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Over the past decades, video salient object detection has achieved rapid development. However, the effect of edge information on spatial and temporal features extraction has not been explored. The method STEG-Net is proposed to solve the problem, we use the extracted edge information to guide the extrction of spatial and temporal features simultaneously. We combine deep texture information with shallow edge information, which can not only retain the edge of the object but also enhance the global information, leading to the more accurate location of the object. At the same time, the shallow edge information and the deep texture information are complementary. As a result, edge information and salient features could advance each other while extracting features. Finally, we evaluate the 16 state-of-the-art VSOD methods on ViSal and FBMS datasets. At the same time we also evaluate the 13 state-of-the-art VSOD methods on UVSD and MCL datasets. The experiment results indicate that the proposed method outperforms the state-of-the-art approaches.

The changes that have taken place in the work of Cuban news agencies during the digitalization of media production are analyzed. Over the past few years, they have been undergoing a process of transformation, changing their approaches to providing information and introducing new products. To study these processes, the authors resort to the digital platforms of two news agencies - Prensa Latina and Agencia Cubana de Noticias , as well as the online news portal Cubadebate . Comparative and analytical methods of research reveal the specific features of functioning of these media resources at the present moment. The authors conclude that despite the international isolation Cuba experienced for more than sixty years and relatively limited financial resources allocated for media development, the country's media market has entered a period of modernization. All the changes taking place will greatly expand the possibilities for integrating Cuban news agencies into global information processes.

Existing methods that use document-level information for event detection ignore the dependencies between sentences and also have shortcomings in modeling the dependencies among words. In this paper, we propose a novel Hierarchical Graph Enhanced Event Detection (HGEED) framework to make full use of syntax and document information for the task of event detection. First, a sentence graph is used to model word-to-word dependencies, enriching the local information of words by incorporating syntactic features. Then, a document graph is built to model sentence-to-sentence dependencies, obtaining global semantic representations for word-level prediction. The experiment results on the widely used ACE 2005 and TAC KBP 2015 corpora show that our model can capture local and global information with dependencies and achieve significant improvements as compared to all baselines.

The author systematize history of Russian university libraries, analyze their progressing in digital environment, and discuss new possibilities for modernizing education and academic libraries, in particular. The laws and regulations for university libraries are reviewed; their functions and services are specified. As a result of global information processes intensive growth, the demand for elibraries and their services have been increasing. The students of Cherepovets State University were surveyed on the issues of library service efficiency. The survey proved the significance and relevance of library services for the students; the strengths and weaknesses of library services were identified. The authors emphasize the need for intensifying marketing component in library activities and promotion of its services, and for studying target audience demands. The prospects for academic libraries are also discussed.

School Library Educators in developing countries have, in the main, been training School Infomration Professionals using models of education developed in the United States and Europe. While there have been some local modifications to these western models few countries have developed truly local education programs for Infomation Servie Professional. This is due, in part, to the fact that the Information Services that students are being educated to work with are global information services often based on series developed in the United States and Europe. Many of the advisers who helped to set up school library training programs are from Western Countries and a large proportion of the staff lecturing in these programs have been educated in Western Countries. The paper will conclude by discussing if western models of education for School Information Service Professionals are the most appropriate models given the increased globilization of information services or whether locally developed models can best service the needs of those training to work in developing countries.

In order to ensure safe operations of industrial processes, it is especially important to find fault types accurately and quickly based on historical data and then deal with faults in time. Unfortunately, due to the tricky characteristics of industrial process data such as massive number, high dimensionality, and nonlinearity, it turns out that timely and accurate diagnosis of faults becomes of great difficulty in industrial processes. To address this problem, novel effective fault diagnosis using an improved global and local dimensionality reduction (DR) method named discrimination locality preserving projections integrated with sparse autoencoder (SAEDLPP) is proposed in this article. In SAEDLPP, the global DR information of data is first obtained by sparse autoencoder (SAE); next, the DR data obtained through SAE are passed through discrimination locality preserving projections (DLPP) to obtain local DR information, preserving not only the global information but the local information of the extracted features. Finally, fault diagnosis is achieved by separating the extracted features by SAEDLPP using an AdaBoost classifier to recognize fault types. Simulations are conducted on the Tennessee Eastman process (TEP) and the results indicate that the provided SAEDLPP-based fault diagnosis methodology can achieve much higher accuracy in fault diagnosis than other associated methods.

