Reviewerly/exHarmony huggingface dataset:
A Semantics for Hyperintensional Belief Revision Based on Information Bases

Abstract I propose a novel hyperintensional semantics for belief revision and a corresponding system of dynamic doxastic logic. The main goal of the framework is to reduce some of the idealisations that are common in the belief revision literature and in dynamic epistemic logic. The models of the new framework are primarily based on potentially incomplete or inconsistent collections of information, represented by situations in a situation space. I propose that by shifting the representational focus of doxastic models from belief sets to collections of information, and by defining changes of beliefs as artifacts of changes of information, we can achieve a more realistic account of belief representation and belief change. The proposed dynamic operation suggests a non-classical way of changing beliefs: belief revision occurs in non-explosive environments which allow for a non-monotonic and hyperintensional belief dynamics. A logic that is sound with respect to the semantics is also provided.
MasterFace Watermarking for IPR Protection of Siamese Network for Face Verification

Deep Neural Network (DNN) watermarking is receiving increasing attention as means to protect the Intellectual Property Rights (IPR) of DNN models. Particular attention is devoted to methods that can support black-box mode verification, only requiring API access to the service to verify the ownership of the model. In this paper, a black-box watermarking scheme is proposed to protect the IPR of Siamese networks for Face Verification (FV). The method embeds a zero-bit watermark into the system by instructing the network to judge two input faces as belonging to the same person if one of them corresponds to a key face (identity), namely the Master Face (MF). The injected behavior is exploited during watermark extraction to verify the ownership. Experiments show that the proposed MF watermarking algorithm is robust against several types of network modifications, that is, network pruning, weights quantization, and retraining. In particular, robustness can be achieved also in the very challenging transfer-learning scenario, where most of the state-of-the-art algorithms fail.
Analysis of the Results of a Complex of Monitoring Studies on Socio-Economic Mechanisms for the Implementation of E-Learning and Distance Learning Technologies

The article discusses the features of the transition of Russian education to distance learning. The most common problems that have arisen in connection with the new training format have been identified. The need to assess the compliance and readiness of educational organizations, teachers, students and their families to introduce a distance learning regime is indicated. In connection with this need, the importance of conducting a set of monitoring studies related to various socio-economic mechanisms for the implementation of e-learning and distance learning technologies, including models of financial, economic and resource support for distance learning at the level of the constituent entities of the Russian Federation, educational organizations, teachers, students and their families in the context of the transition to the specified format of classes. The results of the analysis of a complex of monitoring studies on the socio-economic mechanisms of the implementation of e-learning and distance learning technologies of various target groups are considered. Studies of strategies / models / organizational forms of ensuring the implementation of e-learning were carried out, an assessment of the financial, economic, methodological and resource support of distance learning at the level of regions, educational organizations and families was carried out, possible ways of forming an ecosystem of the digital economy in the general education system at the regional level were determined, studying the current conditions for ensuring the implementation of e-learning and the use of distance learning technologies in the context of the region, educational organization and family, assessed the level of the teacher's skills in working in the context of the implementation of e-learning and the use of distance learning technologies in the educational organization, as well as assessing the degree of parental satisfaction with the quality of educational process in the context of the implementation of e-learning. The advantages and disadvantages of the transition of educational organizations to distance learning are noted.
Traffic Forecasting of Core Network Based on Improved Logistic Regression

Traffic forecasting of core network plays an important role in network planning, traffic management, etc. Therefore, a predictive model that can accurately predict core network traffic is needed. This article proposes a new traffic forecasting method of core network based on logistic regression (LR). In order to get an accurate logistic model, new LR parameter estimation algorithm is proposed. First, the unknown parameters of LR are replaced by the minimum variance unbiased estimator to ensure the accuracy. In order to reduce the computational complexity, a statistical model of LR is introduced. Then, the unknown parameters of the LR are estimated based on the Cramer-Rao lower bound, and then the LR is further obtained based on the proposed estimator. Finally, the accuracy of the model is verified through experiments based on traffic data of core network. Experimental result shows that the improved logistic model proposed in this paper is more accurate than other methods.
Evaluating of the Resistance of Crypto-Compression Image Codograms to Errors in the Data Transmission Channel

The resistance of crypto-compression codograms to errors in the data channel is evaluated. The quality of reconstructions of images presented in BMP, JPEG, PNG and TIFF formats under conditions of data channel errors is evaluated. Different variants of errors in different components of crypto-compression codograms are considered. It is shown that crypto-compression codograms of images have good resistance to errors in the transmission channel, which occur in the code values of the information component of the first processing stage in the absence of errors in the second processing stage and service components.
Non-invasive Image Quality Assessment Based on Eye-tracking

