Reviewerly/exHarmony huggingface dataset:
Data Distribution Scheme for Information Service Based on Blockchain

The main characteristics of data in information service are multi-source, heterogeneous and cross systems, while information service users are scattered throughout the network. There are consequently some urgent problems should be solved in information transmission, distribution security, subject trust, circulation control, etc. Using the advantages of blockchain technology in traceability, tamper proof and distributed storage, and combined with the information service business process, an information service data distribution scheme based on blockchain is proposed. Firstly, the information service data would be encrypted and stored in the Inter Planetary File System (IPFS), and the data addresses are stored in the directory library of information service centers; Secondly, according to the information service subscription/distribution process, a distribution mechanism based on Proxy Re-encryption is designed; then, the information service distribution chain is constructed by using the Consortium Blockchain mode, and the information data and key distribution information are stored on the chain; which is able to better realize distributed data access and centerless access of information services.
By the Numbers: Autism funding over time, West African research, racial reporting

Welcome back to By the Numbers, Spectrum's data-centric take on autism research
A multi-scale deformable convolution network model for text recognition

Natural scene text recognition is one of the most challenging tasks in recent years. Compared with traditional document text, natural scene text has the characteristics of various shapes and different directions, so the accuracy of scene text recognition still needs to be improved. In order to locate the text region better and identify the text content more accurate, we present a multi-scale deformable convolution network model for text recognition. The initial image is irregularly corrected through the rectified network, and the ResNet with FPN structure is used as the backbone network to achieve multi-scale feature extraction. In addition, the feature fusion method of Add is adopted to reduce feature information losing and increase the strength of feature extraction in the text area. The deformable convolution block is introduced in the deep convolution to improve the deformation modeling ability of convolution and expand the receptive field. The prediction module adopts the Transformer and abandons the inherent pre and post attributes of RNN to realize parallel operation and solve the problem of path length between remote dependencies. In order to evaluate the effectiveness of the proposed method, we trained our model on two mixed data sets, MJSynth and SynthText, and tested it on some regular and irregular data sets. The experiment results demonstrate that this method performs well in irregular scene text recognition, especially in CUTE80.
Exploring the promotion method and pathway of energy big data service business

Abstract In the wave of China’s new energy revolution and digital revolution, advanced information technologies such as big data, cloud computing, Internet of Things, mobile Internet, and artificial intelligence have been deeply integrated with the energy industry, and energy big data has emerged as the times require. Energy big data promotes new models and new formats in the energy sector. The vigorous rise of energy big data has injected new impetus into the energy transition. With the explosive growth of energy data and the continuous improvement of data processing capabilities, energy big data has become a new element of social production, and different social entities have corresponding business needs. Therefore, this article is based on the three basic business promotion theories of growth cycle, growth framework and growth strategy with growth hacking theory as the core, and considers the different characteristics of government, enterprises and residents in terms of customer acquisition cost and life value. Finally, this article puts forward energy big data service business promotion models for governments, enterprises and residents which are based on the five stages of the user life cycle.
Evaluating the Parallel Execution Schemes of Smart Contract Transactions in Different Blockchains: An Empirical Study

In order to increase throughput, more and more blockchains begin to provide the ability to execute smart contract transactions in parallel. However, there is currently no research work on evaluating parallel execution schemes in different blockchains, which makes it difficult for developers to find a blockchain technology suitable for their application scenarios. In this paper, we firstly summarize existing parallel execution schemes of smart contract transactions. Then, based on their characteristics, we propose a comprehensive evaluation framework for parallel execution schemes of smart contract transactions and implement a benchmark tool, which can compare different parallel execution schemes and discover their limitations. Finally, we utilize the evaluation framework and the tool to evaluate several excellent blockchains in China and obtain some useful findings.
Modern Tools for Creative Learning of English in a Blended Learning Environment: Practices of Ukrainian Universities

