Reviewerly/exHarmony huggingface dataset:
Development of Learning Materials Based on E-Learning and Website as Learning Media in the Subject of Coaching Children with Special Needs for Students of Guidance and Counseling Department State University of Medan - Indonesia

The purpose of this research is to develop teaching materials based on e-learning and website learning media in the subject of Coaching Children with Special Needs for students of BK study programs, Medan State University. This research and development is motivated by facts about the use of smartphones and the high activity on the internet among students. The trial subjects in this study were 30 students of Class A semester 7 at the Faculty of Science Education, State University of Medan, with the consideration that the class had never used teaching materials based on e-learning and website media in all subjects, and learning interest was low. Data collection techniques conducted using non-test. The development model used in this study uses 3D concepts with development stages including the defining stage, the design stage, the development stage. The research product developed was validated beforehand by the material expert, instructional design expert, and instructional media expert. Material experts assess the learning media based on three aspects namely (1) the quality of the material (2) the quality of the learning strategy (3) the quality of the material delivery system. The evaluation of each of the three aspects shows the percentage of each assessment is 95% in the aspect of feasibility of content. In the aspect of presentation, the assessment score of the material expert is 91.6%. In the aspect of linguistic and graphic material experts give an assessment with a score of 94%. Based on the overall assessment of learning media included in good categories. The research data were analyzed by t test to see the difference in learning interest scores between the control group and the experimental group. In the experimental group, students who were taught by using a product that was developed experienced a learning interest of 85.15. Higher than the control group learned with conventional learning materials and media that is equal to 76.91.
Utilization of the IoT-based early warning system (EWS) for early detection of floods in Setu District, South Tangerang City

Cisadane River is the largest river whose overflow often causes flooding in several locations in South Tangerang City. One of them is located in Pesona Serpong Housing, Setu District, South Tangerang City. The Cisadane Environmental Echo Community (GEMALA) is a community that cares about the sustainability of the Cisadane River. This community service aims to improve understanding of river maintenance and socialize the IoT-based Early Warning System (EWS) tool to the GEMALA community as an early flood detection tool. The method used is to use an ultrasonic sensor HC-SR04 which will measure the water level of the river and will send a signal via the Telegram messaging application. At the end of this activity, an IoT-based Early Warning System (EWS) tool was produced that could be utilized by the GEMALA community as an early flood detection tool.
Learned Spatial Representations for Few-Shot Talking-Head Synthesis

We propose a novel approach for few-shot talking-head synthesis. While recent works in neural talking heads have produced promising results, they can still produce images that do not preserve the identity of the subject in source images. We posit this is a result of the entangled representation of each subject in a single latent code that models 3D shape information, identity cues, colors, lighting and even background details. In contrast, we propose to factorize the representation of a subject into its spatial and style components. Our method generates a target frame in two steps. First, it predicts a dense spatial layout for the target image. Second, an image generator utilizes the predicted layout for spatial denormalization and synthesizes the target frame. We experimentally show that this disentangled representation leads to a significant improvement over previous methods, both quantitatively and qualitatively.
A Novel Cuckoo Search Algorithm and Its Application

In this paper, the principle of Cuckoo algorithm is introduced, and the traditional Cuckoo algorithm is improved to establish a mathematical model of multi-objective optimization scheduling.Based on the improved algorithm, the model is optimized to a certain extent.Through analysis, it is proved that the improved algorithm has higher computational accuracy and can effectively improve the global convergence.
Public Relations Specialist, and Communications, Marketing and Event Specialist, OverDrive Digital Bookmobile, United States

The Digital Bookmobile is a community outreach vehicle for public libraries and schools to create events that promote each library's or school's digital collection of e-books, audiobooks and magazines. Many of the author's collection development specialists who work with libraries and schools in the Over-Drive network are trained and degreed librarians. The Digital Bookmobile creates awareness of each library's and school's digital services by providing a powerful visual as well as a fun demonstration and learning experience. The Digital Bookmobile promotes the unique digital collections of e-books, audiobooks, magazines and more for each library and school, while the traditional bookmobile provides physical materials to lend. At the authors Digital Bookmobile events, they have engaging crafts and sample titles for kids to try out, but they also introduce families to their Read-Along titles during events.
A Novel Approach to Determining the Radius of the Neighborhood Required for the DBSCAN Algorithm

