Reviewerly/exHarmony huggingface dataset:
On the Relationship between Self-Attention and Convolutional Layers

Recent trends of incorporating attention mechanisms in vision have led researchers to reconsider the supremacy of convolutional layers as a primary building block. Beyond helping CNNs to handle long-range dependencies, Ramachandran et al. (2019) showed that attention can completely replace convolution and achieve state-of-the-art performance on vision tasks. This raises the question: do learned attention layers operate similarly to convolutional layers? This work provides evidence that attention layers can perform convolution and, indeed, they often learn to do so in practice. Specifically, we prove that a multi-head self-attention layer with sufficient number of heads is at least as powerful as any convolutional layer. Our numerical experiments then show that the phenomenon also occurs in practice, corroborating our analysis. Our code is publicly available.
Generating Diverse Structure for Image Inpainting With Hierarchical VQ-VAE

Given an incomplete image without additional constraint, image inpainting natively allows for multiple solutions as long as they appear plausible. Recently, multiple-solution inpainting methods have been proposed and shown the potential of generating diverse results. However, these methods have difficulty in ensuring the quality of each solution, e.g. they produce distorted structure and/or blurry texture. We propose a two-stage model for diverse inpainting, where the first stage generates multiple coarse results each of which has a different structure, and the second stage refines each coarse result separately by augmenting texture. The proposed model is inspired by the hierarchical vector quantized variational auto-encoder (VQ-VAE), whose hierarchical architecture disentangles structural and textural information. In addition, the vector quantization in VQVAE enables autoregressive modeling of the discrete distribution over the structural information. Sampling from the distribution can easily generate diverse and high-quality structures, making up the first stage of our model. In the second stage, we propose a structural attention module inside the texture generation network, where the module utilizes the structural information to capture distant correlations. We further reuse the VQ-VAE to calculate two feature losses, which help improve structure coherence and texture realism, respectively. Experimental results on CelebA-HQ, Places2, and ImageNet datasets show that our method not only enhances the diversity of the inpainting solutions but also improves the visual quality of the generated multiple images. Code and models are available at: https://github.com/USTC-JialunPeng/Diverse-Structure-Inpainting.
Automatic knee meniscus tear detection and orientation classification with Mask-RCNN

This work presents our contribution to a data challenge organized by the French Radiology Society during the Journées Francophones de Radiologie in October 2018. This challenge consisted in classifying MR images of the knee with respect to the presence of tears in the knee menisci, on meniscal tear location, and meniscal tear orientation.We trained a mask region-based convolutional neural network (R-CNN) to explicitly localize normal and torn menisci, made it more robust with ensemble aggregation, and cascaded it into a shallow ConvNet to classify the orientation of the tear.Our approach predicted accurately tears in the database provided for the challenge. This strategy yielded a weighted AUC score of 0.906 for all three tasks, ranking first in this challenge.The extension of the database or the use of 3D data could contribute to further improve the performances especially for non-typical cases of extensively damaged menisci or multiple tears.
Predicting Alzheimer’s Disease Using LSTM

Alzheimer's Disease (AD) is a chronic neurodegenerative disease. Early diagnosis will considerably decrease the risk of further deterioration. Unfortunately, current studies mainly focus on classifying the states of disease in its current stage, instead of predicting the possible development of the disease. Long short-term memory (LSTM) is a special kind of recurrent neural network, which might be able to connect previous information to the present task. Noticing that the temporal data for a patient are potentially meaningful for predicting the development of the disease, we propose a predicting model based on LSTM. Therefore an LSTM network, with fully connected layer and activation layers, is built to encode the temporal relation between features and the next stage of Alzheimer's Disease. The Experiments show that our model outperforms most of the existing models.
Deep Learning for SAR Image Despeckling