The existence of tax havens is an integral phenomenon of the modern stage of the world economy development. They compete with each other on the basis of tax rates, privacy level, quality and speed of the service offered. Determining the impact of tax havens is now a rather pressing issue, deepening which has largely contributed to leakages of information, particularly the Panama Papers. However, the leaks of information from tax havens are not sufficiently covered in the research of both domestic and foreign scholars. The article is aimed at examining the impact of tax havens and the Panama Papers information leak on the world economy. The definition of tax havens and their scale in modern conditions of globalization economy is considered. In the systematization of scientific works the relatively different leaks of information from tax havens were compared and the largest of them was identified. The essence of the activities of Mossack Fonseca, the company that was the victim of the data leak, is disclosed. The dynamics of registered offshore companies within the framework of the Panama Papers leak are substantiated. With the help of a mathematical model, the authors computed a trend vector of movement of the number of companies, which are using the services of offshore zones. As a result of regression trend analysis, it was defined that the number of companies registered by Mossack Fonseca tends to grow (an average of 292 units annually). This indicates that, despite the publication of classified information, the popularity of tax havens continues to increase. The main intermediary countries, which are most popular in tax speculation, are provided. It is determined that the simplicity of formalization and registration of companies, the lack of control over subsidiaries of multinational business groups are key attributes of the activities of tax havens that contribute to their use to avoid paying income taxes and money laundering. The consequences caused by the activities of tax havens and information leaks from them have been formulated. The most important among them are: sanctions provisions and monitoring of offshore activities.

With the changing of times and the rapid development of science and technology, the world is faced with global information. And the access to information becomes very important, especially in such special times as epidemic crisis. The media in every country are quickly gathering information to spread news internationally, which should be paid great attention to, for they are the focuses of the world, and the events in the news relate to the image of a country. However, every country has its own national condition, which naturally decides the differences existing in their media reports. China Daily and the New York Times are selected as research subjects, with chosen news ranging from February 2020 to May 2020. China Daily is the major media for foreign publicity in China, while the New York Times is also famous since the United States is the representative of the West. This paper explores international news on COVID-19 of the above two media to understand their differences and find out the reasons. In this paper, the content analysis method is adopted to extract part of international news for qualitative analysis. The ultimate goal of the comparative study is to improve China’s external publicity capacity.

The purpose of this article is to analyze the current state of application of the most common information and communication technologies in the educational process of institutions of general high school in Ukraine and in the world under the influence of information and the technological revolution to modernize. General scientific methods were used, such as analysis and synthesis, the theory of innovation by I. Schumpeter with additions by B. Twiss and M. Lapin, as well as the theory of social interaction by P. Sorokin. An empirical study was carried out on the advantages and disadvantages of the introduction of information and communication technologies in the educational process, research was carried out with 120 people, including 20 parents, 50 students and 50 teachers. As a result of the study, the basic trends of modern reform of general secondary education under the influence of information and communication technologies were investigated and the main positive and negative aspects of global information and communication trends were analyzed, as well as the consequences of incorporations as well. and delayed urgent reforms.

The global information and communication technologies (ICT) industry considers the Internet of Things (IoT) to be a promising and innovative area for development. The IoT concept encompasses both t...

Human-Object Interaction (HOI) Detection is a new genre of human-centric visual relationship detection task, which is significant to deep understanding of visual scenes. Due to the complexity of the visual scene in the image, HOI detection is still a challenging task, the most critical part of which is feature extraction and representation. Some existing approaches rely solely on local region information for HOI detection without using global contextual information, but global contextual information contributes to this task in some HOI categories. Other approaches incorporate global contextual information for HOI detection while losing local region information. In this work, we propose a multi-stream neural network architecture composed of three special module that employs both local region information and global contextual information for HOI detection. This model can detect not only the HOI categories based on local region information but also on global contextual information. Our model more fully considers all HOI categories in the dataset. Compared with other existing approaches, the proposed model shows improved performance on V-COCO and HICO-DET benchmark datasets, especially when predicting rare HOI categories.

Ethnocentrism affects perception and choices, and scholars, teachers, and students may not see or seek the “global” in our “global information revolution,” especially in such vast monolingual socia...

These days, China is deeply integrated into global information capitalism, but what was China like before the Internet era? Xiao Liu’s new book, Information Fantasies: Precarious Mediation in Posts...

