Reviewerly/exHarmony huggingface dataset:
Video localized caption generation framework for industrial videos

In this information age, there is exponential growth in visual content and video captioning can address many real-life applications. Automatic generation of video captions can be beneficial to comprehend a video in a short time, assist in faster information retrieval, video analysis, indexing, report generation, etc. Captioning of industrial videos is of importance to get a visual and textual summary of the work ongoing in the industry. The generated captioned summary of the video can assist in remote monitoring of industries and these captions can be utilized for video question-answering, video segment extraction, productivity analysis, etc. Due to the presence of diverse events processing of industrial videos are more challenging compared to other domains. In this paper, we address the real-life application of generating the descriptions for the videos of a labor-intensive industry. We propose a keyframe-based approach for the generation of video captions. The framework produces a video summary by extraction of keyframes, thereby reducing the video captioning task to image captioning. These keyframes are passed to the image captioning model for description generation. Utilizing these individual frame captions, multi-caption descriptions of a video are generated with a unique start and end time of each caption. For image captioning, a merge encoder-decoder model with a stacked decoder for caption generation is used. We have performed experimentation on a dataset specifically created for the small-scale industry. We have also shown that data augmentation on the small dataset can greatly benefit the generation of remarkably good video descriptions. Results of extensive experimentation performed by utilizing different image encoders, language encoders, and decoders in the merge encoder-decoder model are reported. Apart from presenting the results on domain-specific data, results on domain-independent datasets are also presented to show the applicability of the technique in general. Performance comparison with existing datasets - OVSD and Flickr8k and Flickr30k are reported to demonstrate the scalability of our method.
Design of Assessment Judging Model for Physical Education Professional Skills Course Based on Convolutional Neural Network and Few-Shot Learning

In recent years, the promotion of quality education and the development of curriculum and teaching materials reform have put forward higher goals and requirements for professional skills in physical education. However, there are still many shortcomings in the nuclear assessment of physical education professional skills, such as lack of clarity in evaluation objectives, lack of scientific in evaluation indexes, lack of systematization in evaluation contents, lack of diversity in evaluation methods, lack of authority in evaluation results, and lack of timely prediction and analysis of students' mastery of classroom teaching skills, thus not giving good play to all the functions that the nuclear assessment should have, thus to a certain extent fettering the further enhancement of physical education. With the development of information technology, artificial intelligence, as a new technology, can guide the improvement of the assessment and judging mode of physical education professional skills courses, and is also an important guiding idea for physical education majors to meet the development demands of information-based society. Based on the analysis of the connotation and characteristics of deep learning, this paper points out the insufficiency of the assessment and evaluation of traditional physical education professional skills courses and proposes a method of assessment and evaluation of physical education professional skills courses based on convolutional neural networks and small sample learning. In the case of a small amount of data in the course assessment, we use a small number of samples to learn, and only need a small number of samples to learn quickly. Using the improvement measures under the teaching concept of deep learning, physical education personnel are required to truly change in terms of professional skills mastery and evaluation. We effectively implement improvement measures, promote the improvement of physical education professional skills, and realize the migration and innovation of sports knowledge and skills.
A Knowledge Point Labeling Method by Introduce Knowledge Points Labels Information

Manual annotation of knowledge points is a high cost and labor-intensive task, while automatic annotation of knowledge points is a challenging task. The labels information of knowledge points can guide model annotation, but most of the existing automatic labeling methods ignore the labels information of knowledge points. By introducing knowledge point labels information, this paper proposes an end-to-end knowledge point annotation framework. The proposed framework is composed of feature extraction module and knowledge points annotation module introducing the labels information. We introduce and capture the information between labels based on graph convolutional networks (GCN), and combine Skip Connection to accelerate model convergence. We evaluated the proposed framework on a real-world dataset labeled by four high school courses, 29,813 sample exam questions, and 97 knowledge points. The proposed model achieves similar or better performance (2&#x0025; to 15&#x0025; improvement in Micro F1) than the most advanced knowledge point annotation model.
THE CHALLENGES OF “INCLUSIVE EDUCATION” IN PROFESSIONAL TECHNICAL EDUCATION

