Reviewerly/exHarmony huggingface dataset:
Research on curriculum teaching team building under the background of project-based teaching reform

In recent years, the state has issued a series of policy documents on vocational education reform, in which it has repeatedly mentioned innovative teaching models and methods to improve the quality of class teaching. Combined with the project-based teaching reform that the institute is vigorously promoting, the research group has studied and practiced the application of the project-based teaching mode based on modules in the core course of building intelligent engineering technology "Security and Prevention Technology", summarized the measures and achievements of the construction of the curriculum teaching team, and believed that the modular teacher team is the key to the project-based teaching reform, close and in-depth school-enterprise cooperation is the prerequisite for the normal operation of the teaching team, and the support of national and institute policies and systems at all levels is the guarantee for the growth and development of the teacher team.
Archimedes’ Revenge

Click to increase image sizeClick to decrease image size AcknowledgmentWe are grateful to Oliver Knill for sharing the image in Figure 1 that appears on his webpage [Citation2] (which also includes Runze Li’s suggested solution), and for several helpful comments.
Developing the system of interconnected classifications: Comparing the State Rubricator of Sci-tech Information and Universal Decimal Classification

The authors discuss the findings of the study to compare relevant master versions of the State Rubricator of Sci-tech Information and Universal Decimal Classification (UDC) in Russian. The classifications are compared through the method of intellectual analysis of intrinsic and semantic characteristics of SRSTI subject headings and UDC main schedules and its general and special subdivision tables. The UDC compound numbers are assigned to over the half of all rubrication vertexes. Over one third of SRSTI headings are assigned the compound UDC numbers made of two+ simple or compound numbers. The semantic matches between SRSTI headings and assigned UDC numbers are established as logical match, inclusion or extension overlapping with provision for terminological, structural and semantic features of the classifications being compared, as well as of the substance of the subject areas and their indexing principles. The interreflection of SRSTI and UDC numbers is executed in three types of relations: equivalence, hierarchy, association. Based on the analysis of coefficients of distribution by types of relations between semantic SRSTI headings and UDC numbers, the conclusion is made on the relatively high matching of terminology and structure of the given classification systems in almost every thematic class of subject areas. The value of the study is that the “converter” between SRSTI and UDC is to expand functionality of the interrelated classifications. Russian National Public Library for Science and Technology, within the framework of the Government Order No. 075-01235-23-01, R&amp;D theme No. 1021070112681-2-1.2.1;5.8.3, has been pursuing research for building the single network of links between classifications, development of “converters” between them for the purpose of computer-aided thematic systematization, and integration of dispersed information resources. The State Rubricator of Scientific and Technical Information (SRSTI) is the core classification of the unified networked classification links.
VIRTUAL RESTORATION OF STONE INSCRIPTIONS BASED ON IMAGE ENHANCEMENT AND EDGE DETECTION

Abstract. A method of virtual restoration of inscribed texts is proposed for blurred inscriptions. Firstly, the image enhancement method is used to enhance the image of the current topos, and the optimal method is selected to highlight the text information. Secondly, the extracted text outline is extracted from the enhanced image, and the extracted text outline is partially processed to obtain a smooth outline, and finally the fill function is used to fill the text outline to complete the virtual restoration of the inscribed text. The experimental results prove that the method can restore for blurred text and retain the original stele text writing style. The results of this experiment can provide a reference basis for the virtual restoration of other inscriptions on the central axis.
IMPROVING DATA QUALITY AND MANAGEMENT FOR REMOTE SENSING ANALYSIS: USE-CASES AND EMERGING RESEARCH QUESTIONS

Abstract. During the last decades satellite remote sensing has become an emerging technology producing big data for various application fields every day. However, data quality checking as well as the long-time management of data and models are still issues to be improved. They are indispensable to guarantee smooth data integration and the reproducibility of data analysis such as carried out by machine learning models. In this paper we clarify the emerging need of improving data quality and the management of data and models in a geospatial database management system before and during data analysis. In different use cases various processes of data preparation and quality checking, integration of data across different scales and references systems, efficient data and model management, and advanced data analysis are presented in detail. Motivated by these use cases we then discuss emerging research questions concerning data preparation and data quality checking, data management, model management and data integration. Finally conclusions drawn from the paper are presented and an outlook on future research work is given.
The Proof is in the Pudding