In order to measure the perceptual quality of images, it is important to find suitable Image Quality Assessment (IQA) methods. Compared with the traditional objective IQA methods, the subjective IQA methods can more truly reflect users’ subjective feelings on image quality. However, the evaluation steps in subjective experiments will interfere with the process of subjects viewing images. We propose a non-intrusive IQA method based on eye-tracking technology to overcome the shortcoming. Through subjective experiments, we collect the eye movement data of subjects and calculate four main features from them. The analysis results of the eye movement parameters indicate that users pay attention to different areas of images with different qualities. Then, we use the eye movement parameters to design the Non-Intrusive Subjective (NIS) IQA algorithm which is a new subjective quality metric of images. Experimental results reveal that the validity and feasibility of the proposed metric.
Implementation of Face Detection and Eye Tracking with Convolution Neutral Network and Edge Computing on Electromobility

The eye tracking obtains the position data of both eyes; the fuzzy intent decision maker chooses the desired action for embedded vehicle. The action operation instructions are sent to edge computing to control the appliance and algorithm. The proposed article adopts eye aspect ratio and facial landmark detection with built-in pop count of XNOR-net to judgment driver monitoring system (DMS) on APEX2 and ARM NEON for NXP S32V234 platform. Verification of facial landmark detection and human behavior based on the human face image feature by camera is studied. Implementation network of the computing time upgrade to 59 ms from original 1624 ms under the accuracy loss is smaller than 3%. The overall algorithm has been improved to 16 fps from 0.5 fps.
Towards Machine Learning to Machine Wisdom: A Potential Quest

In the present era of artificial intelligence (AI) enabled solutions, the world is observing a tremendous influx in machine learning (ML) approaches across various application domains like healthcare, industry, document analysis, audio-video processing, etc. All existing machine learning approaches claim for intelligent solutions, but till date the learning is guided by the human wisdom i.e. all the proposed machine intelligence algorithms are data centric and infer knowledge without understanding the scenarios. The wisdom is an ability to take wise decisions based on the inferred knowledge to satisfy W5HH principle which outlines the series of answers to the questions such as why, what, who, when, where, how and how much, in a given context. This paper discusses the scope of machine wisdom (artificial wisdom)over conventional machine learning strategies along with its significance and how it can be achieved.
Research on Feature Selection of Human Physical Activity Recognition for IOT Wearable Devices

Using wearable devices to obtain daily physical activity data makes great contributions to health monitoring and evaluation of human. Wearable devices such as bracelets and watches have also developed rapidly in recent years. However, wearable devices cannot perform complex calculations or transmit large amounts of data on a chip because of limited computing power and strict power control. Therefore, it is necessary to extract the most effective features with the least amount of computation. In this paper, physical activity data is obtained by attaching sensor nodes to multiple parts of the human body, then time-domain features and frequency-domain features are extracted from sliding windows of different lengths to study the implementation scheme of efficient recognition of human activity. It is found that the recognition effect is better when two sensor nodes are tied to trunk and left leg respectively and 250 sampling points are selected as the sliding window length. Experimental results show that compared with using all features directly, the time-domain feature selected by Genetic Algorithm is the most effective feature. The energy consumption of data transmission is reduced by 40.5%, the accuracy increased from 95.2% to 96.67%, and it also has superior real-time performance.
Early Prediction of Hate Speech Propagation

Online hate speech has disrupted the social connectedness in online communities and raises public safety concerns in our societies. Motivated by this rising issue, researchers have developed many machine learning and deep learning methods to detect hate speech in social media automatically. However, most of the existing automated solutions have focused on detecting hate speech in a single post, neglecting the network and information propagation effects of social media platforms. Ideally, the content moderators would want to identify the hateful posts and monitor posts and threads that are likely to induce hate. This paper aims to address this research gap by defining a new problem of early hate speech propagation prediction. We also propose HEAR, which is a deep learning model that utilizes a post&#x2019;s semantic, propagation structure, and temporal features to predict hateful propagation in social media. Through extensive experiments on two publicly available large Twitter datasets, we demonstrate HEAR&#x2019;s ability to outperform the state-of-the-art baselines in the early prediction of hateful propagation task. Specifically, with just 15 minutes of observation on a post&#x2019;s propagation, HEAR outperforms the best baselines by more than 10% (F1 score) in predicting the eventual amount of hateful posts it will induce.
ARHome: Object Selection and Manipulation using Raycasting Technique with 3D-model Tracking in Handheld Augmented Reality