School inclusion is a delicate and complex process of high importance for a society that needs to develop coexistence strategies that consider the wonderful diversity of its citizens.The inclusive policy, currently, is based on the action of the Libras interpreter, as most teachers are not qualified to welcome this clientele.The research that originated this article sought to identify the difficulties of inclusion of the deaf student in the Vocational Technical Education of the Federal Institute of Piauí -IFPI from the analysis of the social representations of servers and students on the subject.Forty-five (45) questionnaires were applied to identify social representations regarding the inclusion of deaf students on campus, generating 72 (seventy-two) evocations.The analysis was based on the theory of the Collective Subject Discourse and used DSCsoft2.0(Tolteca).Inclusion is linked to equality -it was the strongest evocation.Listeners and non-hearers are in fragile territory in the construction of an effective communication process, and the Libras interpreter is the interface between these two worlds, with an urgent need to improve their working conditions, considering programs for the prevention of occupational diseases and expansion of the framework of these professionals.For teachers, Inclusion needs to be linked to a "dignified education" that creates opportunities for the development and autonomy of students, and that gives them the opportunity to enter the job market.This requires serious planning, adequate training and investments in improving educational instruments and environments.
Analytical Evaluation of SOA and SCRUM Business Process Management Approaches for IoT-Based Services Development

The SCRUM approach and Service-Oriented Architecture (SOA) framework are critical in assessing the factors that influence the efficiency of a business process and ensuring that business objectives are fulfilled, and the process is on track to meet those objectives. Flexibility and change adoption are critical features for both SCRUM and SOA approaches. Even though both sides encourage agility, the integration of the two independent concepts (SOA is the architectural framework while SCRUM is the development process) should be considered before being used in software management and development projects. This study assessed and analyzed both SCRUM and SOA’s diverse and different software architectural frameworks and development methodologies as well as their environment, which is integrated with the context of software project management and development setup for the software development industry. In addition, this study explores the similarities between the SCRUM process model and the SOA architectural framework to see if they are compatible and, if so, how they may be combined to enhance SOA-based projects. This research also looks at how to build and use a SCRUM methodology for large-scale SOA projects. As a result, SCRUM was chosen as the software development methodology for a research and development project based on SOA. In terms of project development and implementation, the complete project structure is made up of eight main parts.
K-Means-Clustering and Self-Organizing Maps Comparison to Classify the Electricity Generation in Kurdistan

Most fundamental unsupervised learning challenge is clustering. It is the process of grouping elements into groups whose components are &#x201C;similar but not identical&#x201D; to objects in other groups. It is a technique for classifying subjects into clusters of similar components; we attempt to identify related items based on their properties. We develop homogenous groupings, or clusters, that are distinct from one another. A Self-Organizing Maps (SOM) network&#x0027;s output, from the other hand, does not really provide groups of locations on the map. The SOM map is now arranged so that the network size on the map corresponds to the number of clusters required. Clustering aim is to find the intrinsic grouping in a group of data set. In this study, two methods for generating power in the Kurdistan region were compared by using methods (Self Organize Maps and K-Mean Clustering) to classify the months the data was in (Iraq-Kurdistan Regional Government Ministry of Electricity General Directorate of Control &#x0026;Communication Kurdistan Dispatch Control Centre) from 2006 to 2019. The research found that the months were divided into three groups, and that the Self-Organizing Maps algorithm outperformed the K-means strategy in getting the results with the lowest distance in a cluster. As a result, the SOM is the best and most accurate method for data classification, which supports its implementation. The classification process can also be adjusted by the neural network to account for changes in the power generated by electric patterns over time.
Migration-Based Load Balance of Virtual Machineservers in Cloud Computing by Load Predictionusing Genetic-Based Methods

Distributed computing is fast evolving, and a flurry of new cloud providers are cropping up to meet the need. Asset cost and cost-effectiveness multiplication have two big issues have emerged for cloud service producers who want to the appear serious when enjoying the rewards. The benefit increase issue It has been looked on raising the amount of transistors during uniform fog conditions. From the target of a cloud producer working only, diagram storey financial matters prompted asset identification components to deal with the benefit growth issue. It has been recommended that confirmation control systems be customized inside a Benefits the executives architecture to decrease asset cost. Current methods to the in storage on clusters, such as heterogeneous distributed recollection, name - value storage, hardware, and Piccolo, give a perfectly alright interface based on dynamic state updates. It has been programmed to predict the heap of its contemporaries. By collecting the heaps of each bunch. The proposed load determination technique in Dazzling Matrix has two key advantages. 1) Learning client behaviors increases prediction precision while demanding minimal computational resources. 2) SCCRF can successfully display and anticipating issue while also selecting important highlights to differentiate its energy utilization design. The only ways to react to internal failure with this interface are to replicated the knowledge across apparatus or log refreshing across appliance. When the supplier maintains in various commercial locations, a closeout-based powerful valuing instrument is used. An acknowledgement of the propose valuing component. Cost Efficiency of the Cloud: Savvy asset categorization is based on the following techniques. Costs fall and benefits climb; pay more only when costs rise; number of co ramifications. Management and asset allocation as a cost-effective arrangement: Abuse of usage attributes, expression of client experience/satisfaction.
Time Efficient Solution for Formula Student Driverless Competition: A Unmanned Aerial Vehicle Scouting Approach