Speckle filtering is an unavoidable step when dealing with applications that involve amplitude or intensity images acquired by coherent systems, such as Synthetic Aperture Radar (SAR). Speckle is a target-dependent phenomenon; thus, its estimation and reduction require the individuation of specific properties of the image features. Speckle filtering is one of the most prominent topics in the SAR image processing research community, who has first tackled this issue using handcrafted feature-based filters. Even if classical algorithms have slowly and progressively achieved better and better performance, the more recent Convolutional-Neural-Networks (CNNs) have proven to be a promising alternative, in the light of the outstanding capabilities in efficiently learning task-specific filters. Currently, only simplistic CNN architectures have been exploited for the speckle filtering task. While these architectures outperform classical algorithms, they still show some weakness in the texture preservation. In this work, a deep encoder–decoder CNN architecture, focused in the specific context of SAR images, is proposed in order to enhance speckle filtering capabilities alongside texture preservation. This objective has been addressed through the adaptation of the U-Net CNN, which has been modified and optimized accordingly. This architecture allows for the extraction of features at different scales, and it is capable of producing detailed reconstructions through its system of skip connections. In this work, a two-phase learning strategy is adopted, by first pre-training the model on a synthetic dataset and by adapting the learned network to the real SAR image domain through a fast fine-tuning procedure. During the fine-tuning phase, a modified version of the total variation (TV) regularization was introduced to improve the network performance when dealing with real SAR data. Finally, experiments were carried out on simulated and real data to compare the performance of the proposed method with respect to the state-of-the-art methodologies.
Blockchain in Energy Efficiency: Potential Applications and Benefits

Blockchain technology is ready to disrupt nearly every industry and business model, and the energy sector is no exception. Energy businesses across the world have already started exploring the use of blockchain technology in large-scale energy trading systems, peer-to-peer energy trading, project financing, supply chain tracking, and asset management among other applications. Information and Communication Technologies (ICTs) recently started revolutionizing the energy landscape, and now blockchain technology is providing an additional opportunity to make the energy system more intelligent, efficient, transparent, and secure in the longer term. The idea of this paper is to examine more closely the use of blockchain technology for its possible application in the energy efficiency industry and to determine how it could make energy efficiency markets more secure and transparent in the longer term. This paper examines in detail the key benefits and implications of using blockchain in the energy efficiency sector through the presentation and discussion of two case studies as possible blockchain applications—(i) the UK Energy Company Obligation scheme and (ii) the Italian White Certificate Scheme. We have presented how the key issues around trading energy efficiency savings—correctly estimating the savings, data transparency among stakeholders, and inefficient administrative processes—can be solved through the application of a blockchain-based smart contract system. Finally, this paper presents an implementation of a smart contract for trading of energy-saving certificates achieved via execution of smart contract transactions on the Ethereum blockchain.
TRANSPARENT COGNITIVE TECHNOLOGIES TO ENSURE SUSTAINABLE SOCIETY DEVELOPMENT

The article formalizes theoretical and methodological foundations of the use of parametric artificial intelligence technologies to ensure the security of sustainable society development.An algorithm for using an artificial neuron to describe a model of social development is proposed.Optimization of the processes of using neural networks in creating an expert system for forecasting safe social development is conducted.
Incentive alignment for blockchain adoption in medicine supply chains

In recent years, blockchain technology has been increasingly adopted in OTC medicine supply chains, enabling customers to track the entire process from raw material purchasing to finished medicine distribution. This improves the brand image and hence expands the market. With the use of blockchain, information transparency can be achieved because data are stored immutably and safely in a distributed database that is accessible by all supply chain members. However, will the incentives for supply chain members to participate in blockchain for larger-scale demand come at the cost of information disclosure? In this paper, to investigate the supply chain members’ incentive alignment opportunities towards the adoption of blockchain technology, we consider a two-stage supply chain comprising two medicine manufacturers and a common retailer that has more accurate demand information than the manufacturers have. We find that, interestingly, the retailer has incentives to participate in blockchain when the manufacturers’ competition is mild and the demand variance is low. We further investigate the impact of blockchain on total surplus and customer surplus and find that the adoption of blockchain always benefits customers and society; therefore, blockchain can be particularly useful for social goods such as OTC medicine.
Blockchain based Proxy Re-Encryption Scheme for Secure IoT Data Sharing

Data is central to the Internet of Things IoT ecosystem. Most of the current IoT systems are using centralized cloud-based data sharing systems. Involvement of such third-party service provider requires also trust from both sensor owner and sensor data user. Moreover, the fees need to be paid for their services. To tackle both the scalability and trust issues and to automatize the payments, this paper presents a blockchain based proxy re-encryption scheme. The system stores the IoT data in a distributed cloud after encryption. To share the collected IoT data, the system establishes runtime dynamic smart contracts between the sensor and the data user without the involvement of a trusted third party. It also uses an efficient proxy re-encryption scheme which allows that the data is only visible by the owner and the person present in the smart contract. The proposed system is implemented in an Ethereum based testbed to analyze the performance and security properties.
Meta-Learning with Implicit Processes