Data clustering is one of the most important methods used to discover naturally occurring structures in datasets. One of the most popular clustering algorithms is the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). This algorithm can discover clusters of arbitrary shapes in datasets and thus it has been widely applied in many different applications. However, the DBSCAN requires two input parameters, i.e. the radius of the neighborhood (eps) and the minimum number of points required to form a dense region (MinPts). The right choice of the two parameters is a fundamental issue. In this paper, a new method is proposed to determine the radius parameter. In this approach the distances between each element in the dataset and its k-th nearest neighbor are used, and then in these distances abrupt changes in values are identified. The performance of the new approach has been demonstrated for several different datasets.
Image Deblurring using Wiener Filtering and Siamese Neural Network

Blurred images are difficult to avoid in situations when minor Atmospheric turbulence or camera movement results in low-quality images. We propose a system that takes a blurred image as input and produces a deblurred image by utilizing various filtering techniques. Additionally, we utilize the Siamese Network to match local image segments. A Siamese Neural Network model is used that is trained to account for image matching in the spatial domain. The best-matched image returned by the model is then further used for Signal-to-Noise ratio and Point Spread Function estimation. The Wiener filter is then used to deblur the image. Finally, the results of the deblurring techniques with existing algorithms are compared and it is shown that the error in deblurring an image using the techniques presented in this paper is considerably lesser than other techniques.
Artificial chemical reaction optimisation of recurrent functional link neural networks for efficient modelling and forecasting of financial time series

In contrast to multilayer neural networks, a functional link artificial neural network uses functional expansion units for transferring lower input space to higher dimensions. It achieves enhanced ...
Analysis Of Machine Sensing Of Hate Speech On Twitter In Nigeria

The Advent Of Social Media Has No Doubt Liberated The Communication Space, Deepened Participatory Democracy And Granted Citizens Platform To Ventilate Their Anger On National And Important Discourse. Twitter Have Gained Prominence In Recent Years Because Of Nature And Quality Of Information Available On Them. Policy Statements That Are Sourced On The Conventional Media Are Replete On Twitter. However, The Platform Has Also Become Breeding Ground For Obfuscation Of Many Absurdities. Therefore, This Study Investigates The Prevalence Of Hate Speech On Twitter In Nigeria From 2015 To 2019. Using Twitter Api As Data Mining Method, The StudyContent Analyzed The Mined Tweets By Categorizing The Tweets Under Political, Religious, Ethnic, Region, Gender And Disability Categories. Findings Of The Study Show That Hate Speech On Twitter Is More Frequent During The Electioneering Years. Also, Unverified Handles On Twitter Tend To Tweet More Hate Speech Than The Verified Handles. Findings Show That Twitter Users Tend To ‘Like’ Hate Tweets Than ‘Retweet’ Them. Similarly, Political Hate Speeches Are Dominant On Twitter. The Study Therefore Recommend For Regulatory Legislation On Hate Speech Within The Confinement Of Freedom Of Speech In Order Not To Infringe On The Fundamental Human Right Of The People.
Interpretation of Emergent Communication in Heterogeneous Collaborative Embodied Agents

Communication between embodied AI agents has received increasing attention in recent years. Despite its use, it is still unclear whether the learned communication is interpretable and grounded in perception. To study the grounding of emergent forms of communication, we first introduce the collaborative multi-object navigation task CoMON. In this task, an oracle agent has detailed environment information in the form of a map. It communicates with a navigator agent that perceives the environment visually and is tasked to find a sequence of goals. To succeed at the task, effective communication is essential. CoMON hence serves as a basis to study different communication mechanisms between heterogeneous agents, that is, agents with different capabilities and roles. We study two common communication mechanisms and analyze their communication patterns through an egocentric and spatial lens. We show that the emergent communication can be grounded to the agent observations and the spatial structure of the 3D environment. Video summary: this https URL
FastGarble: an optimised garbled circuit construction framework

The emerging field of cryptography, Secure Computation, can be used to solve a number of distributed computing applications without loss of privacy of sensitive/private data. The applications can b...
Application of Semantics Analysis in Text Classification of Computer Technology

Text classification is a basic problem in the field of natural language processing. It is a very active research direction in the field of machine learning, and has many important practical applications. This paper mainly studies the application of semantic analysis in text classification of computer technology. In view of the fact that most current methods ignore the important correlation between features, this paper adopts the LSA method, which fully considers the semantic relationship between features and reduces the dimension of feature space, so as to construct a new semantic space. Experimental results show that the proposed method can effectively reduce the dimension of feature space and improve the performance of text classification.
Automated Organic Web Harvesting on Web Data for Analytics

