well as the history of private libraries. The first book in the series, published in 2019, was dedicated to the book collection of the largest Irkutsk merchant, the philanthropist and bibliophile V.N. Basnin, whose estate was one of the largest cultural salons in Irkutsk in the first half of the 19th century. The book under review contains the most complete catalog of Basnin’s library, which he had been collecting for more than 40 years. It should be noted that he was not only a reader, but also quite a professional bibliographer, ordered by mail novelty books, making their systematization and description. The second book in the series is dedicated to other merchant libraries. Its compilers have done a lot of research work to find these publications, systematize them and identify their owners. The catalog presented by the Irkutsk State University library most fully reflects the merchants’ interest in printed publications. It includes 969 book titles, reflecting 1 194 editions with exlibrises, autographs and notes of their owners. Despite the purely bibliographic nature of the peer-reviewed publications, which are considered in the research, they are of interest to different groups of people. The author notes the importance of the books not only to library workers, but also to all the readers who are not indifferent to the history of cultural life in Irkutsk, as well as to the role of books and home libraries in personality development and formation of human abilities.
A Convention for a Type-free Language

This chapter and the two that follow it lay out an extensive semantic-conventionalist system, and are therefore technical in nature. Here, the author develops the specific gappy logic that he thinks is most useful for a mathematical system that is to include real numbers. Though it is strongly inspired by Saul Kripke’s gappy logic, it suggests that the latter is just a little too gappy. This problem is alleviated by adding a conditional with a special kind of supervaluational semantics that validates hypothetical proofs. The author proves that the result retains the virtues of consistency and “trustworthiness”.This chapter presupposes Chaps. 1, 5, and 6.
Syntax in End-to-End Natural Language Processing

This tutorial surveys the latest technical progress of syntactic parsing and the role of syntax in end-to-end natural language processing (NLP) tasks, in which semantic role labeling (SRL) and machine translation (MT) are the representative NLP tasks that have always been beneficial from informative syntactic clues since a long time ago, though the advance from end-to-end deep learning models shows new results. In this tutorial, we will first introduce the background and the latest progress of syntactic parsing and SRL/NMT. Then, we will summarize the key evidence about the syntactic impacts over these two concerning tasks, and explore the behind reasons from both computational and linguistic backgrounds.
A Spatial Informance Design Method to Elicit Early Interface Prototypes for Augmented Reality

Abstract Designing for augmented reality (AR) applications is difficult and expensive. A rapid system for the early design process of spatial interfaces is required. Previous research has used video for mobile AR design, but this is not extensible to head-mounted AR. AR is an emergent technology with no prior design precedent, requiring designers to allow free speculation or risk the pitfalls of “path dependence.” In this article, a participatory elicitation method we call “spatial informance design” is presented. We found combining “informance design,” “Wizard of Oz,” improvisation, and “paper prototyping,” to be a fast and lightweight solution for ideation of rich designs for spatial interfaces. A study using our method with 11 participants, produced similar and wildly different interface configurations and interactions for an augmented reality email application. Based on our findings, we propose design implications and an evaluation of our method using spatial informance for the design of head-mounted AR applications.
Modeling Multi-Attribute and Implicit Relationship Factors for Collaborative Filtering Recommender System

Interactions of users and items can be naturally modeled as a user-item bipartite graph in recommender systems, and emerging research is devoted to exploring user-item graphs for collaborative filtering methods. In reality, user-item interaction usually stems from more complex underlying factors, such as the users’ specific preferences. A user-item bipartite graph could be used to understand the differences in motivation. However, existing research has not clearly proposed and modeled the factors that affect the differences, ignoring the similarities between user pairs and item pairs, preventing them from capturing fine-grained user preferences more effectively. This paper has developed a novel recommendation model MI-CF, which explicitly proposes and models multi-attribute and implicit relationship factors for collaborative filtering recommendation. MI-CF aggregates multi-attribute spaces through the user-item bipartite graph and additionally establishes user-user and item-item graphs to model the similar relationship information of neighbor pairs through a memory model. In addition, the sparse regularizer is utilized to alleviate the overfitting problem. Extensive experimental results on three public datasets not only show the significant performance gain of the proposed model but also prove the effectiveness and interpretability of fine-grained implicit factors modeling.
Cartoonize Images using TinyML Strategies with Transfer Learning

The gradual advancements towards the development of Deep Neural Networks has ultimately led to the development of certain specialised hardware for the application of such Networks with superior performance. But, many of the consumer grade hardwares and Single Board Computers (SBCs) which are used in embedded scenarios are not (yet) computationally efficient enough to perform the execution of even an already trained model within feasible limits. Thus in this paper, we focus on the implication of Post Training Quantization (PTQ) strategies to mitigate the great computational demands arising due to evolution of complex models. As the title implies, the original model incorporates a Generative Adversarial Network to generate Cartoonized versions of provided Real-World input images. This model, in its original state, takes nearly twice as much time to process the output on a single threaded workload. Our Solution to the above stated issue involves Quantizing the pre-trained model from 32-bit Floating Point values to a minimum of 8-bit Integer Values with the addition of Transfer Learning in-general. The results from our testing show that PTQ allowed the model to be compressed to a smaller size as compared to the original novelty, making it ready to be deployed on resource-constrained environments. In addition to that, a significant increase in the Inference Engine's processing performance has also been observed on general purpose hardware.
A lightweight convolutional neural network student learning behavior analysis based on facial expression recognition