Formula Student Driverless (FSD) is a famous self-driving race car competition in which the participating autonomous cars race on an unknown track. Many race cars, including the 2018 and 2019 champions, operate with a two-stage approach. The first stage is the training stage, which is used by the cars to observe the track information; the second stage is the execution stage, in which the cars move at full speed based on the information obtained in the first stage. However, a major limitation of this approach is that the cars have to move slowly during the training stage, since they need to gradually learn the track information and reserve enough time (e.g., 2 seconds) ahead of the operation to avoid collision. In addition to the above issue, previous cars are based on algorithms that are cannot be timely executed, which causes large operational delay and increases the risk of collision. To overcome these limitations, this paper presents a novel framework to enhance the performance of race cars. Specifically, the car is guided by a scouting unmanned aerial vehicle (UAV) that obtains the global track information with a monocular camera at the training stage. To implement the proposed framework, a set of algorithms are proposed to support various functionalities, including perception, simultaneous localization and mapping (SLAM), and path planning. Moreover, the proposed algorithms are highly time efficient, which can adapt to the environment at a faster rate than existing methods, thus supporting timely operation of cars and reducing the risk of collision. Our test results indicate that, with the proposed approach, the race car can obtain global trace information 50 seconds before the car reaches the finish line, which enables the race car to safely achieve a better racing performance.
The hashtag heard around the world: social media users’ perceptions and responses to the #MeToo hashtag

Since the advent of the #MeToo hashtag, there have been both praises and criticisms of its use as part of the #MeToo movement. The present study uses expectancy violation theory (EVT) as a guiding framework to examine response expectations among posters of the #MeToo hashtag on social media platforms such as Facebook and Twitter, and the extent to which these expectations were confirmed or violated by observers of the #MeToo hashtag postings online. This investigation is the first of its kind to provide an initial assessment of the effectiveness of the social media component for the #MeToo campaign (i.e., use of the #MeToo hashtag). We analyzed the survey responses for 193 participants (26 posters; 167 observers), asking posters what they expected and received as responses to their #MeToo hashtag posts, and observers about their expected and actual responses to the #MeToo postings of others in their social network. A thematic analysis was performed, and results offered some insights into response expectations for both posters and observers, as well as extent to which expectations were met, and what that means for the #MeToo campaign.
On the road again: Friedrich A. Hayeks (1899-1992) Denationalisation of Money

The real star behind the creation of bitcoin and cryptocurrencies was Hayek; especially with his "Denationalisation of Money". There he called for the creation of competing private fiat monies, divorced from the anchor of gold, stabilized by various composite anchors. It is something of a guide, inspiration, and warning to the creators of bitcoin and cryptocurrencies working decades later. The work is criticized for its relative incomprehensibility in explaining monetary and macroeconomic theory; its rhetoric; Hayek's misunderstanding that attempting to control the price level/sale price of money is a form of price control; his use of the term and theory of "spontaneous order"; his sympathy to dictators. At the same time, it is admired for its clarity in presenting his scheme for competing private fiat currencies - a surprising piece of proposed social engineering - and the various political obstacles facing its implementation. It is a work of creative genius.
A Deep Learning Embedded System for 3D Human Face Shape Reconstruction From a Single Image

A large body of recent researches has done in the field of 3D-reconstruction, around deep neural networks. In spite of the fact that many significant advances have been made by deep neural networks in 3D reconstruction, the high architectural and computational complexity still pose a big challenge for using these methods on edge devices such as mobiles, drones and vehicles due to limitation on the memory, bandwidth and energy resources. In this paper, a model-based 3D reconstruction framework from a single image, onto a mobile device Jetson TX2 is proposed, to build an embedded system for 3D reconstruction. The input to the model is a set of standard landmarks on the human face region in a single input image, and the output is the coefficients of a 3D morphable model (3DMM) for reconstruction of a complete human head. We performed experiments, using the synthetic dataset generated by Besel Face Model (BFM) 3DMM, with rendered 2D images from different poses. The obtained results show the superiority of the proposed framework compared to MobileFace framework, which is also a 3D reconstruction framework designed for embedded devices, in terms of the accuracy, noise robustness, number of parameters and basic operations. All have been done in this paper is available on https://github.com/hamed-tgh/3D-reconstruction
IXCI:The Immersive eXperimenter Control Interface