Automated Web search and web data extraction has become an inevitable part of research in the area of web mining. The web scraping has immense influence on ecommerce, market research, web indexing and much more. Most of the web information is presented in an unstructured or free format. Web scraping helps every user to retrieve, analyze and use the data suitably according to their requirement. There exist different methodologies for web scraping. Major web scraping tools are rule based systems. In the proposed work, an automated method for web information extraction using Computer Vision is proposed and developed. The proposed automated web scraping method comprises of automated URL extraction virtual extraction of required data and storing the data in a structured format which is useful in market research.
Cross-Functional Policy Development for a Data Repository

Policy can articulate the scope of work. For repositories that house data, policy can help users manage expectations, especially for individuals who are new to data sharing, or where expectations for sharing data have changed. We cover some of the current literature around the process for writing policy, specifically focusing on policy for data collections and repositories, factors that encouraged us to create a repository policy, our collaborative process for creating the policy, and lessons learned. We hope that others can use our processes to build their own policy that reflects the needs of their campuses and scholars and further moves the needle toward the “Library as Publisher” model.
Segmentation Quality Evaluation Algorithm Based on Classification

Image segmentation plays a vital role in computer vision. Image segmentation quality evaluation is an essential task in image segmentation and an important step to quantify the performance of the segmentation algorithm. Most of the existing evaluation methods need to use ground truth to evaluate the segmentation quality. However, the annotation of ground truth varies from person to person and takes a long time. In this paper, we proposed a new segmentation quality evaluation network. In the evaluation of segmentation quality, only the segmentation results to be evaluated and the original image are required without specially annotated ground truth. At the same time, we also propose a new space to squeeze module (STS) for segmentation quality evaluation. STS module autonomously learns the edge features of the segmentation object and increases the weight of edge features. Experiments on the dataset constructed in this paper show that the performance of the proposed network is better than other network structures such as ECA-Net, CBAM, SE-Net, and the evaluation accuracy is higher than the existing supervised and unsupervised segmentation quality evaluation methods.
From Continent to Ocean: Investigating the Multi-Element and Precious Metal Geochemistry of the Paraná-Etendeka Large Igneous Province Using Machine Learning Tools

Large Igneous Provinces, and by extension the mantle plumes that generate them, are frequently associated with platinum-group element (PGE) ore deposits, yet the processes controlling the metal budget in plume-derived magmas remains debated. In this paper, we present a new whole-rock geochemical data set from the 135 Ma Paraná-Etendeka Large Igneous Province (PELIP) in the South Atlantic, which includes major and trace elements, PGE, and Au concentrations for onshore and offshore lavas from different developmental stages in the province, which underwent significant syn-magmatic continental rifting from 134 Ma onwards. The PELIP presents an opportunity to observe magma geochemistry as the continent and sub-continental lithospheric mantle (SCLM) are progressively removed from a melting environment. Here, we use an unsupervised machine learning approach (featuring the PCA, t-SNE and k- means clustering algorithms) to investigate the geochemistry of a set of (primarily basaltic) onshore and offshore PELIP lavas. We test the hypothesis that plume-derived magmas can scavenge precious metals including PGE from the SCLM and explore how metal concentrations might change the metal content in intraplate magmas throughout rifting. Onshore lavas on the Etendeka side of the PELIP are classified as the products of deep partial melts of the mantle below the African craton but without significant PGE enrichment. Offshore lavas on both continents exhibit similarities through the multi-element space to their onshore equivalents, but they again lack PGE enrichment. Of the four onshore lava types on the Paraná side of the PELIP, the Type 1 (Southern) and Type 1 (Central-Northern) localities exhibit separate PGE-enriched assemblages (Ir-Ru-Rh and Pd-Au-Cu, respectively). It follows that there is a significant asymmetry to the metallogenic character of the PELIP, with enrichment focused specifically on lavas from the South American continent edge in Paraná. This asymmetry contrasts with the North Atlantic Igneous Province (NAIP), a similar geodynamic environment in which continent-edge lavas are also PGE-enriched, albeit on both sides of the plume-rift system. We conclude that, given the similarities in PGE studies of plume-rift environments, SCLM incorporation under progressively shallowing (i.e., rifting) asthenospheric conditions promotes the acquisition of metasomatic and residual PGE-bearing minerals, boosting the magma metal budget.
Research on Query Expansion Based on Deep Learning