In Augmented Reality (AR), virtual objects are superimposed over the real world when viewed on a display device. It allows users to manipulate and engage with virtual objects. This paper explores the virtual object selection and manipulation using the raycasting technique when well suited with 3D model-based tracking technique in AR workspace of a handheld application called ARHome. This paper will explain the design stage involves in achieving the aim. It will then integrate with 3D model-based tracking. The significance of this study is the interaction technique can be applied in other fields such as education, training, commercial or simulation. Based on the results, the aim of this paper was achieved.
Undecidability, incompleteness, and completeness of second-order logic in Coq

We mechanise central metatheoretic results about second-order logic (SOL) using the Coq proof assistant. Concretely, we consider undecidability via many-one reduction from Diophantine equations (Hilbert's tenth problem), incompleteness regarding full semantics via categoricity of second-order Peano arithmetic, and completeness regarding Henkin semantics via translation to mono-sorted first-order logic (FOL). Moreover, this translation is used to transport further characteristic properties of FOL to SOL, namely the compactness and Löwenheim-Skolem theorems.
DeHIN: A Decentralized Framework for Embedding Large-Scale Heterogeneous Information Networks

Modeling heterogeneity by extraction and exploitation of high-order information from heterogeneous information networks (HINs) has been attracting immense research attention in recent times. Such heterogeneous network embedding (HNE) methods effectively harness the heterogeneity of small-scale HINs. However, in the real world, the size of HINs grow exponentially with the continuous introduction of new nodes and different types of links, making it a billion-scale network. Learning node embeddings on such HINs creates a performance bottleneck for existing HNE methods that are commonly centralized, i.e., complete data and the model are both on a single machine. To address large-scale HNE tasks with strong efficiency and effectiveness guarantee, we present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Decentralized Embedding Framework for Heterogeneous Information Network</i> (DeHIN) in this paper. In DeHIN, we generate a distributed parallel pipeline that utilizes hypergraphs in order to infuse parallelization into the HNE task. DeHIN presents a context preserving partition mechanism that innovatively formulates a large HIN as a hypergraph, whose hyperedges connect semantically similar nodes. Our framework then adopts a decentralized strategy to efficiently partition HINs by adopting a tree-like pipeline. Then, each resulting subnetwork is assigned to a distributed worker, which employs the deep information maximization theorem to locally learn node embeddings from the partition it receives. We further devise a novel embedding alignment scheme to precisely project independently learned node embeddings from all subnetworks onto a common vector space, thus allowing for downstream tasks like link prediction and node classification. As shown from our experimental results, DeHIN significantly improves the efficiency and accuracy of existing HNE models as well as outperforms the large-scale graph embedding frameworks by efficiently scaling up to large-scale HINs.
Box-driven Weakly Supervised Images Semantic Segmentation Algorithm Based on Attention Model

Image semantic segmentation has made great progress using deep neural networks. However, methods based on deep neural networks rely on numerous pixel-level annotations, and such pixel-level annotations require considerable time and money. To overcome this problem, researchers have proposed a weakly supervised image semantic segmentation, which requires only simple annotations to achieve high segmentation accuracy. In this article, we propose an algorithm that uses bounding box annotations to achieve image semantic segmentation. The basic steps are divided into two steps: generating temporary semantic segmentation labels and training the network and then iterating these two steps. We have conducted many experiments on PASCAL VOC 2012 to prove that this algorithm is effective, and the performance improved, which is close to the performance of the corresponding fully supervised network.
Identification of Anomalies in Images Using CNN and Autoencoders Techniques

Identification of anomalies in a given arrangement of images is a responsibility of high practical significance for visual quality review, observation, or clinical picture examination. Autoencoder neural systems figure out how to reproduce typical pictures and, henceforth, can characterize those pictures as inconsistencies, where the remaking blunder exceeds some edge. Here, we examine an essential issue of this methodology when the preparation set is corrupted with a little portion of exceptions. We locate the reserved preparing of autoencoders unavoidably lessens the reproduction mistake of exceptions and henceforth corrupts the abnormality identification execution. So as to neutralize this impact, a compatible autoencoder design is adjusted, which forces earlier dissemination on the inactive portrayal, regularly putting irregularities into low-probability areas. Using the probability model, potential irregularities can be distinguished and dismissed previously during preparing, which bring about an abnormality indicator that is overall progressively strong to the nearness of anomalies during preparing. Ordered approaches are frequently hard to get a lot of named preparing information. In this paper, the proposed method is based on convolutional neural system to reconstruct the errors in pixel.
Approximate Top-k Inner Product Join with a Proximity Graph