In recent years, algorithms for multiple object tracking tasks have benefited from great progresses in deep models and video quality. However, in challenging scenarios like drone videos, they still suffer from problems, such as small objects, camera movements and view changes. In this paper, we propose a new multiple object tracker, which employs Global Information And some Optimizing strategies, named GIAOTracker. It consists of three stages, i.e., online tracking, global link and post-processing. Given detections in every frame, the first stage generates reliable tracklets using information of camera motion, object motion and object appearance. Then they are associated into trajectories by exploiting global clues and refined through four post-processing methods. With the effectiveness of the three stages, GIAOTracker achieves state-of-the-art performance on the VisDrone MOT dataset and wins the 3rd place in the VisDrone2021 MOT Challenge., ICCV 2021 Workshop

Face attribute editing is to edit the face image by modifying single or multiple attributes while maintaining the face identity. In the paper, we propose a method for attribute editing of face images by using the generative adversarial networks: conditional generative adversarial nets is used as the backbone of the framework and input attributes as conditions to the generator, the generator combines the encoder–decoder with U-Net, and the attribute classifier is added to guarantee the correct attribute operation on the generated image. The receptive field of a single discriminator is very limited, especially when the size of the training picture becomes larger, which will affect the extraction of information. In this paper, we tackle these limitations by using multi-scale discriminators to guide the generator to generate better details. It can macroscopically grasp the global information of the generated pictures and obtain more information of the receptive field. We demonstrate the effectiveness of our method and generate well-preserved facial detail images on CelebA dataset. The fidelity of the generated image is improved, and the method has better flexibility. The experiments show that our method is effective on the real-world dataset.

Global information resources and sophisticated technology have accelerated the availability and utilization of Online Information Resources (OIR) in the Open Distance Learning environment. The stud...

Intensity inhomogeneity and noise are two major parts in image segmentation. Aiming at these problems, this work proposes a novel hybrid active contour method which combines local and global statistical information into an improved signed pressure force (SPF) function. First, by considering the global information extracted from a region of interest, a new global-based SPF function is created that effectively adjusts the signs of the pressure force inside and outside the evolving curve. Second, a new local-based SPF function utilizes the normalized local intensity differences as the coefficients of local internal and external regions, which can segment complicated areas easily. Third, by combing the global-based SPF and the local-based SPF functions, an improved hybrid SPF function is constructed based on active contour approach. Experiments on many kinds of real and synthetic images show that our method makes superior segmentation accuracy and is more robust to initial contour and noises.

Background subtraction is the most popular technique for foreground detection to automatically separate foreground objects from background in video sequences. Much research effort has been directed into exploiting different features for background modeling to improve robustness of background models. However, most features used for background modeling are local features (such as intensities, colors and textures) which may have limitations when dealing with environments with complex and diverse changes in backgrounds, since changes may not emerge only within local regions. In this paper, inspired by mature human visual system (HVS), we propose a background subtraction method based on modified pulse coupled neural network (PCNN) in compressive domain, extracting global information for background modeling by making use of global coupling characteristics of the PCNN. Enlightened by theory of compressive sensing, we first employ compressive projection matrices to reduce data dimensionality. Then, we modify the PCNN to receive multi-channel color information as external stimuli and adjust linking strength and linking range for each neuron to enhance the ability of the network in global coupling. The modified PCNN is utilized to generate output responses containing global information of scenes for background modeling. Experimental results have demonstrated that our method has excellent performance and outperforms several advanced techniques in terms of visual and quantitative evaluations.

Feature recalibration is a very effective strategy of further improving performance in deep networks. The commonly used global pooling operation will lose the information of distinguishing features, which requires additional fully connected layers to adjust the relationship between feature maps. In this paper, we propose a novel architectural unit that aims to recalibrate feature maps based on the discriminative feature information contained in each feature map. To achieve this, we use two-layer depthwise convolution instead of global pooling to extract the distinguishing feature information of each feature map. Since convolution can discriminately learn the global information of each feature map, we discard the fully connected layer to ensure independence when adjusting the feature map. After getting the global information of each feature map, the nonlinear function is used directly to implement the refinement of the feature map to enhance the distinguishing feature map and suppress useless ones. Based on the design characteristics of the new unit, we codename it as the “Depthwise Squeeze and Refinement” (DSR) block. And it can be easily embedded into existing state-of-the-art deep structures to significantly improve the performance at a slight computational cost.