In order to overcome the problem of low retrieval efficiency. The paper proposes a method of deep learning-based distributed vector representation for query expansion. The classic task of improving queries to improve retrieval performance is query expansion, refining user intent by filling in extended terms to fully understand the needs to achieve retrieval accuracy. In the query expansion, how to select the extended words is the key issue, and the quality of the extended words determines the performance of retrieval. The main feature of this method is to optimize the expansion words, improve the extended word relevance labeling strategy based on learning to rank, and use the word vector to construct features for the extended words for the construction and optimization of the extended word ranking model. Experimental results show that the method has a high accuracy rate on TREC public dataset, with a 4.45% improvement compared to the traditional method, which has important implications for the research of deep learning in information retrieval.
Adaptive firefly algorithm based on reverse search strategy

Firefly algorithm is proposed by Prof. Yang Xinshe for solving global optimization problems, which uses the principle of mutual attraction of fireflies in nature. The firefly algorithm is a branch of evolutionary algorithms, which is often used to solve single-objective global optimization problems with fewer parameters, easy to implement and easy to understand. However, the traditional firefly algorithm uses the full-attraction model in updating, which is easy to fall into local optimum. Therefore, a firefly algorithm that performs backward search with Poisson distributed probabilities is proposed, which enables the firefly to search more widely in the solution space and easily jump out of the local optimum. Comparative experiments are conducted on 28 functions of the CEC2013 test set. The experimental results show that in 22 of the functions the firefly with the reverse search strategy performs more accurately than the other improved firefly algorithms and in 26 of the functions the firefly with the reverse search strategy converges faster than the other fireflies.
Performance Enhancement in Cloud Computing Using Efficient Resource Scheduling

Inefficient utilization of computational and storage resources in the cloud increases the infrastructural as well as the operational expenditure of networking and also has an impact on the network performance. In this chapter, with the objective to investigate and introduce an efficient resource scheduling technique that has a positive impact on computational cloud performance, we propose a resource scheduling algorithm whose aim is to achieve and demonstrate the two types of resource management techniques, viz. a priority-based algorithm and an algorithm based on dynamic workload. Between the two, the priority-based resource scheduling algorithm assigns priority to each job in the process queue based on its execution time and time taken for submitting these jobs to the data center for execution. During this experiment, the aim is to measure the memory utilization of each task in the virtual machine (VM). The second algorithm is based on scheduling the dynamically appeared workload, which is an extension over the priority-based algorithm. While implementing both the solutions using the CloudSim tool with a random workload generation system, the experimental analysis shows that these approaches are useful for dealing with the cloud’s increasing workloads and also to demonstrate the improvement in the cloud’s computational performance.
Corrigendum: Pragmatic Prediction in the Processing of Referring Expressions Containing Scalar Quantifiers

[This corrects the article DOI: 10.3389/fpsyg.2021.662050.].
Optimizing the Configuration of Financial Supervision Equipment by Using Key Privacy Protection Technologies of Blockchain

The use of blockchain technology in the financial field can effectively break information barriers; track and audit the entire funding cycle, and help improve the transparency of fund use. It can simplify the transaction process, help reduce transaction costs and improve efficiency. However, compared with Western developed countries, the application of blockchain technology in China is still in its infancy, and its application in the financial field faces challenges such as the limitations of traditional concepts, low technology levels, and technology shortages. Research and application talents, laws and regulations are not perfect. It is necessary to innovate ideas, strengthen technology research and development, vigorously cultivate technology research and application talents, and improve relevant laws and regulations to promote the wide application of blockchain technology in the financial field. Based on this, this article studies the use of blockchain key privacy protection technologies to optimize the configuration of financial supervision equipment. This article mainly focuses on blockchain technology and its application in the financial industry, combined with an overview of blockchain technology as the main basis, and then analyzes the application of blockchain technology in the promotion of the financial industry, and makes recommendations. Use blockchain technology to better promote the development of the financial industry. Research shows that blockchain is a new application model of computers. Blockchain is second only to network technology. Mainframes and personal computers have the greatest potential and widest influence, and are essential for triggering industrial change power. Therefore, blockchain technology has great potential in the financial governance system.
Certification of Non-Gaussian States using Double Homodyne Detection

Certifying reliable sources for quantum information processing is still one of the major challenges towards practical applications. Continuous variable quantum information provides a very promising platform where important resources such as scalable entanglement come "for free" [1] . In that context, a crucial quantum feature to look for and investigate is the so-called non-Gaussianity, which, beyond its conceptual relevance, is a necessary resource for any quantum computational advantage as well as for effective error correction, entanglement distillation and more. Non-Gaussian states have been produced and probed using full tomography techniques [2] . However, performing a full tomography of multimode continuous variable systems is a hard problem. Indeed, systems aimed at overcoming classical approach are by definition too complex to be fully characterized, thus alternative approaches are required.