This paper addresses the problem of top-k inner product join, which, given two sets of high-dimensional vectors and a result size k, outputs k pairs of vectors that have the largest inner product. This problem has important applications, such as recommendation, information extraction, and finding outlier correlation. Unfortunately, computing the exact answer incurs an expensive cost for large high-dimensional datasets. We therefore consider an approximate solution framework that efficiently retrieves k pairs of vectors with large inner products. To exploit this framework and obtain an accurate answer, we extend a state-of-the-art proximity graph for inner product search. We conduct experiments on real datasets, and the results show that our solution is faster and more accurate than baselines with state-of-the-art techniques.
Multi-scale visual attention for attribute disambiguation in zero-shot learning

Observing the phenomenon that the discriminative visual features and unambiguous attribute descriptions are important in zero-shot learning (ZSL), we propose a Multi-scale Visual Attention for Attribute Disambiguation (MVAAD). MVAAD contains a Multi-Scale Visual Attention Network (MSVAN) to realize attentions on image regions, which helps MVAAD to learn more discriminative visual features. Based on the multi-scale visual features in MSVAN, we also develop a Coarse-to-fine Visual-guided Attribute Selection Module (CVASM) to use the multi-scale visual attentive features for attribute disambiguation. Both of MSVAN and CVASM can be jointly trained in an end-to-end manner by minimizing the visual-semantic classification loss and the latent-visual contrastive triplet loss. Experimental results on four popular ZSL benchmarks, AwA2, CUB, SUN and FLO, illustrate that MVAAD is able to not only achieve the state-of-the-art performance, but also give meaningful and explainable visualizations on the visual attention and the attribute selection. • We propose an embedding-based attentional model for zero-shot image recognition. • Our method combines multi-scale visual attention (VA) and attribute selection (AS). • The two parts, VA and AS, are optimized in a unified supervised framework. • Our method can discover the relationships between visual and semantic spaces. • Experimental results show our method performs better than other related methods.
Coexistence of Entangled and Classical Light Over 45 km of Installed Fiber

We show coexistence of quantum entangled and classical light over 45.6 km of installed optical fiber. Wavelength engineering/filtering allows milliwatts copropagating power with quantum light from a photon-pair source with broad entangled spectrum.
Multi-View Representation Learning

Representation of multiple views on machine learning and data mining are two fields where learning is a very fast-growing direction. The data for multi-view representation learning is gathered. In a single object, there are several views of data in detail. During the testing process, only one view of the data will be used. In this chapter, multi-view representation alignment and fusion are the methods of multi-view representation learning. As a result, multi-view representation learning utilizes a correlation-based alignment perspective. As compared to other representation learning algorithms, canonical correlation analysis (CCA) is one of the most successful. From the standpoint of representation fusion, the progression of multi-view representation learning, which involves generative methods such as multimodal topic learning, is discussed. Multi-view convolutional neural networks and multimodal recurrent neural networks, as well as multi-view sparse coding and multi-view latent space Markov networks, are neural network-based approaches. The theoretical foundations and current developments in the field of multi-view representation learning were addressed in this chapter, as well as a number of important applications of multi-view representation learning.
MANAGING SELF-DEVELOPMENT (SELF-CREATION) BY THE TEACHER

The aim of the presented article is to draw attention to the considerations related to self-formation, which are more and more vivid in the pedeutological discourse. Constant professional development in the teaching professions determines the effectiveness of teaching as students’ achievements depend mainly on teacher’s skills. Contemporary expectations towards teachers are definitely different than years ago. It results from changing reality, which imposes the necessity to constantly increase one's own competencies, not only the professional ones, but mainly those related to personal development. Personal growth is a significant aspect of professional development. Managing one's own development (self-creation) assumes that a man is a self-creator and the space of self-creation is a new method of experiencing the world and one's place in it. The article analyses the determinants of the self-creation process and its structure as well as the transgressive behaviours involved in the process. The analysis of the teacher's functioning as a co-creator of himself is embedded in the trend of the concept of humanistic, emancipatory and cognitive psychology