This paper presents FASTFOOD, a rule-based natural language generation (NLG) program for cooking recipes. We consider the representation of cooking recipes as discourse representation, because the meaning of each sentence needs to consider the context of the others. Our discourse representation system is based on states of affairs and transtions between states of affairs, and does not use discourse referents. Recipes are generated by using an automated theorem-proving procedure to select the ingredients and instructions, with ingredients corresponding to axioms and instructions to implications. FASTFOOD also contains a temporal optimization module which can rearrange the recipe to make it more time efficient for the user, e.g. the recipe specifies to chop the vegetables while the rice is boiling. The system is described in detail, including the decision to forgo discourse referents and how plausible representations of nouns and verbs emerge purely as a by-product of the practical requirements of efficiently representing recipe content. A comparison is then made with existing recipe generation systems, NLG systems more generally, and automated theorem provers.
Study the Effect of Environmental Scanning on the Relationship Between Individual and Organizational Factors and the Use of Information by the Electronic Publishing Managers

Objetive. To investigate the effect of environmental scanning on the relationship between individual and organizational factors and the use of information by the electronic publishing managers. Method. The current research is completely practical. The survey-analytical method was applied on 152 managers which were active in the electronic publishing industry. Data was collected through a researcher-made questionnaire. Results. As the average results indicate, individual factors (information awareness = 16.21 and information disclosure =7.039), organizational factors (information climate = 7.105 and external attention = 9.96), T value in environmental scanning (50.197) and the use of information (62.92) are above the average level. The analysis and explanation of the findings in this case reveals that after imposing of a mediator, the β value was changes subsequently from (β=0.576) and (β=0.0383) to (β =0.267) and (β=0.341). This means and reinforces the trivial but crucial effect of the mediator. Conclusions. The positive attitudes toward the informational based activities, intermittent interaction with experts with high and enriched informational backgrounds and resources, the amount of conditional and organizational access to information and finally the reciprocal action with the environment has a great impact on the amount of environmental scanning and information to which the managers use.
Poetry as Code as Interactive Fiction: Engaging Multiple Text-Based Literacies in Scarlet Portrait Parlor

&lt;p&gt;In Prismatik’s &lt;em&gt;Scarlet Portrait Parlor&lt;/em&gt; (2020) poetry and code uncannily appear one and the same. This results in a work that is both familiar and strange, and this, along with &lt;em&gt;Scarlet Portrait Parlor&lt;/em&gt;’s brevity, simplicity of construction, and immediate recognizability as a work of literature (a sonnet) that is also executable source code producing a work of electronic literature, has the potential to intrigue students and textual scholars unfamiliar with and perhaps resistant to Critical Code Studies (CCS). A study of Prismatik’s work also has the potential to refine some simplistic judgements in CCS scholarship about the efficacy of code that emulates natural, human language. This case study aims to elaborate the value of &lt;em&gt;Scarlet Portrait Parlor&lt;/em&gt; as a rich example of how poetry, programming, and interactive fiction can be intertwined if not blurred in a single text and to act as a catalyst for generative discussions about the overlapping and intertwining of natural languages, programming languages, creative writing, and coding.&lt;/p&gt;
Discriminative fusion of moments-aligned latent representation of multimodality medical data

Abstract Fusion of multimodal medical data provides multifaceted, disease-relevant information for diagnosis or prognosis prediction modeling. Traditional fusion strategies such as feature concatenation often fail to learn hidden complementary and discriminative manifestations from high-dimensional multimodal data. To this end, we proposed a methodology for the integration of multimodality medical data by matching their moments in a latent space, where the hidden, shared information of multimodal data is gradually learned by optimization with multiple feature collinearity and correlation constrains. We first obtained the multimodal hidden representations by learning mappings between the original domain and shared latent space. Within this shared space, we utilized several relational regularizations, including data attribute preservation, feature collinearity and feature-task correlation, to encourage learning of the underlying associations inherent in multimodal data. The fused multimodal latent features were finally fed to a logistic regression classifier for diagnostic prediction. Extensive evaluations on three independent clinical datasets have demonstrated the effectiveness of the proposed method in fusing multimodal data for medical prediction modeling.
Electrofacies analysis using a geostatistical approach, Northern Iraq case study

The distribution of petrophysical parameters is governed by lithology, hence understanding the spatial variation in lithology is essential for reservoir characterisation. This study points out the use of well logs to estimate the lithology of carbonate rocks (limestone, marly limestone, dolomite, and dolomitic limestone) found in Upper Cretaceous to Miocene formations at the Khabbaz Oil Field in Northern Iraq. Applying the multivariate regression technique to neutron, and density data enabled accurate lithology prediction. By using independent values (well log data) to predict a dependent value (lithology), this technique is a progression of regression analysis. The formations under study comprised a large amount of marly limestone, dolomite, and dolomitic limestone, according to the well log data results. It is possible to calculate the shale content using gamma-ray logs. R2 = 0.72 showed a statistically significant association between the described and anticipated lithologies. For subsequent prediction for other formations in the same well, the model with a higher coefficient of determination (0.72) might be evaluated. In the absence of cores, this work is helpful for lithological and petrophysical characterization of carbonate reservoirs and facies analysis.
Pandemic Preparedness With Pervasive Computing