This paper presents a novel implicit process-based meta-learning (IPML) algorithm that, in contrast to existing works, explicitly represents each task as a continuous latent vector and models its probabilistic belief within the highly expressive IP framework. Unfortunately, meta-training in IPML is computationally challenging due to its need to perform intractable exact IP inference in task adaptation. To resolve this, we propose a novel expectation-maximization algorithm based on the stochastic gradient Hamiltonian Monte Carlo sampling method to perform meta-training. Our delicate design of the neural network architecture for meta-training in IPML allows competitive meta-learning performance to be achieved. Unlike existing works, IPML offers the benefits of being amenable to the characterization of a principled distance measure between tasks using the maximum mean discrepancy, active task selection without needing the assumption of known task contexts, and synthetic task generation by modeling task-dependent input distributions. Empirical evaluation on benchmark datasets shows that IPML outperforms existing Bayesian meta-learning algorithms. We have also empirically demonstrated on an e-commerce company's real-world dataset that IPML outperforms the baselines and identifies outliers tasks which can potentially degrade meta-testing performance.
Application of Python Script in National Geographic Conditions Monitoring Project of China

&amp;lt;p&amp;gt;The geographic conditions monitoring is an important mission in geosciences. Its aim is to study, analyze and describe national conditions in the view of geography. The National Geographic Conditions Monitoring Project of China, based on remote sensing and geospatial information technology, has acquired large-scale and various kinds of geographic data in China, such as remote sensing images, land cover information and geographic conditional elements. The goal of this project is to build National Geographic Conditions Monitoring Database, which is aimed to offer reliable fundamental geoinformation for government decision-making. It plays an important role in natural resources supervision, environmental protection and emergency management. Moreover, it also contributes to the development of geosciences. However, as China is such a huge country, large quantity of data is produced by many institutions and companies. It makes it difficult to finish data quality check manually before importing data into oracle spatial database. Besides, there are many data applications from lots of institutions every year, which also spends plenty of time.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Python is an open source computer programming language. It has the characteristics of friendly, clear syntax and easy to learn. There are large numbers of standard libraries and third-party libraries. Based on python, we developed lots of python scripts for this project. From the viewpoint of geodatabase construction, we developed scripts to check collected data, mainly include directory check, structure check, attribute check and topology check to ensure data is standardized and correct. Spatial analysis and statistical calculation can also be finished rapidly and accurately using python script. For production supply, we also developed scripts which can distribute data from database automatically according to any region.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Tools are critical to the progress and development of science. The application of python scripts improves the efficiency of our work to some extent, which can make sure the project is successfully completed on time every year. Geographic data is obtained that covered all over the country, which contributes to the economic and social development, national strategic decision and planning. The source code of these scripts is public. It also helps to optimize and improve these scripts. I believe open source software will play a greater role in the future. Geoscience will get better and better when geographic data is processed and analyzed using open source software.&amp;lt;/p&amp;gt;
Pengembangan Asrama Mahasiswa Universitas Diponegoro dengan Pendekatan Arsitektur Berkelanjutan

Diponegoro University is one of the best State Higher Education institutions in Indonesia, is ranked 6th according to the 2019 Kemenristekdikti Press Release and the number of students increases every year. It shows that the need for rental housing for students on campus is very important, because the decrease in available land is not directly proportional to the increase in the number of students. With the existence of vertical housing such as dormitories, it will be very useful for the situation which is now lacking in land. The increase in the number of new students every year which is directly proportional to the needs of student rental housing in the Undip environment, encourages Undip student dormitories to increase the number of rental room capacity for prospective student new residents, especially first-year students who come from outside Semarang. The current state of the bedrooms in each dormitory building can be said to be in good condition, but the facilities at this dormitory also have several problems. Currently, the development of the concept of sustainable architecture in development projects is being discussed. The Undip dormitory complex itself has not implemented the concept of sustainability optimally, therefore it is necessary to develop a sustainable concept in Undip student dormitories. The author proposes a plan to develop the Undip student dormitory to provide student housing that has better facilities and also has a good impact on the environment with the sustainable concept being applied.
LibGuides: COMM-105 Speech Sources: Informative Meeting Speech

This guide serves as a jumping off point for COMM-105 students who are researching and preparing to give an Informative Meeting Speech or a Speech to Actuate. For more help, contact your friendly neighborhood librarian!
ANALISIS CIRI KEBAHASAAN DAN EJAAN TEKS EKSPLANASI SISWA KELAS XI SMA NEGERI 1 PAYAKUMBUH TAHUN AJARAN 2018