Local community detection refers to finding the community that contains the given node based on local information, which becomes very meaningful when global information about the network is unavailable or expensive to acquire. Most studies on local community detection focus on finding non-overlapping communities. However, many real-world networks contain overlapping communities like social networks. Given an overlapping node that belongs to multiple communities, the problem is to find communities to which it belongs according to local information. We propose a framework for local overlapping community detection. The framework has three steps. First, find nodes in multiple communities to which the given node belongs. Second, select representative nodes from nodes obtained above, which tends to be in different communities. Third, discover the communities to which these representative nodes belong. In addition, to demonstrate the effectiveness of the framework, we implement six versions of this framework. Experimental results demonstrate that the six implementation versions outperform the other algorithms.

Information diffusion is a fundamental process taking place on the Internet. Every day, various pieces of information are published and further disseminated through news articles and user messages. Even though this process can be explicitly observed, it is difficult to determine individual information pathways. The penetration of the global information environment into all spheres of everyday life radically changes the rate and ways of information diffusion. In this review, we discuss models of online information flows while dividing them into two groups: explanatory models (which imply the existence of an underlying network over which information propagates) and predictive models (which are used to investigate the spread of individual information flows). Despite the complexity of analyzing basic characteristics of information diffusion, this analysis is necessary to understand general processes that occur in the information society.

In this paper, an improved region-based active contour model in a novel variational level set formulation employing global information constraints is proposed for image segmentation. Using the imag...

Purpose The purpose of this paper is to perform a content analysis of information literacy specific journals to determine what contributing countries and predominant themes are represented in the field leading to a global understanding of information literacy. Design/methodology/approach The data for this study were collected through a content analysis of journal article publication information, titles, abstracts and keywords in three journals: Communications in Information Literacy (the USA), Journal of Information Literacy (the UK) and Nordic Journal of Information Literacy in Higher Education (Norway) from 2012 to 2017. Findings The USA contributed the most to the literature, though the top information literacy themes were addressed by each of the 18 countries that contributed articles to the journals under study. Some themes emerged that suggest the existence of a common global discourse around shared information literacy concepts, including themes related to information literacy theory and practice. Practical implications The shared information literacy themes can be used to help solidify a global understanding of information literacy. Originality/value This study fills a gap in the content analysis of information literacy journals by identifying themes that demonstrate a shared global understanding of information literacy.

Ordered Weighted Averaging (OWA) operators have been integrated in Convolutional Neural Networks (CNNs) for image classification through the OWA layer. This layer lets the CNN integrate global information about the image in the early stages, where most CNN architectures only allow for the exploitation of local information. As a side effect of this integration, the OWA layer becomes a practical method for the determination of OWA operator weights, which is usually a difficult task that complicates the integration of these operators in other fields. In this paper, we explore the weights learned for the OWA operators inside the OWA layer, characterizing them through their basic properties of orness and dispersion. We also compare them to some families of OWA operators, namely the Binomial OWA operator, the Stancu OWA operator and the expo-nential RIM OWA operator, finding examples that are currently impossible to generalize through these parameterizations. This work was funded by a predoctoral fellowship of the Research Service of Universidad Pública de Navarra, the Universidad Pública de Navarra under project PJUPNA1926, and the Spanish MICIN (PID2019-108392GB-I00 / AEI / 10.13039/501100011033).

Accurate lesions detection on retinopathy images is crucial for the diagnosis of diabetes. However, it is always hampered by various characteristics of lesions such as shape, color, texture, and similarities. Most advanced algorithms still cannot automatically detect common lesions, e.g. exudate, hemorrhage, and cotton-wool spots, being used for comprehensive analysis of disease state. To this end, we present a multi-functional detection model for diabetic retinopathies and further analyze disease mechanisms overall. Specifically, this paper attempts to implement a multi-lesion detector via modified Mask region-oriented CNN, which can be used for the aforementioned retinopathies. Meanwhile, a non-local attention module is introduced to the detector as a spatial attention mechanism for handling the global information missing problem. In addition, to boost the effectiveness of the detector, the dilated operation is implemented for dataset preprocessing. Improvement is achieved both algorithmically and architecturally, via investigating thoroughly the most probable lesion category with a novel ensemble learning framework. Extensive experiments on standard datasets for three different tasks evidence the superior performance of the proposed method over state-of-the-art methods.