The COVID-19 pandemic has stimulated pervasive computing research and development resulting in new, impactful public health tools, including digital contact tracing, crowd dynamics analysis, and symptom tracking, which are broadly recognized by the public and expert groups alike. In the post-COVID age, focus has shifted to establish a level of pandemic preparedness. Across all preparedness measures, there is the need for interoperable data, pervasive computing tools, and data analysis methods. Considering the open technical challenges, further pervasive computing research is a key to fill the opportunity created over the last years and eventually save more lives at global scale. In this special issue, we capture new technical approaches to the pervasive tool inventory that help dealing with a pandemic situation, but also investigations that highlight opportunities for further research. We aim to motivate continued research and discussion of new ideas on pervasive computing for public health tools that spurs pandemic preparedness.
Transfer Learning Models for CNN Fusion with Fisher Vector for Codebook Optimization of Foreground Features

Human action recognition has become one of the main topics in the computer vision field due to its high demand and competitiveness in real-world applications. The main goals of human action recognition are to improve classification accuracy and reduce computational complexity. Previous studies have mainly used two approaches: the hand-crafted feature extraction approach and the deep learning approach. The hand-crafted approach is simple, which confers it with an added advantage in terms of computational complexity. However, this method is low in accuracy. Conversely, the deep learning approach achieves high accuracy even for complex datasets, but it suffers in terms of computational complexity and long training time as it needs to process huge datasets during training. Other approaches include the use of pre-trained deep learning networks to fuse both methods. In this paper, we will introduce a combination of pre-trained convolutional neural networks (CNN) to extract features, an improved Fisher vector (iFV) codebook, and an optimized support vector machine SVM to achieve improved human action recognition. We leveraged three pre-trained CNNs, namely, Inception-ResNet-v2, NASNet-Large, and Xception, to extract the features. Then, we applied the improved Fisher vector codebook to encode them. We subsequently trained the codebook using SVM for classification and re-adjusted the SVM weights using five different optimization techniques, which are SGD, Adagrad, ADAM, Adamax, and Nadam. To evaluate the performance, we utilized UCF101 and HMDB51 datasets. The results demonstrate that the accuracy and computational complexity of our approach are comparable to state-of-the-art techniques.
A Semantic Segment Encoder (SSE): Improving human face inversion quality through minimized learning space

Recently, Generative Adversarial Networks (GAN) has been greatly developed and widely used in image synthesis. A Style-Based Generator Architecture for Generative Adversarial Networks (StyleGAN) which is the foremost, continues to develop human face inversion domain. StyleGAN uses insufficient vector space to express more than one million pixels. It is difficult to apply in real business due to distortion-edit tradeoff problem in latent space. To overcome this, we propose a novel semantic segment encoder (SSE) with improved face inversion quality by narrowing the size of restoration latent space. Encoder’s learning area is minimized to logical semantic-segment units that can be recognized by humans. The proposed encoder does not affect other segments because only one segment is edited at a time. To verify the face inversion quality, we compared with the latest encoders both Pixel2style2Pixel and RestyleEncoder. Experimental result shows that the proposed encoder improved distortion quality around 20% while maintain editing performance.
Simulation and Evaluation of Deep Learning Autoencoders for Image Compression in Multi-UAV Network Systems

Mobile multi-robot systems are versatile alternatives for improving single-robot capacities in many applications, such as logistics, environmental monitoring, search and rescue, photogrammetry, etc. In this sense, this kind of system must have a reliable communication network between the vehicles, ensuring that information exchanged within the nodes has little losses. This work simulates and evaluates the use of autoencoders for image compression in a multi-UAV simulation with ROS and Gazebo for a generic surveillance application. The autoencoder model was developed with the Keras library, presenting good training and validation results, with training and validation accuracy of 70%, and a Peak Signal Noise Ratio (PSNR) of 40dB. The use of the CPU for the simulated UAVs for processing and sending compressed images through the network is 25% faster. The results showed that this compression methodology is a good choice for improving the system’s performance without losing too much information.
FPGA Simulation for Computing Pseudoinverse Matrices

Many scientific and engineering processing models of big data deal with classification tasks. In particular, current intelligent approaches work efficiently for them, as they use neural techniques with shallow architectures. The Extreme Learning Machine (ELM), which is a one-hidden layer feedforward neural network, is capable of achieving high accuracy in these tasks. Moreover, one of its weighting matrices is the pseudoinverse of the hidden data. In this sense, this work presents meaningful results of computing the Moore-Penrose pseudoinverse for the ELM, using a recurrent model. The numerical simulations were based on an FPGA framework used to design a pseudoinverse-computing core.
Deep Learning Based Real Time Fall Detection Model