Abstract The purpose of this study was to describe the language and spelling characteristics in the explanatory text of class XI students of SMA 1 Payakumbuh. This type of research is qualitative research with descriptive methods. Data collection is done through documentation studies. The subjects in this study were class XI students of SMA 1 Payakumbuh. The stage of analyzing the data in this study is as follows. First, identify common elements of data. Second, identifying data based on the theory that is a reference. Data identification is done by making a format for identifying linguistic characteristics and explanatory text spelling. Third, analyze data. Fourth, interpret the data that has been analyzed. Fifth, conclude the results of the data description by writing a report. The results of this study are that there are 22 students who have not used the language characteristics of the explanatory text and 8 students have used the explanatory text language characteristics. The linguistic characteristics found in this study are 3, namely, information on time, chronological conjunction and causality conjunction. The time was found as many as 53 pieces, 15 chronological conjunctions and 54 causality conjunctions. In addition, many found errors in the use of spelling in the form of capital letters, periods and commas. The error of using capital letters has 28 errors, 15 misuse of the dot and 158 errors in the use of commas from 810 sentences and 178 paragraphs. Kata kunci : Ciri Kebahasaan, Ejaan, Teks Eksplanasi
Multi-Modality Non-rigid Image Registration Using Local Similarity Estimations

In this study, we focus on improving the efficiency and accuracy of nonrigid multi-modality registration of medical images. In this regard, we analyze the potentials of using the point similarity measurement approach as an alternative to global computation of mutual information (MI), which is still the most renown multi-modality similarity measure. The improvement capabilities are illustrated using the popular B-spline transformation model. The proposed solution is a combination of three related improvements of the most straightforward implementation, i.e., efficient computation of the voxel displacement field, local estimation of similarity and usage of a static image intensity dependence estimate. Five image registration prototypes were implemented to show contribution and dependence of the proposed improvements. When all the proposed improvements are applied, a significant reduction of computational cost and increased accuracy are obtained. The concept offers additional improvement opportunities by incorporating prior knowledge and machine learning techniques into the static intensity dependence estimation.
The role of culture in the development of society and the possibility of financing it in Russia

The article highlights the importance and problems of financing culture, gives the author's interpretation of cultural policy, and considers solutions to the problem of financing culture.
A Unified Bayesian Framework for Discriminative and Generative Continual Learning

Continual Learning is a learning paradigm where learning systems are trained on a sequence of tasks. The goal here is to perform well on the current task without suffering from a performance drop on the previous tasks. Two notable directions among the recent advances in continual learning with neural networks are (1) variational Bayes based regularization by learning priors from previous tasks, and, (2) learning the structure of deep networks to adapt to new tasks. So far, these two approaches have been orthogonal. We present a novel Bayesian framework for continual learning based on learning the structure of deep neural networks, addressing the shortcomings of both these approaches. The proposed framework learns the deep structure for each task by learning which weights to be used, and supports inter-task transfer through the overlapping of different sparse subsets of weights learned by different tasks. An appealing aspect of our proposed continual learning framework is that it is applicable to both discriminative (supervised) and generative (unsupervised) settings. Experimental results on supervised and unsupervised benchmarks shows that our model performs comparably or better than recent advances in continual learning.
Autonomous Robotic Exploration and Navigation System using Tri-layered Mapping and Geometrical Path Planning Techniques

Abstract In the recent decade, robotic exploration is emerging as a vast research platform as it prolongs to be a challenging task, especially in unknown dynamic environments. Exploration plays a vital role in collecting data from the areas inaccessible to the human. The operation of manual exploration systems in complex environments is cumbersome due to ineffective noise filtering, poor visibility and shuffling of map characteristics. In order to overcome such drawbacks, our approach focuses on the development of autonomous robotic exploration by designing a robust SLAM-based system, incorporating tri-layered mapping technique for automatic map generation and a new geometrical boundary-based path-planning technique to improve the mobility of the robot during navigation. The proposed path-planning algorithm yields better trajectory planning and obstacle avoidance using the pose, odometry and orientation parameters of the robot. The performance of the developed robot is evaluated through real-time experimentation, and it is evident from the results that, the proposed approach is preferable for autonomous exploration applications.
Logic algebra problems solving in the Maple computer algebra system

Тенденции развития науки и образования