Long-term visual tracking is a challenging problem. State-of-the-art long-term trackers, e.g., GlobalTrack, utilize region proposal networks (RPNs) to generate target proposals. However, the performance of the trackers is affected by occlusions and large scale or ratio variations. To address these issues, in this paper, we are the first to propose a novel architecture with transformers for long-term visual tracking. Specifically, the proposed Visual Tracking Transformer (VTT) utilizes a transformer encoder-decoder architecture for aggregating global information to deal with occlusion and large scale or ratio variation. Furthermore, it also shows better discriminative power against instance-level distractors without the need for extra labeling and hard-sample mining. We conduct extensive experiments on three large-scale long-term tracking datasets and have achieved state-of-the-art performance.

Definitely one of the major modern technological achievements is the phenomenon of global information highway called the Internet...

Video anomaly detection (VAD) is currently a challenging task due to the complexity of anomaly as well as the lack of labor-intensive temporal annotations. In this paper, we propose an end-to-end Global Information Guided (GIG) anomaly detection framework for anomaly detection using the video-level annotations (i.e., weak labels). We propose to first mine the global pattern cues by leveraging the weak labels in a GIG module. Then we build a spatial reasoning module to measure the relevance between vectors in spatial domain with the global cue vectors, and select the most related feature vectors for temporal anomaly detection. The experimental results on the CityScene challenge demonstrate the effectiveness of our model.

The global information technology market is growing every year. Russia has great resource potential and a developed material and technical base to become one of the leaders in this area. Now Russia occupies a very modest place in the global IT industry. The situation is gradually changing. The Russian digital industry is growing much faster than the economy as a whole. However, in order to catch up with the leading countries, the IT sector must develop even faster. In recent years, the state has begun to play a decisive role in the economy therefore, the state has been tasked with bringing the Russian digital economy into the ranks of world leaders. This requires significant transformations and changes in the state model for creating a digital economy.

The rapid development of network information technology has led to the explosive growth of global information data volume. People’s lives are more and more full of massive data. This era is vividly called “big data era”. With the application of big data technology, colleges and universities can predict the future by analyzing and mining the laws hidden behind the data, so that teachers and managers can improve their teaching and management decisions.

Coevolution spreading dynamics on complex networks is a hot topic, which attracts much attention in network science. This paper proposes a mathematical model to describe the two competing complex information spreading dynamics on multiplex networks. An individual can only accept one of the two pieces of information. A heterogeneous mean-field theory is developed to describe the spreading dynamics. We reveal different regions through Monte Carlo simulations of the competing complex information spreading dynamics: no global information, one information dominant, and two information coexistence. We finally find that the heterogeneity of the multiplex networks’ degree distributions does not qualitatively affect the results.

Nowadays, most deep learning models ignore Chinese habits and global information when processing Chinese tasks. To solve this problem, we constructed the BERT-BiLSTM-Attention-CRF model. In the model, we embeded the BERT pre-training language model that adopts the Whole Word Mask strategy, and added a document-level attention. Experimental results show that our method achieves good results in the MSRA corpus, and F1 reaches 95.00%.

Extracting relational triples from unstructured text is essential for the construction of large-scale knowledge graphs, QA and other downstream tasks. The purpose of dialogue relation extraction is to extract the relations between entities from the multi-person dialogue texts. The existing dialogue relation extraction models only focused on coarse-grained global information and ignored fine-grained local information. In this paper, we propose a dialogue relation extraction model BERT-MG to capture the features on different granularity at different BERT layers to take advantage of the fine-grained dialogue features. Moreover, we design a type-confidence mechanism to use the entity type information to assist relation inference. Experimental results on the DialogRE dataset prove that our proposed model BERT-MG outperforms the SOTA baselines.

Tento článek podrobně popisuje inovativní techniku vkládání globální (nebo obecně rozsáhlé) míry kvality do hluboké neuronové sítě (DNN) s cílem kompenzovat tendenci DNN zakládat výslednou klasifikaci prakticky ze superpozice lokálních transformací a projekcí okolí. Jako globální míru kvality jsme použili rys podobný pravděpodobnosti stavu AQ1 a injektovali ji do klasifikátoru založeného na DNN nasazeného v konkrétní úloze určení, které části webové stránky jsou určitým zájmem pro další zpracování technikami NLP. Naším cílem bylo rozložit webové stránky různých internetových diskusních fór AQ2 na užitečný obsah, tj. příspěvky uživatelů, a neužitečný obsah, tj. grafiku fóra, menu, bannery, reklamy atd. This paper describes in details an innovative technique of injection of a global (or generally largescale) quality measure into a deep neural network (DNN) in order to compensate for the tendency of DNNs to found the resulting classification virtually from a superposition of local neighbourhood transformations and projections. We used a state AQ1 probability-like feature as the global quality measure and injected it into a DNN-based classifier deployed in a specific task of determining which parts of a web page are of certain interest for further processing by NLP techniques. Our goal was to decompose web sites of various internet AQ2 discussion forums to useful content, i.e. the posts of users, and useless content, i.e. forum graphics, menus, banners, advertisements, etc.