The practice of teaching English in higher education institutions confirms the efficiency and the popularity of using the blended learning methodology. The purpose of this study is to identify the tools of creative learning of English in a blended learning environment and establish their impact on the level of English language proficiency. To cover the purpose of this study, the authors employed theoretical and empirical methods, namely abstraction, theoretical analysis, synthesis, induction and deduction, observation, questionnaire survey, description, comparison, and generalisation. It is established that blended learning, as a new method of learning English, involves the use of modern interactive and innovative technologies in the educational process, so that the conventional educational process transforms into a creative process with an innovative approach to obtaining professional competencies by students. It is determined that the use of information and communication technologies in a blended learning environment opens up opportunities for students to independently investigate educational material, but this process should take place under the guidance of a teacher. It is proved that online platforms are equally important for creative learning of English, the advantages of which are mobility, systematicity, interactivity, and controllability of knowledge. It is established that teachers of the Departments of English and foreign languages of the Olesya Gonchar Dnipro National University, Hryhorii Skovoroda University in Pereiaslav, and Kamianets-Podilskyi Ivan Ohiienko National University offer students to use such online platforms as Quizlet, Freerice, Learn English from the British Council Vocabulary.com, Microsoft Teams and the Flipgrid application for creative learning of English the effectiveness of which has been confirmed by a survey of students. The practical significance of the results obtained in this study indicates that the approach, according to which it is planned to survey students using a questionnaire method to identify the influence of information and communication technologies and creative learning tools on the academic achievements of students in learning English in Ukrainian higher education institutions, is of universal importance
Accelerating DCNNs via Cooperative Weight/Activation Compression

Deep convolutional neural networks (DCNNs) have achieved great success in various applications. Nevertheless, training and deploying such DCNNs models require a huge amount of computation and storage resources. Weight pruning has emerged as an effective compression technique for DCNNs to reduce the consumption of hardware resources. However, existing weight pruning schemes neglect to simultaneously consider accuracy, compression ratio and hardware-efficiency in the design space, which inevitably leads to performance degradation. To overcome these limitations, in this work, we propose a cooperative weight/activation compression approach. Initially, we observe spatially insensitive consistency, namely the insensitive weight values that have no impact on accuracy distributed at the same spatial position in a mass of channels. Based on the key observation, we propose a cluster-based weight pattern pruning technique, which converges the channels with spatially insensitive consistency into a cluster and prunes them into the weight pattern with a uniform shape. In addition, there are many sparsity rows in the activation matrix due to the effect of the activation function–Rectifying Linear Unit (ReLU). We study the sparsity rows and find that rows with larger sparsity degree are insensitive to accuracy. Hence, we further propose a sparsity-row-based activation removal technique, which directly eliminates insensitive rows of activation. The two proposed techniques allow the hardware to execute at excellent parallel granularity and achieve better compression ratio with negligible accuracy loss. Experiment results on several popular DCNNs models show that our scheme reduces the computation by $$63\%$$ on average.
Spatially bound categorical attention network for semantic segmentation

Semantic segmentation is a key step of image prehension. Single use of convolutional networks for semantic segmentation makes it difficult to distinguish the same class of objects with large contour deviations, while the higherlevel features will lose some the detailed information. Currently, Networks such as ACFNet and DANet have introduced attention mechanism to improve scene classification by obtaining rich contextual information through self-controlled system, but they do not combine both global scope and class feature relationships in local space to further advance intraclass consistency and inter-class divisibility of features. In terms of this problem, semantic segmentation network of categorical attention with spatial constraints has been proposed, which contains two submodules, one using the category spatial distribution to introduce local spatial location information of features, and the other using the global category average strength to introduce global strength information of category features. By selecting a kind of appropriate backbone network, this network model obtains the feature map from the backbone network and stacks the features into the original features after two submodules of global category strength and category local space processing, finally, performing classification processing by the classification layer and up-samples to the input image size to complete the pixel-level label prediction. The experiment result demonstrates that this proposed segmentation network has higher accuracy than existing segmentation networks.
Identity Card Detection System using YOLOv3 and Image Rectification

— In the age of modern technology like today, object detection is something that is really needed. Object detection is done by considering the type of data collected. This research focused on Identity card detection from an input image taken by a smartphone camera. The Identity card detection is a preliminary step toward automatic identity data collection. Identity data collection is usually performed in a conventional way. This way leads to several problems such as bad data result, unreliable data verification, and long duration on confirming data validity. In this project, we will make an object detection system with YOLOv3 algorithm, and then the result will be used for an edge detection with Canny algorithm and image rectification. The testing result show that YOLOv3 algorithm could reach 92.59 mAP with 5s detection time. While the corner detection for image rectification manage to get an average error of 37.06 pixels. The establishment of this model has important practical significance for improving Identity Card detection process. Keywords—Identity Card Detection, Object Detection, YOLOv3 Model, Corner Detection, Image Rectification
Image Caption Generation Using Attention Model