Health professionals are enormously utilising the advantages of most advanced technologies, leading to a scalable development in the field of health care. There has been a paradigm shift from manual monitoring towards more accurate virtual monitoring with a high accuracy rate. The world is very much concerned about taking care of old people. One of the worst things that can happen to older individuals is a fall. The creation of fall detection systems is urgently needed due to the aging population's rapid growth. With the development of technology, fall detection and rescue systems are able to provide quick emergency assistance to victims of accidental falls, thereby reducing the number of fatalities by detection of fall and providing adequate care. Here, we propose a model for real-time fall detection that uses deep convolutional neural networks. An automated fall detection model is essential for elderly care, since it offers a real time monitoring and fall detection whereas manual fall detection and immediate attention depends on timely availability of many factors. A real time monitoring system coupled with Deep learning techniques can accomplish this task. For fall detection, many deep learning networks can be utilized. Here, we propose an efficient model for real-time fall detection that uses deep convolutional neural networks and torch tensors. The system will recognize Fall and Non Fall from a set of real time input videos and respond according to scenario. We tried a hardware demonstration of an Automated Fall Detection System in an Up Extreme Series single board computer with LSTM neural network model.
Direct Current Circuit

This chapter solves problems on direct currentCurrent circuitsCircuit by applying Kirchhoff’s rulesKirchhoff’s rules. The rules are (1) the sum of the currentsCurrent into any junction is zero and (2) the sum of potential differencesPotential difference across each element around a closed loop is zero. Problems to determine the equivalentResistance resistanceEquivalent resistance of resistorsResistor in series and in parallel and to determine currentCurrent and charge in direct currentCurrent RC circuitsCircuit are also tackled. Solutions are by analytical means and computer calculation.
DESIGNING A PICTURE E-BOOK INTERFACE WITH A READ-ALOUD FUNCTION TO IMPROVE ELEMENTARY SCHOOL STUDENTS' READING INTEREST

This study used the online software system, Publuu, to design an e-book reading interface, with the aim of enhancing students' reading interest and motivation through applying the reading aloud strategy in the process of reading picture e-books. We
THE TUTOR'S PERCEPTION ON THE ACTIVITY OF A STUDENT IN GROUP ACTIVITY IN REMOTE MODE

This paper seeks to respond to one of the difficulties founded by a tutor in a situation of monitoring a group activity at a distance activity (remote mode), namely the perception of the individual activity of the students in the group activity.
YOUNG PROFESSIONALS' CDW RECYCLING OBSERVATIONS

Finland was the first country in the world to prepare a national road map to the circular economy (CE) in 2016. Since that many industrial fields have been planning and implementing various business applications and the authorities have been
VR GAME FOR IMPROVING SPATIAL UNDERSTANDING

Some engineering students at the TU Dortmund University have trouble with spatial understanding regarding technical drawings in engineering. This is an important skill that is needed for higher education studies and future professions. To support
THE ROLE OF NEW TECHNOLOGY IN EDUCATION OF PROGRAMMING NOVICES

Constant efforts to develop more and better information systems have become a critical factor of successful functioning of virtually all major systems. The world has become highly dependent on well-designed and well-developed information
Trust aspects in usage of web cloud computing for website development

Cloud computing is a technique that employs less hardware and software to allocate services online in real-time. End-user systems, communications networks, access management systems, and software structures are just a few examples of the various parts that make up existing cloud computing models. To accomplish complete cloud security, all cloud platforms must be safeguarded against both known and unknowable threats on the data and the cloud infrastructure. Web site frameworks are considered an informative tool that helps to develop dynamic webpages as well as static webpages. This paper provides a comparison of various PHP frameworks and an overview. The PHP frameworks extend the structure to develop software and it saves money and time. This article collects many studies talking about several PHP frameworks and compares each other in many ways. In other words, memory usage, response time, and various PHP frameworks like Laravel, CakePHP, CodeIgniter, Symfony2, Phalcon, Symfony, and Yii. Laravel performed better than other MVC frames from the second application, memory usage. On the other hand, Phalcon is more used than other MVC frames in terms of response time. Many researchers agree that using open-source PHP in the cloud would fundamentally alter IT strategy and the whole IT sector. Numerous analysts think PHP, an open source, combined with cloud computing would fundamentally alter IT processes and the whole IT industry. Thanks to cloud computing services provided by cloud computing providers, users may access applications, storage, and application development frameworks like PHP’s XAMPP and others over the Internet using a range of devices such PCs, laptops, cell phones, and PDAs. With a user-friendly platform, cloud computing technology provides cost benefits, high reliability, and easy scalability.