Mobile telephones have become an elementary part of our life today, and are also one of the most relevant media to communicate. Several portable towers, especially in huddled urban areas and concrete areas, are therefore planted to hide additional areas. Now the bottom stations at those locations have transceivers using RF waves to determine contact between users in the mobile network. Due to the variety of base stations, it is needed, with greater portable usage and competition from the market and with new technical capacities. Thermal and non-thermal effects arise from the use of microwaves in mobile contact and have detrimental implications on the biological system. In this paper, a study on the quantification of electromagnetic field (EMF) radiation from mobile towers and their health impacts on human beings has been carried out in Samawa city of Iraq. The aim of this paper is to conduct a separate analysis of the recent document on potential biological and health effects and expected methods for microwave radiation to minimize their effects. This investigation is concentrated on the information and data that we are obtained from Samawa hospital are environment institutes of the same copy this information stapled and compared with global information of the distribution of electronic tower final in Samawa and its effects of city and the numbers inherent overseas and risk.

Group activity recognition has received significant interest due to its widely practical applications in sports analysis, intelligent surveillance and abnormal behavior detection. In a complex multi-person scenario, only a few key actors participate in the overall group activity and others may bring irrelevant information for recognition. However, most previous approaches model all the actors’ actions in the scene equivalently. To this end, we propose a relation-guided actor attention (RGAA) module to learn reinforced feature representations for effective group activity recognition. First, a location-aware relation module (LARM) is designed to explore the relation among pairwise actors’ features in which appearance and position information are both considered. We propose to stack all the pairwise relation features and the features themselves of an actor to learn actor attention which determines the importance degree from local and global information. Extensive experiments on two publicly benchmarks demonstrate the effectiveness of our method and the state-of-the-art performance is achieved.

Recently, in order to address the unsupervised domain adaptation (UDA) problem, extensive studies have been proposed to achieve transferrable models. Among them, the most prevalent method is adversarial domain adaptation, which can shorten the distance between the source domain and the target domain. Although adversarial learning is very effective, it still leads to the instability of the network and the drawbacks of confusing category information. In this paper, we propose a Robust Ensembling Network (REN) for UDA, which applies a robust time ensembling teacher network to learn global information for domain transfer. Specifically, REN mainly includes a teacher network and a student network, which performs standard domain adaptation training and updates weights of the teacher network. In addition, we also propose a dual-network conditional adversarial loss to improve the ability of the discriminator. Finally, for the purpose of improving the basic ability of the student network, we utilize the consistency constraint to balance the error between the student network and the teacher network. Extensive experimental results on several UDA datasets have demonstrated the effectiveness of our model by comparing with other state-of-the-art UDA algorithms.

Most of recent generative image inpainting methods have shown promising performance by adopting attention mechanisms to fill hole regions with known-region features. However, these methods tend to neglect the impact of reliable hole-region information, which leads to discontinuities in structure and texture of final results. Besides, they always fail to predict plausible contents with realistic details in hole regions due to the ineffectiveness of vanilla decoder in capturing long-range information at each level. To handle these problems, we propose a confidence-based global attention guided network (CGAG-Net) consisting of coarse and fine steps, where each step is built upon the encoder-decoder architecture. CGAG-Net utilizes reliable global information to missing contents through an attention mechanism, and uses attention scores learned from high-level features to guide the reconstruction of low-level features. Specifically, we propose a confidence-based global attention layer (CGA) embedded in the encoder to fill hole regions with reliable global features weighted by learned attention scores, where reliability of features is measured by automatically generated confidence values. Meanwhile, the attention scores learned by CGA are repeatedly used to guide the feature prediction at each level of the attention guided decoder (AG Decoder) we proposed. Thus, AG Decoder can obtain semantically-coherent and texture-coherent features from global regions to predict missing contents. Extensive experiments on Paris StreetView and CelebA datasets validate the superiority of our proposed approach through quantitative and qualitative comparisons with existing methods.