Standalone Head-Mounted Displays open up new possibilities to study full-room, bodily interactions in immersive environments. User studies with these devices, however, are complex to design, setup and run, since remote access is required to track the user view and actions, detect errors and perform adjustments during experimental sessions. We designed IXCI as a research support tool to streamline immersive user studies by explicitly addressing these issues. Through a use case, we show how the tool effectively supports the researcher in tasks such as remote debugging, rapid prototyping, run-time parameter configuration, monitoring participants’ progress, providing help to users, recovering from errors and minimizing data loss.
Radial Basis Function Attention for Named Entity Recognition

Attention mechanism is an increasingly important approach in the field of natural language processing (NLP). In the attention-based named entity recognition (NER) model, most attention mechanisms can calculate attention coefficient to express the importance of sentence semantic information but cannot adjust the position distribution of contextual feature vectors in the semantic space. To address this issue, a radial basis function attention (RBF-attention) layer is proposed to adaptively regulate the position distribution of sequence contextual feature vectors, which can minimize the relative distance of within-category named entities and maximize the relative distance of between-category named entities in the semantic space. The experimental results on CoNLL2003 English and MSRA Chinese NER datasets indicate that the proposed model performs better than other baseline approaches without relying on any external feature engineering.
Allocation of English Distance Teaching Resources based on Deep Reinforcement Learning and Multi-Objective Optimization

This work employs deep reinforcement learning and multi-objective optimization algorithms to the allocation of English distance teaching resources in order to increase their allocation efficiency. Moreover, based on the analysis of current regression correction, this paper discusses the algorithm of partition regression correction in depth, and proposes two different neighborhood regression correction algorithms. The proposal of neighborhood further expands the original concept of partition and solves various problems in partition correction. In order to reduce the model complexity of the neighborhood regression algorithm, this paper proposes to solve the problem through structural risk minimization and principal component extraction. The simulation results suggest that the English distance teaching resource allocation approach described in this research, which is based on deep reinforcement learning and multi-objective optimization, may significantly enhance the English distance teaching resource allocation impact.
Investigation on CO2 absorption and compressive strength of paver blocks incorporating seaweeds and potato peels

The challenge that we face in our daily life is the increasing amount of emissions of CO2 which has led to the increased concentration in the atmosphere that results in global warming. The global warming occurs mainly due to the emission of greenhouse gases in the atmosphere. Out of the emitted greenhouse gases, CO2 counts for 72%, 18% counts for methane and 9% counts for nitrous oxide (NOX). From this it is very clear that CO2 emission is the major area of concern for global warming. The CO2 emission occurs due to the incomplete combustion occurring in the vehicle engines that involve fuels like oil, natural gas, diesel, organic-diesel, petrol, organic-petrol and ethanol. The emission rate of CO2 keeps increasing on daily basis and to put this under control, CO2 absorbing paver blocks had been casted which includes the addition of lime, potassium hydroxide, seaweeds and potato peels. By using the above mentioned materials, four different types of paver blocks were casted. The first type composition had been carried out by using 0.5% lime and 5% weight of cement. The second type composition had been carried out by using 0.5% potassium hydroxide and 5% weight of cement. The third type composition had been carried out by using 0.5% seaweeds and 5% of weight of cement. The fourth type composition had been carried out by using 0.5% seaweeds and potato peels and 5% weight of cement and finally control specimens were casted. The mix proportion was adopted by using M35 as it is the suggested grade for mild traffic. The Carbonation tests were carried out by employing two methods such as carbonation test and titration method. In the carbonation test, the paver block was placed in normal atmosphere and testing was carried out on the 7th day, 14th day and 28th day using phenolphthalein indicator. The Titration method was carried out for all the four types of paver blocks by using sodium hydroxide and HCL. The compressive strength had been tested for all the four types paver block on the 7th, 14th and 28th day. The experimental results obtained reveals that the paver block casted by using the addition of seaweeds and potato peels in the combination of 5% by weight of cement had absorbed more amount of carbon dioxide and the compressive strength of this specimen is equal to the compressive strength of the conventional specimen.
Next-generation single photon sources for satellite-based quantum communication

We present a room temperature single photon source based on a color center in hexagonal boron nitride for satellite-based quantum networks. The resonator-coupled emitter is characterized by a narrowband tuneable spectrum, high photon purity, and high quantum efficiency. The photon source is currently integrated on a 3U CubeSat to qualify it for use in a future satellite-based global quantum-encrypted network. The satellite also performs a fundamental test of quantum gravity.