In order to better grasp and analyze the classroom teaching situation and solve the classification problem of facial expressions in small data sets, a lightweight convolutional neural network CNN model is proposed. This model uses deep separable convolution to reduce the convolution operation The number of training parameters reduces the complexity of the model. The pre-activated residual unit is adopted in the residual module to make model optimization easier. At the same time, the use of BN in pre-activation improves the regularization of the model and improves the generalization ability of the model. Use FER2013 data set for training. The research results show that this model has achieved a good recognition rate on the test set of the FER2013 data set, and the network parameters and calculations are small, which is convenient for real-time detection.
On the separability of cyclotomic schemes over finite fields

It is proved that with finitely many possible exceptions, each cyclotomic scheme over a finite field is determined up to isomorphism by the tensor of <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="2"> <mml:semantics> <mml:mn>2</mml:mn> <mml:annotation encoding="application/x-tex">2</mml:annotation> </mml:semantics> </mml:math> </inline-formula>-dimensional intersection numbers; for infinitely many schemes, this result cannot be improved. As a consequence, the Weisfeiler–Leman dimension of a Paley graph or tournament is at most <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="3"> <mml:semantics> <mml:mn>3</mml:mn> <mml:annotation encoding="application/x-tex">3</mml:annotation> </mml:semantics> </mml:math> </inline-formula> with possible exception of several small graphs.
Color Based Object Segmentation on Wheeled Goalkeeper Robot

The ball detection system is a basic ability that must be possessed by soccer player robots, including goalkeeper robots. With the ball detection system, the robot can find out the position of the ball on the field. The position of the ball relative to the goalkeeping robot and the direction of its motion can affect the decisions and movements that will be made by the robot. In other words, the ball detection system has an important role for the goalkeeper robot in blocking the ball that threatens the goal. The problem that is often encountered with this detection system is, the system will detect all objects of the same color in the image even outside the field. This causes the robot to process the wrong information. To overcome this error, an elimination process is carried out on the area outside the field in the input image. This paper presents an object segmentation and detection system. To get the position of the object, it is done by calculating the moment of the contour. After doing some experiments, the result obtained that system successfully eliminates some areas outside the field from the image. This system also only detects the ball and robot in the field, with an average of processed frames per second (fps) is 31 fps.
New approch of opinion analysis from big social data environment using a supervised machine learning algirithm

Sentiment analysis is a very substantial area of research in our environment. Many studies have focused on the topic in recent years. It has rapidly gained interest due to the unusual volume of opinion-bearing data on the Internet (Big Social Data). In this paper, we focus on sentiment environment analysis from Amazon customer reviews shared by a machine learning based approach. This process starts with the collection of reviews and their annotation followed by a text pre-processing phase in order to extract words that are reduced to their root. These words will be used for the construction of input variables using several combinations of extraction and weighting schemes. Classification is then performed by a supervised Machine Learning classifier. The results obtained from the experiments are very promising.
Structured abbreviation expansion in context

Ad hoc abbreviations are commonly found in informal communication channels that favor shorter messages. We consider the task of reversing these abbreviations in context to recover normalized, expanded versions of abbreviated messages. The problem is related to, but distinct from, spelling correction, as ad hoc abbreviations are intentional and can involve more substantial differences from the original words. Ad hoc abbreviations are also productively generated on-the-fly, so they cannot be resolved solely by dictionary lookup. We generate a large, open-source data set of ad hoc abbreviations. This data is used to study abbreviation strategies and to develop two strong baselines for abbreviation expansion.
Intelligent live video dispatching framework for work from home setup in 5G Networks

Social network video sharing popularity is compelling the service providers to sustain the high QoS (Quality of Service). On the other hand, video sharing trend is becoming real time due to upsurging live video streaming over social networks. The current video dispatching framework, bifurcate across network operators and content providers. This fails to strengthen the cross-delivery information. We claim that unicast video traffic can be routed intelligently if network operators utilize the knowledge about location of the users and topology of the network. This in turn can enrich QoS for live video sharing over social networks. We manifest different scenarios in the current mobile network, where QoS suffers due to existing video dispatching framework. We propose that the future generation mobile networks can utilize location of the receiver with respect to the sender to enrich video quality experience. Our simulation experiment substantiate that on every QoS metrics lies the possibility for remarkable enhancement of live video sharing experience with the help of relative location of senders and receivers.
Prototyping VR Training Tools for Healthcare With Off-the-Shelf CGI

Cloud computing, big data, wearables, the internet of things, artificial intelligence, robotics, and virtual reality (VR), when seamlessly combined, will create the healthcare of the future. In the presented study, the authors aim to provide tools and methodologies to efficiently create 3D virtual learning environments (VLEs) to immerse participants in 3600, six degrees of freedom (6DoF) patient examination simulations. Furthermore, the authors will discuss specific methods and features to improve visual realism in VR, such as post-processing effects (ambient occlusion, bloom, depth of field, anti-aliasing), texturing (normal maps, transparent, and reflective materials), and realistic lighting (spotlights and custom lights). The presented VLE creation techniques will be used as a testbed for medical simulation, created using the Unity game engine.