Nuclei Segmentation plays an important role in cancer malignancy grading and assisted diagnosis. Due to the high variations of nuclei appearances among diverse cell types, this task remains challenging. Current box-free instance segmentation methods typically rely on pixel-level information. These methods tend to over- or under -segmentation due to the lack of global information. On the contrary, the box-based instance segmentation methods with object detectors perform better when locating and segmenting individual instances. In this paper, we propose a Context Augmentation Aggregation Network (CAANet) to extract discriminant features of objects of different sizes to improve the performance of segmentation. We investigate a new branch which supplements context information from multiple receptive fields. Experimental results on a publicly available Kaggle 2018 Data Science Bowl dataset shows that our method is comparable to existing methods.

The article contains proposals for the development of modeling of the national industry using IT solutions based on the “economic cross” methodology. The reserves of the digitalization of the planning procedure are considered, taking into account the modern achievements of IT technologies and the development of the global information and communication infrastructure. The conclusions and suggestions contained in the article are formulated taking into account the extrapolation of current trends of nuclear energy development.

The article contains proposals for the development of modeling of the national industry using IT solutions based on the “economic cross” methodology. The reserves of the digitalization of the planning procedure are considered, taking into account the modern achievements of IT technologies and the development of the global information and communication infrastructure. The conclusions and suggestions contained in the article are formulated taking into account the extrapolation of current trends of nuclear energy development.

Software companies have been using Design Thinking to support software development, fostering the creation of innovative features and products. However, there is not so much knowledge of what matters for the application of Design Thinking being successful. Thus, this study aims to describe how does the adoption of Design Thinking with software development take place in a global information technology company through an interview-based case study with 16 professionals. Our analysis indicates that Design Thinking is used in software products, software improvements, processes, user experience identification and solution discovery. Also, there are perceived benefits and challenges during the Design Thinking activities. As a result, this paper describes the Design Thinking phenomenon in software development, serving as a guide for practitioners on how to set up and implement Design Thinking activities and publishing more academic research.

With the advancement of science and technology and the constant improvement of people's living standard, the network and communications industry have been closely linked with the people and inseparable from the normal operation of individuals' lives, offices and enterprises to large-scale national and even global information. It is enough to see that the communications industry with unlimited potential plays a decisive role in society. This paper focuses on how the communications industry is applied in various fields in the development process and forecasts the future direction of this important industry.

The authors dwell on the basic principles and technologies for the implementation of the large-scale program “Creation of Internet centers in 33 leading classical Universities in Russia”. The aim of the program was to provide open access to global information resources for students, teachers, and employees of cultural and scientific organizations. The program was carried out in 1996–2000 by the government of the Russian Federation in collaboration with the Open Society Institute and regional administrations. The main results of the program are presented. Its impact on the development of organizations of culture, science, and healthcare, as well as on the creation of the telecommunication basis for the university educational process is assessed.

We propose a Deep Lightening Network (DLN) for low-light image enhancement. Inspire by the domain transfer study, we propose a novel cycle learning structure to learn the mapping relationship between low- and normal-light images. Each DLN consists of several Lightening Back-Projection (LBP) blocks that learn the residual between low- and normal-light images. To efficiently estimate the local and global information, we fuse the features from different LBP results. Experimental results on different datasets show that our proposed DLN approach outperforms other approaches in all objective and subjective measures.

Fine-tuning with pre-trained models has achieved exceptional results for many language tasks. In this study, we focused on one such self-attention network model, namely BERT, which has performed well in terms of stacking layers across diverse language-understanding benchmarks. However, in many downstream tasks, information between layers is ignored by BERT for fine-tuning. In addition, although self-attention networks are well-known for their ability to capture global dependencies, room for improvement remains in terms of emphasizing the importance of local contexts. In light of these advantages and disadvantages, this paper proposes SesameBERT, a generalized fine-tuning method that (1) enables the extraction of global information among all layers through Squeeze and Excitation and (2) enriches local information by capturing neighboring contexts via Gaussian blurring. Furthermore, we demonstrated the effectiveness of our approach in the HANS dataset, which is used to determine whether models have adopted shallow heuristics instead of learning underlying generalizations. The experiments revealed that SesameBERT outperformed BERT with respect to GLUE benchmark and the HANS evaluation set.