The process of generating a caption for a given image using the techniques of computer vision and natural language processing is called image caption generation. During recent times, many deep learning models have been used to increase the performance of the caption generating models. But the drawback of these models is that they lack proper focus on the pertinent part of the image while generating the caption which leads to a vague caption generation. To get the better of these drawbacks, we are proposing a model, which gives a caption by selecting pertinent objects in a particular image and providing a perceivable explanation using them.
NEW TECHNOLOGICAL MODE (BY THE EXAMPLE OF SYSTEM ADAPTERS OF THE LIGHT)

The article examines the role of Svetlica system adapters as harbingers of a new technological order combining the achievements of electronics and informatization and the resources of the human brain.
Blockchain Technology for Enhancing Sustainability in Agricultural Supply Chains

Nowadays, information and communication technologyInformation and communication technology (ICT) plays a vital role in enhancing the sustainabilitySustainability performance in an organization. Technological disruptions such as the Industry 4.0 technologies have had significant positive impacts across various sectors, and agricultural sector is no exception. The current study highlights the impact of blockchain technologyBlockchain technology (BCT) as a sustainabilitySustainability enabler in the agricultural supply chains (ASCs). The study begins by identifying the BCT enablers from the literature that will lead to potential sustainabilitySustainability outcomes in the ASCs. These sustainability outcomes are then mapped with the United Nation’s Sustainable Development Goals (UNSDGs). We therefore contend that BCT may hold the key in driving the ASCs toward sustainabilitySustainability. Finally, the possible implications from the study are suggested.
A Neural Network based Social Distance Detection

The global breakout of the most spreading viruses like COVID-19 has fundamentally changed the way of interacting with one another. By avoiding people’s physical contact, the spread of virus can be lowered. So, one such measure called social distancing can minimize the reproduction rate (RO) of viruses among communities. This paper proposes a system that can automatically estimate the interpersonal distance between people using MobileNet SSD deep learning algorithms. The proposed model first detects the persons in a video stream, and then, it characterizes people based on their distance using different color indications. To make people aware about the social distance violation, this model also provides alert signal. The proposed system allows a quick monitoring of individuals potential behavior to avoid health risk, especially in current pandemic situation. The proposed system can also be used in surveillance systems to alert the government in harmful situations by analyzing people’s movement.
A Survey of E-bidding System using Blockchain

With the increasing use of the internet, the e-auction system is becoming popular. E-Auction is an efficient e-commerce system that allows buyers and sellers to make a transaction through online platforms. Blockchain technology and smart contracts are being used in an e-auction to make it secure, transparent, and decentralized In this paper, we use different bidding techniques that use cryptography to ensure security and to keep private information confidential of any participants in the auction. Furthermore, we have explained the concept of public bidding and sealed bidding which will be used in E-auction, we have explained UAE that is used to keep stakeholders identity autonomous, the paper also explains the hash functions which provides the advantage of encryption and decentralization Thus blockchain and smart contracts make it more efficient and secure compared to other traditional and e-auction systems.
Factors Affecting the Level of Disclosure of Human Resources for Saudi Listed Companies

Abstract&#x0D; Human resources (HR) information is a critical tool for assessing the effectiveness of HR management and encouraging competitive advantage between companies. Disclosing this information is especially useful for prospective employees, where they are informed on the HR services available within companies. The study aims to examine the factors affecting the level of Human Resources Disclosure (HRD) in the annual reports of listed Saudi companies. Factors are classified into factors related to governance mechanisms are the size of the board of directors, the independence of board members, the number of board meetings, and the type of auditor. Factors associated with the company characteristics are profitability, leverage, liquidity, the company's age, the size of the firm, and industry type. The final study sample consists of 363 observations of 121 Saudi non-financial companies listed on the Saudi financial market during the period from 2016 to 2018. The content analysis method was used to examine the disclosure list of 19 elements of human resources, which was developed based on previous studies. EViews software was used to analyze panel data and use a multiple regression model to test the statistical relationship between relevant factors and human resource disclosure. The regression results indicate that the independence of the board of directors, the company's profitability, the company's age, and the size of the company have a significant positive effect on the level of human resources disclosure for the listed Saudi companies. Whilst the size and number of board meetings, auditor type, financial leverage, liquidity, and industry type have no impact on the level of human resource information disclosure. &#x0D; Keywords: Human Resources Disclosure, Financial Reporting, Governance mechanisms, Company characteristics.
SECURITY ENHANCED ROI BASED REVERSIBLE WATERMARKING FOR MEDICAL IMAGES

The ever-increasing number of digital medical images and the need to exchange them between medical experts and hospitals for a better and more precise diagnosis demand that the privacy of patients be protected. As a result, medical images sent over the internet must be watermarked with the patient's information so that doctors can validate the medical image. In this research article, a novel watermarking scheme combining two techniques, Difference Expansion and Integer-to-Integer wavelet transformation is proposed. The patient data is inserted into ROI1 using difference expansion and the ROI1 coordinates are embedded in ROI2 using Integer-to-Integer wavelet transform. The ROI2 coordinates are then embedded into predefined secret region using difference expansion technique. Simulation experiments are carried out to compute PSNR, SSIM and TPM to assess the imperceptibility of water-marked medical images. By applying various attacks on watermarked images, the robustness and reversibility of the proposed watermarking strategy is examined by computing bit error rate and normalized cross correlation co-efficient, respectively. Key words: Reversible watermarking, Medical image, Difference Expansion, Integer-to-Integer wavelet transform.
The Design of the Benchmark Land Price Release and Management Information System Based on Webgis

The In this paper, on the premise of benchmark land price production data and, starting from the management and application direction of technology based on JavaScript, Java Web technology and ArcGIS for Server technology, the WebGIS system design, the MVC development mode, B/S (browser/Server) multi-tier architecture model design to achieve the collection of maps, land management, land price data query, data statistical analysis, and system integrated management and so on five big modules as one of the benchmark land price distribution and management information system.
Soft Skills Formation in Students Pursuing an Undergraduate Degree in the Field of Nature Conservancy by Means of Online Educational Environmental Projects

The study aims to substantiate the need for soft skills formation in students pursuing an undergraduate degree in the field of nature conservancy according to the requirements actualised by federal state educational standards and the requirements set for graduates by the labour market. The paper presents the sought-after soft skills in the field of environmental safety. The reasons for teachers’ lack of readiness to form soft skills in the educational process at university are revealed. It has been found that online educational environmental projects have a significant potential in soft skills formation in students pursuing an undergraduate degree in the field of nature conservancy. Scientific novelty of the study lies in clarifying the content of soft skills, which are considered as a set of universal skills and personal qualities (internal potential) necessary for graduates in the field of nature conservancy for the effective implementation of their labour functions. A comparative study of various classification features of soft skills, as well as a content analysis of job vacancies and a survey of experts in the field of environmental protection made it possible to specify the basic list of soft skills, which include interpersonal communication skills, teamwork skills, self-organisation skills and management skills. As a result, it has been proved that the use of online educational environmental projects in the educational process at university contributes to the formation of a set of skills and qualities in undergraduate students (teamwork, technical literacy, business communication skills, cooperation in solving tasks, responsibility, sociability, self-discipline and self-organisation), which are regarded by employers as a predictor of professional success of graduates in the field of nature conservancy.
9 Kanashi basic vocabulary

In this chapter we have compiled a (slightly extended) basic vocabulary of Kanashi, based on the IDS/LWT standard set of concepts.
Preliminary testing of eye gaze interfaces for controlling a haptic system intended to support play in children with physical impairments: Attentive versus explicit interfaces

Children with physical impairments may face challenges to play because of their motor impairments, which could lead to negative impacts in their development. The objective of this article was to compare two eye gaze interfaces that identified the desired toy a user wanted to reach with a haptic-enabled telerobotic system in a play activity.One of the interfaces was an attentive user interface predicted the toy that children wanted to reach by observing where they incidentally focused their gaze. The other was an explicit eye input interface determined the toy after the child dwelled for 500 ms on a selection point. Five typically developing children, an adult with cerebral palsy (CP) and a child with CP participated in this study. They controlled the robotic system to play a whack-a-mole game.The prediction accuracy of the attentive interface was higher than 89% in average, for all participants. All participants did the activity faster with the attentive interface than with the explicit interface.Overall, the attentive interface was faster and easier to use, especially for children. Children needed constant prompting and were not 100% successful at using the explicit interface.